International audience

Massive access or communication, emerging as one of six usage scenarios in 6G, has attracted considerable recent attention due to its potential to empower next-generation industrial cyber-physical systems such as smart grids, factory automation, industrial internet-of-things (IIoT), etc. However, to guarantee its QoS, the associated power control becomes computationally intractable with a huge number of users. In this paper, we present a tractable algorithm for power control in massive access, based on mean-field approximations. In particular, our aim is to maximize the overall throughput in each scheduling period, at the beginning of which each user has a finite number of backlogged bits. To achieve this goal and overcome the curse of dimensionality, a mean-field game (MFG) is formulated. Unfortunately, the formulated MFG is still a non-convex optimization problem. Enlightened by MAPEL, an efficient solver for non-convex power control problem, we leverage multiplicative linear fractional programming (MLFP) to tackle the non-convexity in our formulated MFG. Furthermore, the mean-field approximation assisted power control strategy requires low signaling overhead consumed for estimation and feedback of channel state information (CSI). Simulation results demonstrate that the proposed tractable power control attains substantial performance gains in both the overall throughput and computational complexity.

With the unprecedented capability of configuring antenna positions, fluid antenna systems (FASs) have been recognized as a key enabler for secure communications. However, it is challenging to optimize the secrecy rate by reconfiguring positions of fluid antennas based on the fast-changing instantaneous channel state information (CSI). Considering the effectiveness of regularized zero-forcing (RZF) and zero-forcing (ZF) precoding in mitigating information leakage in physical layer security, we propose a two-timescale design to maximize ergodic secrecy sum rate (ESSR), where only the statistical CSI is utilized for the port selection of FASs. For that purpose, we first derive the analytical expression for the ESSR of FASs with RZF/ZF precoding by utilizing random matrix theory (RMT). Then, based on the evaluation results, we propose a two-timescale algorithm to maximize the ESSR by optimizing both port selection of FASs and regularization factor of RZF. Numerical simulations validate the accuracy of the proposed ESSR evaluation and show that the proposed two-timescale design could improve the ESSR performance significantly when compared with the uniform port selection.

Robust signal detection in colored noise with unknown covariance is essential in radar, cognitive radio, integrated sensing and communication (ISAC), and quantum sensing applications. This paper develops a unified analytical framework for the Standard Condition Number (SCN) detector, which employs the ratio of the largest to smallest eigenvalues of the whitened sample covariance matrix. The framework jointly covers both ideal conditions in which the training and sensing noise statistics are identical and disturbed conditions in which interference or jamming alters the sensing covariance. Despite the SCN's practical relevance, its finite-sample false-alarm and detection behavior has not been analytically characterized. Using random matrix theory (RMT), we derive general expressions for these probabilities, provide closed-form results for special cases, and show that the SCN preserves the Constant False Alarm Rate (CFAR) property under covariance mismatch. Analytical and simulation results confirm that the proposed unified framework delivers consistent detection performance and greater robustness than conventional eigenvalue- and LRT-based detectors.

This study investigates energy-efficient resource allocation for covert vehicular communications with age constraints, where vehicle-to-vehicle (V2V) links leverage spectrum sharing to conceal covert transmissions. Vehicle-to-infrastructure (V2I) links serve as friendly jammers, simultaneously disrupting detection and maintaining connectivity with the base station. To maximize V2V links’ covert energy efficiency (CEE) and V2I links’ throughput under quality of service (QoS), communication covertness, and freshness constraints, a novel matching-based resource allocation framework is proposed. Specifically, we derive the minimum error detection rate and the optimal detection threshold at warden. The transmit probability and power are jointly optimized using the successive convex approximation method. Jamming selection is then modeled as a stable marriage problem, solved via the Gale-Shapley algorithm for stable matching between V2V and V2I links. Additionally, we explore a coalition falsification strategy to further enhance the CEE of certain V2V links without hurting the performance of the rest. Extensive simulations validate the proposed approach, showing significant improvements over existing baselines.

Direction-of-arrival (DOA) estimation for incoherently distributed (ID) sources is crucial for Industrial Internet of Things (IIoT) applications operating in complex multipath environments, yet it remains challenging due to the combined effects of angular spread and gain-phase uncertainties in cost-sensitive antenna arrays. This paper presents a two-stage sparse DOA estimation framework, transitioning from partial calibration to full potential, under the generalized array manifold (GAM) framework. In the first stage, coarse DOA estimates are obtained by exploiting the output from a subset of partly-calibrated arrays (PCAs). In the second stage, these estimates are utilized to determine and compensate for gain-phase uncertainties across all array elements. Then a sparse total least-squares optimization problem is formulated and solved via alternating descent to refine the DOA estimates. Simulation results demonstrate that the proposed method achieves superior estimation accuracy compared to existing approaches, while maintaining robustness against both noise and angular spread effects in practical industrial environments.

A polarization-aware direction-of-arrival (DoA) detection scheme is conceived that leverages the intrinsic vector sensitivity of a single Rydberg atomic vapor cell to achieve quantum-enhanced angle resolution. Our core idea lies in the fact that the vector nature of an electromagnetic wave is uniquely determined by its orthogonal electric and magnetic field components, both of which can be retrieved by a single Rydberg atomic receiver via electromagnetically induced transparency (EIT)- based spectroscopy. To be specific, in the presence of a static magnetic bias field that defines a stable quantization axis, a pair of sequential EIT measurements is carried out in the same vapor cell. Firstly, the electric-field polarization angle is extracted from the Zeeman-resolved EIT spectrum associated with an electricdipole transition driven by the radio frequency (RF) field. Within the same experimental cycle, the RF field is then retuned to a magnetic-dipole resonance, producing Zeeman-resolved EIT peaks for decoding the RF magnetic-field orientation. This scheme exhibits a dual yet independent sensitivity on both angles, allowing for precise DoA reconstruction without the need for spatial diversity or phase referencing. Building on this foundation, we derive the quantum Fisher-information matrix (QFIM) and obtain a closed-form quantum Cramér-Rao bound (QCRB) for the joint estimation of polarization and orientation angles. Finally, simulation results spanning various quantum parameters validate the proposed approach and identify optimal operating regimes. With appropriately chosen polarization and magnetic-field geometries, a single vapor cell is expected to achieve sub-0.1° angle resolution at moderate RF-field driving strengths.

With the advent of 6G communications, intelligent communication systems face multiple challenges, including constrained perception and response capabilities, limited scalability, and low adaptability in dynamic environments. To address these challenges, this tutorial provides a systematic and comprehensive introduction to the principles, design, and applications of Large Artificial Intelligence Models (LAMs) and Agentic AI technologies in intelligent communication systems, aiming to offer researchers an integrated overview of cutting-edge methodologies and practical insights. First, the tutorial outlines the background of 6G communications and reviews the technological evolution from LAMs to Agentic AI. It then systematically examines the key components required for constructing LAMs, classifies various types of LAMs, and analyzes their applicability in communication. A LAM-centric design paradigm tailored for communication systems is subsequently proposed, encompassing dataset construction, internal learning, and external learning approaches. Building upon this foundation, the tutorial develops an LAM-based Agentic AI system for intelligent communications, elaborating on its core components—including agents, world models, planners, knowledge bases, tools, and memory modules— as well as their interaction mechanisms. Finally, it provides an in-depth review of representative applications of LAMs and Agentic AI in communication scenarios, and summarizes the current research challenges and future directions, with the goal of fostering the development of efficient, secure, and sustainable next-generation intelligent communication systems.

Fluid antenna systems (FAS) have emerged as a promising technology to achieve high spatial diversity by dynamically reconfiguring multiple closely spacedNantenna ports. However, the inherent spatial correlation among these ports poses significant challenges for accurate performance analysis. Traditional block-correlation modeling algorithms, which partition theN×NToeplitz-structured correlation matrix into independentDblocks with constant correlation coefficients, often yield substantial approximation errors to block-correlation models, especially in scenarios with limited ports. In this paper, we revisit the spatial block-correlation model for FAS and introduce a novel block-correlation modeling algorithm in tuning the model parameters, which realizes the variable block-correlation model in practice. Our proposed approach derives closed-form expressions for the optimal block-specific correlation coefficients and develops a low-complexity heuristic algorithm that reduces the computational complexity from exponentialDN–Dto linear (N–D) ×Dsearches,thereby achieving significantly lower approximation error compared to constant correlation models. To validate the effectiveness of our variable block-correlation modeling algorithm, we first apply it to point-to-point FAS communications with closely spaced ports, deriving analytical expressions for the joint probability density function (PDF) of channel amplitudes and outage probability. Our analysis shows that the proposed algorithm offers tractable performance evaluation and superior accuracy, particularly when the number of ports is small (NThese results underscore the practical value of our approach for the design and optimization of next-generation FAS-based wireless networks.

The reconfigurability of fluid antenna systems (FASs) and reconfigurable intelligent surfaces (RISs) provides significant flexibility in optimizing channel conditions by jointly adjusting the positions of fluid antennas and the phase shifts of RISs. However, it is challenging to acquire the instantaneous channel state information (CSI) for both fluid antennas and RISs, while frequent adjustment of antenna positions and phase shifts will significantly increase the system complexity. To tackle this issue, this paper investigates the two-timescale design for FAS-RIS multi-user systems with linear precoding, where only the linear precoder design requires instantaneous CSI of the end-to-end channel, while the FAS and RIS optimization relies on statistical CSI. The main challenge comes from the complex structure of channel and inverse operations in linear precoding, such as regularized zero-forcing (RZF) and zero-forcing (ZF). Leveraging on random matrix theory (RMT), we first investigate the fundamental limits of FAS-RIS systems with RZF/ZF precoding by deriving the ergodic sum rate (ESR). This result is utilized to determine the minimum number of selected antennas to achieve a given ESR. Based on the evaluation result, we propose an algorithm to jointly optimize the antenna selection, regularization factor of RZF, and phase shifts at the RIS. Numerical results validate the accuracy of performance evaluation and demonstrate that the performance gain brought by joint FAS and RIS design is more pronounced with a larger number of users.

While stacked intelligent metasurfaces (SIMs) have demonstrated significant technical and cost advantages in multiuser scenarios, existing literature universally assumes ideal transceiver hardware. Addressing this gap, this paper investigates the design and optimization of a SIM-assisted multiuser downlink multiple-input single-output (MISO) system under practical transceiver hardware impairments (HWIs). To accurately capture distortion effects at both the base station (BS) and user equipment, we adopt an aggregate HWI model based on improper Gaussian statistics. The considered impairments include finite-resolution digital-to-analog converters (DACs), power amplifier (PA) nonlinearities, in-phase/quadrature (I/Q) imbalance, and other radio-frequency (RF) front-end non-idealities. We formulate a sum-rate (SR) maximization problem that jointly optimizes digital beamforming at the BS and multi-layer analog beamforming at the SIM. To tackle this highly non-convex optimization challenge, we propose a closed-form-based iterative algorithm that alternately updates BS and SIM beamforming with guaranteed convergence. Extensive simulations validate the effectiveness of the proposed algorithm, quantify the impact of different HWI sources, and demonstrate that SIM deployment significantly improves system robustness, mitigates HWI-induced performance degradation, and reduces DAC resolution requirements without substantial performance loss.

Stacked intelligent metasurface (SIM) extends the concept of single-layer reconfigurable holographic surfaces (RHS) by incorporating a multi-layered structure, thereby providing enhanced control over electromagnetic wave propagation and improved signal processing capabilities. This study investigates the potential of SIM in enhancing the rate fairness in multiuser downlink systems by addressing two key optimization problems: maximizing the minimum rate (MR) and maximizing the geometric mean of rates (GMR). The former strives to enhance the minimum user rate, thereby ensuring fairness among users, while the latter relaxes fairness requirements to strike a better trade-off between user fairness and system sum-rate (SR). For the MR maximization, we adopt a consensus alternating direction method of multipliers (ADMM)-based approach, which decomposes the approximated problem into sub-problems with closed-form solutions. For GMR maximization, we develop an alternating optimization (AO)-based algorithm that also yields closed-form solutions and can be seamlessly adapted for SR maximization. Numerical results validate the effectiveness and convergence of the proposed algorithms. Comparative evaluations show that MR maximization ensures near-perfect fairness, while GMR maximization balances fairness and system SR. Furthermore, the two proposed algorithms respectively outperform existing related works in terms of MR and SR performance. Lastly, SIM with lower power consumption achieves performance comparable to that of multi-antenna digital beamforming.

The significant progress of quantum sensing technologies offer numerous radical solutions for measuring a multitude of physical quantities at an unprecedented precision. Among them, Rydberg atomic quantum receivers (RAQRs) emerge as an eminent solution for detecting the electric field of radio frequency (RF) signals, exhibiting great potential in assisting classical wireless communications and sensing. So far, most experimental studies have aimed for the proof of physical concepts to reveal its promise, while the practical signal model of RAQR-aided wireless communications and sensing remained under-explored. Furthermore, the performance of RAQR-based wireless receivers and their advantages over classical RF receivers have not been fully characterized. To fill these gaps, we introduce the RAQR to the wireless community by presenting an end-to-end reception scheme. We then develop a corresponding equivalent baseband signal model relying on a realistic reception flow. Our scheme and model provide explicit design guidance to RAQR-aided wireless systems. We next study the performance of RAQR-aided wireless systems based on our model, and compare them to classical RF receivers. The results show that Doppler broadening-free RAQRs are capable of achieving a substantial received signal-to-noise ratio (SNR) gain of over 27 decibel (dB) and 40 dB in the photon shot limit and standard quantum limit regimes, respectively.

The communication performance of traditional two-dimensional (2D) antenna arrays is approaching its theoretical limit under constraints of physical size and hardware costs, thus failing to meet the escalating demands of wireless communications. While double-layer three-dimensional (3D) antenna arrays presents a breakthrough for overcoming this bottleneck by exploiting the additional degrees of freedom, its implementation is hindered by several challenges, notably the issues of codebook design. In this paper, we propose a novel codebook scheme tailored for 3D antenna array structures. Specifically, an angle-distance-aware codebook for 3D antenna arrays is designed to cater to both near-field and far-field scenarios by minimizing inter-beam interference, with proven asymptotic orthogonality. Furthermore, evanescent codewords for both regions are effectively eliminated to improve codebook construction efficiency. Simulation results illustrate the superior performance of the proposed codebook over 2D baselines, with a 29% and 12% narrower angular and distance beamwidth ofh=λ, and a 27% gain in spectral efficiency ofh=0.5λ, owing to the vertical dimension. Moreover, practical mutual coupling that manifests as beam deviations and broadening is analyzed to establish a basis for future work.

With its capability to obtain a good tradeoff between complexity and performance, regularized zero-forcing (RZF) has been widely investigated to enhance the physical layer security. However, the associated reliability performance, i.e., secrecy outage probability (SOP), is not yet available in the literature. In this paper, we characterize the secrecy performance of RZF in the multi-user, downlink multiple-input single-output system. For this purpose, we first set up a central limit theorem for the joint distribution of users’ signal-to-interference-plus-noise ratio and eavesdropper’s signal-to-noise ratio by leveraging random matrix theory. The result is then utilized to obtain a closed-form approximation for the ergodic secrecy rate and SOP of three typical scenarios: the case with only external Eves, the case with only internal Eves, and that with both. The derived results are then used to evaluate the percentage of users in secrecy outage and the required number of transmit antennas to achieve a positive secrecy rate. It is shown that, with equally-capable Eves, the secrecy loss caused by external Eves is higher than that caused by internal Eves. Numerical simulations validate the accuracy of the theoretical results and demonstrate the advantage of RZF over other linear transmitters such as ZF.

Environment sensing and fusion via onboard sensors are envisioned to be widely applied in future autonomous driving networks. This paper considers a vehicular system with multiple self-driving vehicles that is assisted by multi-access edge computing (MEC), where image data collected by the sensors is offloaded from cellular vehicles to the MEC server using vehicle-to-infrastructure (V2I) links. Sensory data can also be shared among surrounding vehicles via vehicle-to-vehicle (V2V) communication links. To improve spectrum utilization, the V2V links may reuse the same frequency spectrum as the V2I links, which may cause severe interference. To tackle this issue, we leverage reconfigurable intelligent computational surfaces (RICSs) to jointly enable V2I reflective links and mitigate interference appearing at the V2V links. Considering the limitations of traditional algorithms in addressing this problem, such as the assumption of quasi-static channel state information, which restricts their ability to adapt to dynamic environmental changes and leads to poor performance under frequently varying channel conditions, in this paper, we formulate the problem at hand as a Markov game. Our novel formulation is applied to time-varying channels subject to multi-user interference and introduces a collaborative learning mechanism among users. The considered optimization problem is solved via a driving safety-enabled multi-agent deep reinforcement learning (DS-MADRL) approach that capitalizes on the RICS presence. Our extensive numerical investigations showcase that the proposed reinforcement learning approach achieves faster convergence and significant enhancements in both data rate and driving safety, as compared to various state-of-the-art benchmarks.

Despite the widespread adoption of vision sensors in edge applications, such as surveillance, video transmission consumes substantial spectrum resources. Semantic communication (SC) offers a solution by extracting and compressing information at the semantic level, but traditional SC without sensing capabilities faces inefficiencies due to the repeated transmission of static frames in edge videos. To address this challenge, we propose an SC with computer vision sensing (SCCVS) framework for edge video transmission. The framework first introduces a compression ratio (CR) adaptive SC (CRSC) model, capable of adjusting CR based on whether the frames are static or dynamic, effectively conserving spectrum resources. Simultaneously, we present a knowledge distillation (KD)-based approach to ensure the efficient learning of the CRSC model. Additionally, we implement a computer vision (CV)-based sensing model (CVSM) scheme, which intelligently perceives the scene changes by detecting the movement of the sensing targets. Therefore, CVSM can assess the significance of each frame through in-context analysis and provide CR prompts to the CRSC model based on real-time sensing results. Moreover, both CRSC and CVSM are designed as lightweight models, ensuring compatibility with resource-constrained sensors commonly used in practical edge applications. Experimental results show that SCCVS improves transmission accuracy by approximately 70% and reduces transmission latency by about 89% compared with baselines. We also deploy this framework on an NVIDIA Jetson Orin NX Super, achieving an inference speed of 14 ms per frame with TensorRT acceleration and demonstrating its real-time capability and effectiveness in efficient semantic video transmission.

Unmanned aerial vehicles (UAVs) have been extensively employed in the low-altitude intelligent network (LAIN) on data collection and transmission, enabling predictive maintenance, enhanced safety, and improved operational efficiency. However, the openness of wireless communication networks makes UAVs vulnerable to numerous security threats. To secure the critical transmitted data, many authenticated key agreement (AKA) schemes have been developed. Nevertheless, most existing AKA schemes fail to efficiently and securely authenticate communications between a single user and multiple UAVs in IIoT environments. To this end, we propose an extensible multi-party AKA scheme for LAINs. Specifically, we employ the physical unclonable functions and the Chinese remainder theorem to facilitate efficient authentication and data aggregation. Furthermore, by leveraging the additive homomorphic cryptography and blockchain, our scheme ensures privacy even in the presence of semi-trusted mobile operators. Formal security analyses and performance evaluations indicate that the proposed scheme meets the security requirements for LAINs while maintaining lightweight and extensible energy consumption.

Vertical federated learning (VFL) has been widely adopted in various domains for collaborative decision-making. However, recent studies have revealed critical privacy vulnerabilities in VFL, particularly label inference attacks, which significantly undermine label confidentiality and limit the applicability of VFL in privacy-sensitive scenarios. To mitigate such threats, several defense methods have been proposed by incorporating diverse privacy-preserving techniques. Nevertheless, existing defenses fail to effectively prevent the recently proposed model completion-based label inference attacks. To address this limitation, we propose a novel defense method, termed Label Compression-Based Defense (LCD), to defend against this class of attacks. The core idea of LCD is to train the VFL model using fake labels, thereby decoupling the ground-truth labels from the outputs of the malicious bottom model, which constitute the critical component exploited in the model completion-based attacks. Specifically, we introduce a multi-stage training strategy that decomposes the training process into different stages to deceive the malicious bottom model without affecting the original task. In addition, we design a deep feature-based label compression mechanism to generate fake labels for misleading the attacker. To further enhance the defense effectiveness, we propose an embedding compaction strategy based on center loss, which substantially increases the difficulty of label inference. Moreover, we theoretically prove the effectiveness of LCD from an information-theoretic perspective. Extensive experiments on both tabular and image datasets demonstrate that LCD can effectively defend against label inference attacks. The source code of LCD is publicly available at GitHub:https://github.com/YuanShunJie1/LCD.

In this paper, we propose a multi-SNR adaptive Semantic Communication (SemCom) System based on Recon figurable Intelligent Surface (RIS) to solve the problem of insufficient adaptability of traditional SemCom in dynamic chan nel environments. We firstly design a RIS-enhanced Semantic Communication (RISemCom) System that innovatively combines a programmable wireless environment with Deep Learning (DL) to achieve joint optimization of channel environment and se mantic feature extraction. Next, two training algorithms are proposed: Dynamic Random Environment Adaptive Multi-SNR (DREAMS) algorithm and Two-Stage Training (TST) algorithm. The DREAMS dynamically adjusts SNR values during training, allowing a single model to adapt to a wide range of SNR conditions while significantly reducing deployment complexity. The TST serves as a comparison baseline, providing a dedicated optimized model for each specific SNR environment. Numerical results are demonstrated to confirm that the DREAMS algorithm maintains excellent performance across a wide range of SNRs with a single model, and significantly improves the PSNR and SSIM metrics compared to traditional methods under low SNR conditions. The performance gain is particularly notable in challenging low SNR environments, proving the system's robustness in adverse channel conditions. This work not only expands the applicability of SemCom but also provides new insights for reliable communication in variable channel environments in future 6G networks.

The intrinsic integration of Rydberg atomic receivers into wireless communication systems is proposed, by harnessing the principles of quantum physics in wireless communications. More particularly, we conceive a pair of Rydberg atomic receivers, one incorporates a local oscillator (LO), referred to as an LO-dressed receiver, while the other operates without an LO and is termed an LO-free receiver. The appropriate wireless model is developed for each configuration, elaborating on the receiver's responses to the radio frequency (RF) signal, on the potential noise sources, and on the signal-to-noise ratio (SNR) performance. The developed wireless model conforms to the classical RF framework, facilitating compatibility with established signal processing methodologies. Next, we investigate the associated distortion effects that might occur, specifically identifying the conditions under which distortion arises and demonstrating the boundaries of linear dynamic ranges. This provides critical insights into its practical implementations in wireless systems. Finally, extensive simulation results are provided for characterizing the performance of wireless systems, harnessing this pair of Rydberg atomic receivers. Our results demonstrate that LO-dressed systems achieve a significant SNR gain of approximately 40~50 dB over conventional RF receivers in the standard quantum limit regime. This SNR head-room translates into reduced symbol error rates, enabling efficient and reliable transmission with higher-order constellations.

Stacked intelligent metasurfaces (SIMs), which integrate multiple programmable metasurface layers, have recently emerged as a promising technology for advanced wave-domain signal processing. SIMs benefit from flexible spatial degree-of-freedom (DoF) while reducing the requirement for costly radio-frequency (RF) chains. However, current state-of-the-art SIM designs face challenges such as complex phase shift optimization and energy attenuation from multiple layers. To address these aspects, we propose incorporating meta-fibers into SIMs, with the aim of reducing the number of layers and enhancing the energy efficiency. First, we introduce a meta-fiber-connected 2-layer SIM that exhibits the same flexible signal processing capabilities as conventional multi-layer structures, and explains the operating principle. Subsequently, we formulate and solve the optimization problem of minimizing the mean square error (MSE) between the SIM channel and the desired channel matrices. Specifically, by designing the phase shifts of the meta-atoms associated with the transmitting-SIM and receiving-SIM, a non-interference system with parallel subchannels is established. In order to reduce the computational complexity, a closed-form expression for each phase shift at each iteration of an alternating optimization (AO) algorithm is proposed. We show that the proposed algorithm is applicable to conventional multi-layer SIMs. The channel capacity bound and computational complexity are analyzed to provide design insights. Finally, numerical results are illustrated, demonstrating that the proposed two-layer SIM with meta-fiber achieves over a 25% improvement in channel capacity while reducing the total number of meta-atoms by 59% as compared with a conventional seven-layer SIM.

Artificial noise (AN) has been recognized as an effective physical-layer security scheme impairing the eavesdropper (Eve). Recently, artificial noise elimination (ANE) has emerged as a promising strategy to mitigate the impact of AN at Eves. However, conventional ANE schemes rely on prior knowledge, such as legitimate channel state information (CSI) or classification information, which may limit their practical applicability. To address these practical challenges, we propose an ANE scheme beyond prior knowledge (BPK) by leveraging machine learning algorithms. Firstly, a coarse projection is applied to partially eliminate the impact of AN using maximum likelihood estimation on the equivalent AN matrix. Secondly, a density clustering algorithm is introduced to obtain classification information based on the coarsely-projected observed vectors. Thirdly, a generalized principal component analysis (PCA)-based ANE algorithm is developed to effectively mitigate the residual AN using the obtained classification information. Furthermore, the artificial-noise-to-signal ratio (ANSR) and computational complexity are analyzed for performance revaluation, and a redefinition of several AN design principles is provided for scenarios involving a powerful Eve equipped with the BPK-ANE scheme by deriving the validity boundary. Finally, numerical results reveal key insights into four principles of AN: 1) Allocating less power to AN; 2) Reducing the randomness of AN; 3) Increasing the number of transmit antennas; and 4) Increasing the modulation order.

We propose a novel doubly-dispersive (DD) multiple-input multiple-output (MIMO) channel model incorporating flexible intelligent metasurfaces (FIMs), which is suitable for integrated sensing and communications (ISAC) in high-mobility scenarios. We then discuss how the proposed FIM-parameterized DD (FPDD) channel model can be applied in a logical manner to multicarrier waveforms that are known to perform well in DD environments, namely, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM). Leveraging the proposed model, we formulate an achievable rate maximization problem with a strong sensing constraint for all the aforementioned waveforms, which we then solve via a gradient ascent algorithm with closed-form gradients presented as a bonus. Our numerical results indicate that the achievable rate is significantly impacted by the emerging FIM technology with careful parametrization essential in obtaining strong ISAC performance across all waveforms suitable to mitigating the effects of DD channels.

The rapid expansion of latency-sensitive applications has sparked renewed interest in deploying edge computing capabilities aboard satellite constellations, aiming to achieve truly global and seamless service coverage. On one hand, it is essential to allocate the limited onboard computational and communication resources efficiently to serve geographically distributed users. On the other hand, the dynamic nature of satellite orbits necessitates effective service migration strategies to maintain service continuity and quality as the coverage areas of satellites evolve. We formulate this problem as a spatio-temporal Markov decision process, where satellites, ground users, and flight users are modeled as nodes in a time-varying graph. The node features incorporate queuing dynamics to characterize packet loss probabilities. To solve this problem, we propose a Graph-Aware Temporal Encoder (GATE) that jointly models spatial correlations and temporal dynamics. GATE uses a two-layer graph convolutional network to extract inter-satellite and user dependencies and a temporal convolutional network to capture their short-term evolution, producing unified spatio-temporal representations. The resulting spatial-temporal representations are passed into a Hybrid Proximal Policy Optimization (HPPO) framework. This framework features a multi-head actor that outputs both discrete service migration decisions and continuous resource allocation ratios, along with a critic for value estimation. We conduct extensive simulations involving both persistent and intermittent users distributed across real-world population centers. The results validate that the proposed framework consistently achieves superior performance compared to Proximal Policy Optimization (PPO), Soft Actor Critic (SAC), and ablated baselines in terms of reward, failure rate, and migration overhead, demonstrating the effectiveness of the proposed spatio-temporal modeling and hybrid reinforcement learning approach in dynamic satellite edge environments.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation has recently been regarded as a promising technology to provide reliable communications in high-mobility situations. Accurate and low-complexity channel estimation is one of the most critical challenges for massive multiple input multiple output (MIMO) ODDM systems, mainly due to the extremely large antenna arrays and high-mobility environments. To overcome these challenges, this paper addresses the issue of channel estimation in downlink massive MIMO-ODDM systems and proposes a low-complexity algorithm based on memory approximate message passing (MAMP) to estimate the channel state information (CSI). Specifically, we first establish the effective channel model of the massive MIMO-ODDM systems, where the magnitudes of the elements in the equivalent channel vector follow a Bernoulli-Gaussian distribution. Further, as the number of antennas grows, the elements in the equivalent coefficient matrix tend to become completely random. Leveraging these characteristics, we utilize the MAMP method to determine the gains, delays, and Doppler effects of the multi-path channel, while the channel angles are estimated through the discrete Fourier transform method. Finally, numerical results show that the proposed channel estimation algorithm approaches the Bayesian optimal results when the number of antennas tends to infinity and improves the channel estimation accuracy by about 30% compared with the existing algorithms in terms of the normalized mean square error.

Despite the success of large language models (LLMs) across domains, their potential for efficient channel state information (CSI) compression and feedback in frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems remains largely unexplored yet increasingly important. In this paper, we propose a novel LLM-based framework for CSI feedback to exploit the potential of LLMs. We first reformulate the CSI compression feedback task as a masked token prediction task that aligns more closely with the functionality of LLMs. Subsequently, we design an information-theoretic mask selection strategy based on self-information, identifying and selecting CSI elements with the highest self-information at the user equipment (UE) for feedback. This ensures that masked tokens correspond to elements with lower self-information, while visible tokens correspond to elements with higher self-information, thus maximizing the accuracy of LLM predictions. Finally, the LLM leverages its robust modeling capabilities to reconstruct complete CSI representations through contextual inference. This self-information-driven masking strategy integrates the LLM-based masked token prediction mechanism into a coherent, information-driven framework. Numerical results indicate that the proposed LLM-based CSI feedback framework significantly outperforms traditional small models in CSI reconstruction accuracy, leading to substantial improvements in communication rates in multi-user MIMO scenarios. This approach has the potential to address the limitations of CSI reconstruction accuracy that restrict multi-user communication rates. Moreover, the method deploys a lightweight network at the UE, with additional network complexity overhead only at the base station (BS). Finally, the method demonstrates strong generalization across different compression ratios and exhibits excellent transfer learning capabilities across various channel scenarios. These findings pave the way for integrating LLMs into next-generation wireless communication systems.

Affine frequency division multiplexing (AFDM) and orthogonal time frequency space (OTFS) are two promising advanced waveforms proposed for reliable communications in high-mobility scenarios. In this paper, we introduce a simple transmit diversity technique, termed cyclic delay-Doppler shift (CDDS), for these two advanced waveforms to achieve ultra-reliable communications in doubly selective channels (DSCs). Two simple CDDS schemes, named modulation-domain CDDS (MD-CDDS) and time-domain CDDS (TD-CDDS), are proposed, which perform CDDS in advance at the transmitter before and after the modulation, respectively. We demonstrate that both of the two proposed CDDS schemes can be implemented efficiently and flexibly by multiplying the transmit vector with a well-designed precoding matrix, which is nothing but a sparse phase-compensated permutation matrix. Moreover, we theoretically and numerically prove that CDDS can provide MIMO-AFDM and MIMO-OTFS with optimal transmit diversity gain when a proper CDDS step is adopted. Compared to the conventional transmit diversity techniques, the proposed CDDS scheme enjoys the advantages of lower channel estimation overhead, implementation complexity, and signal processing latency, making it particularly suitable for ultra-reliable communications in high-mobility scenarios.

Rate-splitting multiple access (RSMA) has been studied for multiuser multiple-input multiple-output (MU-MIMO) systems especially in the presence of imperfect channel state information (CSI) at the transmitter. However, its precoding designs that maximize the sum rate normally have high computational complexity. To implement an efficient RSMA scheme for the MU-MIMO system, in this work, we propose a novel robust precoding design, which can handle imperfect CSI. Specifically, we first adopt the generalized mutual information to construct a lower bound of the objective function in the sum rate maximization problem. Then, we apply a smooth lower bound of the non-smooth sum rate objective function to construct a new optimization problem. By revealing the relationship between the generalized signal-to-interference-plus-noise ratio and the minimum mean square error matrices, we transform the constructed problem into a tractable one. After decomposing the transformed problem into three subproblems, we investigate a new alternating precoding design based on sequential solutions. Simulation results demonstrate that the proposed precoding scheme achieves comparable performance to conventional methods, while significantly reducing the computational complexity.

Satellite-Air-Ground Integrated Networks (SAGINs) provide ubiquitous connectivity, global coverage and flexible deployment convenience for terrestrial users, which are beneficial to optimizing network resources and achieving task scheduling functions. However, the corresponding SAGIN nodes are dynamic and complex, leading to intractable multi-modal features and high network latency when graph model is used for collaborative task completion. Therefore, we establish a directed SAGIN federated graph model to minimize the total transmission latency via computation offloading and quantization methods. Specifically, we utilize the federated graph learning to process the time-varying graph nodes and sizes, and then perform deep reinforcement learning (DRL) to optimize the computation and quantization resources. Moreover, federated learning is convoked to accelerate the convergence speed. Finally, our simulation results show that the proposed method outperforms some advanced benchmarks in terms of convergence performance and transmission latency for multiple data modals.

The reconfigurability of fluid antenna systems (FASs) and reconfigurable intelligent surfaces (RISs) can be jointly utilized to achieve unprecedented degrees of freedom for wireless communication systems. However, adjusting fluid antennas and RISs based on instantaneous channel state information (CSI) is highly challenging. To tackle this challenge, we propose a two-timescale approach for FAS-RIS-aided multi-user systems with regularized zero-forcing (RZF)/zero-forcing (ZF) precoding, where only statistical CSI is required for FAS and RIS optimization. To achieve this goal, we first obtain the closed-form evaluation for the ergodic sum rate (ESR) of FAS-RIS aided multi-user systems with RZF/ZF precoding by exploiting random matrix theory (RMT). Then, we propose an ESR maximization algorithm by jointly optimizing the port selection for FASs, phase shifts at the RIS, and regularization factor of RZF. Numerical results validate the approximation accuracy of the derived ESR evaluation and demonstrate that the performance enhancement benefiting from the joint design of FASs and RISs becomes more prominent when the number of users becomes larger.

This study seeks to utilize large language models (LLMs) to forecast the moving ports of fluid antenna (FA). By repositioning the antenna to the locations identified by our proposed model, we intend to address the mobility challenges faced by user equipment (UE). To the best of our knowledge, this paper introduces, for the first time, the application of LLMs in the prediction of FA ports, presenting a novel model termed Port-LLM. The architecture of our model is based on the pre-trained GPT-2 framework. We designed specialized data preprocessing, input embedding, and output projection modules to effectively bridge the disparities between the wireless communication data and the data format utilized by the pre-trained LLM. Simulation results demonstrate that our model exhibits superior predictive performance under different numbers of base station (BS) antennas and varying UE speeds, indicating strong generalization and robustness ability. Furthermore, the spectral efficiency (SE) attained by our model surpasses that achieved by traditional methods in both medium and high-speed mobile environments.

Cooperative multi-Unmanned Aerial Vehicle (UAV) systems for dynamic tasks, such as target tracking, face challenges in maintaining efficient coordination when agents become inactive upon task completion. This dynamic behavior introduces heterogeneous input streams to centralized state evaluation components (Critics) in multi-agent reinforcement learning frameworks, impairing coordination and increasing network resource demands, such as bandwidth and latency. This work proposes a novel Learned Inactive Feature Mixture-of- Experts (LIF-MoE) Critic to address the above issue, which jointly learns a compact inactive representation and applies expert-based specialization to diverse agent inputs. LIF-MoE replaces uninformative inactive observations with a learnable feature vector to provide meaningful representations for inactive states, while employing per-agent MoE processing with sparse routing to enable specialized handling of heterogeneous inputs. This approach enhances state representation for accurate value estimation, thus facilitating efficient coordination of the UAV swarms. Simulation results validate that LIF-MoE significantly improves task performance and reduces mission times compared to baselines, with pronounced advantages in complex scenarios.

Unmanned aerial vehicle (UAV) communication plays a pivotal role in achieving ubiquitous connectivity for the sixth-generation (6G) networks. Accurate and real-time direction of arrival (DOA) estimation is crucial for beamforming in UAV communication systems. However, the existing high-precision DOA estimation algorithms encounter high computational complexity when being implemented on a UAV with the on-board signal processing constraints. To tackle this issue, a hybrid optical-electric neural network (HOENN) is utilized for DOA estimation, which is capable of generating angular spectrum based solely on amplitude observation. The proposed HOENN consists of two components: a stacked intelligent metasurfaces (SIM)-enabled diffractive neural network, which is mounted on UAV and can process signals in the wave domain at the speed of light with low energy consumption, and a fully connected layer for processing the received amplitude signal. Finally, the simulation results validate that the HOENN achieves significant performance gain compared to the conventional beamforming (CBF) method, albeit with its lower cost and RF-related power consumption.

Flexible intelligent metasurfaces (FIMs) constitute a promising technology that could significantly boost the wireless network capacity. An FIM is essentially a soft array made up of many low-cost radiating elements that can independently emit electromagnetic signals. What's more, each element can flexibly adjust its position, even perpendicularly to the surface, to morph the overall 3D shape. In this paper, we study the potential of FIMs in point-to-point multiple-input multiple-output (MIMO) communications, where two FIMs are used as transceivers. In order to characterize the capacity limits of FIM-aided narrowband MIMO transmissions, we formulate an optimization problem for maximizing the MIMO channel capacity by jointly optimizing the 3D surface shapes of the transmitting and receiving FIMs, as well as the transmit covariance matrix, subject to a specific total transmit power constraint and to the maximum morphing range of the FIM. To solve this problem, we develop an efficient block coordinate descent (BCD) algorithm. The BCD algorithm iteratively updates the 3D surface shapes of the FIMs and the transmit covariance matrix, while keeping the other fixed. Numerical results verify that FIMs can achieve higher MIMO capacity than traditional rigid arrays. In some cases, the MIMO channel capacity can be doubled by employing FIMs.

Rydberg atomic quantum receivers exhibit great potential in assisting classical wireless communications due to their outstanding advantages in detecting radio frequency signals. To realize this potential, we integrate a Rydberg atomic quantum receiver into a classical multi-user multiple-input multiple-output (MIMO) scheme to form a multi-user Rydberg atomic quantum MIMO (RAQ-MIMO) system for the uplink. To study this system, we first construct an equivalent baseband signal model, which facilitates convenient system design, signal processing and optimizations. We then study the ergodic achievable rates under both the maximum ratio combining (MRC) and zero-forcing (ZF) schemes by deriving their tight lower bounds. We next compare the ergodic achievable rates of the RAQ-MIMO and the conventional massive MIMO schemes by offering a closed-form expression for the difference of their ergodic achievable rates, which allows us to directly compare the two systems. Our results show that RAQ-MIMO allows the average transmit power of users to be$>25 \text{d B m}$lower than that of the conventional massive MIMO. Viewed from a different perspective, an extra$\sim 8.8$bits/s/Hz/user rate becomes achievable by ZF RAQ-MIMO.

Stacked intelligent metasurfaces (SIMs), composed of a multi-layered structure, have emerged as a powerful computing tool and analog signal processing platform for enabling task-oriented semantic communications (SemCom). However, SIMs lack nonlinear inference capabilities, thus motivating the emergence of the hybrid optical-electronic neural network (HOENN) that cascades a SIM and an electronic neural network (ENN). In this work, we investigate a disaster recognition taskoriented SemCom setting by leveraging the HOENN technology. Specifically, the HOENN is made of an optical neural network (ONN) using SIM mounted on an unmanned aerial vehicle (UAV) and a shallow ENN at the ground receiving station (GRS). The SIM automatically processes semantic information modulated on electromagnetic waves, with reduced energy consumption and ultrafast processing speed. At the GRS, the energy signals are processed by the shallow ENN to enhance the system's inference capability. The aim is to recognize the disaster according to the captured geomorphic images. To this end, we utilize a stochastic gradient descent algorithm to train the HOENN efficiently to minimize the cross-entropy between the recognized and actual semantics. Numerical results show that the HOENN surpasses the performance of using either ONN or ENN alone, achieving$\mathbf{9 7 \%}$recognition accuracy.

Recently, with the users' growing demand for communication rate and capacity in wireless networks, Unmanned Aerial Vehicles (UAVs) have attracted widespread attention due to their mobility, flexibility, and robust line-of-sight communication links. By equipping UAVs with multiple communication payloads, we can construct an aerial wireless network with three-dimensional coverage. However, due to the limitations of UAV onboard energy and communication resources, the lifetime and performance of UAV-assisted wireless networks are significantly constrained. This paper mainly focuses on equipping UAVs with mobile base stations to enhance wireless communication coverage and capacity. We propose a Joint Optimization of 3D Trajectory and Resource Allocation (JOTRA) scheme to maximize energy efficiency in complex scenarios with multi-user mobility and diverse requirements (e.g., UAV-assisted post-disaster search and rescue). Specifically, we apply Dinkelbach's iterative method and Block Coordinate Descent (BCD) method to solve the formulated multivariable and non-convex maximization problem. The algorithm's convergence has been analyzed. According to the simulation, the proposed algorithm can converge faster while maximizing energy efficiency in complex wireless communication scenarios.

The increasing demand for high-quality real-time 360° video streams from mobile platforms, such as 5G-connected Unmanned Aerial Vehicles (UAVs), is challenging modern B5G networks. Vehicular mobility and fluctuating conditions in high-altitude, high-speed scenarios, known as high volatility, complicate maintaining an effective Quality of Experience (QoE) for cellular networks. This work introduces FlyBit, a Deep Reinforcement Learning (DRL)-based bitrate selection framework for live 360° video streaming in 5G-connected UAV applications, designed to enhance video quality, reduce packet loss, and minimize End-to-End (E2E) latency. We developed and deployed a real-world testbed to evaluate the impact of dynamic network conditions, UAV mobility, and trajectory on streaming performance, analyzing FlyBit with real-world data. Experimental results show that FlyBit improves Video Multimethod Assessment Fusion (VMAF) by ~29% and average bitrate by ~50%, while maintaining low latency and packet loss compared to baseline approaches, demonstrating its ability to adjust bitrate in real-time and significantly improve QoE for ultra-low-latency video streaming.

This paper introduces a novel framework for designing efficient neural network architectures specifically tailored to tiny machine learning (TinyML) platforms. By leveraging large language models (LLMs) for neural architecture search (NAS), a vision transformer (ViT)-based knowledge distillation (KD) strategy, and an explainability module, the approach strikes an optimal balance between accuracy, computational efficiency, and memory usage. The LLM-guided search explores a hierarchical search space, refining candidate architectures through Pareto optimization based on accuracy, multiply-accumulate operations (MACs), and memory metrics. The best-performing architectures are further fine-tuned using logits-based KD with a pre-trained ViT-B/16 model, which enhances generalization without increasing model size. Evaluated on the CIFAR-100 dataset and deployed on an STM32H7 microcontroller (MCU), the three proposed models, LMaNet-Elite, LMaNet-Core, and QwNet-Core, achieve accuracy scores of 74.50%, 74.20% and 73.00%, respectively. All three models surpass current state-of-the-art (SOTA) models, such as MCUNet-in3/in4 (69.62% / 72.86%) and XiNet (72.27%), while maintaining a low computational cost of less than 100 million MACs and adhering to the stringent 320 KB static random-access memory (SRAM) constraint. These results demonstrate the efficiency and performance of the proposed framework for TinyML platforms, underscoring the potential of combining LLM-driven search, Pareto optimization, KD, and explainability to develop accurate, efficient, and interpretable models. This approach opens new possibilities in NAS, enabling the design of efficient architectures specifically suited for TinyML. To facilitate further research and development in this field, the proposed framework and the best-performing architectures are made publicly available at Link.

Due to the continuous traffic of ubiquitous cellular networks and the ultra low-power low-cost characteristics of backscatter communication, cellular backscatter communication is a crucial technology for passive Internet of Things. However, existing cellular backscatter systems suffer from the lack of downlink control ability, low-efficiency modulation, and unreliable demodulation for megabit-rate backscatter transmission. This paper proposes CellScatter to overcome such drawbacks. First, we design a synchronization-and-control module for a CellScatter tag, which exploits cellular reference signals to achieve accurate synchronization and reliable multi-tag control without extra time overhead. Then, we design a single-sideband high-order modulation module to focus the backscatter signal power to the desired band and improve the spectrum utilization efficiency. Furthermore, we establish an explicit model to represent the spectrum-expanded backscatter signal without requiring a high-speed analog-to-digital converter, and derive a closed-form solution for the user equipment to recover the data of the tag at low complexity. We prototype the CellScatter system and evaluate its performance via ambient 4G LTE and 5G NR signals. Experimental results show that CellScatter can achieve 2.24 Mbps backscatter communication within a range of 30 meters, 6.7× lower BER and 33% higher rate than state of the art solutions.

With a massive number of antennas densely deployed in a compact area, holographic multiple-input multiple-output (HMIMO) systems are envisioned to be a key enabling technology for improving the data rate and coverage of 6 G networks. Unfortunately, the reduced spacing between radiation elements, which enables HMIMO to better exploit the channel propagation characteristics, also causes increased mutual coupling (MC) and reduced radiation efficiency. It is thus critical to understand the effect of MC on the capacity of HMIMO systems, which is not yet available in the literature. In this paper, we investigate the ergodic mutual information (EMI) and associated capacity-achieving transmit covariance design for HMIMO systems with MC. To this end, we first derive the closed-form expression for the EMI of HMIMO systems with MC, by leveraging random matrix theory (RMT). Then, based on the derived results, we propose an MC-aware algorithm to maximize the EMI by optimizing the transmit covariance matrix. Numerical simulations validate the accuracy of the theoretical analysis and the effectiveness of the proposed MC-aware algorithm. It is observed that the halfwavelength antenna spacing is not optimal especially with low signal-to-noise ratio.

The parameter size of modern large language models (LLMs) can be scaled up to the trillion-level via the sparsely-activated Mixture-of-Experts (MoE) technique to avoid excessive increase of the computational costs. To further improve training efficiency, pipelining computation and communication has become a promising solution for distributed MoE training. However, existing work primarily focuses on scheduling tasks within the MoE layer, such as expert computing and all-to-all (A2A) communication, while neglecting other key operations including multi-head attention (MHA) computing, gating, and all-reduce communication. In this paper, we propose FlowMoE, a scalable framework for scheduling multi-type task pipelines. First, FlowMoE constructs a unified pipeline to consistently scheduling MHA computing, gating, expert computing, and A2A communication. Second, FlowMoE introduces a tensor chunk-based priority scheduling mechanism to overlap the all-reduce communication with all computing tasks. We implement FlowMoE as an adaptive and generic framework atop PyTorch. Extensive experiments with 675 typical MoE layers and four real-world MoE models across two GPU clusters demonstrate that our proposed FlowMoE framework outperforms state-of-the-art MoE training frameworks, reducing training time by14%-57%, energy consumption by 10%-39%, and memory usage by 7%-32%. FlowMoE’s code is anonymously available at https://anonymous.4open.science/r/FlowMoE.

This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction method, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. In the performance analysis, we derive the asymptotical lower and upper bounds of the prediction error for a multipath channel, when the number of base station (BS) antennas and the port density of the FA are large enough. When the UEs move at a speed of 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction method.

As Uncrewed Aerial Vehicle (UAV) technology matures and spreads, non-cooperative UAV intrusions have significantly increased airspace safety risks. To achieve all-weather airspace awareness and multi-level threat response, integrating UAV detection and warning into existing cellular base stations is a promising solution. Currently, most UAV detection techniques involve extracting Micro-Doppler features during UAV movement. However, extracting salient Micro-Doppler motion features is challenging and requires complex mathematical algorithms. We present a novel detection network called the Shallow Brain Residual Network (SBRN) to address this critical need for timely detection and early warning systems. The SBRN is inspired by the Shallow Brain architecture, which emulates the parallel processing hierarchy of the human visual system and fundamentally differs from classical deep learning frameworks. To the best of our knowledge, the proposed SBRN model is the first architecture that systematically implements a complete shallow-brain biological architecture. Our framework achieves detection accuracy of 99.45% while eliminating complex data preprocessing requirements. The model demonstrates robust multi-class discrimination capabilities, distinguishing between six distinct UAV types and multiple avian species. Unlike conventional deep learning models, the SBRN with only 17 million parameters, enabling efficient deployment. The network exhibits fast convergence speed, achieving > 96% accuracy within the first training epoch.

Large language models (LLMs) face significant challenges in specialized domains like telecommunication (Tele-com) due to technical complexity, specialized terminology, and rapidly evolving knowledge. Traditional methods, such as scaling model parameters or retraining on domain-specific corpora, are computationally expensive and yield diminishing returns, while existing approaches like retrieval-augmented generation, mixture of experts, and fine-tuning struggle with accuracy, efficiency, and coordination. To address this issue, we propose Telecom mixture of models (TeleMoM), a consensus-driven ensemble framework that integrates multiple LLMs for enhanced decision-making in Telecom. TeleMoM employs a two-stage process: proponent models generate justified responses, and an adjudicator finalizes decisions, supported by a quality-checking mechanism. This approach leverages strengths of diverse models to improve accuracy, reduce biases, and handle domain-specific complexities effectively. Evaluation results demonstrate that TeleMoM achieves a 9.7% increase in answer accuracy, highlighting its effectiveness in Telecom applications.

The telecommunications industry’s rapid evolution demands intelligent systems capable of managing complex networks and adapting to emerging technologies. While large language models (LLMs) show promise in addressing these challenges, their deployment in telecom environments faces significant constraints due to edge device limitations and inconsistent documentation. To bridge this gap, we present TeleOracle, a telecom-specialized retrieval-augmented generation (RAG) system built on the Phi-2 small language model (SLM). To improve context retrieval, TeleOracle employs a two-stage retriever that incorporates semantic chunking and hybrid key-word and semantic search. Additionally, we expand the context window during inference to enhance the model’s performance on open-ended queries. We also employ low-rank adaption for efficient fine-tuning. A thorough analysis of the model’s performance indicates that our RAG framework is effective in aligning Phi-2 to the telecom domain in a downstream question and answer (QnA) task, achieving a 30% improvement in accuracy over the base Phi-2 model, reaching an overall accuracy of 81.20%. Notably, we show that our model not only performs on par with the much larger LLMs but also achieves a higher faithfulness score, indicating higher adherence to the retrieved context.

Unmanned Aerial Vehicles (UAVs) are critical in modern emergency response due to their high mobility. However, limited computing resources and energy supplies necessitate the use of UAV networks for collaborative inference. UAV intelligent tasks are often Deep Neural Networks (DNN)-based, with DNN partitioning enabling collaborative inference. However, executing DNN partitioning in a highly dynamic UAV network faces two challenges that have not been addressed in existing research: the time gap between the state sampling and the execution of the corresponding action based on that state, and the unknown trajectories in advance. The time gap requires predictive action decision-making. To address this, we model DNN partitioning and edge offloading with hybrid action decisions in dynamic, energy-harvesting UAV networks as a Predictive Markov Decision Process (P-MDP). The rapidly changing and previously unknown network topology significantly impacts channel and data transmission energy consumption, affecting DNN partitioning decisions. To better solve the action prediction problem, we use the Transformer module to extract motion features from recent time slots in the proposed Transformer-enhanced Multi-Agent Hybrid Action Proximal Policy Optimization (TE-MHAPPO) framework. Simulation results show that TE-MHAPPO reduces the reward which comprehensively considers task delay and energy consumption, by at least 12.1% compared to the state-of-theart MHAPPO. Additionally, its reward performance degradation with the increase in prediction time is at most 55.2% of that observed in the baseline.

Network optimization is a fundamental challenge in the Internet of Things (IoT) network, often characterized by complex features that make it difficult to solve these problems. Recently, generative diffusion models (GDMs) have emerged as a promising new approach to network optimization, with the potential to directly address these optimization problems. However, the application of GDMs in this field is still in its early stages, and there is a noticeable lack of theoretical research and empirical findings. In this study, we first explore the intrinsic characteristics of generative models. Next, we provide a concise theoretical proof and intuitive demonstration of the advantages of generative models over discriminative models in network optimization. Based on this exploration, we implement GDMs as optimizers aimed at learning high-quality solution distributions for given inputs, sampling from these distributions during inference to approximate or achieve optimal solutions. Specifically, we utilize denoising diffusion probabilistic models (DDPMs) and employ a classifier-free guidance mechanism to manage conditional guidance based on input parameters. We conduct extensive experiments across three challenging network optimization problems. By investigating various model configurations and the principles of GDMs as optimizers, we demonstrate the ability to overcome prediction errors and validate the convergence of generated solutions to optimal solutions. We provide code and data athttps://github.com/qiyu3816/DiffSG.

Fluid antenna systems (FAS) enable dynamic antenna positioning, offering new opportunities to enhance integrated sensing and communication (ISAC) performance. However, existing studies primarily focus on communication enhancement or single-target sensing, leaving multi-target scenarios underexplored. Additionally, the joint optimization of beamforming and antenna positions poses a highly non-convex problem, with traditional methods becoming impractical as the number of fluid antennas increases. To address these challenges, this letter proposes a block coordinate descent (BCD) framework integrated with a deep reinforcement learning (DRL)-based approach for intelligent antenna positioning. By leveraging the deep deterministic policy gradient (DDPG) algorithm, the proposed framework efficiently balances sensing and communication performance. Simulation results demonstrate the scalability and effectiveness of the proposed approach. Unlike traditional optimization approaches that suffer from exponential complexity growth, our DRL-based method achieves real-time decision-making with superior scalability for complex multi-target scenarios while maintaining computational efficiency.

How to reduce the pilot overhead required for channel estimation? How to deal with the channel dynamic changes and error propagation in channel prediction? To jointly address these two critical issues in next-generation transceiver design, in this paper, we propose a novel framework named channel deduction for high-dimensional channel acquisition in multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems. Specifically, it makes use of the outdated channel information of past time slots, performs coarse estimation for the current channel with a relatively small number of pilots, and then fuses these two information to obtain a complete representation of the present channel. The rationale is to align the current channel representation to both the latent channel features within the past samples and the coarse estimate of current channel at the pilots, which, in a sense, behaves as a complementary combination of estimation and prediction and thus reduces the overall overhead. To fully exploit the highly nonlinear correlations in time, space, and frequency domains, we resort to learning-based implementation approaches. By using the highly efficient complex-domain multilayer perceptron (MLP)-mixer for across-space-frequency-domain representation and the recurrence-based or attention-based mechanisms for the past-present interaction, we respectively design two different channel deduction neural networks (CDNets). We provide a general procedure of data collection, training, and deployment to standardize the application of CDNets. Comprehensive experimental evaluations in accuracy, robustness, and efficiency demonstrate the superiority of the proposed approach, which reduces the pilot overhead by up to 88.9% compared to state-of-the-art estimation approaches and enables continuous operating even under unknown user movement and error propagation.

With the rapid growth of multimedia data volume, there is an increasing need for efficient video transmission in applications such as virtual reality and future video streaming services. Semantic communication is emerging as a vital technique for ensuring efficient and reliable transmission in low-bandwidth, high-noise settings. However, most current approaches focus on joint source-channel coding (JSCC) that depends on end-to-end training. These methods often lack an interpretable semantic representation and struggle with adaptability to various downstream tasks. In this paper, we introduce the use of object-attribute-relation (OAR) as a semantic framework for videos to facilitate low bit-rate coding and enhance the JSCC process for more effective video transmission. We utilize OAR sequences for both low bit-rate representation and generative video reconstruction. Additionally, we incorporate OAR into the image JSCC model to prioritize communication resources for areas more critical to downstream tasks. Our experiments on traffic surveillance video datasets assess the effectiveness of our approach in terms of video transmission performance. The empirical findings demonstrate that our OAR-based video coding method not only outperforms H.265 coding at lower bit-rates but also synergizes with JSCC to deliver robust and efficient video transmission.

Presents corrections to the paper, Coverage Rate Analysis for Integrated Sensing and Communication Networks.

In this paper, a novel learning-based Wyner-Ziv coding framework is considered under a distributed image transmission scenario, where the correlated source is only available at the receiver. Unlike other learnable frameworks, our approach demonstrates robustness to non-stationary source correlation, where the overlapping information between image pairs varies. Specifically, we first model the affine relationship between correlated images and leverage this model for learnable mask generation and rate-adaptive joint source-channel coding. Moreover, we also provide a warping-prediction network to remove the distortion from channel interference and affine transform. Intuitively, the observed performance improvement is largely due to focusing on the simple geometric relationship, rather than the complex joint distribution between the sources. Numerical results show that our framework achieves a 1.5 dB gain in PSNR and a 0.2 improvement in MS-SSIM, along with a significant superiority in perceptual metric, compared to state-of-the-art methods when applied to real-world samples with non-stationary correlations.

Building the next-generation wireless systems that could support services such as the metaverse, digital twins (DTs), and holographic teleportation is challenging to achieve exclusively through incremental advances to conventional wireless technologies like metasurfaces or holographic antennas. While the 6G concept of artificial intelligence (AI)-native networks promises to overcome some of the limitations of existing wireless technologies, current developments of AI-native wireless systems rely mostly on conventional AI tools such as auto-encoders and off-the-shelf artificial neural networks. However, those tools struggle to manage and cope with the complex, nontrivial scenarios faced in real-world wireless environments and the growing quality-of-experience (QoE) requirements of the aforementioned, emerging wireless use cases. In contrast, in this article, we propose to fundamentally revisit the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems. Our envisioned AGI-native wireless systems acquire common sense by exploiting different cognitive abilities such as reasoning and analogy. These abilities in our proposed AGI-native wireless system are mainly founded on three fundamental components: a perception module, a world model, and an action-planning component. Collectively, these three fundamental components enable the four pillars of common sense that include dealing with unforeseen scenarios through horizontal generalizability, capturing intuitive physics, performing analogical reasoning, and filling in the blanks. Toward developing these components, we start by showing how the perception module can be built through abstracting real-world elements into generalizable representations. These representations are then used to create a world model, founded on principles of causality and hyperdimensional (HD) computing. Specifically, we propose a concrete definition of a world model, viewing it as an HD causal vector space that aligns with the intuitive physics of the real world—a cornerstone of common sense. In addition,we discuss how this proposed world model can enable analogical reasoning and manipulation of the abstract representations. Then, we show how the world model can drive an action-planning feature of the AGI-native network. In particular, we propose an intent-driven and objective-driven planning method that can maneuver the AGI-native network to plan its actions. These planning methods are based on brain-inspired frameworks such as integrated information theory and hierarchical abstractions that play a crucial role in enabling human-like decision-making. Next, we explain how an AGI-native network can be further exploited to enable three use cases related to human users and autonomous agent applications: 1) analogical reasoning for the next-generation DTs; 2) synchronized and resilient experiences for cognitive avatars; and 3) brain-level metaverse experiences exemplified by holographic teleportation. Finally, we conclude with a set of recommendations to ignite the quest for AGI-native systems. Ultimately, we envision this article as a roadmap for the next generation of wireless systems beyond 6G.

Flexible intelligent metasurfaces (FIMs) show great potential for improving the wireless network capacity in an energy-efficient manner. An FIM is a soft array consisting of several low-cost radiating elements. Each element can independently emit electromagnetic signals, while flexibly adjusting its position even perpendicularly to the overall surface to ‘morph’ its 3D shape. More explicitly, compared to a conventional rigid antenna array, an FIM is capable of finding an optimal 3D surface shape that provides improved signal quality. In this paper, we study point-to-point multiple-input multiple-output (MIMO) communications between a pair of FIMs. In order to characterize the capacity limits of FIM-aided MIMO transmissions over frequency-flat fading channels, we formulate a transmit optimization problem for maximizing the MIMO channel capacity by jointly optimizing the 3D surface shapes of the transmitting and receiving FIMs as well as the MIMO transmit covariance matrix, subject to the total transmit power constraint and to the maximum perpendicular morphing range of the FIM. To solve this problem, we develop an efficient block coordinate descent (BCD) algorithm. The BCD algorithm iteratively updates the 3D surface shapes of the FIMs and the transmit covariance matrix, while keeping the other fixed, to find a locally optimal solution. Numerical results verify that FIMs can achieve higher MIMO capacity than that of the conventional rigid arrays. In particular, the MIMO channel capacity can be doubled by the proposed BCD algorithm under some setups.

Massive multi-input multi-output (MIMO) in Frequency Division Duplex (FDD) mode suffers from heavy feedback overhead for Channel State Information (CSI). In this paper, a novel manifold learning-based CSI feedback framework (MLCF) is proposed to reduce the feedback and improve the spectral efficiency for FDD massive MIMO. Manifold learning (ML) is an effective method for dimensionality reduction. However, most ML algorithms focus only on data compression, and lack the corresponding recovery methods. Moreover, the computational complexity is high when dealing with incremental data. Considering to utilize the intrinsic manifold structure where the CSI samples reside, we propose a landmark selection algorithm to describe the topological skeleton of this manifold. Based on the learned skeleton, the local patch of the incremental CSI on the manifold can be easily determined by its nearest landmarks. This motivates us to propose an incremental CSI compression and reconstruction scheme by keeping the local geometric relationships with landmarks invariant. We theoretically prove the convergence of the proposed landmark selection algorithm. Meanwhile, the upper bound on the error of approximating CSI with landmarks is derived. Simulation results under an industrial channel model of 3GPP demonstrate that the proposed MLCF outperforms existing deep learning based algorithms.

Future wireless networks are expected to incorporate diverse services that often lack general mathematical models. To address such black-box network management tasks, the large language model (LLM) optimizer framework, which leverages pretrained LLMs as optimization agents, has recently been promoted as a promising solution. This framework utilizes natural language prompts describing the given optimization problems along with past solutions generated by LLMs themselves. As a result, LLMs can obtain efficient solutions autonomously without knowing the mathematical models of the objective functions. Although the viability of the LLM optimizer (LLMO) framework has been studied in various black-box scenarios, it has so far been limited to numerical simulations. For the first time, this paper establishes a theoretical foundation for the LLMO framework. With careful investigations of LLM inference steps, we can interpret the LLMO procedure as a finite-state Markov chain, and prove the convergence of the framework. Our results are extended to a more advanced multiple LLM architecture, where the impact of multiple LLMs is rigorously verified in terms of the convergence rate. Comprehensive numerical simulations validate our theoretical results and provide a deeper understanding of the underlying mechanisms of the LLMO framework.

The transition from fifth-generation (5G) to sixth-generation (6G) mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.

The objective of this study is to address the mobility challenges faced by user equipment (UE) through the implementation of fluid antenna (FA) on the UE side. This approach aims to maintain the time-varying channel in a relatively stable state by strategically relocating the FA to an appropriate port. To the best of our knowledge, this paper introduces, for the first time, the application of large language models (LLMs) in the prediction of FA ports, presenting a novel model termed Port-LLM. Our proposed method for predicting the moving port of the FA is a two-step prediction method. To enhance the learning efficacy of our proposed Port-LLM model, we integrate low-rank adaptation (LoRA) fine-tuning technology. Additionally, to further exploit the natural language processing capabilities of pre-trained LLMs, we propose a framework named Prompt-Port-LLM, which is constructed upon the Port-LLM architecture and incorporates prompt fine-tuning techniques along with a specialized prompt encoder module. The simulation results show that our proposed models all exhibit strong generalization ability and robustness under different numbers of base station antennas and medium-to-high mobility speeds of UE. In comparison to existing methods, the performance of the port predicted by our models demonstrates superior efficacy. Moreover, both of our proposed models achieve millimeter-level inference speed.

This paper focuses on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to a multi-access edge computing (MEC) server. Considering that the V2I links sometimes can be reused by vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of the V2I link may suffer from severe interference, causing outages during the task offloading. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Simulation results show that the proposed RICS-assisted offloading framework significantly improves the safety of the autonomous driving network, in which the safety coefficient of the CVs is improved by nearly 34%. The V2V data rate is improved by around 60%, which indicates that the RICS’s adjustment of the signals can effectively mitigate the interference of the V2V link.

Satellite-ground twin networks (SGTNs) are regarded as a promising service paradigm, which can provide mega access services and powerful computation offloading capabilities via cloud-fog automation functions. Specifically, cloud-fog automation technologies are collaboratively leveraged to enable dense connectivity, pervasive computing, and intelligent control in terrestrial industrial cyber-physical systems, whose system-level privacy security can be strengthened via blockchain based consensus protocol. Moreover, digital twin (DT) can shorten the gap between physical unities and digital space to enable instant data mapping in SGTNs environments. However, complex multi-modal network environments, such as stochastic task size, dynamic low earth orbit location, and time-varying channel gains, hinder better performance metrics in terms of energy consumption, throughput and privacy overhead. Hence, we establish a SGTN integrated cloud-fog automation model to transfer task data to low earth orbit satellites, and then execute broad communication access, powerful computation offloading, and efficient twin control. Next, we propose a Lyapunov stability theory based multi-modal federated learning (LST-MMFL) method to optimize the battery energy, the size of block, computation frequency, and the number of twin control for minimizing the total energy consumption and privacy overhead. Furthermore, we design a novel blockchain based transaction verification protocol to strengthen privacy security, derive performance upper bounds of SGTN model, and fulfill the long-term average task as well as energy queue constraints. Finally, massive simulation results show that the proposed LST-MMFL algorithm outperforms existing state-of-the-art benchmarks in line with energy consumption, available battery level, networked control and privacy protection overhead.

Optimization is crucial for the efficiency and reliability of multi-access edge computing (MEC) networks. Many optimization problems in this field are NP-hard and do not have effective approximation algorithms. Consequently, there is often a lack of optimal (ground-truth) data, which limits the effectiveness of traditional deep learning approaches. Most existing learning-based methods require a large amount of optimal data and do not leverage the potential advantages of using suboptimal data, which can be obtained more efficiently. To illustrate this point, we focus on the multi-server multi-user computation offloading (MSCO) problem, a common issue in MEC networks that lacks efficient optimal solution methods. In this paper, we introduce the graph diffusion-based solution generator (GDSG), designed to work with suboptimal datasets while still achieving convergence to the optimal solution with high probability. We reformulate the network optimization challenge as a distribution-learning problem and provide a clear explanation of how to learn from suboptimal training datasets. We develop GDSG, a multi-task diffusion generative model that employs a graph neural network (GNN) to capture the distribution of high-quality solutions. Our approach includes a straightforward and efficient heuristic method to generate a sufficient amount of training data composed entirely of suboptimal solutions. In our implementation, we enhance the GNN architecture to achieve better generalization. Moreover, the proposed GDSG can achieve nearly 100% task orthogonality, which helps prevent negative interference between the discrete and continuous solution generation training objectives. We demonstrate that this orthogonality arises from the diffusion-related training loss in GDSG, rather than from the GNN architecture itself. Finally, our experiments show that the proposed GDSG outperforms other benchmark methods on both optimal and suboptimal training datasets. Regarding the minimization of computation offloading costs, GDSG achieves savings of up to 56.62% on the ground-truth training set and 41.06% on the suboptimal training set compared to existing discriminative methods.

Generative diffusion models like Stable Diffusion are at the forefront of the thriving field of generative models today, celebrated for their robust training methodologies and high-quality photorealistic generation capabilities. These models excel in producing rich content, establishing them as essential tools in the industry. Building on this foundation, the field has seen the rise ofpersonalized content synthesisas a particularly exciting application. However, the large model sizes and iterative nature of inference make it difficult to deploy personalized diffusion models broadly on local devices with heterogeneous computational power. To address this, we propose a novel framework for efficient multi-user offloading of personalized diffusion models. This framework accommodates a variable number of users, each with different computational capabilities, and adapts to the fluctuating computational resources available on edge servers. To enhance computational efficiency and alleviate the storage burden on edge servers, we propose a tailored multi-user hybrid inference approach. This method splits the inference process for each user into two phases, with an optimizable split point. Initially, a cluster-wide model processes low-level semantic information for each user's prompt using batching techniques. Subsequently, users employ their personalized models to refine these details during the later phase of inference. Given the constraints on edge server computational resources and users' preferences for low latency and high accuracy, we model the joint optimization of each user's offloading request handling and split point as an extension of the Generalized Quadratic Assignment Problem (GQAP). Our objective is to maximize a comprehensive metric that balances both latency and accuracy across all users. To solve this NP-hard problem, we transform the GQAP into an adaptive decision sequence, model it as a Markov decision process, and develop a hybrid solution combining deep reinforcement learning with convex optimization techniques. Simulation results validate the effectiveness of our framework, demonstrating superior optimality and low complexity compared to traditional methods. All related code, datasets, and fine-tuned models are available athttps://github.com/wty2011jl/E-MOPDM.

Intelligent metasurfaces have recently emerged as a promising technology that enables the customization of wireless environments by harnessing large numbers of low-cost reconfigurable scattering elements. However, prior studies have predominantly focused on single-layer metasurfaces, which have limitations in terms of wave-domain processing capabilities due to practical hardware limitations. In contrast, this paper introduces a novel stacked intelligent metasurface (SIM) design. Specifically, we investigate the integration of SIM into the downlink of a multiuser multiple-input single-output (MISO) communication system, where an SIM, consisting of a multilayer metasurface structure, is deployed at the base station (BS) to facilitate transmit beamforming in the electromagnetic wave domain. This eliminates the need for conventional digital beamforming and high-resolution digital-to-analog converters at the BS. To this end, an optimization problem is formulated to maximize the sum rate of all user equipments by jointly optimizing the transmit power allocation at the BS and the wave-based beamforming at the SIM, subject to constraints on the transmit power budget and discrete phase shifts. Furthermore, we propose a computationally efficient algorithm for solving the formulated joint optimization problem and elaborate on the potential benefits of employing SIM in wireless networks. Numerical results are illustrated to corroborate the effectiveness of the proposed SIM-enabled wave-based beamforming design and to evaluate the performance improvement achieved by the proposed algorithm compared to various benchmark schemes. It is demonstrated that considering the same number of transmit antennas, the proposed SIM-based system achieves about 200% improvement in terms of sum rate compared to conventional MISO systems. The code for this paper is available athttps://github.com/JianchengAn.

Flexible intelligent metasurface (FIM) technology shows promise in terms of enhancing both the spectral and energy efficiency of wireless networks. An FIM is composed of an array of low-cost radiating elements, each of which can independently radiate electromagnetic signals, while flexibly adjusting its position along the direction perpendicular to the surface by a process termed as “morphing”. This is of particular interest for wireless communication systems operating at millimeter-wave and terahertz frequencies, where deep fading generally occurs within a few millimeters. Hence, in contrast to conventional rigid 2D antenna arrays, the FIM surface shape may be reconfigured to improve the channel quality by beneficial 3D morphing. In this paper, we investigate the multiuser downlink, where an FIM deployed at a base station (BS) communicates with multiple single-antenna users. We formulate an optimization problem for minimizing the total downlink transmit power at the BS, by jointly optimizing the transmit beamforming and FIM surface shape, subject to an individual signal-to-interference-plus-noise ratio (SINR) constraint of each user as well as a constraint on the maximum FIM morphing range. To solve this problem, we first consider a simple single-user scenario and show that the optimal 3D surface shape is achieved by independently adjusting each FIM element to the position having the strongest channel gain. However, in realistic multiuser scenarios, FIM surface-shape morphing involves complex tradeoffs. To address this issue, an efficient alternating optimization method is proposed to iteratively update the FIM surface shape and the transmit beamformer to gradually reduce the transmit power. Additionally, we analyze the performance gain of the FIM, showcasing a logarithmic received power scaling law versus its maximum morphing range. Finally, simulation results show that the FIM reduces the transmit power by about 3 dB compared to conventional rigid 2D arrays at a given data rate. The code for this paper is available athttps://github.com/JianchengAn.

Integrated sensing and communication (ISAC) has emerged as a promising technology to facilitate high-rate communications and super-resolution sensing, particularly operating in the millimeter wave (mmWave) band. However, the vulnerability of mmWave signals to blockages severely impairs ISAC capabilities and coverage. To tackle this, an efficient and low-cost solution is to deploy distributed reconfigurable intelligent surfaces (RISs) to construct virtual links between the base stations (BSs) and users in a controllable fashion. In this paper, we model the generalized RIS-assisted mmWave ISAC networks considering the blockage effect, and examine the beneficial impact of RISs on the coverage rate utilizing stochastic geometry. Based on the proposed beam patterns and user association policies, we derive the conditional coverage probability and ergodic rate of communication and sensing dual functions for two association cases, as well as the marginal coverage rate using the distance-dependent thinning method. Taking into account the coupling effect of ISAC dual functions within the same network topology, we further calculate the joint coverage probability of ISAC performance. Simulation results verify the accuracy of derived theoretical formulations, and illustrate the impact of the RIS aperture, blockage, BS and RIS densities on ISAC coverage rates, which provide valuable guidelines for the practical network deployment. Specifically, our results indicate the superiority of the RIS deployment with the density of 40 km${}^{-2}$BSs, and that the joint coverage rate of ISAC performance exhibits potential growth from 62% to 97% with the deployment of RISs.

This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction methods, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. Our analysis shows that for a multi-path channel with a strong line-of-sight (LoS) path, the prediction error of our proposed MPMP method nearly converges to zero, as the number of BS antennas and the port density of the FA are large enough. For a general multi-path channel, we also derive the upper and lower bounds of the prediction error when the number of paths is large enough. When the UEs move at a speed of 60 or 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction methods.

Reconfigurable intelligent surface (RIS) is emerging as a promising technology for next-generation wireless communication networks, offering a variety of merits such as the ability to tailor the communication environment. Moreover, deploying multiple RISs helps mitigate severe signal blocking between the base station (BS) and users, providing a practical and efficient solution to enhance the service coverage. However, fully reaping the potential of a multi-RIS aided communication system requires solving a non-convex optimization problem. This challenge motivates the adoption of learning-based methods for determining the optimal policy. In this paper, we introduce a novel heterogeneous graph neural network (GNN) to effectively leverage the graph topology of a wireless communication environment. Specifically, we design an association scheme that selects a suitable RIS for each user. Then, we maximize the weighted sum rate (WSR) of all the users by iteratively optimizing the RIS association scheme, and beamforming designs until the considered heterogeneous GNN converges. Based on the proposed approach, each user is associated with the best RIS, which is shown to significantly improve the system capacity in multi-RIS multi-user millimeter wave (mmWave) communications. Specifically, simulation results demonstrate that the proposed heterogeneous GNN closely approaches the performance of the high-complexity alternating optimization (AO) algorithm in the considered multi-RIS aided communication system, and it outperforms other benchmark schemes. Moreover, the performance improvement achieved through the RIS association scheme is shown to be of the order of 30%.

Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless communication systems. To accurately characterize the achievable performance limits of these systems, it is crucial to develop a universal EM-compliant channel model. This paper addresses this necessity by proposing a comprehensive EM channel model tailored for realistic multi-path environments, accounting for the combined effects of antenna array configurations and propagation conditions in HMIMO communications. Both polarization phenomena and spatial correlation are incorporated into this probabilistic channel model. Additionally, physical constraints of antenna configurations, such as mutual coupling effects and energy consumption, are integrated into the channel modeling framework. Simulation results validate the effectiveness of the proposed probabilistic channel model, indicating that traditional Rician and Rayleigh fading models cannot accurately depict the channel characteristics and underestimate the channel capacity. More importantly, the proposed channel model outperforms free-space Green’s functions in accurately depicting both near-field gain and multi-path effects in radiative near-field regions. These gains are much more evident in tri-polarized systems, highlighting the necessity of polarization interference elimination techniques. Moreover, the theoretical analysis accurately verifies that capacity decreases with expanding communication regions of two-user communications.

In this paper, we propose a novel semantic-aided image communication framework for supporting the compatibility with practical separation-based coding architectures. Particularly, the deep learning (DL)-based joint source-channel coding (JSCC) is integrated into the classical separate source-channel coding (SSCC) to transmit the images via the combination of semantic stream and image stream from DL networks and SSCC respectively, which we name as parallel-stream transmission. The positive coding gain stems from the sophisticated design of the JSCC encoder, which leverages the residual information neglected by the SSCC to enhance the learnable image features. Furthermore, a conditional rate adaptation mechanism is introduced to adjust the transmission rate of semantic stream according to residual, rendering the framework more flexible and efficient to bandwidth allocation. We also design a dynamic stream aggregation strategy at the receiver, which provides the composite framework with more robustness to signal-to-noise ratio (SNR) fluctuations in wireless systems compared to a single conventional codec. Finally, the proposed framework is verified to surpass the performance of both traditional and DL-based competitors in a large range of scenarios and meanwhile, maintains lightweight in terms of the transmission and computational complexity of semantic stream, which exhibits the potential to be applied in real systems.

Flexible antenna arrays (FAAs), distinguished by their rotatable, bendable, and foldable properties, are extensively employed in flexible radio systems to achieve customized radiation patterns. This paper aims to illustrate that FAAs, capable of dynamically adjusting surface shapes, can enhance communication performances with both omni-directional and directional antenna patterns, in terms of multi-path channel power and channel angle Cramér-Rao bounds. To this end, we develop a mathematical model that elucidates the impacts of the variations in antenna positions and orientations as the array transitions from a flat to a rotated, bent, and folded state, all contingent on the flexible degree-of-freedom. Moreover, since the array shape adjustment operates across the entire beamspace, especially with directional patterns, we discuss the sum-rate in the multi-sector base station that covers the 360° communication area. Particularly, to thoroughly explore the multi-sector sum-rate, we propose separate flexible precoding (SFP), joint flexible precoding (JFP), and semi-joint flexible precoding (SJFP), respectively. In our numerical analysis comparing the optimized FAA to the fixed uniform planar array, we find that the bendable FAA achieves a remarkable 156% sum-rate improvement compared to the fixed planar array in the case of JFP with the directional pattern. Furthermore, the rotatable FAA exhibits notably superior performance in SFP and SJFP cases with omni-directional patterns, with respective 35% and 281%.

In the era of 6G, with compelling visions of intelligent transportation systems and digital twins, remote surveillance is poised to become a ubiquitous practice. Substantial data volume and frequent updates present challenges in wireless networks. To address these challenges, we propose a novel agent-driven generative semantic communication (A-GSC) framework based on reinforcement learning. In contrast to the existing research on semantic communication (SemCom), which mainly focuses on either semantic extraction or semantic sampling, we seamlessly integrate both by jointly considering the intrinsic attributes of source information and the contextual information regarding the task. Notably, the introduction of generative artificial intelligence (GAI) enables the independent design of semantic encoders and decoders. In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling. Accordingly, we design a semantic decoder with both predictive and generative capabilities, consisting of two tailored modules. Moreover, the effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework in both energy saving and reconstruction accuracy.

Conventional wireless communication receivers guided by Bayesian inference methods need to know the exact statistical relationship among variables, which is hard to obtain accurately in wireless contexts, thus limiting the system performance. The recently emerging artificial intelligence (AI)-empowered algorithms have shown striking performances in exploring the implicit relationship among variables with specially designed Neural Networks (NNs). Therefore, it is preferable to integrate NNs with BPs in receiver design. Such approaches also leverage NNs’ lack of reasoning ability in large state spaces and traditional BPs’ lack of reasoning depth. However, conventional receiver modules are usually designed based on explicit mathematical derivations, which cannot be easily substituted with data-driven NNs as they may break the overall inner relationship of the algorithm. In this paper, we investigate how to beneficially incorporate NNs into the existing Belief Propagation (BP)-based framework, taking the traditional semi-blind estimation problem in an Orthogonal Frequency-Division Multiplexing (OFDM) receiver as an example. Unlike existing deep-unfolding approaches, we simply utilize NNs as embedded functional units rather than duplicate denoising modules. Through qualitative discussions and numerical results, we illustrate the characteristics, principles, and differences of our proposed architecture compared to the traditional BP framework and show the dramatic performance improvements brought by incorporating NNs with BP in this well-investigated problem. Recalling that the state evolution of NNs is different from that of traditional BP methods, we give some new insights and design principles which are somehow counterfactual. We also raise some open issues on the incorporated framework.

Large Language Models (LLMs) have demonstrated zero-shot generalization capabilities in analyzing and predicting protein properties through natural language interactions. However, existing protein-focused datasets for LLM fine-tuning, such as ProteinLMDataset with its 17.46 billion tokens for pre-training and 893,000 instructions for fine-tuning, face limitations. Specifically, they include insufficient coverage of protein functional properties, inadequate protein-protein interaction data, and limited integration of contextual information from biomedical literature. To overcome these challenges, we present ProteinPFAIDataset, which integrates data from UniProt and PubMed, comprising 72.8 million tokens for Supervised Fine-Tuning (SFT). ProteinPFAIDataset encompasses important protein characteristics including enzyme activities, molecular functions, pH dependence, tissue specificity, temperature sensitivity, subunit structure, and disease associations. Additionally, we propose a novel knowledge graph-based approach that incorporates over 300,000 biomedical literature entries, providing rich contextual information about protein functions and interactions. To validate the effectiveness of our dataset, we fine-tuned Falcon2-11B LLM, resulting in a model we call Falcon2-11B-PFAI. The fine-tuned model achieved state-of-the-art performance on ProteinLMBench, improving accuracy from 47.10% to 58.37%. The dataset is available at https://huggingface.co/datasets/xiaorui1/PFAI. The fine-tuned model is available at https://huggingface.co/xiaorui1/PFAI/tree/main.

As machine intelligence evolves, the need to test and compare the problem-solving abilities of different AI models grows. However, current benchmarks are often simplistic, allowing models to perform uniformly well and making it difficult to distinguish their capabilities. Additionally, benchmarks typically rely on static question-answer pairs that the models might memorize or guess. To address these limitations, we introduce Dynamic Intelligence Assessment (DIA), a novel methodology for testing AI models using dynamic question templates and improved metrics across multiple disciplines such as mathematics, cryptography, cybersecurity, and computer science. The accompanying dataset, DIA-Bench, contains a diverse collection of challenge templates with mutable parameters presented in various formats, including text, PDFs, compiled binaries, visual puzzles, and CTF-style cybersecurity challenges. Our framework introduces four new metrics to assess a model’s reliability and confidence across multiple attempts. These metrics revealed that even simple questions are frequently answered incorrectly when posed in varying forms, highlighting significant gaps in models’ reliability. Notably, API models like GPT-4o often overestimated their mathematical capabilities, while ChatGPT-4o demonstrated better performance due to effective tool usage. In self-assessment OpenAI’s o1-mini proved to have the best judgement on what tasks it should attempt to solve. We evaluated 25 state-of-the-art LLMs using DIA-Bench, showing that current models struggle with complex tasks and often display unexpectedly low confidence, even with simpler questions. The DIA framework sets a new standard for assessing not only problem-solving, but also a model’s adaptive intelligence and ability to assess its limitations. The dataset is publicly available on the project’s page: https://github.com/DIA-Bench.

A flexible intelligent metasurface (FIM) is composed of an array of low-cost radiating elements, each of which can independently radiate electromagnetic signals and flexibly adjust its position through a 3D surface-morphing process. In our system, an FIM is deployed at a base station (BS) that transmits to multiple single-antenna users. We formulate an optimization problem for minimizing the total downlink transmit power at the BS by jointly optimizing the transmit beamforming and the FIM’s surface shape, subject to an individual signal-to-interference-plus-noise ratio (SINR) constraint for each user as well as to a constraint on the maximum morphing range of the FIM. To address this problem, an efficient alternating optimization method is proposed to iteratively update the FIM’s surface shape and the transmit beamformer to gradually reduce the transmit power. Finally, our simulation results show that at a given data rate the FIM reduces the transmit power by about 3 dB compared to conventional rigid 2D arrays.

Orthogonal delay-Doppler division multiplexing (ODDM) modulation has recently been considered a promising technology for enhancing communication system performance in high-mobility scenarios. Accurate and low-complexity channel estimation is one of the most significant challenges for massive multiple-input multiple-output (MIMO) ODDM systems, mainly due to the massive antenna arrays and high-mobility environments. In this paper, we focus on the downlink massive MIMO-ODDM communication systems, and propose a two-stage low-complexity channel estimation algorithm. Specifically, we first derive the effective channel model of the massive MIMO-ODDM systems, where the elements of the channel matrix do not follow a Bernoulli-Gaussian distribution, but their magnitudes do. Utilizing this characteristic, we employ the memory approximate message passing method to estimate the gains, delay, and Doppler of the multi-path channel, while the angles of the channel are estimated using the discrete Fourier transform method, achieving low-complexity Bayes-optimal results. Finally, numerical results demonstrate that the proposed algorithm can achieve improved estimation results, surpassing existing algorithms by approximately 2 dB.

The multiple-input and multiple-output (MIMO) technique is considered as a promising approach for improving the throughput and reliability of optical fiber communications. However, the finite blocklength (FBL) analysis of optical fiber MIMO systems is not available in the literature. Considering the Jacobi MIMO channel, which was proposed to model the nearly lossless propagation and the crosstalks in optical fiber channels, this paper studies the optimal average error probability (OAEP) of optical fiber multicore/multimode systems in the FBL regime. In particular, we consider the case where the coding rate is in the ${\mathcal{O}}\left({\frac{1}{{\sqrt {LM} }}}\right)$ proximity of the capacity, with M and L denoting the number of transmit channels and blocklength, respectively. To this end, a central limit theorem (CLT) for the information density is first established in the asymptotic regime where the blocklength and the number of transmit, receive, and available channels approach infinity with fixed ratios. With the aid of the CLT, the closed-form upper and lower bounds for the OAEP with the concerned rate are then derived. It is shown that the derived bounds could degenerate to those for Rayleigh MIMO channels if the number of available channels goes to infinity. Numerical simulations indicate that the derived bounds are closer to the performance of low-density parity check (LDPC) coding schemes than outage probability, thus providing a better characterization with the concerned the rate.

Millimeter-wave (mmWave) technology is increasingly recognized as a pivotal technology of the sixth-generation communication networks due to the large amounts of available spectrum at high frequencies. However, the huge overhead associated with beam training imposes a significant challenge in mmWave communications, particularly in urban environments with high background noise. To reduce this high overhead, we propose a novel solution for robust continuous-time beam tracking with liquid neural network, which dynamically adjust the narrow mmWave beams to ensure real-time beam alignment with mobile users. Through extensive simulations, we validate the effectiveness of our proposed method and demonstrate its superiority over existing state-of-the-art deep-learning-based approaches. Specifically, our scheme achieves at most 46.9% higher normalized spectral efficiency than the baselines when the user is moving at 5 m/s, demonstrating the potential of liquid neural networks to enhance mmWave mobile communication performance.

Staked intelligent metasurface (SIM) based techniques are developed to perform two-dimensional (2D) direction-of-arrival (DOA) estimation. In contrast to conventional designs, an advanced SIM in front of a receiver array automatically performs the 2D discrete Fourier transform (DFT) as the incident waves propagate through it. To arrange for the SIM to carry out this task, we design a gradient descent algorithm for iteratively updating the phase shift of each meta-atom in the SIM to minimize the fitting error between the SIM's response and the 2D DFT matrix. To further improve the DOA estimation accuracy, we configure the phase shifts in the input layer of the SIM to generate a set of 2D DFT matrices having orthogonal spatial frequency bins. Extensive numerical simulations verify the capability of a well-trained SIM to perform the 2D DFT. Specifically, it is demonstrated that a SIM having an optical computational speed achieves an MSE of 10–4in 2D DOA estimation.

The emerging concept of extremely-large holographic multiple-input multiple-output (HMIMO), beneficial from compactly and densely packed cost-efficient radiating metaatoms, has been demonstrated for enhanced degrees of freedom even in pure line-of-sight conditions, enabling tremendous multiplexing gain for the next-generation communication systems. Most of the reported works focus on energy and spectrum efficiency, path loss analyses, and channel modeling. The extension to secure communications remains unexplored. In this paper, we theoretically characterize the secrecy capacity of the HMIMO network with multiple legitimate users and one eavesdropper while taking into consideration artificial noise and max-min fairness. We formulate the power allocation (PA) problem and address it by following successive convex approximation and Taylor expansion. We further study the effect of fixed PA coefficients, imperfect channel state information, inter-element spacing, and the number of Eve's antennas on the sum secrecy rate. Simulation results show that significant performance gain with more than 100% increment in the high signal-to-noise ratio (SNR) regime for the two-user case is obtained by exploiting adaptive/flexible PA compared to the case with fixed PA coefficients.

This paper addresses the sensing space identification of arbitrarily shaped continuous antennas. In the context of holographic multiple-input multiple-output (MIMO), a.k.a. large intelligent surfaces, these antennas offer benefits such as super-directivity and near-field operability. The sensing space reveals two key aspects: (a) its dimension specifies the maximally achiev-able spatial degrees of freedom (DoFs), and (b) the finite basis spanning this space accurately describes the sampled field. Ear-lier studies focus on specific geometries, bringing forth the need for extendable analysis to real-world conformal antennas. Thus, we introduce a universal framework to determine the antenna sensing space, regardless of its shape. The findings underscore both spatial and spectral concentration of sampled fields to define a generic eigenvalue problem of Slepian concentration. Results show that this approach precisely estimates the DoFs of well-known geometries, and verify its flexible extension to conformal antennas.

Reconfigurable intelligent surfaces (RISs) have become a promising technology to meet the requirements of energy efficiency and scalability in future six-generation (6G) communications. However, a significant challenge in RISs-aided communications is the joint optimization of active and passive beamforming at base stations (BSs) and RISs respectively. Specif-ically, the main difficulty is attributed to the highly non-convex optimization space of beamforming matrices at both BSs and RISs, as well as the diversity and mobility of communication scenarios. To address this, we present a greenly gradient based meta learning beamforming (GMLB) approach. Unlike traditional deep learning based methods which take channel information directly as input, GMLB feeds the gradient of sum rate into neural networks. Coherently, we design a differential regulator to address the phase shift optimization of RISs. Moreover, we use the meta learning to iteratively optimize the beamforming matrices of BSs and RISs. These techniques make the proposed method to work well without requiring energy-consuming pretraining. Simulations show that GMLB could achieve higher sum rate than that of typical alternating optimization algorithms with the energy consumption by two orders of magnitude less.

The large communication and computation overhead of federated learning (FL) is one of the main challenges facing its practical deployment over resource-constrained clients and systems. In this work, SpaFL: a communication-efficient FL framework is proposed to optimize sparse model structures with low computational overhead. In SpaFL, a trainable threshold is defined for each filter/neuron to prune its all connected parameters, thereby leading to structured sparsity. To optimize the pruning process itself, only thresholds are communicated between a server and clients instead of parameters, thereby learning how to prune. Further, global thresholds are used to update model parameters by extracting aggregated parameter importance. The generalization bound of SpaFL is also derived, thereby proving key insights on the relation between sparsity and performance. Experimental results show that SpaFL improves accuracy while requiring much less communication and computing resources compared to sparse baselines. The code is available at https://github.com/news-vt/SpaFL_NeruIPS_2024

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functionalities coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage rate of sensing and communication performance under resource constraints. Further, we present theoretical results for the coverage rate of unified ISAC performance, taking into account the coupling effects of dual functions in coexistence networks. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from 1 km-2to 10 km-2can boost the ISAC coverage rate from 1.4% to 39.8%.

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover all space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) algorithm based on 3D superdirective holographic antenna arrays. Specifically, EHB consists of antenna excitation current vectors (analog beamforming) and digital precoding matrices, where the implementation of analog beamforming involves real-time adjustments to the radiation pattern to adapt to the wireless environment. Meanwhile, the digital beamforming is optimized based on the channel characteristics of analog beam-forming to further improve the achievable rate of communication systems. An electromagnetic channel model incorporating array radiation pattern and coupling effect is also developed to evaluate the benefits of our proposed scheme. Simulation results show that the proposed scheme achieves a sum rate gain of over 150 % compared to traditional beamforming algorithms.

Intelligent surfaces represent a breakthrough technology capable of customizing the wireless channel cost-effectively. However, the existing works generally focus on planar wavefront, neglecting near-field spherical wavefront characteristics caused by large array aperture and high operation frequencies in the terahertz (THz). Additionally, the single-layer reconfigurable intelligent surface (RIS) lacks the signal processing ability to mitigate the computational complexity at the base station (BS). To address this issue, we introduce a novel stacked intelligent metasurfaces (SIM) comprised of an array of programmable metasurface layers. The SIM aims to substitute conventional digital baseband architecture to execute computing tasks with ultra-low processing delay, albeit with a reduced number of radio-frequency (RF) chains and low-resolution digital-to-analog converters. In this paper, we present a SIM-aided multiuser multiple-input single-output (MU-MISO) near-field system, where the SIM is integrated into the BS to perform beamfocusing in the wave domain and customize an end-to-end channel with minimized inter-user interference. Finally, the numerical results demonstrate that near-field communication achieves superior spatial gain over the far-field, and the SIM effectively suppresses inter-user interference as the wireless signals propagate through it.

Accurate localization of mobile terminals is crucial for integrated sensing and communication systems. Existing fingerprint localization methods, which deduce coordinates from channel information in pre-defined rectangular areas, struggle with the heterogeneous fingerprint distribution inherent in non-line-of-sight (NLOS) scenarios. To address the problem, we introduce a novel multi-source information fusion learning framework referred to as the Autosync Multi-Domain NLOS Localization (AMDNLoc). Specifically, AMDNLoc employs a two-stage matched filter fused with a target tracking algorithm and iterative centroid-based clustering to automatically and irregularly segment NLOS regions, ensuring uniform fingerprint distribution within channel state information across frequency, power, and time-delay domains. Additionally, the framework utilizes a segment-specific linear classifier array, coupled with deep residual network-based feature extraction and fusion, to establish the correlation function between fingerprint features and coordinates within these regions. Simulation results demonstrate that AMDNLoc significantly enhances localization accuracy by over 40% compared with traditional convolutional neural networks on the wireless artificial intelligence research dataset.

Millimeter wave (mmWave) has attracted considerable attention due to its wide bandwidth and high frequency. However, it is highly susceptible to blockages, resulting in significant degradation of the coverage and the sum rate. A promising approach is deploying distributed reconfigurable intelligent surfaces (RISs), which can establish extra communication links. In this paper, we investigate the impact of distributed RISs on the coverage probability and the sum rate in mmWave wireless communication systems. Specifically, we first introduce the system model, which includes the blockage, the RIS and the user distribution models, leveraging the Poisson point process. Then, we define the association criterion and derive the conditional coverage probabilities for the two cases of direct association and reflective association through RISs. Finally, we combine the two cases using Campbell's theorem and the total probability theorem to obtain the closed-form expressions for the ergodic coverage probability and the sum rate. Simulation results validate the effectiveness of the proposed analytical approach, demonstrating that the deployment of distributed RISs significantly improves the ergodic coverage probability by 45.4% and the sum rate by over 1.5 times.

Limited feedback is a key technique for conveying channel state information (CSI) back to the base station (BS). However, its reliance on quantization to select the optimum code-word from a predefined codebook results in severe degradation in achievable rate due to quantization error. To address this issue, robust techniques should be developed. In this paper, we first examine an approximation for the second-order statistics of quantized CSI. Based on the proposed approximation, we then propose a novel robust precoding design that minimizes the conditional expectation based mean square error (MSE). Numerical results show that the proposed design significantly improves the achievable rate compared to conventional precoding schemes.

The Lip Reading Sentences-3 (LRS3) benchmark has primarily been the focus of intense research in visual speech recognition (VSR) during the last few years. As a result, there is an increased risk of overfitting to its excessively used test set, which is only one hour duration. To alleviate this issue, we build a new VSR test set named WildVSR, by closely following the LRS3 dataset creation processes. We then evaluate and analyse the extent to which the current VSR models generalize to the new test data. We evaluate a broad range of publicly available VSR models and find significant drops in performance on our test set, compared to their corresponding LRS3 results. Our results suggest that the increase in word error rates is caused by the models’ inability to generalize to slightly "harder" and in the wild lip sequences than those found in the LRS3 test set. Our new test benchmark is made public in order to enable future research towards more robust VSR models.

This paper addresses the mobility problem in extremely large antenna array (ELAA) communication systems. In order to account for the performance loss caused by the spherical wavefront of ELAA in the mobility scenarios, we propose a wavefront transformation-based matrix pencil (WTMP) channel prediction method. In particular, we design a matrix to transform the spherical wavefront into a new wavefront, which is closer to the plane wave. We also design a time-frequency projection matrix to capture the time-varying path delay due to user movement. Furthermore, we adopt the matrix pencil (MP) method to estimate channel parameters. Our proposed WTMP method can mitigate the effect of near-field radiation when predicting future channels. For an ELAA communication system in the mobility scenarios, we prove that the prediction error converges to zero with the increasing number of base station antennas. Simulation results demonstrate that our designed transform matrix efficiently mitigates the near-field effect, and that our proposed WTMP method can overcome the ELAA mobility challenge and approach the performance in stationary settings.

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.

Autonomous underwater vehicles (AUVs) play a crucial role in data collection for underwater acoustic sensor networks (UWASNs). The limited capacity of individual AUV and the need for low-latency data collection necessitate the deployment of AUV swarms to achieve efficient and secure cooperative data collection. However, most existing works assume prior knowledge of sensor node locations, which is impractical in real-world AUV networks. Additionally, continuous data collection needs to be considered due to the sustained operation of sensors and cluster head replacement. To address these challenges, we propose a target uncertainty map assisted data collection scheme for AUV swarms based on the multiagent proximal policy optimization (MAPPO) algorithm. Specifically, the target uncertainty map is established by leveraging current and past search and collection results, guiding the AUV swarm to prioritize areas with higher probabilities of containing sensor nodes. Moreover, a digital pheromone mechanism incorporating repulsive and attractive pheromones is designed to establish an artificial potential field for adjusting the target uncertainty map. To further enable a comprehensive exploration of unknown environments, we introduce the Age of Information (AoI) as an indicator. Additionally, we consider the energy consumption associated with data collection to strike a balance between collection and energy efficiency, and derive a lower bound on the policy improvement achieved by the MAPPO algorithm. Simulation results have validated that the proposed scheme has a superior performance compared to the baselines, achieving an approximately 15% increase in the collection rate while reducing the energy consumption of data collection and AoI as well.

The integration of nonorthogonal multiple access (NOMA) with integrated sensing and communication (ISAC) heralds a novel and promising paradigm, advancing the frontier of wireless communication technologies. Nevertheless, this synergistic integration may confront security challenges attributable to intrinsic vulnerabilities within the ISAC frameworks, i.e., sensing targets assume the role of potential eavesdroppers. In this article, we exploit the additional degrees of freedom afforded by reconfigurable intelligent surfaces (RISs) to enhance secure communication and achieve target detection within the NOMA-assisted ISAC system. Specifically, the coexisting radar and NOMA signals are concurrently propagated through both the direct and reflected links. Subsequently, we formulate a secure optimization problem by collaboratively designing the beamforming vectors of the base station and the phase shifts of RIS under the constraints of total transmit power, communication quality of service, and sensing quality. Due to nonconvexity, the optimization problem is decomposed into two subproblems and addressed separately, employing the successive convex approximation approach based on the first-order Taylor expansion and the second-order cone. Finally, we obtain the solution to the original problem utilizing alternating optimization. Simulation results demonstrate that our proposed approach exhibits superior capabilities in secure communication and precise target detection.

This article presents a cooperative multiagent deep reinforcement learning (MADRL) approach for unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks. An UAV with computing capability can provide task offlaoding services to ground Internet of Things devices (IDs). With partial observation of the entire network state, the UAV and the IDs individually determine their MEC strategies, i.e., UAV trajectory, resource allocation, and task offloading policy. This requires joint optimization of decision-making process and coordination strategies among the UAV and the IDs. To address this difficulty, the proposed cooperative MADRL approach computes two types of action variables, namely, message action and solution action, each of which is generated by dedicated actor neural networks (NNs). As a result, each agent can automatically encapsulate its coordination messages to enhance the MEC performance in the decentralized manner. The proposed actor structure is designed based on graph attention networks such that operations are possible regardless of the number of IDs. A scalable training algorithm is also proposed to train a group of NNs for arbitrary network configurations. Numerical results demonstrate the superiority of the proposed cooperative MADRL approach over conventional methods.

This paper presents a flexible deep learning strategy that tackles decentralized optimization tasks in multi-tier networks where wireless nodes are deployed in a hierarchical structure. Practical multi-tier networks have arbitrary node populations as well as their backhaul connections. Thus, node operations in the multi-tier network request versatile inference rules for arbitrary network configurations. To this end, we present a tree-based learning strategy which transforms the multi-tier network optimization into a collaborative inference process over random trees. For the decentralized structure, each node in a tree is equipped with dedicated deep neural network (DNN) modules. A group of these component DNNs builds a tree deep neural network (TNN) where forward pass calculations define the node interaction policy. The TNN is carefully designed such that it can be universally applied to random trees. The training mechanism is developed to involve a number of random tree instances so that the TNN can be generalized to arbitrary network configurations. As a consequence, the TNN can scale up with a large number of nodes which requires only a single training process. The scalability of the proposed framework is validated for various multi-tier network optimization problems. Numerical results demonstrate the effectiveness of the TNN over existing approaches.

Mobile edge computing (MEC) offloads computation intensive tasks of individual users to computing clouds to alleviate the computing loads. Virtual machines (VMs), in practice, are often adopted to realize the parallel computing feature of MEC clouds. A careful local interaction among VMs further reduces the overall computing latency. However, their management turns out quite challenging in practical wireless MEC networks. This paper aims at minimizing the latency of the overall MEC task with the min-max criterion. To this end, a novel distributed strategy is developed for the joint management of the task allocation and the offloading balance among VMs. This task offloading protocol is carried out through a message-passing framework that enables a simultaneous consideration of the min-max criterion about multiple MEC tasks. The numerical results demonstrate that the proposed scheduling for distributed MEC operations achieves a 40% improvement in network utility performance over existing optimization techniques.

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems.

Stacked intelligent metasurfaces (SIMs) are capable of emulating reconfigurable physical neural networks by utilizing electromagnetic (EM) waves as carriers. They can also perform various complex computational and signal processing tasks. An SIM is constructed by densely integrating multiple metasurface layers, each consisting of a large number of small meta-atoms that can control the EM waves passing through it. In this paper, we harness an SIM for two-dimensional (2D) direction-of-arrival (DOA) estimation. In contrast to conventional designs, an advanced SIM in front of a receiver array can be designed to automatically compute the 2D discrete Fourier transform (DFT) as the incident waves propagate through it. As a result, a receiver array can directly observe the angular spectrum of the incoming signal, and it can estimate the DOA by simply using probes to detect the energy distribution on the receiver array. This avoids the need for power inefficient radio frequency chains. To enable an SIM to perform the 2D DFT in the wave domain, we formulate an optimization problem that minimizes the mean square error (MSE) between the SIM’s EM response and the 2D DFT matrix. Then, a gradient descent algorithm is customized for iteratively updating the phase shift applied by each meta-atom of the SIM. To further improve the DOA estimation accuracy, we configure the phase shifts of the input layer of the SIM to generate a set of 2D DFT matrices associated with orthogonal spatial frequency bins. Additionally, we analytically evaluate the performance of the proposed SIM-based DOA estimator by deriving a tight upper bound for the MSE. Extensive numerical simulations verify the capability of an optimized SIM to perform DOA estimation and corroborate the theoretical analysis. Specifically, we show that an SIM is capable of performing DOA estimation with an MSE of the order of$10^{-4}$.

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functions coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage and ergodic rate of sensing and communication performance under resource constraints. Then, we shed light on the fundamental limits of ISAC networks by presenting theoretical results for the coverage rate of the unified performance, taking into account the coupling effects of dual functions in coexistence networks. Further, we obtain the analytical formulations for evaluating the ergodic sensing rate constrained by the maximum communication rate, and the ergodic communication rate constrained by the maximum sensing rate. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from$1~\text {km}^{-2}$to$10~\text {km}^{-2}$can boost the ISAC coverage rate from 1.4% to 39.8%. Further, results also reveal that with the increase of the constrained sensing rate, the ergodic communication rate improves significantly, but the reverse is not obvious.

Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and achieving fundamental wireless limits. Empowered by the nearly continuous, large and energy-efficient surfaces with powerful electromagnetic (EM) wave control capabilities, H-MIMO opens up the opportunity for signal processing in a more fundamental EM-domain, paving the way for realizing holographic imaging level communications in supporting the extremely high spectral efficiency and energy efficiency in future networks. In this article, we propose a generalized EM-domain near-field channel modeling and study its capacity limit of point-to-point H-MIMO systems that equips arbitrarily placed surfaces in a line-of-sight (LoS) environment. Two effective and computational-efficient channel models are established from their integral counterpart, where one is with a sophisticated formula but showcases more accurate, and another is concise with a slight precision sacrifice. Furthermore, we unveil the capacity limit using our channel model, and derive a tight upper bound based upon an elaborately built analytical framework. Our result reveals that the capacity limit grows logarithmically with the product of transmit element area, receive element area, and the combined effects of 1/d2mn, 1/d4mn, and 1/d6mnover all transmit and receive antenna elements, wheredmnindicates the distance between each transmit elementnand receive elementm. Particularly, 1/d6mndominates in the near-field region whereas 1/d2mndominates in the far-field region. Numerical evaluations validate the effectiveness of our channel models, and showcase the slight disparity between the upper bound and the exact capacity, which is beneficial for predicting practical system performance.

As the backbone of the fifth-generation (5G) cellular network, massive multiple-input multiple-output (MIMO) encounters a significant challenge in practical applications: how to deploy a large number of antenna elements within limited spaces. Recently, holographic communication has emerged as a potential solution to this issue. It employs dense antenna arrays and provides a tractable model. Nevertheless, some challenges must be addressed to actualize this innovative concept. One is the mutual coupling among antenna elements within an array. When the element spacing is small, near-field coupling becomes the dominant factor that strongly restricts the array performance. Another is the polarization of electromagnetic waves. As an intrinsic property, it was not fully considered in the previous channel modeling of holographic communication. The third is the lack of real-world experiments to show the potential and possible defects of a holographic communication system. In this paper, we propose an electromagnetic channel model based on the characteristics of electromagnetic waves. This model encompasses the impact of mutual coupling in the transceiver sides and the depolarization in the propagation environment. Furthermore, by approximating an infinite array, the performance restrictions of large-scale dense antenna arrays are also studied theoretically to exploit the potential of the proposed channel. In addition, numerical simulations and a channel measurement experiment are conducted. The findings reveal that within limited spaces, the coupling effect, particularly for element spacing smaller than half of the wavelength, is the primary factor leading to the inflection point for the performance of holographic communications.

In integrated ground-air-space (IGAS) wireless networks, numerous services require sensing knowledge including location, angle, distance information, etc., which usually can be acquired during the beam training stage. On the other hand, IGAS networks employ large-scale antenna arrays to mitigate obstacle occlusion and path loss. However, large-scale arrays generate pencil-shaped beams, which necessitate a higher number of training beams to cover the desired space. These factors motivate our investigation into the IGAS beam training problem to achieve effective sensing services. To address the high complexity and low identification accuracy of existing beam training techniques, we propose an efficient hashing multi-arm beam (HMB) training scheme. Specifically, we first construct an IGAS single-beam training codebook for the uniform planar arrays. Then, the hash functions are chosen independently to construct the multi-arm beam training codebooks for each AP. All APs traverse the predefined multi-arm beam training codeword simultaneously and the multi-AP superimposed signals at the user are recorded. Finally, the soft decision and voting methods are applied to obtain the correctly aligned beams only based on the signal powers. In addition, we logically prove that the traversal complexity is at the logarithmic level. Simulation results show that our proposed IGAS HMB training method can achieve 96.4% identification accuracy of the exhaustive beam training method and greatly reduce the training overhead.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

It has been proved that a long-range (LoRa) system can achieve long-distance and low-power transmission. However, the performance of LoRa systems can be severely degraded by fading. In addition, LoRa technology typically adopts an ALOHA-based access mechanism, which inevitably produces interfering signals for the target user. To overcome the effects of fading and interference, we introduce a reconfigurable intelligent surface (RIS) to LoRa systems. In this context, both non-coherent and coherent detections are considered and their bit error rate (BER) performance analyses are conducted. Moreover, we derive the closed-form BER expressions for the proposed system over Nakagami-m fading channels. Simulation results are used to verify the accuracy of our proposed analytical results. It is shown that in the presence of the interference, the proposed system outperforms RIS-free LoRa systems, and RIS-aided LoRa systems adopting blind transmission. In addition, we also compare the proposed system to single-user RIS-aided LoRa systems adopting blind transmission, and the results show that the proposed system maintains its superior performance even in the presence of the interference. Furthermore, the impacts of the spreading factor (SF), the number of reflecting elements, and the Nakagami-m fading parameters are investigated. It is shown that increasing the number of reflecting elements can remarkably enhance the BER performance, which is an affective measure for the proposed system to balance the trade-off between data rate and coverage range. We further observe that the BER performance of the proposed system is more sensitive to the fading parameter m at high signal-to-noise ratios.

Unmanned aerial vehicles (UAVs) are envisioned to provide diverse services from the air. The service quality may rely on the wireless performance which is affected by the UAV’s position. In this paper, we focus on the UAV placement problem in the Internet of Vehicles, where the UAV is deployed to monitor the road traffic and sends the monitored videos to vehicles. The studied problem is formulated as video resolution maximization by optimizing over the UAV’s position. Moreover, we take into account the maximal transmission delay and impose a probabilistic constraint. To solve the formulated problem, we first leverage the techniques in extreme value theory (EVT) and Gaussian process regression (GPR) to characterize the influence of the UAV’s position on the delay performance. Based on this characterization, we subsequently propose a proactive resolution selection and UAV placement approach, which adaptively places the UAV according to the geographic distribution of vehicles. Numerical results justify the joint usage of EVT and GPR for maximal delay characterization. Through investigating the maximal transmission delay, the proposed approach nearly achieves the optimal performance when vehicles are evenly distributed, and reduces 10% and 19% of the 999-th 1000-quantile over two baselines when vehicles are biased distributed.

In this paper a novel joint sensing, communication, and artificial intelligence (AI) framework is proposed so as to optimize extended reality (XR) experiences over terahertz (THz) wireless systems. Within this framework, active reconfigurable intelligent surfaces (RISs) are incorporated as as pivotal elements, serving as enhanced base stations in the THz band to enhance Line-of-Sight (LoS) communication. The proposed framework consists of three main components.First, a tensor decomposition framework is proposed to extract unique sensing parameters for XR users and their environment by exploiting the THz channel sparsity. Essentially, THz band’s quasi-opticality is exploited and the sensing parameters are extracted from the uplink communication signal, thereby allowing for the use of thesame waveform, spectrum, and hardware for both communication and sensing functionalities. Then, the Cramer-Rao lower bound is derived to assess the accuracy of the estimated sensing parameters.Second, a non-autoregressive multi-resolution generative artificial intelligence (AI) framework integrated with an adversarial transformer is proposed to predict missing and future sensing information. The proposed framework offers robust and comprehensive historical sensing information and anticipatory forecasts of future environmental changes, which aregeneralizable to fluctuations in both known and unforeseen user behaviors and environmental conditions.Third, a multi-agent deep recurrent hysteretic Q-neural network is developed to control the handover policy of RIS subarrays, leveraging the informative nature of sensing information to minimize handover cost, maximize the individual quality of personal experiences (QoPEs), and improve the robustness and resilience of THz links. Simulation results show a high generalizability of the proposed unsupervised generative AI framework to fluctuations in user behavior and velocity, leading to a 61% improvement in instantaneous reliability compared to schemes with known channel state information.

In this work, we investigate the performance of reconfigurable intelligent surface (RIS) assisted near-field wideband system. By considering the large-scale effect of a high-dimensional RIS and frequency-selective channels, we derive an accurate array manifold of the RIS in the near-field from the scattering point of view. Subsequently, we conceive a near-optimal RIS phase design for a single-user scenario to alleviate the beam-squint effect of the wideband system. As for the multi-user case, we provide a virtual-subarray-based phase shift design, which mitigates the beam-squint effect as well as the mitigates deleterious effects of beam concentration. Numerical results show that the achievable data rate can be significantly improved by the proposed schemes compared to the benchmarks both in the single-user and multi-user cases. Explicitly, in the multi-user system, by leveraging the virtual-subarray-based phase design, the achievable sum-rate can be doubled compared to the conventional benchmarks.

Massive multiple-input multiple-output (MIMO) networks are highly vulnerable to an active eavesdropping attack called pilot spoofing attack. The pilot spoofing attack causes information leakage to the active eavesdropper (ED) and also weakens the strength of the signal received by the attacked legitimate user equipment (UE) during the downlink transmission. In this paper, a deep neural network, called identification network (IDNet), is proposed to detect asynchronous pilot spoofing attacks and identify the attacked UE. We show that an asynchronous pilot spoofing attack leads to increasing the signal subspace dimension by one unlike the synchronous one. This property is then exploited to improve the attack detection/identification accuracy. In the proposed IDNet, the input features are the eigenvalues of the sample covariance matrix of the received signal at the base station (BS) as well as the ratio between the power of the received signal at the BS projected onto the pilot signals and its expected value. Numerical results show the effectiveness of IDNet in identifying the attacked UE and reveal that the larger the timing and/or frequency mismatches of the ED, the higher the identification accuracy confirming that asynchronous pilot spoofing attacks can be identified more accurately than synchronous pilot spoofing attacks.

Satellite-Ground integrated networks (SGINs) are regarded as promising network architecture, which can provide global coverage, large broadband and mega access services for massive terrestrial users. Furthermore, it is beneficial to reducing network congestion, releasing network resources and achieving computation offloading functions. However, the SGIN graph structure is time-varying and highly complex, lack of fixed node orders or reference nodes, which result in dynamic multi-modal features. Hence, we consider a SGIN directed graph model to minimize the total latency while improving the model prediction accuracy, and then perform the computation offloading and quantization schemes. Specifically, we envision a spatial graph convolutional neural network framework to adapt to the dynamic SGIN graph nodes and size, and then propose a centrally deep reinforcement learning aided multi-node federated learning (CDRFL) framework to optimize the CPU cycle frequency, transmission bandwidth and the number of quantization bits to accelerate the convergence round. Extensive theoretical analyses verify the graph permutation property between SGIN graph structure and optimization problems, and demonstrate the upper bound of quantization error via massive mathematical derivation. Finally, the experimental results indicate that the proposed CDRFL framework outperforms some existing benchmarks with reference to FL convergence analysis, average latency and transmission energy consumption for all independent identically distribution (IID) and non-IID data.

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover the whole space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) scheme based on a three-dimensional (3D) superdirective holographic antenna array. Specifically, EHB consists of antenna excitation current vectors (analog beamforming) and digital precoding matrices, where the implementation of analog beamforming involves the real-time adjustment of the radiation pattern to adapt it to the dynamic wireless environment. Meanwhile, the digital beamforming is optimized based on the channel characteristics of analog beamforming to further improve the achievable rate of communication systems. An electromagnetic channel model incorporating array radiation patterns and the mutual coupling effect is also developed to evaluate the benefits of our proposed scheme. Simulation results demonstrate that our proposed EHB scheme with a 3D holographic array achieves a relatively flat superdirective beamforming gain and allows for programmable focusing directions throughout the entire spatial domain. Furthermore, they also verify that the proposed scheme achieves a sum rate gain of over 150% compared to traditional beamforming algorithms.

The development of Unmanned Aerial Vehicles (UAVs) offers new prospects for emerging applications in the Industrial Internet of Things (IIoT) networks. With the assistance of Digital Twin (DT), a real-time understanding of physical entities can be constructed for dynamic perception and decision-making. However, DT modeling requires distributed data aggregation, resulting in privacy disclosure and communication burden. Therefore, we propose the digital twin edge network by integrating the DT technology and edge computing, which leverages an over-the-air computation enabled federated learning architecture for an efficient and secure DT model construction. Specifically, we propose a heterogeneity-aware and energy-conscious device scheduling mechanism, considering the update importance, channel condition, and computation capacity based on a probabilistic scheduling framework. To enhance energy efficiency, we introduce a virtual queue to track the difference between the cumulative energy consumption and budget. Additionally, we design a low-complexity scheduling algorithm to solve the optimization problem. Simulation results validate the superiority of our proposed mechanism in improving the test accuracy and energy efficiency in a heterogeneous and energy-constrained environment. Moreover, the proposed mechanism demonstrates significant advantages when employed to highly heterogeneous datasets, and exhibits a certain level of robustness to mapping errors arising from the utilization of DT technique.

The practical deployment of federated learning (FL) over wireless networks requires balancing energy efficiency, convergence rate, and a target accuracy due to the limited available resources of devices. Prior art on FL often trains deep neural networks (DNNs) to achieve high accuracy and fast convergence using 32 bits of precision level. However, such scenarios will be impractical for resource-constrained devices since DNNs typically have high computational complexity and memory requirements. Thus, there is a need to reduce the precision level in DNNs to reduce the energy expenditure. In this paper, a green-quantized FL framework, which represents data with a finite precision level in both local training and uplink transmission, is proposed. Here, the finite precision level is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format. In the considered FL model, each device trains its QNN and transmits a quantized training result to the base station. Energy models for the local training and the transmission with quantization are rigorously derived. To minimize the energy consumption and the number of communication rounds simultaneously, a multi-objective optimization problem is formulated with respect to the number of local iterations, the number of selected devices, and the precision levels for both local training and transmission while ensuring convergence under a target accuracy constraint. To solve this problem, the convergence rate of the proposed FL system is analytically derived with respect to the system control variables. Then, the Pareto boundary of the problem is characterized to provide efficient solutions using the normal boundary inspection method. Design insights on balancing the tradeoff between the two objectives while achieving a target accuracy are drawn from using the Nash bargaining solution and analyzing the derived convergence rate. Simulation results show that the proposed FL framework can reduce energy consumption until convergence by up to 70% compared to a baseline FL algorithm that represents data with full precision without damaging the convergence rate.

This paper addresses the mobility problem in extremely large antenna array (ELAA) communication systems. In order to account for the performance loss caused by the spherical wavefront of ELAA in the mobility scenario, we propose a wavefront transformation-based matrix pencil (WTMP) channel prediction method. In particular, we design a matrix to transform the spherical wavefront into a new wavefront, which is closer to the plane wave. We also design a time-frequency projection matrix to capture the time-varying path delay. Furthermore, we adopt the matrix pencil (MP) method to estimate channel parameters. Our proposed WTMP method can mitigate the effect of near-field radiation when predicting future channels. Theoretical analysis shows that the designed matrix is asymptotically determined by the angles and distance between the base station (BS) antenna array and the scatterers or the user when the number of BS antennas is large enough. For an ELAA communication system in the mobility scenario, we prove that the prediction error converges to zero with the increasing number of BS antennas. Simulation results demonstrate that our designed transform matrix efficiently mitigates the near-field effect, and that our proposed WTMP method can overcome the ELAA mobility challenge and approach the performance in stationary setting.

Data collection and processing timely is crucial for mobile crowd integrated sensing, communication, and computation (ISCC) systems with various applications such as smart home and connected cars, which requires numerous integrated sensing and communication (ISAC) devices to sense the targets and offload the data to the base station (BS) for further processing. However, as the number of ISAC devices grows, there exists intensive interactions among ISAC devices in the processes of data collection and processing since they share the common network resources. In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game (MFG). Specifically, to handle the complex interactions among large-scale ISAC devices, we first utilize the MFG method to transform the influence from other ISAC devices into the mean field term and derive the Fokker-Planck-Kolmogorov equation, which models the evolution of the system state. Then, we derive the cost function based on the mean field term and reformulate the waveform precoding design problem. Next, we utilize the G-prox primal-dual hybrid gradient algorithm to solve the reformulated problem and analyze the computational complexity of the proposed algorithm. Finally, simulation results demonstrate that the proposed algorithm can solve the interactions among large-scale ISAC devices effectively in the ISCC process. In addition, compared with other baselines, the proposed waveform precoding design algorithm has advantages in improving communication performance and reducing cost function.

Semantic-aware communication is a novel paradigm that draws inspiration from human communication focusing on the delivery of the meaning of messages. It has attracted significant interest recently due to its potential to improve the efficiency and reliability of communication and enhance users’ quality-of-experience (QoE). Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal. This paper investigates the implicit semantic-aware communication in which the hidden information, e.g., hidden relations, concepts and implicit reasoning mechanisms of users, that cannot be directly observed from the source signal must be recognized and interpreted by the intended users. To this end, a novel implicit semantic-aware communication (iSAC) architecture is proposed for representing, communicating, and interpreting the implicit semantic meaning between source and destination users. A graph-inspired structure is first developed to represent the complete semantics, including both explicit and implicit, of a message. A projection-based semantic encoder is then proposed to convert the high-dimensional graphical representation of explicit semantics into a low-dimensional semantic constellation space for efficient physical channel transmission. To enable the destination user to learn and imitate the implicit semantic reasoning process of source user, a generative adversarial imitation learning-based solution, called G-RML, is proposed. Different from existing communication solutions, the source user in G-RML does not focus only on sending as much of the useful messages as possible; but, instead, it tries to guide the destination user to learn a reasoning mechanism to map any observed explicit semantics to the corresponding implicit semantics that are most relevant to the semantic meaning. By applying G-RML, we prove that the destination user can accurately imitate the reasoning process of the source user and automatically generate a set of implicit reasoning paths following the same probability distribution as the expert paths. Compared to the existing solutions, our proposed G-RML requires much less communication and computational resources and scales well to the scenarios involving the communication of rich semantic meanings consisting of a large number of concepts and relations. Numerical results show that the proposed solution achieves up to 92% accuracy of implicit meaning interpretation.

Though achieving marvelous progress in various scenarios, existing semantic communication frameworks mainly consider single-input single-output Gaussian channels or Rayleigh fading channels, neglecting the widely-used multiple-input multiple-output (MIMO) channels, which hinders the application into practical systems. One common solution to combat MIMO fading is to utilize feedback MIMO channel state information (CSI). In this paper, we incorporate MIMO CSI into system designs from a new perspective and propose the learnable CSI fusion semantic communication (LCFSC) framework, where CSI is treated as side information by the semantic extractor to enhance the semantic coding. To avoid feature fusion due to abrupt combination of CSI with features, we present a non-invasive CSI fusion multi-head attention module inside the Swin Transformer. With the learned attention masking map determined by both source and channel states, more robust attention distribution could be generated. Furthermore, the percentage of mask elements could be flexibly adjusted by the learnable mask ratio, which is produced based on the conditional variational interference in an unsupervised manner. In this way, CSI-aware semantic coding is achieved through learnable CSI fusion masking. Experiment results testify the superiority of LCFSC over traditional schemes and state-of-the-art Swin Transformer-based semantic communication frameworks in MIMO fading channels.

The millimeter wave (mmWave) has received considerable interest due to its expansive bandwidth and high frequency. However, a noteworthy challenge arises from its vulnerability to blockages, leading to reduced coverage and achievable rate. To address these limitations, a potential solution is to deploy distributed reconfigurable intelligent surfaces (RISs), which comprise many low-cost and passively reflected elements, and can facilitate the establishment of extra communication links. In this paper, we leverage stochastic geometry to investigate the ergodic coverage probability and the achievable rate in both distributed RISs-assisted single-cell and multi-cell mmWave wireless communication systems. Specifically, we first establish the system model considering the stochastically distributed blockages, RISs and users by the Poisson point process. Then we give the association criterion and derive the association probabilities, the distance distributions, and the conditional coverage probabilities, for two cases of associations between base stations and users without or with RISs. Finally, we use Campbell’s theorem and the total probability theorem to obtain the closed-form expressions of the ergodic coverage probability and the achievable rate. Simulation results verify the effectiveness of our analysis method, and demonstrate that by deploying distributed RISs, the ergodic coverage probability is significantly improved by approximately 50%, and the achievable rate is increased by more than 1.5 times.

It has been well known that the achievable rate of multiuser multiple-input multiple-output systems with limited feedback is severely degraded by quantization errors when the number of feedback bits is not sufficient. To overcome such a rate degradation, we propose new robust precoding designs which can compensate for the quantization errors. In this paper, we first analyze the achievable rate of traditional precoding designs for limited feedback systems. Then, we obtain an approximation of the second-order statistics of quantized channel state information. With the aid of the derived approximation, we propose robust precoding designs in terms of the mean square error (MSE) with conditional expectation in non-iterative and iterative fashions. For the non-iterative precoding design, we study a robust minimum MSE (MMSE) precoding algorithm by extending a new channel decomposition. Also, in the case of iterative precoding, we investigate a robust weighted MMSE (WMMSE) precoding to further improve the achievable rate. Simulation results show that the proposed precoding schemes yield significant improvements over traditional precoding designs.

Reconfigurable intelligent surface (RIS) has become a promising technology to realize the programmable wireless environment via steering the incident signal in fully customizable ways. However, a major challenge in RIS-aided communication systems is the simultaneous design of the precoding matrix at the base station (BS) and the phase shifting matrix of the RIS elements. This is mainly attributed to the highly non-convex optimization space of variables at both the BS and the RIS, and the diversity of communication environments. Generally, traditional optimization methods for this problem suffer from the high complexity, while existing deep learning based methods are lacking in robustness in various scenarios. To address these issues, we introduce a gradient-based manifold meta learning method (GMML), which works without pre-training and has strong robustness for RIS-aided communications. Specifically, the proposed method fuses meta learning and manifold learning to improve the overall spectral efficiency, and reduce the overhead of the high-dimensional signal process. Unlike traditional deep learning based methods which directly take channel state information as input, GMML feeds the gradients of the precoding matrix and phase shifting matrix into neural networks. Coherently, we design a differential regulator to constrain the phase shifting matrix of the RIS. Numerical results show that the proposed GMML can improve the spectral efficiency by up to 7.31%, and speed up the convergence by 23 times faster compared to traditional approaches. Moreover, they also demonstrate remarkable robustness and adaptability in dynamic settings.

The rapid development of 6G millimeter-wave (mmWave) networks has introduced new challenges for network security. Adversarial attacks on beamforming algorithms in these networks can lead to severe communication performance degradation. This paper proposes an optimization framework for Deep Learning (DL) hyperparameters that enhances adversarial security in 6G mmWave networks through beam prediction. We develop a robust DL model that can adapt to various adversarial attacks and maintain high prediction accuracy. The proposed framework optimizes hyperparameters using hybrid Particle Swarm Optimization (PSO) with Multi-Verse Optimizer (MVO) for improved security. The framework is evaluated through extensive simulations, demonstrating its effectiveness in improving network security and robustness against adversarial attacks. Under normal conditions, the optimized model achieves the lowest mean squared error (MSE) of 9.4410E – 05 for beamforming codeword predictions. Subjected to Fast Gradient Sign Method (FGSM) adversarial attacks, the optimized model maintains the lowest MSE of 2.2910E – 03, indicating greater resilience against adversarial perturbations. With adversarial training, the optimized model achieves the lowest MSE of 2.7110E – 03, demonstrating the most robust defense against adversarial attacks. In contrast, the non-optimized model suffers significant performance degradation under adversarial and defended conditions. The source code is available at [1].

The recent progress of artificial intelligence (AI) opens up new frontiers in the possibility of automating many tasks involved in Telecom networks design, implementation, and deployment. This has been further pushed forward with the evolution of generative artificial intelligence (AI), including the emergence of large language models (LLMs), which is believed to be the cornerstone toward realizing self-governed, interactive AI agents. Motivated by this, in this paper, we aim to adapt the paradigm of LLMs to the Telecom domain. In particular, we fine-tune several LLMs including BERT, distilled BERT, RoBERTa and GPT-2, to the Telecom domain languages, and demonstrate a use case for identifying the 3rd Generation Partnership Project (3GPP) standard working groups. We consider training the selected models on 3GPP technical documents (Tdoc) pertinent to years 2009-2019 and predict the Tdoc categories in years 2020-2023. The results demonstrate that fine-tuning BERT and RoBERTa model achieves 84.6% accuracy, while GPT-2 model achieves 83% in identifying 3GPP working groups. The distilled BERT model with around 50% less parameters achieves similar performance as others. This corroborates that fine-tuning pretrained LLM can effectively identify the categories of Telecom language. The developed framework shows a stepping stone towards realizing intent-driven and self-evolving wireless networks from Telecom languages, and paves the way for the implementation of generative AI in the Telecom domain.

Enabling quantum switches (QSs) to serve requests submitted by quantum end nodes in quantum communication networks (QCNs) is a challenging problem due to the heterogeneous fidelity requirements of the submitted requests and the limited resources of the QCN. Effectively determining which requests are served by a given QS is fundamental to foster developments in practical QCN applications, like quantum data centers. However, the state-of-the-art on QS operation has overlooked this association problem, and it mainly focused on QCNs with a single QS. In this paper, the request-QS association problem in QCNs is formulated as a matching game that captures the limited QCN resources, heterogeneous application-specific fidelity requirements, and scheduling of the different QS operations. To solve this game, a swap-stable request-QS association (RQSA) algorithm is proposed while considering partial QCN information availability. Extensive simulations are conducted to validate the effectiveness of the proposed RQSA algorithm. Simulation results show that the proposed RQSA algorithm achieves a near-optimal (within 5%) performance in terms of the percentage of served requests and overall achieved fidelity, while outperforming benchmark greedy solutions by over 13%. Moreover, the proposed RQSA algorithm is shown to be scalable and maintain its near-optimal performance even when the size of the QCN increases.

To support the extremely high spectral efficiency and energy efficiency requirements, and emerging applications of future wireless communications, holographic multiple-input multiple-output (H-MIMO) technology is envisioned as one of the most promising enablers. It can potentially bring extra degrees-of-freedom for communications and signal processing, including spatial multiplexing in line-of-sight (LoS) channels and electromagnetic (EM) field processing performed using specialized devices, to attain the fundamental limits of wireless communications. In this context, EM-domain channel modeling is critical to harvest the benefits offered by H-MIMO. Existing EM-domain channel models are built based on the tensor Green function, which require prior knowledge of the global position and/or the relative distances and directions of the transmit/receive antenna elements. Such knowledge may be difficult to acquire in real-world applications due to extensive measurements needed for obtaining this data. To overcome this limitation, we propose a transmit-receive parameter separable channel model method-ology in which the EM-domain (or holographic) channel can be simply acquired from the distance/direction measured between the center-points between the transmit and receive surfaces, and the local positions between the transmit and receive elements, thus avoiding extensive global parameter measurements. Analysis and numerical results showcase the effectiveness of the proposed channel modeling approach in approximating the H-MIMO channel, and achieving the theoretical channel capacity.

Ransomware, a malicious piece of software responsible for several high-profile attacks in recent years, poses a significant threat to organizations of all sizes. Such attacks can cause significant operational and financial harm, including system interruptions and compromises of system integrity. By developing the ability to detect and prevent ransomware attacks, we can contribute to the creation of a more secure and safe digital ecosystem. In this research, we propose FLDetect, a unique Federated Learning (FL)-based method for identifying ransomware on Windows machines. Windows machines, integral to Internet of Things (loT) networks, can act as brokers to other sensor nodes, rendering them susceptible to such attacks. Our approach utilizes distributed computing to train a Machine Learning (ML) model using data from various devices without relying on centralized data storage. The API-call-pattern-based detection method is the preferred approach for detecting ransomware in this paper. We made use of an open-source dataset, known as ransomwaredataset2016, for a comparable objective. The global model's accuracy was 93.1% after we trained it with twenty different devices. Our results demonstrate that our method is effective in identifying ransomware while maintaining the privacy and security of the training data by utilizing FL.

This paper addresses the challenging problem of enabling reliable immersive teleoperation in scenarios where an Unmanned Aerial Vehicle (UAV) is remotely controlled by an operator via a cellular network. Such scenarios can be quite critical particularly when the UAV lacks advanced equipment (e.g., Lidar-based auto stop) or when the network is subject to some performance constraints (e.g., delay). To tackle these challenges, we propose a novel architecture leveraging Digital Twin (DT) technology to create a virtual representation of the physical environment. This virtual environment accurately mirrors the physical world, accounting for 3D surroundings, weather constraints, and network limitations. To enhance tele-operation, the UAV in the virtual environment is equipped with advanced features that may be absent in the real UAV. Furthermore, the proposed architecture introduces an intelligent logic that utilizes information from both virtual and physical environments to approve, deny, or correct actions initiated by the UAV operator. This anticipatory approach helps to mitigate potential risks. Through a series of field trials, we demonstrate the effectiveness of the proposed architecture in significantly improving the reliability of UAV teleoperation.

In this work, we study the problem of semantic communication and inference, in which a student agent (i.e. mobile device) queries a teacher agent (i.e. cloud sever) to generate higher-order data semantics living in a simplicial complex. Specifically, the teacher first maps its data into a k-order simplicial complex and learns its high-order correlations. For effective communication and inference, the teacher seeks minimally sufficient and invariant semantic structures prior to conveying information. These minimal simplicial structures are found via judiciously removing simplices selected by the Hodge Laplacians without compromising the inference query accuracy. Subsequently, the student locally runs its own set of queries based on a masked simplicial convolutional autoencoder (SCAE) leveraging both local and remote teacher's knowledge. Numerical results corroborate the effectiveness of the proposed approach in terms of improving inference query accuracy under different channel conditions and simplicial structures. Experiments on a coauthorship dataset show that removing simplices by ranking the Laplacian values yields a 85% reduction in payload size without sacrificing accuracy. Joint semantic communication and inference by masked SCAE improves query accuracy by 25% compared to local student based query and 15% compared to remote teacher based query. Finally, incorporating channel semantics is shown to effectively improve inference accuracy, notably at low signal-to-noise ratio (SNR) values.

Reconfigurable intelligent surface has recently emerged as a promising technology for shaping the wireless environment by leveraging massive low-cost reconfigurable elements. Prior works mainly focus on a single-layer metasurface that lacks the capability of suppressing multiuser interference. By contrast, we propose a stacked intelligent metasurface (SIM)-enabled transceiver design for multiuser multiple-input single-output downlink communications. Specifically, the SIM is endowed with a multilayer structure and is deployed at the base station to perform transmit beamforming directly in the electromagnetic wave domain. As a result, an SIM-enabled transceiver overcomes the need for digital beamforming and operates with low-resolution digital-to-analog converters and a moderate number of radio-frequency chains, which significantly reduces the hardware cost and energy consumption, while substantially decreasing the pre-coding delay benefiting from the processing performed in the wave domain. To leverage the benefits of SIM-enabled transceivers, we formulate an optimization problem for maximizing the sum rate of all the users by jointly designing the transmit power allocated to them and the analog beamforming in the wave domain. Numerical results based on a customized alternating optimization algorithm corroborate the effectiveness of the proposed SIM-enabled analog beamforming design as compared with various benchmark schemes. Most notably, the proposed analog beamforming scheme is capable of substantially decreasing the precoding delay compared to its digital counterpart.

The development of the Internet of Things (IoT) has dramatically expanded our daily lives, playing a pivotal role in the enablement of smart cities, healthcare, and buildings. Emerging technologies, such as IoT, seek to improve the quality of service in cognitive cities. Although IoT applications are helpful in smart building applications, they present a real risk as the large number of interconnected devices in those buildings, using heterogeneous networks, increases the number of potential IoT attacks. IoT applications can collect and transfer sensitive data. Therefore, it is necessary to develop new methods to detect hacked IoT devices. This paper proposes a Feature Selection (FS) model based on Harris Hawks Optimization (HHO) and Random Weight Network (RWN) to detect IoT botnet attacks launched from compromised IoT devices. Distributed Machine Learning (DML) aims to train models locally on edge devices without sharing data to a central server. Therefore, we apply the proposed approach using centralized and distributed ML models. Both learning models are evaluated under two benchmark datasets for IoT botnet attacks and compared with other well-known classification techniques using different evaluation indicators. The experimental results show an improvement in terms of accuracy, precision, recall, and F-measure in most cases. The proposed method achieves an average F-measure up to 99.9%. The results show that the DML model achieves competitive performance against centralized ML while maintaining the data locally.

Reconfigurable intelligent surface (RIS) is considered as a promising solution for next-generation wireless communication networks due to a variety of merits, e.g., customizing the communication environment. Therefore, deploying multiple RISs helps overcome severe signal blocking between the base station (BS) and users, which is also a practical and effective solution to achieve better service coverage. However, reaping the full benefits of a multi-RISs aided communication system requires solving a non-convex, infinite-dimensional optimization problem, which motivates the use of learning-based methods to configure the optimal policy. This paper adopts a novel heterogeneous graph neural network (GNN) to effectively exploit the graph topology in the wireless communication optimization problem. First, we characterize all communication link features and interference relations in our system with a heterogeneous graph structure. Then, we endeavor to maximize the weighted sum rate (WSR) of all users by jointly optimizing the active beamforming at the BS, the passive beamforming vector of the RIS elements, as well as the RISs association strategy. Unlike most existing work, we consider a more general scenario where the cascaded link for each user is not fixed but dynamically selected by maximizing the WSR. Simulation results show that our proposed heterogeneous GNNs perform about 10 times better than other benchmarks, and a suitable RISs association strategy is also validated to be effective in improving the quality services of users by 30%.

Dual-polarized rate-splitting multiple access (RSMA) with polarization multiplexing has appeared recently as an attractive downlink transmission technique for dual-polarized massive multiple-input multiple-output (MIMO) systems. Dual-polarized RSMA does not require successive interference cancellation (SIC), which avoids practical issues of imperfect SIC decoding. Nevertheless, depolarization phenomena can still degrade the performance of this featured technique. In this work, we exploit the advanced capabilities of a dual-polarized intelligent reflecting surface (IRS) for unleashing an enhanced RSMA polarization multiplexing. To optimize the IRSs, we develop a multi-objective Frank-Wolfe-based algorithm for jointly mitigating cross-polar interference and improving the reception of common and private data streams at multiple users. Representative simulation examples demonstrate that dual-polarized IRS-MIMO-RSMA can efficiently mitigate detrimental depolarization phenomena and outperform conventional systems.

This paper studies the exploitation of triple polarization (TP) for multi-user (MU) holographic multiple-input multiple-output surface (HMIMOS) wireless communication systems, aiming at capacity boosting without enlarging the antenna array size. We specifically consider that both the transmitter and receiver are equipped with an HMIMOS comprising compact sub-wavelength TP patch antennas. To characterize TP MU-HMIMOS systems, a TP near-field channel model is proposed using the dyadic Green's function, whose characteristics are leveraged to design a user-cluster-based precoding scheme for mitigating the cross-polarization and inter-user interference contributions. A theoretical correlation analysis for HMIMOS with infinitely small patch antennas is also presented. According to the proposed scheme, the users are assigned to one of the three polarizations, which is easy to implement, at the cost, however, of reducing the system's diversity. Our numerical results showcase that the cross-polarization channel components have a non-negligible impact on the system performance, which is efficiently eliminated with the proposed MU precoding scheme.

Visual Speech Recognition (VSR) differs from the common perception tasks as it requires deeper reasoning over the video sequence, even by human experts. Despite the recent advances in VSR, current approaches rely on labeled data to fully train or finetune their models predicting the target speech. This hinders their ability to generalize well beyond the training set and leads to performance degeneration under out-of-distribution challenging scenarios. Unlike previous works that involve auxiliary losses or complex training procedures and architectures, we propose a simple approach, named Lip2Vec that is based on learning a prior model. Given a robust visual speech encoder, this network maps the encoded latent representations of the lip sequence to their corresponding latents from the audio pair, which are sufficiently invariant for effective text decoding. The generated audio representation is then decoded to text using an off-the-shelf Audio Speech Recognition (ASR) model. The proposed model compares favorably with fully-supervised learning methods on the LRS3 dataset achieving 26 WER. Unlike SoTA approaches, our model keeps a reasonable performance on the VoxCeleb2-en test set. We believe that reprogramming the VSR as an ASR task narrows the performance gap between the two and paves the way for more flexible formulations of lip reading.

Accurate and robust coarse semantic segmentation plays a key role in the pursuit of autonomous driving. We present an algorithm that regularizes the representation space of Semantic Segmentation by Multiple Granularity Learning (SSMGL). This approach explores multiple levels of semantic knowledge in an unified framework, where the fine-grained semantic information can be either labeled or unlabeled. In our experiments, we find that SSMGL can achieve better results (1) on both on-road and off-road benchmarks, (2) under different segmentation architectures, or (3) with different backbones. The method is plug-and-play, not specialized for autonomous driving applications, and can be easily extended to any other segmentation scenario. Moreover, our SSMGL approach does not increase the computational overhead in the inference stage.

The increasing number of applications and devices in the Sixth-generation (6G) networks and the diversity of mobile data, architectures, and technologies make security and privacy a critical concern. Advanced metaheuristics algorithms (MHAs) have recently become a viable solution for optimizing security and privacy in wireless networks, combining game theory and convex optimization, and several other advanced models. As a subfield of Artificial Intelligence (AI), MHAs are inspired by concepts from Evolutionary Algorithms (EAs), Trajectory-based Algorithms (TAs), and Swarm Intelligence (SI). Recent implementations of MHAs in the 6G networks have effectively solved complex security and privacy problems. This study examines MHAs’ utilization in addressing security and privacy challenges in 6G networks. The paper provides a comprehensive overview of MHAs and their use in solving security and privacy problems in 6G. The current limitations of the literature are also identified, and avenues for further research are suggested. The reader will have a clear image of the needed technologies and tools for securing 6G networks using MHAs.

Targeting ultra-reliable and scalable connectivity of extremely high data rates, in the 100 Gbps to Tbps range, at almost “zero-latency” in 6G systems would require taking advantage of breakthrough novel technology concepts, including THz wireless links, broadband and spectrally efficient RF-frontends for a variety of different bands, the employment of intelligent materials (e.g., reconfigurable intelligent surfaces) and the design of machine learning-based models, protocols, and management techniques. To materialize the 6G vision, novel system techniques will need to be devised, including channel modeling and estimation, waveforms, beamforming, and multiple-access schemes, all tailored to the particularities of the adopted breakthrough technologies. As challenging Tbit/s usage scenarios are becoming ever more relevant for 6G systems, including non-line of sight connectivity based on intelligent surfaces and ad hoc connectivity in fast-moving network topologies, e.g., based on drones or V2X links, performance targets need to be reassessed. In such scenarios, apart from the high data rates in the order of Tbit/s other critical parameters may arise as more relevant: range, reliability, adaptability, reconfigurability, and agility, to name just a few.

Goal-oriented semantic communication will be a pillar of next-generation wireless networks. Despite significant recent efforts in this area, most prior works are focused on specific data types (e.g., image or audio), and they ignore the goal and effectiveness aspects of semantic transmissions. In contrast, in this paper, a holistic goal-oriented semantic communication framework is proposed to enable a speaker and a listener to cooperatively execute a set of sequential tasks in a dynamic environment. A common language based on a hierarchical belief set is proposed to enable semantic communications between speaker and listener. The speaker, acting as an observer of the environment, utilizes the beliefs to transmit an initial description of its observation (called event) to the listener. The listener is then able to infer on the transmitted description and complete it by adding related beliefs to the transmitted beliefs of the speaker. As such, the listener reconstructs the observed event based on the completed description, and it then takes appropriate action in the environment based on the reconstructed event. An optimization problem is defined to determine the perfect and abstract description of the events while minimizing the various communication costs with constraints on the task execution time and belief efficiency. Then, a novel bottom-up curriculum learning (CL) framework based on reinforcement learning is proposed to solve the optimization problem and enable the speaker and listener to gradually identify the structure of the belief set and the perfect and abstract description of the events. Simulation results show that the proposed CL method outperforms classical RL and CL without inference scheme in terms of convergence time, task execution cost and time, reliability, and belief efficiency.

In this paper, we investigate a cell-free massive multiple-input multiple-output system with both access points and user equipments equipped with multiple antennas over the Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency (SE) for the fully centralized processing scheme and large-scale fading decoding (LSFD) scheme. To further improve the SE performance, we design the uplink precoding schemes based on the weighted sum SE maximization. Since the weighted sum SE maximization problem is not jointly over all optimization variables, two efficient uplink precoding schemes based on Iteratively Weighted sum-Minimum Mean Square Error (I-WMMSE) algorithms, which rely on the iterative minimization of weighted Mean Square Error (MSE), are proposed for two processing schemes investigated. Furthermore, with maximum ratio combining applied in the LSFD scheme, we derive novel closed-form achievable SE expressions and optimal precoding schemes. Numerical results validate the proposed results and show that the I-WMMSE precoding schemes can achieve excellent sum SE performance with a large number of UE antennas.

This work presents a novel nonorthogonal multiple access (NOMA) cognitive radio (CR) system where the base station (BS) opportunistically multiplexes the secondary user (SU) with the primary user (PU) using power-domain NOMA. As the PU has the priority to transmit and SU is satisfied on best-effort basis, four different transmission-modes (TMs) are produced at the BS, which are PU orthogonal multiple access (PU-OMA), SU-OMA, PU/SU-NOMA, and silent mode. Consequently, the considered protocol can be classified as a hybrid underlay-interweave CR-NOMA. The TM adaptation should be seamless for the PU where its detector configuration remains unchanged regardless of the active TM. In contrast, the SU has to identify the active TM blindly, i.e. without side information, to select the appropriate detector. The identification process is performed using a classifier that is designed based on the maximum likelihood criterion. The performance of the proposed system is analyzed in terms of throughput, packet error rate (PER), and classification error. The Binomial and Multinomial theorems are utilized to simplify and allow a tractable analysis. The derived closed-form expressions, corroborated by Monte-Carlo simulation results, show that the hybrid CR-NOMA can provide substantial throughput improvement over conventional NOMA, which is about a 100%.

Driven by privacy concerns and the promise of Deep Learning, researchers have devoted significant effort to exploring the applicability of Machine Learning (ML). In the domains of communication, network, and service management, ML-based decision-making solutions are eagerly sought to replace traditional model-driven approaches, addressing the growing complexity and heterogeneity of modern systems. In this context, Federated Learning (FL) has gained increasing interest as a decentralized approach that overcomes the limitations of centralized systems for data analysis.

Integrating unmanned aerial vehicles (UAVs) with federated learning (FL) has been seen as a promising paradigm for dealing with the massive amounts of data generated by intelligent devices. Nevertheless, although FL has natural advantages in data security protection, eavesdroppers can also deduce the raw data according to the shared parameters. Existing works mainly focused on encrypting the content of uploaded parameters, but we believe that it can improve security further by hiding the presence of parameter updating. Therefore, in this paper, we conceive a UAV-enabled covert federated learning architecture, where the UAV is not only responsible for orchestrating the operation of FL but also for emitting artificial noise (AN) to interfere with the eavesdropping of unintended users. To strike a balance between the security level and the training cost (including time overhead and energy consumption), we propose a distributed proximal policy optimization-based strategy for the sake of jointly optimizing the trajectory and AN transmitting power of the UAV, the CPU frequency, the transmitting power and the bandwidth allocation of the participated devices, as well as the needed accuracy of the local model. Furthermore, a series of experiments have been conducted to validate the effectiveness of our proposed scheme.

This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), which estimates the path parameters by exploiting the angular-frequency-domain and angular-time-domain structures of the wideband channel. The MDMP method also entails a novel path pairing scheme to pair the delay and Doppler, based on the super-resolution property of the angle estimation. Our method is able to deal with the realistic constraint of time-varying path delays introduced by user movements, which has not been considered so far in the literature. We prove theoretically that in the scenario with time-varying path delays, the prediction error converges to zero with the increasing number of the base station (BS) antennas, providing that only two arbitrary channel samples are known. We also derive a lower-bound of the number of the BS antennas to achieve a satisfactory performance. Simulation results under the industrial channel model of 3GPP demonstrate that our proposed MDMP method approaches the performance of the stationary scenario even when the users’ velocity reaches 120 km/h and the latency of the channel state information is as large as 16 ms.

Reconfigurable Intelligent Surfaces (RISs) have been recently proposed as an enabling technology for programmable wireless environments. In this paper, we present asymptotic closed-form expressions for the mean and variance of the mutual information for a multi-antenna transmitter-receiver pair in the presence of RISs, using statistical physics methods. While nominally valid in the large-system limit, we show that the derived Gaussian approximation for the mutual information can be quite accurate, even for modest-sized antenna arrays and metasurfaces. The above results are particularly useful when fast-fading conditions are present, which renders channel estimation challenging. We find that, when the channel close to an RIS is correlated, for instance due to small angle spread, which is reasonable for wireless systems with increasing carrier frequencies, the communication link benefits significantly from statistical RIS optimization, resulting in gains that are surprisingly higher than the nearly uncorrelated case. Using our novel asymptotic properties of the correlation matrices of the impinging and outgoing signals at the RISs, we can optimize the metasurfaces without brute-force numerical optimization. When the desired reflection from any of the RISs departs significantly from geometrical optics, the metasurfaces can be optimized to provide robust communication links, without significant need for their optimal placement.

Starting from first principles of wave propagation, we consider a multiple-input multiple-output (MIMO) representation of a communication system between two spatially-continuous volumes. This is the concept of holographic MIMO communications. The analysis takes into account the electromagnetic interference, generated by external sources, and the constraint on the physical radiated power. The electromagnetic MIMO model is particularized for a pair of parallel line segments in line-of-sight conditions. Inspired by orthogonal-frequency division-multiplexing, we assume that the spatially-continuous transmit currents and received fields are represented using the Fourier basis functions. In doing so, a wavenumber-division multiplexing (WDM) scheme is obtained, which is not optimal but can be efficiently implemented. The interplay among the different system parameters (e.g., transmission range, wavelength, and sizes of source and receiver) in terms of number of communication modes and level of interference among them is studied with conventional tools of linear systems theory. Due to the non-finite support (in the spatial domain) of the electromagnetic channel, WDM cannot provide non-interfering communication modes. The interference decreases as the receiver size grows, and goes to zero only asymptotically. Different digital processing architectures, operating in the wavenumber domain, are thus used to deal with the interference. The simplest implementation provides the same spectral efficiency of a singular-value decomposition architecture with water-filling when the receiver size is comparable to the transmission range. The developed framework is also used to represent a communication scheme that performs only an integration over short spatial segments. This is equivalent to a classical MIMO system with uniform linear arrays made of electrically small dipoles. Numerical comparisons show that better performance than WDM can be achieved only when a higher number of radio-frequency chains is used.

The polarization domain provides an extra degree of freedom (DoF) for improving the performance of multiple-input multiple-output (MIMO) systems. This paper takes advantage of this additional DoF to alleviate practical issues of successive interference cancellation (SIC) in rate-splitting multiple access (RSMA) schemes. Specifically, we propose three dual-polarized downlink transmission approaches for a massive MIMO-RSMA network under the effects of polarization interference and residual errors of imperfect SIC. The first approach implements polarization multiplexing for transmitting the users’ data messages, which removes the need to execute SIC in the reception. The second approach transmits replicas of users’ messages in the two polarizations, which enables users to exploit diversity through the polarization domain. The third approach, in its turn, employs the original SIC-based RSMA technique per polarization, and this allows the BS to transmit two independent superimposed data streams simultaneously. An in-depth theoretical analysis is carried out, in which we derive tight closed-form approximations for the outage probabilities of the three proposed approaches. Accurate approximations for the ergodic sum-rates of the two first schemes are also derived. Simulation results validate the theoretical analysis and confirm the effectiveness of the proposed schemes. For instance, under low to moderate cross-polar interference, the results show that, even under high levels of residual SIC error, our dual-polarized MIMO-RSMA strategies outperform the conventional single-polarized MIMO-RSMA counterpart. It is also shown that the performance of all RSMA schemes is impressively higher than that of single and dual-polarized massive MIMO systems employing non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) techniques.

This paper investigates the utilization of triple polarization (TP) for multi-user (MU) wireless communication systems with holographic multiple-input multi-output surfaces (HMIMOSs), targeting capacity boosting and diversity exploitation without enlarging the antenna array sizes of the transceivers. We specifically consider that both the transmitter and receiver are equipped with an HMIMOS consisting of compact sub-wavelength TP patch antennas and operating in the near-field (NF) regime. To characterize TP MU-HMIMOS systems, a TP NF channel model is constructed using the dyadic Green’s function, whose characteristics are leveraged to design two precoding schemes for mitigating the cross-polarization and inter-user interference contributions. Specifically, a user-cluster-based precoding scheme that assigns different users to one of three polarizations, at the expense of system’s diversity, is presented together with a two-layer precoding technique that removes interference using a Gaussian elimination method. A theoretical correlation analysis for HMIMOS-based systems operating in the NF region is also derived, revealing that both the spacing of transmit patch antennas and user distance impact transmit correlation factors. Our numerical results showcase that the users located far from the transmit HMIMOS experience higher correlation than those closer in the NF region, resulting in a lower channel capacity. In terms of channel capacity, it is demonstrated that the proposed TP HMIMOS-based systems almost achieve 1.25 and 3 times larger gain compared to their dual-polarized version and conventional HMIMOS systems, respectively. It is also shown that the the proposed two-layer precoding scheme combined with two-layer power allocation realizes the highest spectral efficiency, among compared schemes, without sacrificing diversity.

Aiming at overcoming practical issues of successive interference cancellation (SIC), this paper proposes a dual-polarized rate-splitting multiple access (RSMA) technique for a downlink massive multiple-input multiple-output (MIMO) net-work. By modeling the effects of polarization interference, an in-depth theoretical analysis is carried out, in which we derive tight closed-form approximations for the outage probabilities and ergodic sum-rates. Simulation results validate the accuracy of the theoretical analysis and confirm the effectiveness of the proposed approach. For instance, under low to moderate cross-polar interference, our results show that the proposed dual-polarized MIMO-RSMA strategy outperforms the single-polarized MIMO-RSMA counterpart for all considered levels of residual SIC error.

Massive multi-input multi-output (MIMO) in Frequency Division Duplex (FDD) mode suffers from heavy feedback overhead for Channel State Information (CSI). In this paper, a novel manifold learning-based CSI feedback framework (MLF) is proposed to reduce the amount of feedback and improve the spectral efficiency of FDD massive MIMO. In most traditional manifold learning approaches, the newly sampled data has to be combined with the existing dataset and the training process has to be done all over again, making it complex to process the incremental CSI in a wireless communication system. Also, the number of component functions required for reconstruction is proportional to the dimension of channel matrix, which limits their practicality in wideband systems. In this paper, we solve the incremental problem by introducing two groups of dictionaries. The key idea of our MLF framework is to learn these dictionaries to represent the manifold structure of CSI data. The incremental CSI is reconstructed by preserving the local manifold structure, i.e., sharing the same neighbor and coding relationships in the input space and the feature space. Experimental results under an industrial channel model of 3GPP show that the proposed algorithm outperforms existing algorithms based on compressive sensing and deep learning in terms of CSI reconstruction performance.

Semantic communications will play a critical role in enabling goal-oriented services over next-generation wireless systems. However, most prior art in this domain is restricted to specific applications (e.g., text or image), and it does not enable goal-oriented communications in which the effectiveness of the transmitted information must be considered along with the semantics so as to execute a certain task. In this paper, a comprehensive semantic communications framework is proposed for enabling goal-oriented task execution. To capture the semantics between a speaker and a listener, a common language is defined using the concept of beliefs to enable the speaker to describe the environment observations to the listener. Then, an optimization problem is posed to choose the minimum set of beliefs that perfectly describes the observation while minimizing the task execution time and transmission cost. A novel top-down framework that combines curriculum learning (CL) and reinforcement learning (RL) is proposed to solve this problem. Simulation results show that the proposed CL method outperforms traditional RL in terms of convergence time, task execution time, and transmission cost during training.

Deploying federated learning (FL) over wireless networks with resource-constrained devices requires balancing between accuracy, energy efficiency, and precision. Prior art on FL often requires devices to train deep neural networks (DNNs) using a 32-bit precision level for data representation to improve accuracy. However, such algorithms are impractical for resource-constrained devices since DNNs could require execution of millions of operations. Thus, training DNNs with a high precision level incurs a high energy cost for FL. In this paper, a quantized FL framework, that represents data with a finite level of precision in both local training and uplink transmission, is proposed. Here, the finite level of precision is captured through the use of quantized neural networks (QNNs) that quantize weights and activations in fixed-precision format. In the considered FL model, each device trains its QNN and transmits a quantized training result to the base station. Energy models for the local training and the transmission with the quantization are rigorously derived. An energy minimization problem is formulated with respect to the level of precision while ensuring convergence. To solve the problem, we first analytically derive the FL convergence rate and use a line search method. Simulation results show that our FL framework can reduce energy consumption by up to 53% compared to a standard FL model. The results also shed light on the tradeoff between precision, energy, and accuracy in FL over wireless networks.

This paper addresses the challenge of mobility in massive multiple-input multiple-output (MIMO) communication systems. In the deployment of 5G, this problem leads to alarmingly high performance degradation. In this paper, we propose a novel multi-dimension Matrix Pencil (MDMP) channel prediction method in order to tackle this practical challenge. More specifically, our method calculates accurate estimations of path angles, delays and Doppler simultaneously. In order to do so, we exploit the angular-frequency-domain and angular-time-domain structures of the wideband channel, and propose a path pairing procedure by exploiting the super-resolution property of the estimated angles. Our method is able to deal with the realistic constraint of time-variant path delays introduced by user movements, which have not been considered so far in literature. We prove that with only two arbitrary channel samples given, that prediction error converges to zero in the scenario with time-variant delay and arbitrary delay of channel state information (CSI), if the number of base station (BS) antennas is large enough. Unlike the existing Prony-based angular-delay domain (PAD) prediction method that assumes the CSI delay is an integral multiple of the pilot interval, our MDMP method breaks such a limitation and is therefore more general. Simulation results under the clustered delay line (CDL) model of 3GPP demonstrate that in the high-mobility scenario with time-variant path delays and a large CSI delay of 16 ms, our proposed MDMP method can approach to the performance of the stationary scenario.

The emergent technology of Reconfigurable Intelligent Surfaces (RISs) has the potential to transform wireless environments into controllable systems, through programmable propagation of information-bearing signals. Techniques stemming from the field of Deep Reinforcement Learning (DRL) have recently gained popularity in maximizing the sum-rate performance in multi-user communication systems empowered by RISs. Such approaches are commonly based on Markov Decision Processes (MDPs). In this paper, we instead investigate the sum-rate design problem under the scope of the Multi-Armed Bandits (MAB) setting, which is a relaxation of the MDP framework. Nevertheless, in many cases, the MAB formulation is more appropriate to the channel and system models under the assumptions typically made in the RIS literature. To this end, we propose a simpler DRL approach for orchestrating multiple metasurfaces in RIS-empowered multi-user Multiple-Input Single-Output (MISO) systems, which we numerically show to perform equally well with a state-of-the-art MDP-based approach, while being less demanding computationally.

In this paper, we investigate a cell-free massive MIMO system with both access points and user equipments equipped with multiple antennas over the Weichselberger Rayleigh fading channel. We study the uplink spectral efficiency (SE) based on a two-layer decoding structure with maximum ratio (MR) or local minimum mean-square error (MMSE) combining applied in the first layer and optimal large-scale fading decoding method implemented in the second layer, respectively. To maximize the weighted sum SE, an uplink precoding structure based on an Iteratively Weighted sum-MMSE (I-WMMSE) algorithm using only channel statistics is proposed. Furthermore, with MR combining applied in the first layer, we derive novel achievable SE expressions and optimal precoding structures in closed-form. Numerical results validate our proposed results and show that the I-WMMSE precoding can achieve excellent sum SE performance.

Multi-access edge computing (MEC) is viewed as an integral part of future wireless networks to support new applications with stringent service reliability and latency requirements. However, guaranteeing ultra-reliable and low-latency MEC (URLL MEC) is very challenging due to uncertainties of wireless links, limited communications and computing resources, as well as dynamic network traffic. Enabling URLL MEC man-dates taking into account the statistics of the end-to-end (E2E) latency and reliability across the wireless and edge computing systems. In this paper, a novel framework is proposed to optimize the reliability of MEC networks by considering the distribution of E2E service delay, encompassing over-the-air transmission and edge computing latency. The proposed framework builds on correlated variational autoencoders (VAEs) to estimate the full distribution of the E2E service delay. Using this result, a new optimization problem based on risk theory is formulated to maximize the network reliability by minimizing the Conditional Value at Risk (CVaR) as a risk measure of the E2E service delay. To solve this problem, a new algorithm is developed to efficiently allocate users’ processing tasks to edge computing servers across the MEC network, while considering the statistics of the E2E service delay learned by VAEs. The simulation results show that the proposed scheme outperforms several baselines that do not account for the risk analyses or statistics of the E2E service delay.

A cell-free massive multiple-input multiple-output (MIMO) uplink is investigated in this paper. We address a power allocation design problem that considers two conflicting metrics, namely the sum rate and fairness. Different weights are allocated to the sum rate and fairness of the system, based on the requirements of the mobile operator. The knowledge of the channel statistics is exploited to optimize power allocation. We propose to employ large scale-fading (LSF) coefficients as the input of a twin delayed deep deterministic policy gradient (TD3). This enables us to solve the non-convex sum rate fairness trade-off optimization problem efficiently. Then, we exploit a use-and-then-forget (UatF) technique, which provides a closed-form expression for the achievable rate. The sum rate fairness trade-off optimization problem is subsequently solved through a sequential convex approximation (SCA) technique. Numerical results demonstrate that the proposed algorithms outperform conventional power control algorithms in terms of both the sum rate and minimum user rate. Furthermore, the TD3-based approach can increase the median of sum rate by 16%-46% and the median of minimum user rate by 11%-60% compared to the proposed SCA-based technique. Finally, we investigate the complexity and convergence of the proposed scheme.

In this paper, we aim to efficiently and accurately predict the static channel impulse response (CIR) with only the user’s position information and a set of channel instances obtained within a certain wireless communication environment. Such a problem is by no means trivial since it needs to reconstruct the high-dimensional information (here the CIR everywhere) from the extremely low-dimensional data (here the location coordinates), which often results in overfitting and large prediction error. To this end, we resort to a novel physics-inspired generative approach. Specifically, we first use a forward deep neural network to infer the positions of all possible images of the source reflected by the surrounding scatterers within that environment, and then use the well-known Gaussian Radial Basis Function network (GRBF) to approximate the amplitudes of all possible propagation paths. We further incorporate the most recently developed sinusoidal representation network (SIREN) into the proposed network to implicitly represent the highly dynamic phases of all possible paths, which usually cannot be well predicted by the conventional neural networks with non-periodic activators. The resultant framework of Cosine-Gaussian Radial Basis Function network (C-GRBFnet) is also extended to the MIMO channel case. Key performance measures including prediction accuracy, convergence speed, network scale and robustness to channel estimation error are comprehensively evaluated and compared with existing popular networks, which show that our proposed network is much more efficient in representing, learning and predicting wireless channels in a given communication environment.

The emerging technology of reconfigurable intelligent surfaces (RISs) is provisioned as an enabler of smart wireless environments, offering a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium, ultimately providing increased environmental intelligence for diverse operation objectives. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces with limited, or even the absence of, computing hardware. In this article, we consider multiuser and multi-RIS-empowered wireless systems and present a thorough survey of the online machine learning approaches for the orchestration of their various tunable components. Focusing on the sum-rate maximization as a representative design objective, we present a comprehensive problem formulation based on deep reinforcement learning (DRL). We detail the correspondences among the parameters of the wireless system and the DRL terminology, and devise generic algorithmic steps for the artificial neural network training and deployment while discussing their implementation details. Further practical considerations for multi-RIS-empowered wireless communications in the sixth-generation (6G) era are presented along with some key open research challenges. Different from the DRL-based status quo, we leverage the independence between the configuration of the system design parameters and the future states of the wireless environment, and present efficient multiarmed bandits approaches, whose resulting sum-rate performances are numerically shown to outperform random configurations, while being sufficiently close to the conventional deep$Q$network (DQN) algorithm, but with lower implementation complexity.

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes.

mmWave communication requires accurate and continuous beam steering to overcome the severe propagation loss and user mobility. In this paper, we leverage a self-supervised deep learning approach to exploit sub-6 GHz channels and propose a novel method to predict beamforming vectors in the mmWave band for a single access point– user link. This complex channel-beam mapping is learned via data issued from the DeepMIMO dataset. We then compare our proposed method with existing supervised deep learning and classic reinforcement learning methods. Our simulations show that choosing an appropriate beam steering method depends on the target application and is a tradeoff between data rate and computational complexity. We also investigate tuning the size of our neural network depending on the number of transmit and receive antennas at the access point. Finally, we extend our method to the case of multiple links and introduce a federated learning (FL) approach to efficiently predict their mmWave beams by sharing only the weights of the locally trained neural networks (and not the local data). We investigate both synchronous and asynchronous FL methods. Our numerical simulations show the high potential of our approach, especially when the local available data is scarce or imperfect.

We consider the extra degree of freedom offered by the rotation of the reconfigurable intelligent surface (RIS) plane and investigate its potential in improving the performance of RIS-assisted wireless communication systems. By considering radiation pattern modeling at all involved nodes, we first derive the composite channel gain and present a closed-form upper bound for the system ergodic capacity over cascade Rician fading channels. Then, we reconstruct the composite channel gain by taking the rotations at the RIS plane, transmit antenna, and receive antenna into account, and extract the optimal rotation angles after investigating their impacts on the capacity. Moreover, we present a location-dependent expression of the ergodic capacity and investigate the RIS deployment strategy, i.e. the joint rotation adjustment and location selection. Finally, simulation results verify the accuracy of the theoretical analyses and deployment strategy. Although the RIS location has a big impact on the performance, our results showcase that the RIS rotation plays a more important role. In other words, we can obtain a considerable improvement by properly rotating the RIS rather than moving it over a wide area. For instance, we can achieve more than 200% performance improvement through rotating the RIS by 42.14°, while an 150% improvement is obtained by shifting the RIS over 400 meters.

Massive multiple-input multiple-output (MIMO) is a key technology for improving the spectral and energy efficiency in 5G-and-beyond wireless networks. For a tractable analysis, most of the previous works on Massive MIMO have been focused on the system performance with complex Gaussian channel impulse responses under rich-scattering environments. In contrast, this paper investigates the uplink ergodic spectral efficiency (SE) of each user under the double scattering channel model. We derive a closed-form expression of the uplink ergodic SE by exploiting the maximum ratio (MR) combining technique based on imperfect channel state information. We further study the asymptotic SE behaviors as a function of the number of antennas at each base station (BS) and the number of scatterers available at each radio channel. We then formulate and solve a total energy optimization problem for the uplink data transmission that aims at simultaneously satisfying the required SEs from all the users with limited data power resource. Notably, our proposed algorithms can cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of the closed-form ergodic SE over Monte-Carlo simulations. Besides, the system can still provide the required SEs to many users even under congestion.

In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connected workers. The proposed algorithm, Censored and Quantized Generalized GADMM (CQ-GGADMM), leverages the worker grouping and decentralized learning ideas of Group Alternating Direction Method of Multipliers (GADMM), and pushes the frontier in communication efficiency by extending its applicability to generalized network topologies, while incorporating link censoring for negligible updates after quantization. We theoretically prove that CQ-GGADMM achieves the linear convergence rate when the local objective functions are strongly convex under some mild assumptions. Numerical simulations corroborate that CQ-GGADMM exhibits higher communication efficiency in terms of the number of communication rounds and transmit energy consumption without compromising the accuracy and convergence speed, compared to the censored decentralized ADMM, and the worker grouping method of GADMM.

In this paper, we investigate a cell-free massive MIMO system with both access points (APs) and user equipments (UEs) equipped with multiple antennas over jointly-correlated Rayleigh fading channels. We study four uplink implementations, from fully centralized processing to fully distributed processing, and derive their achievable spectral efficiency (SE) expressions with minimum mean-squared error successive interference cancellation (MMSE-SIC) detectors and arbitrary combining schemes. Furthermore, the global and local MMSE combining schemes are derived based on full and local channel state information (CSI) obtained under pilot contamination, which can maximize the achievable SE for the fully centralized and distributed implementation, respectively. We study a two-layer decoding implementation with an arbitrary combining scheme in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. Besides, we compute novel closed-form SE expressions for the two-layer decoding implementation with maximum ratio (MR) combining. In the numerical results, we compare the SE performance for different implementation levels, combining schemes, and channel models. It is important to note that increasing the number of antennas per UE may degrade the SE performance.

Increasing concerns on intelligent spectrum sensing call for efficient training and inference technologies. In this paper, we propose a novel federated learning (FL) framework, dubbed federated spectrum learning (FSL), which exploits the benefits of reconfigurable intelligent surfaces (RISs) and overcomes the unfavorable impact of deep fading channels. Distinguishingly, we endow conventional RISs with spectrum learning capabilities by leveraging a fully-trained convolutional neural network (CNN) model at each RIS controller, thereby helping the base station to cooperatively infer the users who request to participate in FL at the beginning of each training iteration. To fully exploit the potential of FL and RISs, we address three technical challenges: RISs phase shifts configuration, user-RIS association, and wireless bandwidth allocation. The resulting joint learning, wireless resource allocation, and user-RIS association design is formulated as an optimization problem whose objective is to maximize the system utility while considering the impact of FL prediction accuracy. In this context, the accuracy of FL prediction interplays with the performance of resource optimization. In particular, if the accuracy of the trained CNN model deteriorates, the performance of resource allocation worsens. The proposed FSL framework is tested by using real radio frequency (RF) traces and numerical results demonstrate its advantages in terms of spectrum prediction accuracy and system utility: a better CNN prediction accuracy and FL system utility can be achieved with a larger number of RISs and reflecting elements.

The energy consumption of cellular networks, despite the high energy efficiency of the fifth generation (5G) of mobile technology, is still a challenge. The fundamental problem arises due to the complexity of optimising the operation of the available rich set of energy efficiency features in large-scale deployments. To assist such optimisation, a large body of research -with the resulting understanding and algorithms-exists, particularly on the energy efficiency of single-cell massive multiple-input multiple-output systems. However, other funda-mental cellular features, such as those relating to multi-carrier systems, remain largely unexplored. In this paper, we show how multi-carrier features, such as carrier aggregation, can play a significant role in energy savings, and question the need for hundreds of antennas and transceiver chains at the base stations as an urgent solution to increase the energy efficiency of next generation networks.

Reconfigurable Intelligent Surfaces (RISs), comprising large numbers of low-cost and passive metamaterials with tunable reflection properties, have been recently proposed as an enabler for programmable radio propagation environments. However, the role of the channel conditions near the RISs on their optimizability has not been analyzed adequately. In this paper, we present an asymptotic closed-form expression for the mutual information of a multi-antenna transmitter-receiver pair in the presence of multiple RISs, in the large-antenna limit, using the random matrix and replica theories. Under mild assumptions, asymptotic expressions for the eigenvalues and the eigenvectors of the channel covariance matrices are derived. We find that, when the channel close to an RIS is correlated, for instance due to small angle spread, the communication link benefits significantly from the RIS optimization, resulting in gains that are surprisingly higher than the nearly uncorrelated case. Furthermore, when the desired reflection from the RIS departs significantly from geometrical optics, the surface can be optimized to provide robust communication links. Building on the properties of the eigenvectors of the covariance matrices, we are able to find the optimal response of the RISs in closed form, bypassing the need for brute-force optimization.

The energy consumption and carbon footprint of the fifth-generation (5G) of mobile technology is a current concern to mobile network operators (MNOs). These are currently attempting to lower both their carbon emissions and electricity bills by investigating new schemes that allow adapting the network transmission capabilities to the end-users' quality of service (QoS) requirements. Many of such schemes rely on accurate traffic forecasting, and as a consequence, there is a large effort on investigating novel machine learning (ML) algorithms, which fed by network measurement data and empowered by the computing capabilities of dedicated hardware, can help modelling and predicting users' behaviours. Most of the works in the literature, however, focus on predicting the traffic when energy saving features, e.g. carrier shutdown, are not implemented or activated. However, the prediction task becomes much more challenging when energy saving features are adopted due to their impact to the actual measured traffic. In this paper, we consider a scenario in which part of the base stations implement energy saving schemes, which allow them to dynamically switch off part of their hardware to reduce their power consumption. Then, we present a ML framework based on graph convolutional networks (GCNs) for traffic forecasting in such dynamic scenarios, and compare its performance with other statistical and ML prediction algorithms. The proposed GCN framework provides significant accuracy gains. Moreover, we provide an analysis of the impact of spatial correlation-captured by the GCN model-on the achieved performance.

This paper considers a massive MIMO system under the double scattering channels. We derive a closed-form expression of the uplink ergodic spectral efficiency (SE) by exploiting the maximum-ratio combining technique with imperfect channel state information. We then formulate and solve a total uplink data power optimization problem that aims at simultaneously satisfying the required SEs from all the users with limited power resources. We further propose algorithms to cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of our proposed power optimization. More importantly, our proposed congestion-handling algorithms can guarantee the required SEs to many users under congestion, even when the SE requirement is high.

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted multi-user wireless communication system and present a joint channel estimation and signal recovery algorithm in this paper. Specifically, we propose a bidirectional approximate message passing algorithm that applies the Taylor series expansion and Gaussian approximation to simplify the sum-product algorithm in the formulated problem. Our simulation results show that the proposed algorithm shows the superiority over a state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed algorithms.

We focus on the wireless communication with several independent nodes in the bounded space. In particular, we discuss the existence and uniqueness of solutions for artificially constructing reachable intervals of wireless channel by adjusting the distribution of singular value of the multiple observation points simultaneously, under the point excitation sources in two-dimensional space. In this paper, it is theoretically proved that there is no physical exact solution for more than five observation points under part of the fixed wireless channel. In practical engineering, no amount of RIS arrays or elements can be deployed to construct a physical solution that accommodates more than five receivers simultaneously, when part of the wireless channel is given in advanced and unchangeable. Fundamental limits of wave control in smart environment is given by the number and range of independent variables of underdetermined functions.

Reconfigurable intelligent surfaces (RIS) technology, as the name implies, is a grid of many small intelligent surfaces that can be reconfigured. It is a new and promising concept in wireless communications, expected to help realize the requirements of the future cellular generations. Numerous studies have been done to prove its added advantages and to control these surfaces in beneficial ways. However, these control schemes come with difficulties related to efficient and practical implementation. In this paper, we propose to control multiple RISs in a multi-user scheme with an algorithm that leads to a simple implementation. We formulate a stochastic optimization problem and we propose a new method using a distributed stochastic algorithm that allows the optimization to be done locally at each RIS with low computational requirements and without the need for instantaneous channel knowledge at the RIS. Also, the signaling between the base station (BS) and each RIS is limited to the exchange of a scalar only. Simulation results prove the success and efficiency of this algorithm.

Emerging unmanned aerial vehicles (UAVs) are playing an increasingly important role in military, public, and civilian applications. More recently, UAVs have become a topic of central research interest in the wireless communication community. For example, the 3rd Generation Partnership Project (3GPP) standardisation body has recently worked on a study item to facilitate seamless integration of UAVs into future cellular networks, which is called the cellular Internet of UAVs. UAVs can be exploited in different ways to enhance cellular communications. On the one hand, dedicated UAVs can be used as airborne wireless access points or relay nodes to further improve terrestrial communications, which is referred to as UAV-assisted cellular communications. On the other hand, UAVs may be exploited for sensing purposes by leveraging their advantages such as on-demand deployment, larger service coverage compared with the conventional fixed sensor nodes, and flexible spatial network architecture. We refer to this category of UAV applications as cellular-assisted UAV sensing. Unlike terrestrial cellular networks, UAV communications have many distinctive features such as high dynamic network topologies and weakly connected communication links. Besides, they also suffer from some practical constraints such as battery power, no-fly zones, and sensing requirements. Therefore, it is essential to develop novel communication and signal-processing techniques in support of ultra-reliable and real-time sensing applications. This special issue aims to create a platform for researchers from both academia and industry to disseminate state-of-the-art results and to advance the integration of UAVs into cellular networks. In total, 12 excellent papers were accepted after a rigorous multi-round review process. These papers can be divided into two topics: UAV-assisted cellular communications and cellular-assisted UAV sensing. In the following, we will introduce these papers and highlight their contributions. In their survey paper 'A survey on unmanned aerial vehicle relaying networks', Li et al. comprehensively summarise UAV relaying communications, which is an important paradigm of UAV-assisted cellular communications, and introduce its application scenarios. Key challenges are presented and corresponding technologies to address these challenges are discussed. Furthermore, they also show the research opportunities of UAV relaying communications. Yuan et al., in their paper 'Interference coordination and throughput maximisation in an unmanned aerial vehicle-assisted cellular: User association and three-dimensional trajectory optimisation', consider a UAV as an aerial base station (BS) to serve ground users. To reduce the interference between the UAV and terrestrial BSs, they propose a joint user association and 3D trajectory optimisation method. An improved block successive upper-bound minimisation based penalty algorithm is proposed. In 'Age-optimal path planning for finite-battery UAV-assisted data dissemination in IoT networks', Changizi and Emadi consider using UAVs to assist wireless sensor networks to deliver information with the aim to explore the freshness of data. An UAV trajectory planning for data dissemination is proposed, taking into account both maximal use of energy and the freshness of data. The effect of limited energy for UAVs is also discussed. In 'metaheuristic-based optimal 3D positioning of UAVs forming aerial mesh network to provide emergency communication services', Gupta and Varma study the optimal placement of UAVs to facilitate post-disaster emergency communication services. Coverage, quality-of-services, energy consumption, equal load distribution over UAVs, and fault tolerance are all considered for improving network connectivity and lifetime. Two metaheuristic-based hybrid optimisation algorithms are proposed to integrate these objectives together. Sun et al., in their paper 'An efficient data collection framework in the sky: An affine transformation approach based on Internet of unmanned aerial vehicles', use a UAV as a data collector to collect data from sensors. An efficient data collection framework is proposed and a min-maximum data processing strategy is adopted based on data value to store the collected time-series data. Moreover, an efficient affine transformation method is proposed to improve the efficiency of the system. In their paper 'Advanced squirrel algorithm-trained neural network for efficient spectrum sensing in cognitive radio-based air traffic control application', Eappen et al. utilise a cognitive radio manner to establish a connection between the UAV and the ground controller. A neural network trained by Advanced Squirrel Algorithm (ASA) is proposed for efficient spectrum sensing. Simulation-based evaluation shows that the proposed method is capable of efficiently detecting the spectrum holes with high convergence rate. In 'Blockchain-assisted secure UAV communication in 6G environment: Architecture, opportunities, and challenges', Gupta et al. investigate the security and privacy issues in UAV sensing applications. They propose an Interplanetary File System and blockchain-based secure UAV communication scheme. The proposed scheme ensures data security and privacy, reduces data storage cost, and enhances network performance. Wu et al., in their paper 'Optimisation of virtual cooperative spectrum sensing for UAV-based interweave cognitive radio system', consider UAVs equipped with spectrum sensing for data transmission. Based on a virtual cooperative spectrum sensing model, the authors propose an energy-efficient virtual cooperative spectrum sensing with the sequential 0/1 fusion rule to reduce the average number of decisions without any loss in the detection performance. Moreover, the optimisation problem of virtual cooperative spectrum sensing for UAV-based interweave cognitive ratio systems is formulated and solved. In 'Cellular UAV-to-device communications: Joint trajectory, speed, and power optimisation', Liu et al. study two communication modes for the UAV sensing applications, that is, UAVs can transmit through the BS or to the corresponding mobile devices directly. The authors propose a joint sensing and transmission protocol to schedule UAV sensing and transmission, and formulate an energy utility maximisation problem. A joint trajectory, speed, and transmit power optimisation algorithm is proposed to obtain a suboptimal solution. Ren et al., in their paper 'Computation offloading game in multiple unmanned aerial vehicle-enabled mobile edge computing networks', use mobile edge computing to offload the computation tasks for UAVs. To obtain the minimum computing time, the offloading percentage and the transmission power is optimised through a game theory modelling. Numerical results verify that the proposed schemes can effectively decrease the computing time and energy consumption, especially for a large number of UAVs. In 'An enhanced genetic algorithm for unmanned aerial vehicle logistics scheduling', Yuan et al. examines a scheduling problem in consideration of the loading capacity, the maximum flight time, and the flight speed. A genetic-based algorithm framework is presented for solving the scheduling problem. Moreover, in order to reduce the search space and accelerate the execution of this algorithm, a weight-based loading method is adopted. Finally, in their paper 'Multi-channel underdetermined blind source separation for recorded audio mixture signals using an unmanned aerial vehicle', Xie et al. apply UAVs for locating sound-emitting targets and study the source separation problem when the number of sources is more than the number of sensors. An underdetermined blind source separation algorithm to separate the multi-channel audio mixture signals recorded by an unmanned aerial vehicle is proposed. As a result, the frequency-domain sources are estimated through Wiener filtering and time-domain sources are obtained via inverse short-time Fourier transform. We would like to express our sincere thanks to all the authors for submitting their papers and to the reviewers for their valuable comments and suggestions that significantly enhanced the quality of these articles. We are also grateful to Prof. Liuqing Yang, the Editor-in-Chief of the IET Communications, for her great support throughout the whole review and publication process of this special issue, and, of course, to all the editorial staff. Hongliang Zhang received the B.S. and Ph.D. degrees at the School of Electrical Engineering and Computer Science at Peking University, China, in 2014 and 2019, respectively. Currently, he is a postdoctoral associate in the Department of Electrical Engineering at Princeton University, USA. His current research interest includes reconfigurable intelligent surfaces, aerial access networks, and game theory. He received the best doctoral thesis award from the Chinese Institute of Electronics in 2019. He is an exemplary reviewer for IEEE Transactions on Communications in 2020. He has served as a TPC Member for many IEEE conferences, such as Globecom, ICC, and WCNC. He is currently an associate editor for IET Communications and Frontiers in Signal Processing. He also serves as a Guest Editor for IEEE IoT-J special issue on Internet of UAVs over cellular networks. Walid Saad received the Ph.D. degree from the University of Oslo, Norway, in 2010. He is currently a professor with the Department of Electrical and Computer Engineering, Virginia Tech, USA, where he leads the Network science, Wireless, and Security (NEWS) Laboratory. His research interests include wireless networks, machine learning, game theory, security, unmanned aerial vehicles, cyber-physical systems, and network science. Dr. Saad is a recipient of the NSF CAREER Award in 2013, the AFOSR Summer Faculty Fellowship in 2014, and the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He has authored or co-authored 10 conference best paper awards at WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, IEEE GLOBECOM in 2018, IFIP NTMS in 2019, IEEE ICC in 2020, and IEEE GLOBECOM in 2020. He is also a recipient of the 2015 Fred W. Ellersick Prize from the IEEE Communications Society, the 2017 IEEE ComSoc Best Young Professional in Academia Award, the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award, and the 2019 IEEE ComSoc Communication Theory Technical Committee. He has also co-authored the 2019 IEEE Communications Society Young Author Best Paper. From 2015 to 2017 he was named the Stephen O. Lane Junior Faculty Fellow at Virginia Tech and in 2017 he was named College of Engineering Faculty Fellow. He received the Dean's award for research excellence from Virginia Tech in 2019. He currently serves as an editor for IEEE Transactions on Mobile Computing and IEEE Transactions on Cognitive Communications and Networking. He is an Editor-at-Large of IEEE Transactions on Communications. He is an IEEE Distinguished Lecturer. Mérouane Debbah received the M.Sc. and Ph.D. degrees from Ecole Normale Supérieure Paris-Saclay, France. In 1996, he joined Ecole Normale Supérieure Paris-Saclay. He was with Motorola Labs, France, from 1999 to 2002, and also with the Vienna Research Center for Telecommunications, Austria, until 2003. From 2003 to 2007, he was an assistant professor with the Mobile Communications Department, Institut Eurecom, France. From 2007 to 2014, he was the director of the Alcatel-Lucent Chair on flexible radio. Since 2007, he has been a full professor with CentraleSupelec, France. He has managed eight EU projects and more than 24 national and international projects. His research interests include fundamental mathematics, algorithms, statistics, information, and communication sciences research. He was a recipient of the ERC Grant MORE (Advanced Mathematical Tools for Complex Network Engineering) from 2012 to 2017. He received 20 best paper awards, among which the 2015 IEEE Communications Society Leonard G. Abraham Prize, the 2016 IEEE Communications Society Best Tutorial Paper Award, and the 2018 IEEE Marconi Prize Paper Award. He is an associate editor-in-chief of the journal Random Matrix: Theory and Applications. He was an associate area editor and a senior area editor of IEEE Transactions on Signal Processing from 2011 to 2013 and from 2013 to 2014, respectively. Lingyang Song received the Ph.D. from the University of York, UK, in 2007, where he received the K.M. Stott Prize for excellent research. He worked as a postdoctoral research fellow at the University of Oslo, Norway, and Harvard University, USA, until rejoining Philips Research UK in March 2008. In May 2009, he joined the School of Electronics Engineering and Computer Science, Peking University, China, as a full professor. His main research interests include cooperative and cognitive communications, physical layer security, and wireless ad hoc/sensor networks. He has published extensively, writing six textbooks, and is co-inventor of a number of patents (standard contributions). He received nine paper awards in IEEE journals and conferences including IEEE JSAC 2016, IEEE WCNC 2012, ICC 2014, Globecom 2014, and ICC 2015. He is currently on the editorial board of IEEE Transactions on Wireless Communications and Journal of Network and Computer Applications. He served as the TPC co-chairs for the International Conference on Ubiquitous and Future Networks (ICUFN2011/2012), symposium co-chairs in the International Wireless Communications and Mobile Computing Conference (IWCMC 2009/2010), IEEE International Conference on Communication Technology (ICCT2011), and IEEE International Conference on Communications (ICC 2014, 2015). He is the recipient of the 2012 IEEE Asia Pacific (AP) Young Researcher Award. Dr. Song is a fellow of IEEE and IEEE ComSoc distinguished lecturer since 2015.

The Emerging unmanned aerial vehicles (UAVs) have been widely exploited for sensing purposes due to the larger service coverage compared with the conventional fixed sensor nodes. However, due to the limited computation capability of UAVs, real-time sensory data needs to be transmitted to the BS/server for real-time data processing. In this regard, the cellular networks are necessary to support the data transmission for UAVs, which is called the Internet of UAVs. Very recently, 3GPP has approved a study item on the enhanced support to seamlessly integrate UAVs into future cellular networks.

Wireless communication in the TeraHertz band (0.1--10 THz) is envisioned as one of the key enabling technologies for the future sixth generation (6G) wireless communication systems scaled up beyond massive multiple input multiple output (Massive-MIMO) technology. However, very high propagation attenuations and molecular absorptions of THz frequencies often limit the signal transmission distance and coverage range. Benefited from the recent breakthrough on the reconfigurable intelligent surfaces (RIS) for realizing smart radio propagation environment, we propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies. Particularly, multiple passive and controllable RISs are deployed to assist the transmissions between the base station (BS) and multiple single-antenna users. We investigate the joint design of digital beamforming matrix at the BS and analog beamforming matrices at the RISs, by leveraging the recent advances in deep reinforcement learning (DRL) to combat the propagation loss. To improve the convergence of the proposed DRL-based algorithm, two algorithms are then designed to initialize the digital beamforming and the analog beamforming matrices utilizing the alternating optimization technique. Simulation results show that our proposed scheme is able to improve 50\% more coverage range of THz communications compared with the benchmarks. Furthermore, it is also shown that our proposed DRL-based method is a state-of-the-art method to solve the NP-hard beamforming problem, especially when the signals at RIS-assisted THz communication networks experience multiple hops.

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactively carry out decision-making and, thereby, react to local environmental changes and disturbances while experiencing zero communication latency. To achieve this goal, it is essential to cater for high ML inference accuracy at scale under the time-varying channel and network dynamics, by continuously exchanging fresh data and ML model updates in a distributed way. Taming this new kind of data traffic boils down to improving the communication efficiency of distributed learning by optimizing communication payload types, transmission techniques, and scheduling, as well as ML architectures, algorithms, and data processing methods. To this end, this article aims to provide a holistic overview of relevant communication and ML principles and, thereby, present communication-efficient and distributed learning frameworks with selected use cases.

This article studies the joint problem of uplink-downlink scheduling and power allocation for controlling a large number of control systems that upload their states to remote controllers and download control actions over wireless links. To overcome the lack of wireless resources, we propose a machine learning-based solution, where only one control system is controlled, while the rest of the control systems are actuated by locally predicting the missing state and/or action information using the previous uplink and/or downlink receptions via a Gaussian process regression (GPR). This GPR prediction credibility is determined using the age-of-information (AoI) of the latest reception. Moreover, the successful reception is affected by the transmission power, mandating a co-design of the communication and control operations. To this end, we formulate a network-wide minimization problem of the average AoI and transmission power under communication reliability and control stability constraints. To solve the problem, we propose a dynamic control algorithm using the Lyapunov drift-plus-penalty optimization framework. Numerical results corroborate that the proposed algorithm can stably control$2\times$more number of actuators compared to an event-triggered scheduling baseline with Kalman filtering and frequency division multiple access, which is$18\times$larger than a round-robin scheduling baseline.

In this paper, we consider the downlink transmission of URLLC packets requiring very low latency and ultra-reliability. Because of the low latency constraint, the Base Station may not have enough time to acquire the instantaneous Channel State Information (CSI) of the corresponding device and has then to transmit urgent packets immediately in the absence of CSI. To enhance reliability, we explore frequency diversity where a packet can be simultaneously sent over multiple channels. Using a Markov Decision Process framework, we address the problem of dynamic channel allocation to the URLLC devices in absence of instantaneous CSI. More precisely, we define a multi-agent MDP wherein the state of each device is the packet loss rate experienced in the previous time slots and the decision variable is how to split the available orthogonal channels across the devices. We design a new low-complexity algorithm which avoids the exhaustive enumeration of all possible resource allocations and enables significant computational savings compared to the Value Iteration algorithm. We investigate the gap between our proposed low complexity algorithm and the Value Iteration policy. We provide numerical performance results and show that our algorithm can achieve more than 80% of the optimal reward with substantial computational complexity reduction.

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks due to their fast and low-power configuration, which has increased potential in enabling massive connectivity and low-latency communications. Accurate and low-overhead channel estimation in RIS-based systems is one of the most critical challenges due to the usually large number of RIS unit elements and their distinctive hardware constraints. In this paper, we focus on the uplink of a RIS-empowered multi-user Multiple Input Single Output (MISO) uplink communication systems and propose a channel estimation framework based on the parallel factor decomposition to unfold the resulting cascaded channel model. We present two iterative estimation algorithms for the channels between the base station and RIS, as well as the channels between RIS and users. One is based on alternating least squares (ALS), while the other uses vector approximate message passing to iteratively reconstruct two unknown channels from the estimated vectors. To theoretically assess the performance of the ALS-based algorithm, we derived its estimation Cramér-Rao Bound (CRB). We also discuss the downlink achievable sum rate computation with estimated channels and different precoding schemes for the base station. Our extensive simulation results show that our algorithms outperform benchmark schemes and that the ALS technique achieves the CRB. It is also demonstrated that the sum rate using the estimated channels always reach that of perfect channels under various settings, thus, verifying the effectiveness and robustness of the proposed estimation algorithms.

Reconfigurable intelligent surfaces (RISs) have recently emerged as a promising technology that can achieve high spectrum and energy efficiency for future wireless networks by integrating a massive number of low-cost and passive reflecting elements. An RIS can manipulate the properties of an incident wave, such as the frequency, amplitude, and phase, and, then, reflect this manipulated wave to a desired destination, without the need for complex signal processing. In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations. In particular, a passive beamformer that can achieve the asymptotic optimal performance by controlling the incident wave properties is designed, under a limited RIS control link and practical reflection coefficients. In order to increase the achievable system sum-rate, a modulation scheme that can be used in an RIS without interfering with existing users is proposed and its average symbol error rate is asymptotically derived. Moreover, a new resource allocation algorithm that jointly considers user scheduling and power control is designed, under consideration of the proposed passive beamforming and modulation schemes. Simulation results show that the proposed schemes are in close agreement with their upper bounds in presence of a large number of RIS reflecting elements thereby verifying that the achievable rate in practical RISs satisfies the asymptotic optimality.

Providing ultra reliable and low-latency communication (URLLC) is considered one of the major challenges for wireless communication networks. This article considers a downlink URLLC system in which a base station (BS) serves multiple single-antenna users in the short blocklength regime. With the objective of maximizing the users' minimum rate, three different optimization problems are considered: (i) joint design of bandwidth and power allocation for the case of a single-antenna BS; (ii) beamforming design for the case of a multiple-antenna BS; and (iii) design of power allocation with regularized zero-forcing beamforming for the case of a multiple-antenna BS. In the short blocklength regime, the achievable rate is a complicated function of bandwidth and power allocation coefficients or beamforming vectors, which makes these max-min rate optimization problems challenging to solve. This work develops path-following algorithms, which generate a sequence of improved feasible points and converge at least to a locally optimal solution, to solve these three optimization problems. Performance of the proposed algorithms is analyzed through extensive simulations under various settings of transmit power budget, number of users, total bandwidth, transmission time, and number of transmit antennas at the BS. Simulation results clearly demonstrate the merits of the proposed algorithms.

Reconfigurable intelligent surfaces have emerged as a promising technology for future wireless networks. Given that a large number of reflecting elements is typically used and that the surface has no signal processing capabilities, a major challenge is to cope with the overhead that is required to estimate the channel state information and to report the optimized phase shifts to the surface. This issue has not been addressed by previous works, which do not explicitly consider the overhead during the resource allocation phase. This work aims at filling this gap, by developing an overhead-aware resource allocation framework for wireless networks where reconfigurable intelligent surfaces are used to improve the communication performance. An overhead model is proposed and incorporated in the expressions of the system rate and energy efficiency, which are then optimized with respect to the phase shifts of the reconfigurable intelligent surface, the transmit and receive filters, the power and bandwidth used for the communication and feedback phases. The bi-objective maximization of the rate and energy efficiency is investigated, too. The proposed framework characterizes the trade-off between optimized radio resource allocation policies and the related overhead in networks with reconfigurable intelligent surfaces.

In this paper, a novel framework is proposed to enable a predictive deployment of unmanned aerial vehicles (UAVs) as temporary base stations (BSs) to complement ground cellular systems in face of downlink traffic overload. First, a novel learning approach, based on the weighted expectation maximization (WEM) algorithm, is proposed to estimate the user distribution and the downlink traffic demand. Next, to guarantee a truthful information exchange between the BS and UAVs, using the framework of contract theory, an offload contract is developed, and the sufficient and necessary conditions for having a feasible contract are analytically derived. Subsequently, an optimization problem is formulated to deploy an optimal UAV onto the hotspot area in a way that the utility of the overloaded BS is maximized. Simulation results show that the proposed WEM approach yields a prediction error of around 10%. Compared with the expectation maximization and k-mean approaches, the WEM method shows a significant advantage on the prediction accuracy, as the traffic load in the cellular system becomes spatially uneven. Furthermore, compared with two event-driven deployment schemes based on the closest-distance and maximal-energy metrics, the proposed predictive approach enables UAV operators to provide efficient communication service for hotspot users in terms of the downlink capacity, energy consumption and service delay. Simulation results also show that the proposed method significantly improves the revenues of both the BS and UAV networks, compared with two baseline schemes.

We consider multi-antenna systems carrying OAM waveforms, which are limited by the transmit power and receive antenna aperture. Under the line-of-sight (LOS) channel condition, we propose the multi-mode-multi-spatial (MOMS) solution as a low complex and flexible engineering implementation without any loss of degrees of freedom. The basic idea of MOMS is that the transmitter uses multiple OAM waveforms to carry multiple independent data streams, and the receiver uses a multiple antenna channel equalization algorithm to de-multiplex the multiple eigenchannels. Link-level and Monte Carlo simulation show that our MOMS solution has higher capacity than common uniform linear array LOS-MIMO within the Rayleigh distance of antenna array. However, it has a lower capacity at longer distances due to the faster power intensity decrease of OAM waveforms in comparison with plane waveforms.

We consider a cell-free massive multiple-input multiple-output (MIMO) system where the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU). Zero-forcing technique is used at the CPU to detect the signals transmitted from all users. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is proposed to convert the problem into a geometric programme (GP). Exploiting a deep convolutional neural network (DCNN) allows us to determine both a mapping from the large-scale fading (LSF) coefficients and the optimal power by solving the optimization problem using the quantized channel. Depending on how the optimization problem is solved, different power control schemes are investigated; i) small-scale fading (SSF)-based power control; ii) LSF use-and-then-forget (UatF)-based power control; and iii) LSF deep learning (DL)-based power control. The SSF-based power control scheme needs to be solved for each coherence interval of the SSF, which is practically impossible in real time systems. Numerical results reveal that the proposed LSF-DL-based scheme significantly increases the performance compared to the practical and well-known LSF-UatF-based power control.

Reconfigurable intelligent surfaces (RISs) have recently emerged as a promising technology that can manipulate the properties of an incident wave, such as the frequency, amplitude, and phase, without the need for complex signal processing. In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations. In particular, a passive beamformer that can achieve the asymptotic optimal performance by controlling the incident wave properties is designed, under practical reflection coefficients. In order to increase the achievable system sum-rate, a modulation scheme that can be used in an RIS without interfering with existing users is proposed and its average symbol error rate is asymptotically derived. Simulation results show that the proposed schemes are in close agreement with their upper bounds in presence of a large number of RIS reflecting elements thereby verifying that the achievable rate in practical RISs satisfies the asymptotic optimality.

Next generation networks will support diverse use cases that require higher flexibility in the resource allocation. To this end, we propose a new waveform referred to as Lagrange Vandermonde division multiplexing (LVDM) that generalizes the zero padding orthogonal frequency division multiplexing (ZP-OFDM) while guaranteeing the perfect recovery of the transmitted signal. The LVDM transceiver design relies on the signature roots that have been judiciously selected to 1) provide a flexible resource allocation and 2) keep the complexity low where one-tap equalization is adopted. Carrying out the simulations in both frequency selective and 3GPP channels, our proposed waveform outperforms the ZP-OFDM where performance results are shown in terms of bit error ratio (BER). The LVDM achieves a signal-to-noise ratio gain of 5 dB over the ZP-OFDM when applying the optimized power allocation over subcarriers.

This work deals with the use of artificial neural networks for energy efficiency optimization. Unlike previous works, it addresses the question of how frequently should the neural network be re-trained in order to optimize the long-term energy efficiency of a wireless network. This question is motivated by the fundamental trade-off between frequently updating the configuration of the neural network in response to changes in the propagation channel statistics, and the energy consumption of the training process. In order to shed light on this tradeoff, this work develops energy consumption models that quantity the energy consumption due to the training and use of a neural network. Moreover, the long-term energy efficiency performance of power control based on neural networks is compared to state-of-the-art methods based only on the use of optimization theory.

Multi-user (MU) diversity yields sum-rate gains by scheduling a user for transmission at times when its channel is near its peak. These gains are limited in environments with line- of-sight (LoS) channel components and/or spatial correlation. To remedy this, previous works have proposed opportunistic beamforming (OBF) using multiple antennas at the BS to transmit the same signal, modulated by time-varying gains, to the best user at each time slot. In this paper, we propose reconfigurable surface (RS)-assisted OBF to increase the range of channel fluctuations in a single-antenna broadcast channel (BC), where opportunistic scheduling (OS) strategy achieves the sum-rate capacity. The RS is abstracted as an array of passive reflecting elements that only induce random phase shifts onto the impinging electromagnetic waves. We develop the sum-rate scaling laws under Rayleigh, Rician and correlated Rayleigh fading and show that RS-assisted OBF with a single-antenna BS can outperform multi-antenna BS- assisted OBF using a moderate number of elements.

We consider the dynamic spectrum access (DSA) problem where K Internet of Things (IoT) devices compete for T time slots constituting a frame. Devices collectively monitor M events where each event could be monitored by multiple IoT devices. Each device, when at least one of its monitored events is active, picks an event and a time slot to transmit the corresponding active event information. In the case where multiple devices select the same time slot, a collision occurs and all transmitted packets are discarded. In order to capture the fact that devices observing the same event may transmit redundant information, we consider the maximization of the average sum event rate of the system instead of the classical frame throughput. We propose a multi-agent reinforcement learning approach based on a stochastic version of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to access the frame by exploiting device-level correlation and time correlation of events. Through numerical simulations, we show that the proposed approach is able to efficiently exploit the aforementioned correlations and outperforms benchmark solutions such as standard multiple access protocols and the widely used Independent Deep Q-Network (IDQN) algorithm.

Lagrange Vandermonde division multiplexing (LVDM), that generalizes yet outperforms the zero padding orthogonal frequency division multiplexing (ZP-OFDM) while guaranteeing the perfect recovery of the transmitted signal, has been proposed recently as a new waveform. Therein, the transceiver design relies on the signature roots that have been optimized to keep the implementation complexity low where a simple one-tap equalization has been performed in frequency selective channels. While the beyond 5G solutions ought to be designed to overcome the intercarrier interference in high mobility, a new advanced receiver for LVDM that deals with doubly selective channels is needed. We propose two new schemes using time-frequency domain equalization where performance and implementation complexity have been discussed for each. Simulation results show that both schemes drastically boost the performance while keeping the complexity low compared to the state of the art solutions.

A cell-free massive multiple-input multiple-output (MIMO) uplink is considered, where quantize-and-forward (QF) refers to the case where both the channel estimates and the received signals are quantized at the access points (APs) and forwarded to a central processing unit (CPU) whereas in combine-quantize-and-forward (CQF), the APs send the quantized version of the combined signal to the CPU. To solve the non-convex sum rate maximization problem, a heuristic sub-optimal scheme is exploited to convert the power allocation problem into a standard geometric programme (GP). We exploit the knowledge of the channel statistics to design the power elements. Employing large-scale-fading (LSF) with a deep convolutional neural network (DCNN) enables us to determine a mapping from the LSF coefficients and the optimal power through solving the sum rate maximization problem using the quantized channel. Four possible power control schemes are studied, which we refer to as i) small-scale fading (SSF)-based QF; ii) LSF-based CQF; iii) LSF use-and-then-forget (UatF)-based QF; and iv) LSF deep learning (DL)-based QF, according to where channel estimation is performed and exploited and how the optimization problem is solved. Numerical results show that for the same fronthaul rate, the throughput significantly increases thanks to the mapping obtained using DCNN.

Future wireless networks will be as pervasive as the air we breathe, not only connecting us but embracing us through a web of systems that support personal and societal well-being. That is, the ubiquity, speed and low latency of such networks will allow currently disparate devices and services to become a distributed intelligent communications, sensing, and computing platform.

Reconfigurable intelligent surfaces (RISs) are an emerging transmission technology for application to wireless communications. RISs can be realized in different ways, which include (i) large arrays of inexpensive antennas that are usually spaced half of the wavelength apart; and (ii) metamaterial-based planar or conformal large surfaces whose scattering elements have sizes and inter-distances much smaller than the wavelength. Compared with other transmission technologies, e.g., phased arrays, multi-antenna transmitters, and relays, RISs require the largest number of scattering elements, but each of them needs to be backed by the fewest and least costly components. Also, no power amplifiers are usually needed. For these reasons, RISs constitute a promising software-defined architecture that can be realized at reduced cost, size, weight, and power (C-SWaP design), and are regarded as an enabling technology for realizing the emerging concept of smart radio environments (SREs). In this paper, we (i) introduce the emerging research field of RIS-empowered SREs; (ii) overview the most suitable applications of RISs in wireless networks; (iii) present an electromagnetic-based communication-theoretic framework for analyzing and optimizing metamaterial-based RISs; (iv) provide a comprehensive overview of the current state of research; and (v) discuss the most important research issues to tackle. Owing to the interdisciplinary essence of RIS-empowered SREs, finally, we put forth the need of reconciling and reuniting C. E. Shannon's mathematical theory of communication with G. Green's and J. C. Maxwell's mathematical theories of electromagnetism for appropriately modeling, analyzing, optimizing, and deploying future wireless networks empowered by RISs.

In this paper, the problem of joint power and resource allocation (JPRA) for ultra-reliable low-latency communication (URLLC) in vehicular networks is studied. Therein, the network-wide power consumption of vehicular users (VUEs) is minimized subject to high reliability in terms of probabilistic queuing delays. Using extreme value theory (EVT), a new reliability measure is defined to characterize extreme events pertaining to vehicles' queue lengths exceeding a predefined threshold. To learn these extreme events, assuming they are independently and identically distributed over VUEs, a novel distributed approach based on federated learning (FL) is proposed to estimate the tail distribution of the queue lengths. Considering the communication delays incurred by FL over wireless links, Lyapunov optimization is used to derive the JPRA policies enabling URLLC for each VUE in a distributed manner. The proposed solution is then validated via extensive simulations using a Manhattan mobility model. Simulation results show that FL enables the proposed method to estimate the tail distribution of queues with an accuracy that is close to a centralized solution with up to 79% reductions in the amount of exchanged data. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, while reducing the average power consumption by two folds, compared to an average queue-based baseline.

This paper investigates the capacity scaling of multicell massive MIMO systems in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the “random sparse angular support” model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. To achieve the asymptotic capacity results, statistical spatial despreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users scales linearly with base station antennas, a linear growth of the capacity with respect to the number of antennas is achievable under the sparse angular support model. We also note that the achievable rate lower bound based on massive MIMO “channel hardening”, widely used in the massive MIMO literature, yields rather loose results in the strong spatial correlation regimes and may significantly underestimate the achievable rate of massive MIMO. This work therefore considers an alternative bounding technique which is better suited to the strong correlation regimes. In fading channels with sparse angular support, it is further shown that spatial despreading (spreading) in uplink (downlink) has a more prominent impact on the performance of massive MIMO than channel hardening.

This paper develops a stochastic geometry-based approach for the modeling, analysis, and optimization of wireless cloud caching networks comprised of multiple-antenna radio units (RUs) inside clouds with coordinated multi-point transmissions and guard zones. We consider Poisson cluster processes to model RUs and users, and the probabilistic content placement to cache files in RUs. Accordingly, we study the exact hit probability for a user of interest for two strategies; closest selection, where the user is served by the closest RU that has its requested file, and best power selection, where the serving RU having the requested file provides the maximum instantaneous received power at the user. As key steps for the analyses, the Laplace transform of out of cloud interference, the desired link distance distribution in the closest selection, and the desired link received power distribution in the best power selection are derived. Also, we approximate the derived exact hit probabilities for both the closest and the best power selections in such a way that the related objective functions for the content caching design of the network can lead to tractable concave optimization problems. Solving the optimization problems, we propose algorithms to efficiently find their optimal content placements. Finally, we investigate the impact of different parameters on the caching performance.

During the first phase of the deployment of new radio (NR) networks, base station (BS) and user equipment (UE) are expected to operate alongside the pre-existing long-term evolution (LTE) networks. This paper proposes a solution to be adopted by NR UE to perform an uplink transmission towards NR BS over the same time and frequency resources as the legacy LTE system, which would extend the uplink coverage when operating at the supplementary uplink band. In order to guarantee the absence of interference towards LTE BS, we design a new waveform termed as inverse discrete Fourier transform-based Vandermonde-subspace frequency division multiplexing (IDFT-VFDM) with the following important features. On one hand, an NR UE communicating with an NR BS through IDFT-VFDM would not generate any interference at the legacy LTE BS, regardless of the number of antennas present at both ends of the interference channel. On the other hand, an IDFT-VFDM signal can always be designed to occupy the same signal bandwidth as a legacy single-carrier frequency division multiple access signal for LTE uplink transmission. Numerical results demonstrate the merit of IDFT-VFDM and confirm its potential as a candidate solution to achieve LTE-NR co-existence in multi-antenna multi-user scenarios.

In unmanned aerial vehicle (UAV)-assisted massive multi-input multi-output (MIMO) millimeter-wave (mmWave) networks, beam-steering guarantees reliable and steady connection between flying base stations and ground users with the challenge of strict angular deviation. In this paper, we investigate a joint optimization problem of beamforming and beam-steering in the multi-UAV mmWave networks, considering line-of-sight (LoS) communication for UAVs. For the hybrid beamforming optimization of massive MIMO mmWave, we propose a hybrid beamforming scheme based on the cross-entropy estimation with the robustness algorithm inspired by machine learning, which aims to optimize the hybrid precoding matrix. For the beam-steering optimization, we propose a novel mean field game (MFG)-based massive MIMO angle control scheme to model the optimal mmWave channel optimization problem between UAVs and ground users. In addition, when dealing with the problem of initial sensitivity and difficulty to solve the partial differential equations in the MFG, we utilize reinforcement learning to achieve the mean field equilibrium, which is described as the mean field learning game algorithm. Finally, a joint beamforming and beam-steering optimization algorithm is proposed to maximize the system sum-rate. Simulation results show the significant improvements in sum-rate, energy efficiency, and spectral efficiency, which verify the effectiveness of the proposed algorithm.

This work focuses on the downlink of a single-cell multi-user system in which a base station (BS) equipped with M antennas communicates with K single-antenna users through a reconfigurable intelligent surface (RIS) installed in the line-of-sight (LoS) of the BS. RIS is envisioned to offer unprecedented spectral efficiency gains by utilizing N passive reflecting elements that induce phase shifts on the impinging electromagnetic waves to smartly reconfigure the signal propagation environment. We study the minimum signal-to-interference-plus-noise ratio (SINR) achieved by the optimal linear precoder (OLP), that maximizes the minimum SINR subject to a given power constraint for any given RIS phase matrix, for the cases where the LoS channel matrix between the BS and the RIS is of rank-one and of full-rank. In the former scenario, the minimum SINR achieved by the RIS-assisted link is bounded by a quantity that goes to zero with K. For the high-rank scenario, we develop accurate deterministic approximations for the parameters of the asymptotically OLP, which are then utilized to optimize the RIS phase matrix. Simulation results show that RISs can outperform half-duplex relays with a small number of passive reflecting elements while large RISs are needed to outperform full-duplex relays.

Communication based on Time Reversal (TR) exploits rich multipath radio propagation for high resolution spatiotemporal focusing. It refers to the process of transmitting a received signal in a time reversed order, profiting from channel's spatial reciprocity. Recent theoretical studies have shown that signal processing techniques for TR communication have the potential of realizing the benefits of massive antenna systems using only a single antenna base station and simple receive processing circuitry. In addition, TR can offer highly accurate localization, especially when considered for indoor wireless positioning systems. Particularly, the larger the transmission power and bandwidth are, the more observable are the multiple channel paths, hence, TR capability for localization becomes more profitable. In this paper, we implement TR wireless communication at 3.5GHz using up to 600MHz bandwidth channel sounding signals. We present extensive experimental results showcasing the concept's potential for indoor cm-level localization and signal coverage extension.

In this paper, we propose a deep learning based approach to design online power control policies for large EH networks, which are often intractable stochastic control problems. In the proposed approach, for a given EH network, the optimal on-line power control rule is learned by training a deep neural network (DNN), using the solution of offline policy design problem. Under the proposed scheme, in a given time slot, the transmit power is obtained by feeding the current system state to the trained DNN. Our results illustrate that the DNN based online power control scheme outperforms a Markov decision process based policy. In general, the proposed deep learning based approach can be used to find solutions to large intractable stochastic control problems.

One of the fundamental challenges to realize massive Multiple-Input Multiple-Output (MIMO) communications is the accurate acquisition of channel state information for a plurality of users at the base station. This is usually accomplished in the UpLink (UL) direction profiting from the time division duplexing mode. In practical base station transceivers, there exist inevitably nonlinear hardware components, like signal amplifiers and various analog filters, which complicates the calibration task. To deal with this challenge, we design a deep neural network for channel calibration between the UL and DownLink (DL) directions. During the initial training phase, the deep neural network is trained from both UL and DL channel measurements. We then leverage the trained deep neural network with the instantaneously estimated UL channel to calibrate the DL one, which is not observable during the UL transmission phase. Our numerical results confirm the merits of the proposed approach, and show that it can achieve performance comparable to conventional approaches, like the Agros method and methods based on least squares, that however assume linear hardware behavior models. More importantly, considering generic nonlinear relationships between the UL and DL channels, it is demonstrated that our deep neural network approach exhibits robust performance, even when the number of training sequences is limited.

In this paper, the capacity scaling of multicell massive MIMO systems is investigated in the presence of spatially correlated fading. In particular, we focus on the strong spatial correlation regimes where the covariance matrix of each user channel vector has a rank that scales sublinearly with the number of base station antennas, as the latter grows to infinity. We also consider the case where the covariance eigenvectors corresponding to the non-zero eigenvalues span randomly selected subspaces. For this channel model, referred to as the “random sparse angular support” model, we characterize the asymptotic capacity scaling law in the limit of large number of antennas. In order to achieve the capacity results, spatial (de)spreading based on the second-order channel statistics plays a pivotal role in terms of pilot decontamination and interference suppression. A remarkable result is that even when the number of users per cell scales linearly with base station antennas in multicell environments, unlimited capacity is achievable under the sparse angular support model as long as the effective signal-to-noise ratio is away from zero.

Reliability and latency challenges in future mixed sub-6 GHz/millimeter wave (mmWave) fifth generation (5G) cell-free massive multiple-input multiple-output (MIMO) networks is to guarantee a fast radio resource management in both uplink (UL) and downlink (DL), while tackling the corresponding propagation imbalance that may arise in blockage situations. In this context, we introduce a semi-blind UL/DL decoupling concept where, after its initial activation, the central processing unit (CPU) gathers measurements of the Rician K-factor-reflecting the line-of-sight (LOS) condition of the user equipment (UE)-as well as the DL reference signal receive power (RSRP) for both 2.6 GHz and 28 GHz frequency bands, and then train a non-linear support vector machine (SVM) algorithm. The CPU finally stops the measurements of mmWave definitely, and apply the trained SVM algorithm on the 2.6 GHz data to blindly predict the target frequencies and access points (APs) that can be independently used for the UL and DL. The accuracy score of the proposed classifier reaches 95% for few training samples.

In this paper, we address the issue of automatic tracking areas (TAs) planning in fifth generation (5G) ultra-dense networks (UDNs). By invoking handover (HO) attempts and measurement reports (MRs) statistics of a 4G live network, we first introduce a new kernel function mapping HO attempts, MRs and inter-site distances (ISDs) into the so-called similarity weight. The corresponding matrix is then fed to a self-tuning spectral clustering (STSC) algorithm to automatically define the TAs number and borders. After evaluating its performance in terms of the Q-metric as well as the silhouette score for various kernel parameters, we show that the clustering scheme yields a significant reduction of tracking area updates and average paging requests per TA; optimizing thereby network resources.

In this paper, we study the problem of dynamic channel allocation for URLLC traffic in a multi-user multichannel wireless network where urgent packets have to be successfully received in a timely manner. We formulate the problem as a finite-horizon Markov Decision Process with a stochastic constraint related to the QoS requirement, defined as the packet loss rate for each user. We propose a novel weighted formulation that takes into account both the total expected reward (number of successfully decoded packets) and the risk which we define as the QoS requirement violation. First, we use the value iteration algorithm to find the optimal policy, which assumes a perfect knowledge of the controller of all the parameters, namely the channel statistics. We then propose a Q-learning algorithm where the controller learns the optimal policy without having knowledge of neither the CSI nor the channel statistics. We illustrate the performance of our algorithms with numerical studies.

Due to increasing quality-of-service (QoS) demand in already congested radio spectrum, there is a need for designing energy-efficient free space optical (FSO) communication networks. Considering a realistic fading model incorporating the fluctuations in angle-of-arrival, we minimize the outage probability for error free transmission of high data volumes through optimizing the power allocation (PA) and relay placement (RP) in a dual-hop decode-and-forward (DF) relay-assisted cooperative FSO communication with coherent detection and direct link unavailability. As this problem is nonconvex, first the optimal PA between source and relay is obtained using a global optimization algorithm. Also, a closed form for the solution is obtained using a tight analytical approximation with the assumption that atmospheric turbulence over both the links is nearly same. Next, we optimize the RP followed by the outage probability is jointly minimized using alternating optimization algorithm. Numerical results validate the outage analysis and provide key insights on optimal PA and RP yielding an outage enhancement of around 37% over the benchmark scheme.

The goal in this work is to design online power control policies for large energy harvesting (EH) networks where, due to large energy overhead involved in the exchange of state information among the nodes, it is infeasible to use a centralized policy. Furthermore, typical applications of EH networks concern the scenario where the statistical information, about both the EH process and the wireless channel, is not available. In order to address these challenges, we propose a mean-field multiagent deep reinforcement learning framework. The proposed approach enables the nodes to learn online power control policies in a fully distributed fashion, i.e., it does not require the nodes to exchange the information about their states. Using the underlying structure of the problem, we analytically establish the convergence of the proposed scheme. In particular, we show that the policies obtained using the proposed approach converge to the `stationary' Nash equilibrium. Our simulation results illustrate the efficacy of the power control policies, learned through the proposed approach. In particular, the mean-field multi-agent reinforcement learning scheme achieves a performance close to the state-of-the-art centralized policies which operate using the information about the state of whole network.

Fueled by the availability of more data and computing power, recent breakthroughs in cloud-based machine learning (ML) have transformed every aspect of our lives from face recognition and medical diagnosis to natural language processing. However, classical ML exerts severe demands in terms of energy, memory, and computing resources, limiting their adoption for resource-constrained edge devices. The new breed of intelligent devices and high-stake applications (drones, augmented/virtual reality, autonomous systems, and so on) requires a novel paradigm change calling for distributed, low-latency and reliable ML at the wireless network edge (referred to as edge ML). In edge ML, training data are unevenly distributed over a large number of edge nodes, which have access to a tiny fraction of the data. Moreover, training and inference are carried out collectively over wireless links, where edge devices communicate and exchange their learned models (not their private data). In a first of its kind, this article explores the key building blocks of edge ML, different neural network architectural splits and their inherent tradeoffs, as well as theoretical and technical enablers stemming from a wide range of mathematical disciplines. Finally, several case studies pertaining to various high-stake applications are presented to demonstrate the effectiveness of edge ML in unlocking the full potential of 5G and beyond.

The month’s regular papers issue covers machine learning at the wireless network edge, soft-informationbased localization techniques, and Antenna-in-Package technology.

This paper focuses on developing a decentralized framework for coordinated minimum power beamforming wherein L base stations (BSs), each equipped with N antennas, serve K single-antenna users with specific rate constraints. This is realized by considering user specific intercell interference (ICI) strength as the principal coupling parameter among BSs. First, explicit deterministic expressions for transmit powers are derived for spatially correlated channels in the asymptotic regime in which N and K grow large with a non-trivial ratio K/N. These asymptotic expressions are then used to compute approximations of the optimal ICI values that depend only on the channel statistics. By relying on the approximate ICI values as coordination parameters, a distributed non-iterative coordination algorithm, suitable for large networks with limited backhaul, is proposed. A heuristic algorithm is also proposed relaxing coordination requirements even further as it only needs pathloss values for non-local channels. The proposed algorithms satisfy the target rates for all users even when N and K are relatively small. Finally, the potential benefits of grouping users with similar statistics are investigated to further reduce the overhead and computational effort of the proposed solutions. Simulation results show that the proposed methods yield near-optimal performance.

Cell-free massive multiple-input-multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received signal by the conjugate of the estimated channel, and send back a quantized version of this weighted signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the quantized version of the estimated channel and the quantized signal are available at the CPU, and the case when only the quantized weighted signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. The numerical results demonstrate the superiority of the proposed schemes.

Providing seamless connectivity for wireless virtual reality (VR) users has emerged as a key challenge for future cloud-enabled cellular networks. In this paper, the problem of wireless VR resource management is investigated for a wireless VR network in which VR contents are sent by a cloud to cellular small base stations (SBSs). The SBSs will collect tracking data from the VR users, over the uplink, in order to generate the VR content and transmit it to the end-users using downlink cellular links. For this model, the data requested or transmitted by the users can exhibit correlation, since the VR users may engage in the same immersive virtual environment with different locations and orientations. As such, the proposed resource management framework can factor in such spatial data correlation, so as to better manage uplink and downlink traffic. This potential spatial data correlation can be factored into the resource allocation problem to reduce the traffic load in both the uplink and downlink. In the downlink, the cloud can transmit 360° contents or specific visible contents (e.g., user field of view) that are extracted from the original 360° contents to the users according to the users' data correlation so as to reduce the backhaul traffic load. In the uplink, each SBS can associate with the users that have similar tracking information so as to reduce the tracking data size. This data correlation-aware resource management problem is formulated as an optimization problem whose goal is to maximize the users' successful transmission probability, defined as the probability that the content transmission delay of each user satisfies an instantaneous VR delay target. To solve this problem, a machine learning algorithm that uses echo state networks (ESNs) with transfer learning is introduced. By smartly transferring information on the SBS's utility, the proposed transfer-based ESN algorithm can quickly cope with changes in the wireless networking environment due to users' content requests and content request distributions. Simulation results demonstrate that the developed algorithm achieves up to 15.8% and 29.4% gains in terms of successful transmission probability compared to Q-learning with data correlation and Q-learning without data correlation, respectively.

In this paper, a novel economic approach, based on the framework of contract theory, is proposed for providing incentives for LTE over unlicensed channels (LTE-U) in cellular networks. In this model, a mobile network operator (MNO) designs and offers a set of contracts to the users to motivate them to accept being served over the unlicensed bands. A practical model in which the information about the quality-of-service (QoS) required by every user is not known to the MNO and other users is considered. For this contractual model, the closed-form expression of the price charged by the MNO for every user is derived and the problem of spectrum allocation is formulated as a matching game with incomplete information. For the matching problem, a distributed algorithm is proposed to assign the users to the licensed and unlicensed spectra. The simulation results show that the proposed pricing mechanism can increase the fraction of users that achieve their QoS requirements by up to 45% compared to classical algorithms that do not account for users requirements. Moreover, the performance of the proposed algorithm in the case of incomplete information is shown to approach the performance of the same mechanism with complete information.

Human-centric applications such as virtual reality and immersive gaming are central to future wireless networks. Common features of such services include: 1) their dependence on the human user’s behavior and state and 2) their need for more network resources compared to conventional applications. To successfully deploy such applications over wireless networks, the network must be made cognizant of not only the quality-of-service (QoS) needs of the applications, but also of the perceptions of thehuman userson this QoS. In this paper, by explicitly modeling the limitations of the human brain, a concrete measure for the delay perception of human users is introduced. Then, a learning method, called probability distribution identification, is developed to find a probabilistic model for this delay perception based on the brain features of a human user. Given the learned model for the delay perception of the human brain, a brain-aware resource management algorithm based on Lyapunov optimization is proposed for allocating radio resources to human users while minimizing the transmit power and taking into account the reliability of both machine type devices and human users. Then, a closed-form relationship between the reliability measure and wireless physical layer metrics of the network is derived. Simulation results show that a brain-aware approach can yield savings of up to 78% in power compared to the system that only considers QoS metrics. The results also show that, compared with QoS-aware, brain-unaware systems, the brain-aware approach can save substantially more power in low-latency systems.

To overcome devices' limitations in performing computation-intense applications, mobile edge computing (MEC) enables users to offload tasks to proximal MEC servers for faster task computation. However, the current MEC system design is based on average-based metrics, which fails to account for the ultra-reliable low-latency requirements in mission-critical applications. To tackle this, this paper proposes a new system design, where probabilistic and statistical constraints are imposed on task queue lengths, by applying extreme value theory. The aim is to minimize users' power consumption while trading off the allocated resources for local computation and task offloading. Due to wireless channel dynamics, users are reassociated to MEC servers in order to offload tasks using higher rates or accessing proximal servers. In this regard, a user-server association policy is proposed, taking into account the channel quality as well as the servers' computation capabilities and workloads. By marrying tools from Lyapunov optimization and matching theory, a two-timescale mechanism is proposed, where a user-server association is solved in the long timescale, while a dynamic task offloading and resource allocation policy are executed in the short timescale. The simulation results corroborate the effectiveness of the proposed approach by guaranteeing highly reliable task computation and lower delay performance, compared to several baselines.

In this paper, the effective use of multiple quadrotor drones as an aerial antenna array that provides wireless service to ground users is investigated. In particular, under the goal of minimizing the airborne service time needed for communicating with ground users, a novel framework for deploying and operating a drone-based antenna array system whose elements are single-antenna drones is proposed. In the considered model, the service time is minimized by minimizing the wireless transmission time as well as the control time that is needed for movement and stabilization of the drones. To minimize the transmission time, first, the antenna array gain is maximized by optimizing the drone spacing within the array. In this case, using perturbation techniques, the drone spacing optimization problem is addressed by solving successive, perturbed convex optimization problems. Then, according to the location of each ground user, the optimal locations of the drones around the array's center are derived such that the transmission time for the user is minimized. Given the determined optimal locations of drones, the drones must spend a control time to adjust their positions dynamically so as to serve multiple users. To minimize this control time of the quadrotor drones, the speed of rotors is optimally adjusted based on both the destinations of the drones and external forces (e.g., wind and gravity). In particular, using bang-bang control theory, the optimal rotors' speeds as well as the minimum control time are derived in closed-form. Simulation results show that the proposed approach can significantly reduce the service time to ground users compared with a fixed-array case in which the same number of drones form a fixed uniform antenna array. The results also show that, in comparison with the fixed-array case, the network's spectral efficiency can be improved by 32% while leveraging the drone antenna array system. Finally, the results reveal an inherent tradeoff between the control time and transmission time while varying the number of drones in the array.

An energy harvesting (EH) enabled network is capable of delivering energy to users, who are located sufficiently close to the base stations. However, wireless energy delivery requires much more transmit power than what the normal information delivery does. It is very challenging to provide the quality of wireless information and power delivery simultaneously. It is of practical interest to employ non-orthogonal multiple access (NOMA) to improve the network throughput, while fulfilling the EH requirements. To realize both the EH and information decoding, this paper considers a transmit time-switching (transmit-TS) protocol. Two important problems of users' max-min throughput optimization and energy efficiency maximization under power constraint and EH thresholds, which are non-convex in beamforming vectors, are addressed by efficient path-following algorithms. In addition, the conventional power splitting (PS)-based EH receiver is also considered. The provided numerical results confirm that the proposed transmit-TS-based algorithms clearly outperform the PS-based algorithms in terms of throughput and energy efficiency.

This paper considers a Massive MIMO network with L cells, each comprising a base stations (BS) with M antennas and K single-antenna user equipments. Within this setting, we are interested in deriving approximations of the achievable rates in the uplink and downlink under the assumption that single-cell linear processing is used at each BS and that each intracell link forms an uncorrelated MIMO Rician fading channel matrix; that is, with a deterministic line-of-sight (LoS) path and a stochastic non-LoS component describing a spatial uncorrelated multipath environment. The analysis is conducted assuming that N and K grow large with a given ratio N/K under the assumption that the data transmission in each cell is affected by channel estimation errors, pilot contamination, an arbitrary large scale attenuation and LoS components. Numerical results are used to prove that the approximations are asymptotically tight, but accurate for systems with finite dimensions. The asymptotic results are also used to evaluate the impact of LoS components. In particular, we exemplify how the number of antennas for achieving a target rate can be substantially reduced with LoS links of only a few dBs of strength.

This paper deals with the use of emerging deep learning techniques in future wireless communication networks. It will be shown that the data-driven approaches should not replace, but rather complement, traditional design techniques based on mathematical models. Extensive motivation is given for why deep learning based on artificial neural networks will be an indispensable tool for the design and operation of future wireless communication networks, and our vision of how artificial neural networks should be integrated into the architecture of future wireless communication networks is presented. A thorough description of deep learning methodologies is provided, starting with the general machine learning paradigm, followed by a more in-depth discussion about deep learning and artificial neural networks, covering the most widely used artificial neural network architectures and their training methods. Deep learning will also be connected to other major learning frameworks, such as reinforcement learning and transfer learning. A thorough survey of the literature on deep learning for wireless communication networks is provided, followed by a detailed description of several novel case studies wherein the use of deep learning proves extremely useful for network design. For each case study, it will be shown how the use of (even approximate) mathematical models can significantly reduce the amount of live data that needs to be acquired/measured to implement the data-driven approaches. Finally, concluding remarks describe those that, in our opinion, are the major directions for future research in this field.

In this paper, we focus on the downlink ergodic sum rate of a single-cell large-scale multiuser MIMO system in which the base station employs N antennas to communicate with K single-antenna user equipments (TIEs). A regularized zero-forcing (RZF) scheme is used for precoding under the assumption that each TIE uses a specific power and each link forms a spatially correlated MIMO Rician fading channel. The analysis is conducted assuming that N and K grow large with a given ratio and perfect channel state information is available at the base station. New results from random matrix theory and large system analysis are used to compute an asymptotic expression of the signal-to-interference-plus-noise ratio as a function of system parameters, spatial correlation matrix, and Rician factor. Numerical results are used to validate the accuracy of asymptotic approximations in the finite system regime and to evaluate the performance under different operating conditions. It turns out that the asymptotic expressions provide accurate approximations even for relatively small values of N and K.

A cell-free massive multiple-input multiple-output system is considered using a max-min approach to maximize the minimum user rate with per-user power constraints. First, an approximated uplink user rate is derived based on channel statistics. Then, the original max-min signal-to-interference-plus-noise ratio problem is formulated for the optimization of receiver filter coefficients at a central processing unit and user power allocation. To solve this max-min non-convex problem, we decouple the original problem into two sub-problems, namely, receiver filter coefficient design and power allocation. The receiver filter coefficient design is formulated as a generalized Eigenvalue problem, whereas the geometric programming (GP) is used to solve the user power allocation problem. Based on these two sub-problems, an iterative algorithm is proposed, in which both problems are alternately solved while one of the design variables is fixed. This iterative algorithm obtains a globally optimum solution, whose optimality is proved through establishing an uplink-downlink duality. Moreover, we present a novel sub-optimal scheme which provides a GP formulation to efficiently and globally maximize the minimum uplink user rate. The numerical results demonstrate that the proposed scheme substantially outperforms the existing schemes in the literature.

The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to $300\%$ higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying.

In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and cellular-connected drone users (drone-UEs), is introduced. For this new 3D cellular architecture, a novel framework for network planning for drone-BSs and latency-minimal cell association for drone-UEs is proposed. For network planning, a tractable method for drone-BSs' deployment based on the notion of truncated octahedron shapes is proposed, which ensures full coverage for a given space with a minimum number of drone-BSs. In addition, to characterize frequency planning in such 3D wireless networks, an analytical expression for the feasible integer frequency reuse factors is derived. Subsequently, an optimal 3D cell association scheme is developed for which the drone-UEs' latency, considering transmission, computation, and backhaul delays, is minimized. To this end, first, the spatial distribution of the drone-UEs is estimated using a kernel density estimation method, and the parameters of the estimator are obtained using a cross-validation method. Then, according to the spatial distribution of drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell association for drone-UEs is derived by exploiting tools from an optimal transport theory. The simulation results show that the proposed approach reduces the latency of drone-UEs compared with the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in the average latency compared with the SINR-based association. The results also show that the proposed latency-optimal cell association improves the spectral efficiency of a 3D wireless cellular network of drones.

This paper studies the ergodic rate performance of regularized zero-forcing (RZF) precoding in the downlink of a multi-user multiple-input single-output (MISO) system, where the channel between the base station (BS) and each user is modeled by the double scattering model. This non-Gaussian channel model is a function of both the antenna correlation and the structure of scattering in the propagation environment. This paper makes the preliminary contribution of deriving the minimum-mean-square-error (MMSE) channel estimate for this model. Then under the assumption that the users are divided into groups of common correlation matrices, this paper derives deterministic approximations of the signal-to-interference-plus-noise ratio (SINR) and the ergodic rate, which are almost surely tight in the limit that the number of BS antennas, the number of users, and the number of scatterers in each group grow infinitely large. The derived results are expressed in a closed-form for the special case of multi-keyhole channels. The simulation results confirm the close match provided by the asymptotic analysis for moderate system dimensions. We show that the maximum number of users that can be supported simultaneously, while realizing large-scale MIMO gains, is equal to the number of scatterers.

Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, this paper investigates the problem of path selection and rate allocation for multi-hop self-backhaul millimeter-wave (mm-wave) networks. Enabling multi-hop mm-wave transmissions raises a potential issue of increased latency, and thus, this paper aims at addressing the fundamental questions: how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints? In this regard, a new system design, which exploits multiple antenna diversity, mm-wave bandwidth, and traffic splitting techniques, is proposed to improve the downlink transmission. The studied problem is cast to as a network utility maximization, subject to the upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: 1) path selection and 2) rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a non-convex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, the comprehensive performance analysis and convergence proof are provided for the proposed solution. The numerical results show that the proposed approach ensures reliable communication with a guaranteed probability of up to 99.9999% and reduces latency by 50.64% and 92.9% as compared to baseline models. Furthermore, the results showcase the key tradeoff between latency and network arrival rate.

In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and drone users (drone-UEs), is introduced. For this new 3D cellular network architecture, a novel framework for the deployment of drone-BSs and latency-minimal cell association for drone-UEs is proposed. For drone-BSs' deployment, a tractable method based on the notion of truncated octahedron shapes is proposed that ensures full coverage for a given space with minimum number of drone-BSs. Then, an optimal 3D cell association scheme is determined such that the drone-UEs' latency, considering transmission, computation, and backhaul latencies, is minimized. In particular, using optimal transport theory, the optimal 3D cell partitions are derived according to the spatial distribution of drone-UEs and the drone-BSs' locations. Simulation results show that the proposed approach reduces the latency of drone-UEs compared to the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in average latency compared to the SINR-based association. Also, it is shown that the proposed latency-optimal cell association improves the spectral efficiency of a 3D wireless cellular network of drones.

This paper studies the sum-rate performance of regularized zero-forcing (RZF) precoding in a multi-user multiple-input single-output (MISO) system, where the channel between the base station (BS) and each user is modeled by the double scattering channel model. This non-Gaussian channel accounts for both the spatial correlation in the antenna arrays and the structure of scattering in the propagation environment. The user population is divided into G groups, where the users in the same group experience similar propagation conditions and are characterized by common correlation matrices. Under this setting, we derive deterministic approximations of the signal-to-interference-plus-noise ratio (SINR) and the sum-rate with RZF precoding, which are almost surely tight in the large system limit. Simulation results confirm the close match provided by the asymptotic analysis for moderate system dimensions.

The massive scale of future wireless networks will cause computational bottlenecks in performance optimization. In this paper, we study the problem of connecting mobile traffic to Cloud RAN (C-RAN) stations. To balance station load, we steer the traffic by designing device association rules. The baseline association rule connects each device to the station with the strongest signal, which does not account for interference or traffic hot spots, and leads to load imbalances and performance deterioration. Instead, we can formulate an optimization problem to decide centrally the best association rule at each time instance. However, in practice this optimization has such high dimensions, that even linear programming solvers fail to solve. To address the challenge of massive connectivity, we propose an approach based on the theory of optimal transport, which studies the economical transfer of probability between two distributions. Our proposed methodology can further inspire scalable algorithms for massive optimization problems in wireless networks.

In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilistic queuing delays. In particular, a reliability measure is defined to characterize extreme events (i.e., when vehicles' queue lengths exceed a predefined threshold with non-negligible probability) using extreme value theory (EVT). Leveraging principles from federated learning (FL), the distribution of these extreme events corresponding to the tail distribution of queues is estimated by VUEs in a decentralized manner. Finally, Lyapunov optimization is used to find the joint transmit power and resource allocation policies for each VUE in a distributed manner. The proposed solution is validated via extensive simulations using a Manhattan mobility model. It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79% reductions in the amount of data that need to be exchanged. Furthermore, the proposed method yields up to 60% reductions of VUEs with large queue lengths, without an additional power consumption, compared to an average queue-based baseline. Compared to systems with fixed power consumption and focusing on queue stability while minimizing average power consumption, the reductions in extreme events of the proposed method is about two orders of magnitude.

In this paper, a novel machine learning (ML) framework is proposed for enabling a predictive, efficient deployment of unmanned aerial vehicles (UAVs), acting as aerial base stations (BSs), to provide on-demand wireless service to cellular users. In order to have a comprehensive analysis of cellular traffic, an ML framework based on a Gaussian mixture model and a weighted expectation maximization algorithm is introduced to predict the potential network congestion. Then, the optimal deployment of UAVs is studied with the objective of minimizing the power needed for UAV transmission and mobility, given the predicted traffic. To this end, first, the optimal partition of service areas of each UAV is derived, based on a fairness principle. Next, the optimal location of each UAV that minimizes the total power consumption is derived. Simulation results show that the proposed ML approach can reduce power needed for downlink transmission and mobility by over 20% and 80%, respectively, compared with an optimal deployment of UAVs with no ML prediction.

This paper investigates the use of a Passive Intelligent Mirrors (PIM) to operate a multi-user MISO downlink communication. The transmit powers and the mirror reflection coefficients are designed for sum-rate maximization subject to individual QoS guarantees to the mobile users. The resulting problem is non-convex, and is tackled by combining alternating maximization with the majorization-minimization method. Numerical results show the merits of the proposed approach, and in particular that the use of PIM increases the system throughput by at least 40%, without requiring any additional energy consumption.

In this paper, we consider the max-min signal-to- interference plus noise ratio (SINR) problem for the uplink transmission of a cell-free Massive multiple-input multiple-output (MIMO) system. Assuming that the central processing unit (CPU) and the users exploit only the knowledge of the channel statistics, we first derive a closed-form expression for uplink rate. In particular, we enhance (or maximize) user fairness by solving the max-min optimization problem for user rate, by power allocation and choice of receiver coefficients, where the minimum uplink rate of the users is maximized with available transmit power at the particular user. Based on the derived closed-form expression for the uplink rate, we formulate the original user max-min problem to design the optimal receiver coefficients and user power allocations. However, this max-min SINR problem is not jointly convex in terms of design variables and therefore we decompose this original problem into two sub- problems, namely, receiver coefficient design and user power allocation. By iteratively solving these sub-problems, we develop an iterative algorithm to obtain the optimal receiver coefficient and user power allocations. In particular, the receiver coefficients design for a fixed user power allocation is formulated as generalized eigenvalue problem whereas a geometric programming (GP) approach is utilized to solve the power allocation problem for a given set of receiver coefficients. Numerical results confirm a three-fold increase in system rate over existing schemes in the literature.

We consider a cell-free Massive multiple-input multiple-output (MIMO) system and investigate the system performance for the case when the quantized version of the estimated channel and the quantized received signal are available at the central processing unit (CPU), and the case when only the quantized version of the combined signal with maximum ratio combining (MRC) detector is available at the CPU. Next, we study the max-min optimization problem, where the minimum user uplink rate is maximized with backhaul capacity constraints. To deal with the max-min non-convex problem, we propose to decompose the original problem into two sub-problems. Based on these sub- problems, we develop an iterative scheme which solves the original max-min user uplink rate. Moreover, we present a user assignment algorithm to further improve the performance of cell-free Massive MIMO with limited backhaul links.

In this paper, the effective use of multiple drones as an aerial antenna array that provides wireless service to ground users is investigated. In particular, under the goal of minimizing the service time needed for servicing ground users, a novel framework for deploying a drone- based antenna array system whose elements are single- antenna drones is proposed. To this end, first, the antenna array gain is maximized by optimizing the drone spacing within the array. In this case, using perturbation techniques, the drone spacing optimization problem is addressed by solving successive, perturbed convex optimization problems. In the second step, the optimal locations of the drones around the array''s center are derived such that the service time for each ground user is minimized. Simulation results show that the proposed approach can significantly reduce the service time to ground users compared to a single drone that uses the same amount of power as the array. The results also show that the network''s spectral efficiency can be improved by 78% while leveraging the drone antenna array system.

Classic network control techniques have as sole objective the fulfillment of Quality-of-Service (QoS) metrics, being quantitative and network- centric. Nowadays, the research community envisions a paradigm shift that will put the emphasis on Quality of Experience (QoE) metrics, which relate directly to the user satisfaction. Yet, assessing QoE from QoS measurements is a challenging task that powerful Software Defined Network controllers are now able to tackle via machine learning techniques. In this paper we focus on a few crucial QoE factors and we first propose a Bayesian Network model to predict re- buffering ratio. Then, we derive our own novel Neural Network search method to prove that the BN correctly captures the discovered stalling data patterns. Finally, we show that hidden variable models based and context information boost performance for all QoE related measures.

We consider downlink communications between a Base Station (BS) and various mobile stations, equipped with multiple antennas, based on Orthogonal Frequency Division Multiplexing (OFDM). Transmission is compliant with the Long Term Evolution (LTE) standard operating in Frequency Division Duplex (FDD) mode. Since ideal feedback of channel state information to the BS may be cumbersome, we consider two suboptimal channel estimation algorithms, denoted as Resource Block (RB) and Resource Block Group (RBG). Both approaches approximate the channel as constant over multiples of the fundamental LTE block, known as Physical Resource Block (PRB). Our results show that RB and RBG incur a limited performance loss, yet guaranteeing significant saving in the amount of feedback information.

Pilot based acquisition of channel state information (CSI) is a challenging problem in multiuser multiple-input multiple-output (MU-MIMO) systems that allow unscheduled intermittent transmissions from user terminals (UTs). These challenges stem mainly from the need to account for i) possible intra-cell pilot collisions or interference which are characteristic of unscheduled uplink transmissions and ii) the fact that these same pilots are typically also needed for user activity detection at the base station (BS). The problem gets even more challenging in situations with both scheduled (grant based) and unscheduled (grant free) concurrent uplink transmissions because of the fact that grant free and grant based transmissions are typically different in terms of key performance indicators (KPIs). In this article, we present an assignment and multiplexing scheme for uplink pilot sequences from grant free and grant based transmissions that take place on the same time-frequency resources and we numerically assess its performance in terms of both CSI quality and user activity detection probability. The scheme is based on a novel method that can extend any set of state-of-the-art orthogonal pilots to generate a hybrid (orthogonal/non-orthogonal) pilot set with a built-in trade-off between the maximum achievable sum rate for grant based transmissions and the maximum tolerable level of pilot interference for grant free transmissions.

The 3GPP Release-13 has recently introduced full-dimension multiple-input multiple-output (FD-MIMO) technology as a practical way to deploy massive MIMO arrays within feasible base station (BS) form factors through the use of active antenna systems with two-dimensional (2D) planar array structures. The 2D arrangement of antenna elements, where the elements in each antenna port are fed with downtilt weights, allows for adaptive electronic beamforming in the elevation as well as the conventional azimuth dimensions. This work focuses on the previously unaddressed problem of determining the optimal downtilt weight vectors for the antenna ports in each cell of a multi-cell multi-user system. The optimization criterion is to maximize the minimum signal to intra-cell interference ratio within a cell while constraining the inter-cell interference leakage. The quasi-optimal weight vectors are obtained through the application of semi-definite relaxation and Dinkelbach's method. The proposed algorithm performs better than the existing approximate schemes even under the effects of pilot contamination.

We investigate the problem of multi-hop scheduling in self-backhauled millimeter wave (mmWave) networks. Owing to the high path loss and blockage of mmWave links, multi-hop paths/routes between the macro base station and the intended users via full-duplex small cells need to be carefully selected. This paper addresses the fundamental question: “how to select the best paths and how to allocate rates over these paths subject to latency constraints?” To answer this question, we propose a new system design, which factors in mmWave-specific channel variations and network dynamics. The problem is cast as a network utility maximization subject to a bounded delay constraint and network stability. The studied problem is decoupled into: (i) a path/route selection and (ii) rate allocation, whereby learning the best paths is done by means of a reinforcement learning algorithm, and the rate allocation is solved by applying the successive convex approximation method. Via numerical results, our approach ensures reliable communication with a guaranteed probability of 99.9999%, and reduces latency by 50.64% and 92.9% as compared to baselines.

In this paper, we consider a scenario where the satellite-terrestrial network is overlaid over the legacy cellular network. The established communication system is operated in the millimeter wave (mmWave) frequencies, which enables the massive antennas arrays to be equipped on the satellite and terrestrial base stations (BSs). The secure communication in this coexistence system of the satellite-terrestrial network and cellular network through the physical-layer security techniques is studied in this paper. To maximize the achievable secrecy rate of the eavesdropped fixed satellite service, we design a cooperative secure transmission beamforming scheme, which is realized through the satellite's adaptive beamforming, artificial noise, and BSs' cooperative beamforming implemented by terrestrial BSs. A non-cooperative beamforming scheme is also designed, according to which BSs implement the maximum ratio transmission beamforming strategy. Applying the designed secure beamforming schemes to the coexistence system established, we formulate the secrecy rate maximization problems subjected to the power and transmission quality constraints. To solve the nonconvex optimization problems, we design an approximation and iteration-based genetic algorithm, through which the original problems can be transformed into a series of convex quadratic problems. Simulation results show the impact of multiple antenna arrays at the mmWave on improving the secure communication. Our results also indicate that through the cooperative and adaptive beamforming, the secrecy rate can be greatly increased. In addition, the convergence and efficiency of the proposed iteration-based approximation algorithm are verified by the simulations.

Ensuring ultrareliable and low-latency communication (URLLC) for 5G wireless networks and beyond is of capital importance and is currently receiving tremendous attention in academia and industry. At its core, URLLC mandates a departure from expected utility-based network design approaches, in which relying on average quantities (e.g., average throughput, average delay, and average response time) is no longer an option but a necessity. Instead, a principled and scalable framework which takes into account delay, reliability, packet size, network architecture and topology (across access, edge, and core), and decision-making under uncertainty is sorely lacking. The overarching goal of this paper is a first step to filling this void. Towards this vision, after providing definitions of latency and reliability, we closely examine various enablers of URLLC and their inherent tradeoffs. Subsequently, we focus our attention on a wide variety of techniques and methodologies pertaining to the requirements of URLLC, as well as their applications through selected use cases. These results provide crisp insights for the design of low-latency and high-reliability wireless networks.

Provides an overview of the technical articles and features presented in this issue. Our regular papers this month focus on 5G related topics such as multipleinput– multipleoutput transmission using finite input signals, and achieving ultrareliable and low-latency wireless communication.

This paper discusses full-dimension multiple-input-multiple-output (FD-MIMO) technology, which is currently an active area of research and standardization in wireless communications for evolution toward Fifth Generation (5G) cellular systems. FD-MIMO utilizes an active antenna system (AAS) with a 2-D planar array structure that not only allows a large number of antenna elements to be packed within feasible base station form factors, but also provides the ability of adaptive electronic beamforming in the 3-D space. However, the compact structure of large-scale planar arrays drastically increases the spatial correlation in FD-MIMO systems. In order to account for its effects, the generalized spatial correlation functions for channels constituted by individual elements and overall antenna ports in the AAS are derived. Exploiting the quasi-static channel covariance matrices of users, the problem of determining the optimal downtilt weight vector for antenna ports, which maximizes the minimum signal-to-interference ratio of a multi-user multiple-input-single-output system, is formulated as a fractional optimization problem. A quasi-optimal solution is obtained through the application of semi-definite relaxation and Dinkelbach's method. Finally, the user-group specific elevation beamforming scenario is devised, which offers significant performance gains as confirmed through simulations. These results have direct application in the analysis of 5G FD-MIMO systems.

Massive multiple-input multiple-output (MIMO) systems, where base stations (BSs) are equipped with hundreds of antennas, are an attractive way to handle the rapid growth of data traffic. As the number of user equipments (UEs) increases, the initial access and handover in contemporary networks will be flooded by user collisions. In this paper, a random access protocol is proposed that resolves collisions and performs timing estimation by simply utilizing the large number of antennas envisioned in massive MIMO networks. UEs entering the network perform spreading in both time and frequency domains, and their timing offsets are estimated at the BS in closed form using a subspace decomposition approach. This information is used to compute channel estimates that are subsequently employed by the BS to communicate with the detected UEs. The favorable propagation conditions of massive MIMO suppress interference among UEs, whereas the inherent timing misalignments improve the detection capabilities of the protocol. Numerical results are used to validate the performance of the proposed procedure in massive MIMO networks, under uncorrelated and correlated fading channels. With$2.5 \times 10^{3}$UEs that may simultaneously become active with probability 1%, a total of 16 frequency–time codes, and 100 antennas, a given UE is detected with probability 75%, and the accuracy of its timing estimate is on the order of few samples.

This paper aims at developing a power control framework to jointly optimize energy efficiency (measured in bit/joule) and delay in wireless networks. A multi-objective approach is taken dealing with both performance metrics, while ensuring a minimum quality-of-service to each user in the network. Each user in the network is modeled as a rational agent that engages in a generalized non-cooperative game. Feasibility conditions are derived for the existence of each player's best response, and used to show that if these conditions are met, the game best response dynamics will converge to a unique Nash equilibrium. Based on these results, a convergent power control algorithm is derived, which can be implemented in a fully decentralized fashion. Next, a centralized power control algorithm is proposed, which also serves as a benchmark for the proposed decentralized solution. Due to the non-convexity of the centralized problem, the tool of maximum block improvement is used, to tradeoff complexity with optimality.

This paper characterizes the performance in terms of queueing stability of a network composed of multiple MIMO transmitter-receiver pairs taking into account the dynamic traffic pattern and the probing/feedback cost. We adopt a centralized scheduling scheme that selects a number of active pairs in each time-slot. We consider that the transmitters apply interference alignment (IA) technique if two or more pairs are active, whereas in the special case where one pair is active point-to-point MIMO singular value decomposition (SVD) is used. We consider a time-division duplex (TDD) system where transmitters acquire their channel state information (CSI) by decoding the pilot sequences sent by the receivers. Since global CSI knowledge is required for IA, the transmitters have also to exchange their estimated CSIs over a backhaul of limited capacity (i.e. imperfect case). Under this setting, we characterize in this paper the stability region of the system under both the imperfect and perfect (i.e. unlimited backhaul) cases, then we examine the gap between these two resulting regions. Further, under each case we provide a centralized probing policy that achieves the max stability region. These stability regions and scheduling policies are given for the symmetric system, where all the path loss coefficients are equal to each other, as well as for the general system. For the symmetric system, we provide the conditions under which IA yields a queueing stability gain compared to SVD. Under the general system, the adopted scheduling policy is of a high computational complexity for moderate numbers of pairs, consequently we propose an approximate policy that has a reduced complexity but that achieves only a fraction of the system stability region. A characterization of this fraction is provided.

This paper studies the problem of feedback allocation and scheduling for a multichannel downlink cellular network under limited and delayed feedback. We propose two efficient algorithms that select the link states that should be reported to the base station (BS). A novelty here is that these feedback allocation algorithms are performed at the users' side to take advantage of their local channel state information knowledge in order to achieve higher gains. The first algorithm is suitable for a continuous-time contention scheme and requires only one feedback per channel, whereas the second one is adapted for a discrete-time contention scheme and adopts a threshold-based concept. For this second algorithm, we study some implementation aspects related to the feedback period and investigate the tradeoff between knowing at the BS a small number of accurate link states and a larger but outdated number of link states. We show that these algorithms, combined with the Max-Weight scheduling, achieve good stability performance compared with the ideal system.

In this paper, we analyze and optimize the energy efficiency of downlink cellular networks. With the aid of tools from stochastic geometry, we introduce a new closed-form analytical expression of the potential spectral efficiency (bit/sec/m2). In the interference-limited regime for data transmission, unlike currently available mathematical frameworks, the proposed analytical formulation depends on the transmit power and deployment density of the base stations. This is obtained by generalizing the definition of coverage probability and by accounting for the sensitivity of the receiver not only during the decoding of information data, but during the cell association phase as well. Based on the new formulation of the potential spectral efficiency, the energy efficiency (bit/Joule) is given in a tractable closed-form formula. An optimization problem is formulated and is comprehensively studied. It is mathematically proved, in particular, that the energy efficiency is a unimodal and strictly pseudo-concave function in the transmit power, given the density of the base stations, and in the density of the base stations, given the transmit power. Under these assumptions, therefore, a unique transmit power and density of the base stations exist, which maximize the energy efficiency. Numerical results are illustrated in order to confirm the obtained findings and to prove the usefulness of the proposed framework for optimizing the network planning and deployment of cellular networks from the energy efficiency standpoint.

In this paper, the problem of cell association is studied in an Internet of things (IoT) system in which a set of devices deployed in a given area report physical events to a set of small base stations (SBSs) via uplink communication links. In this model, the key goal is to minimize the number of devices that report the same information to a given SBS by taking into account the spatial correlation between the IoT devices. In particular, the problem of correlation-aware cell association is formulated as a popular matching game in which the IoT devices are assigned to the SBSs to maximize the amount of information that is reported to the SBSs. To this end, the number of devices matched to every SBS must be maximized. For the formulated problem, a distributed two- level matching algorithm is proposed and the algorithm is proved to converge to a popular outcome. In that state, all the SBSs and devices prefer the matching that results from the proposed algorithm to any other possible matching. Simulation results show that the proposed algorithm allows the SBSs to collect up to 40\% more useful information compared to max sum-rate association algorithm.

In this paper, the effective use of unmanned aerial vehicles (UAVs) as flying base stations that can provide wireless service to ground users is investigated. In particular, a novel framework for optimizing the performance of such UAV-based wireless systems, in terms of the average number of bits (data service) transmitted to users under flight time constraints, is proposed. In the considered model, UAVs are deployed over a given geographical area to serve ground users that are distributed within a given area based on an arbitrary spatial distribution function. In this case, based on the maximum possible flight times of the UAVs, the average data service delivered to the users is maximized by finding the optimal cell partitions associated to the UAVs, under a fair resource allocation scheme. To this end, using the powerful mathematical framework of optimal transport theory, a gradient-based algorithm is proposed for optimally partitioning the geographical area based on the users' distribution, flight times, and locations of the UAVs. Simulation results show that the proposed cell partitioning approach yields a significantly higher fairness among the users compared to the classical weighted Voronoi diagram. In particular, by using our approach, the Jain's fairness index is improved by a factor of 2.6.

Millimeter wave (mmW) communications at the 60 GHz unlicensed band is seen as a promising approach for boosting the capacity of wireless local area networks (WLANs). If properly integrated into legacy IEEE 802.11 standards, mmW communications can offer substantial gains by offloading traffic from congested sub-6 GHz unlicensed bands to the 60 GHz mmW frequency band. In this paper, a novel medium access control (MAC) is proposed to dynamically manage the WLAN traffic over the unlicensed mmW and sub-6 GHz bands. The proposed protocol leverages the capability of advanced multi-band wireless stations (STAs) to perform fast session transfers (FST) to the mmW band, while considering the intermittent channel at the 60 GHz band and the level of congestion observed over the sub-6 GHz bands. The performance of the proposed scheme is analytically studied via a new Markov chain model and the probability of transmissions over the mmW and sub-6 GHz bands, as well as the aggregated saturation throughput are derived. In addition, analytical results are validated by simulation results. Simulation results show that the proposed integrated mmW-sub 6 GHz MAC protocol yields significant performance gains, in terms of maximizing the saturation throughput and minimizing the delay experienced by the STAs. The results also shed light on the tradeoffs between the achievable gains and the overhead introduced by the FST procedure.

In this paper, the use of multi-hop, device-to- device communications over millimeter wave (mmW) frequencies is studied for effective wearable communications. In particular, a problem of uplink communications is studied for a wearable network, in which each wearable device aims to form a multihop path over mmW to access a cellular base station, in order to overcome the high channel loss caused by mmW attenuation and blockage. To analyze the optimal selection of the uplink path, a network formation game is formulated between all wearable devices. In this game, each wearable device autonomously chooses the uplink path that maximizes its quality-of-service that captures the tradeoff between rate, delay, and privacy. To solve this game, a novel algorithm that combines best response dynamics with mixed-strategy techniques is proposed to find the mixed Nash network, which corresponds to a stable uplink structure at which no wearable device can improve its utility by changing its network formation decision. Simulation results show that the proposed game approach improves the average utility per wearable device of over 14% and 78%, respectively, compared with the direct transmission and the nearest next-hop schemes.

This work considers the downlink of a multicell massive MIMO system in which L base stations (BSs) of N antennas each communicate with K single-antenna user equipments randomly positioned in the coverage area. Within this setting, we are interested in evaluating the sum rate of the system when MRT and RZF are employed under the assumption that each intracell link forms a MIMO Rician uncorrelated fading channel. The analysis is conducted assuming that N and K grow large with a non-trivial ratio N/K under the assumption that the data transmission in each cell is affected by channel estimation errors, pilot contamination, and an arbitrary large scale attenuation. Numerical results are used to validate the asymptotic analysis in the finite system regime and to evaluate the network performance under different settings. The asymptotic results are also instrumental to get insights into the interplay among system parameters.

This paper investigates a cellular edge caching design under an extremely large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), SBS-user distances shrink, and each user can request a cached content from multiple SBSs. Unfortunately, the complexity of existing caching controls' mechanisms increases with the number of SBSs, making them inapphcable for solving the fundamental caching problem: How to maximize local caching gain while minimizing the replicated content caching? Furthermore, spatial dynamics of interference is no longer negligible in UDCNs due to the surge in interference. In addition, the caching control should consider temporal dynamics of user demands. To overcome such difficulties, we propose a novel caching algorithm weaving together notions of mean-field game theory and stochastic geometry. These enable our caching algorithm to become independent of the number of SBSs and users, while incorporating spatial interference dynamics as well as temporal dynamics of content popularity and storage constraints. Numerical evaluation validates the fact that the proposed algorithm reduces not only the long run average cost by at least 24% but also the number of replicated content by 56% compared to a popularity-based algorithm.

The max-min signal-to-interference-plus-noise ratio (SINR) problem is considered in a coordinated network wherein L base stations (BSs) each equipped with N antennas serve in total K single-antenna users that are uniformly distributed in the network. We conduct the analysis in the asymptotic regime in which N and K grow large to compute a deterministic approximation for the max-min SINR. The results are independent from fast-fading and users' locations and thus allow one to determine the optimal max-min SINR given basic system parameters such as cell radius, K, N and pathloss exponent. The provided framework can be utilized for analyzing the problem without the need to run system level simulations and for finding the optimal N, K, resource allocation and BS placement. Numerical results are used to validate the analytical results in a finite system regime and to evaluate the effects of system parameters on the system performance.

Massive multiple-input multiple-output (massive MIMO) and small cell densification are complementary key 5G enablers. Given a fixed number of the entire basestation antennas per unit area, this paper fairly compares (i) to deploy few base stations (BSs) and concentrate many antennas on each of them, i.e. massive MIMO, and (ii) to deploy more BSs equipped with few antennas, i.e. small cell densification. We observe that small cell densification always outperforms for both signal-to-interference ratio (SIR) coverage and energy efficiency (EE), when each BS serves multiple users via L number of sub-bands (multicarrier transmission). Moreover, we also observe that larger L increases SIR coverage while decreasing EE, thus urging the necessity of optimal 5G network design. These two observations are based on our novel closed-form SIR coverage probability derivation using stochastic geometry, also validated via numerical simulations.

In this article, we investigate how the performance of Massive MIMO cellular systems can be enhanced by introducing D2D communication. We consider a scenario where the base station (BS) is equipped with large, but finite number of antennas and the total number of UEs is kept fixed. The key design question is that what fraction of users should be offloaded to D2D mode in order to maximize the aggregate cell level throughput. We demonstrate that there exists an optimal user offload fraction, which maximizes the overall capacity. This fraction is strongly coupled with the network parameters such as the number of antennas at the BS, D2D link distance and the transmit SNR at both the UE and the BS and careful tuning of the offload fraction can provide up to 5× capacity gains.1

Caching popular contents at the edge of the network can positively impact the performance and future sustainability of wireless networks in several ways, e.g., end-to-end access delay reduction and peak rate increase. In this paper, we aim at showing that non-negligible performance enhancements can be observed in terms of network interference footprint as well. To this end, we consider a full-duplex small-cell network consisting of non-cooperative cache-aided base stations, which communicate simultaneously with both downlink users and wireless backhaul nodes. We propose a novel static caching model seeking to mimic a geographical policy based on local files popularity and calculate the corresponding cache hit probability. Subsequently we study the performance of the considered network in terms of throughput gain with respect to its cache-free half-duplex counterpart. Numerical results corroborate our theoretical findings and highlight remarkable performance gains when moving from cache-free to cache-aided full-duplex small-cell networks.

In this paper, the problem of proactive deployment of cache-enabled unmanned aerial vehicles (UAVs) for optimizing the quality-of-experience (QoE) of wireless devices in a cloud radio access network is studied. In the considered model, the network can leverage human-centric information, such as users' visited locations, requested contents, gender, job, and device type to predict the content request distribution, and mobility pattern of each user. Then, given these behavior predictions, the proposed approach seeks to find the user-UAV associations, the optimal UAVs' locations, and the contents to cache at UAVs. This problem is formulated as an optimization problem whose goal is to maximize the users' QoE while minimizing the transmit power used by the UAVs. To solve this problem, a novel algorithm based on the machine learning framework of conceptor-based echo state networks (ESNs) is proposed. Using ESNs, the network can effectively predict each user's content request distribution and its mobility pattern when limited information on the states of users and the network is available. Based on the predictions of the users' content request distribution and their mobility patterns, we derive the optimal locations of UAVs as well as the content to cache at UAVs. Simulation results using real pedestrian mobility patterns from BUPT and actual content transmission data from Youku show that the proposed algorithm can yield 33.3% and 59.6% gains, respectively, in terms of the average transmit power and the percentage of the users with satisfied QoE compared with a benchmark algorithm without caching and a benchmark solution without UAVs.

In this paper, the problem of uplink (UL) and downlink (DL) resource optimization, mode selection, and power allocation is studied for wireless cellular networks under the assumption of in-band full duplex base stations, non-orthogonal multiple access (NOMA) operation, and queue stability constraints. The problem is formulated as a network utility maximization problem for which a Lyapunov framework is used to decompose it into two disjoint subproblems of auxiliary variable selection and rate maximization. The latter is further decoupled into a user association and mode selection (UAMS) problem and a UL/DL power optimization (UDPO) problem that are solved concurrently. The UAMS problem is modeled as a many-to-one matching problem whose goal is to associate users to small cell base stations and select transmission mode (half-/full-duplex and orthogonal/NOMA). Then, an algorithm is proposed to solve the problem by finding a pairwise stable matching. Subsequently, the UDPO problem is formulated as a sequence of convex problems and is solved using the concave-convex procedure. Simulation results demonstrate that the proposed scheme is effective in allocating UL and DL power levels after dynamically selecting the operating mode and the served users, under different traffic intensity conditions, network density, and self-interference cancellation capability. The proposed scheme is shown to achieve up to 63% and 73% of gains in UL and DL packet throughput, and 21% and 17% in UL and DL cell edge throughput, respectively, compared with the existing baseline schemes.

Consider the problem where a statistician in a two-node system receives rate-limited information from a transmitter about marginal observations of a memoryless process generated from two possible distributions. Using its own observations, this receiver is required to first identify the legitimacy of its sender by declaring the joint distribution of the process, and then depending on such authentication, it generates adequate reconstruction of the observations, satisfying an average per-letter distortion. The performance of this setup is investigated through the corresponding rate-error-distortion region, describing the tradeoff between: the communication rate, the error exponent induced by the detection, and the distortion incurred by the source reconstruction. In the special case of testing against independence, where the alternative hypothesis implies that the sources are independent, the optimal rate-error-distortion region is characterized. An application example to binary symmetric sources is given subsequently and the explicit expression for the rate-error-distortion region is provided as well. The case of “general hypotheses” is also investigated. A new achievable rate-error-distortion region is derived based on the use of non-asymptotic binning, improving the quality of communicated descriptions. Further improvement of performance in the general case is shown to be possible when the requirement of source reconstruction is relaxed, which stands in contrast to the case of general hypotheses.

In this paper, the problem of proactive caching is studied for cloud radio access networks (CRANs). In the studied model, the baseband units (BBUs) can predict the content request distribution and mobility pattern of each user and determine which content to cache at remote radio heads and the BBUs. This problem is formulated as an optimization problem, which jointly incorporates backhaul and fronthaul loads and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks (ESNs) with sublinear algorithms is proposed. Using ESNs, the BBUs can predict each user's content request distribution and mobility pattern while having only limited information on the network's and user's state. In order to predict each user's periodic mobility pattern with minimal complexity, the memory capacity of the corresponding ESN is derived for a periodic input. This memory capacity is shown to capture the maximum amount of user information needed for the proposed ESN model. Then, a sublinear algorithm is proposed to determine which content to cache while using limited content request distribution samples. Simulation results using real data from Youku and the Beijing University of Posts and Telecommunications show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 27.8% and 30.7%, respectively, compared with two baseline algorithms: random caching with clustering and random caching without clustering.

To reap the benefits of cache-enabled small cell networks, new backaul management mechanisms are needed to prevent the predicted files that are downloaded at the small base stations (SBSs) for caching purposes from jeopardizing the urgent requests that need to be served via the backhaul. Such mechanisms must account for the heterogeneity of the backhaul that will encompass both wireless backhaul links (at various frequency bands) and a wired backhaul component. In this paper, the heterogeneous backhaul management problem is formulated as a minority game in which each SBS has to define the number of predicted files to download, without affecting the required transmission rate of the current requests. For the formulated game, it is shown that a unique fair proper mixed Nash equilibrium (PMNE) exists. A self-organizing reinforcement learning algorithm is then proposed and shown to converge to a unique Boltzmann-Gibbs equilibrium, which approximates the desired PMNE. Simulation results show that the performance of the proposed approach can be close to that of the ideal optimal algorithm while it outperforms a centralized greedy approach in terms of the amount of data that is cached without jeopardizing the quality-of-service of current requests.

A multi-user cognitive (secondary) radio system is considered, where the spatial multiplexing mode of operation is implemented amongst the nodes, under the presence of multiple primary transmissions. The secondary receiver carries out minimum mean-squared error detection to effectively decode the secondary data streams, while it performs spectrum sensing at the remaining signal to capture the presence of primary activity or not. New analytical closed-form expressions regarding some important system measures are obtained, namely, the outage and detection probabilities, the transmission power of the secondary nodes, the probability of unexpected interference at the primary nodes, and the detection efficiency with the aid of the area under the receive operating characteristics curve. The realistic scenarios of channel fading time variation and channel estimation errors are encountered for the derived results. Finally, the enclosed numerical results verify the accuracy of the proposed framework, while some useful engineering insights are also revealed, such as the key role of the detection accuracy to the overall performance and the impact of transmission power from the secondary nodes to the primary system.

In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, are investigated. In particular, to enable reliable uplink communications for the IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the 3D placement and the mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs' locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total-transmit power of the IoT devices is reduced by 45% compared with a case, in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28% enhanced system reliability compared with the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs.

In this paper, the effective use of flight-time constrained unmanned aerial vehicles (UAVs) as flying base stations that provide wireless service to ground users is investigated. In particular, a novel framework for optimizing the performance of such UAV-based wireless systems in terms of the average number of bits (data service) transmitted to users as well as the UAVs' hover duration (i.e. flight time) is proposed. In the considered model, UAVs hover over a given geographical area to serve ground users that are distributed within the area based on an arbitrary spatial distribution function. In this case, two practical scenarios are considered. In the first scenario, based on the maximum possible hover times of UAVs, the average data service delivered to the users under a fair resource allocation scheme is maximized by finding the optimal cell partitions associated to the UAVs. Using the powerful mathematical framework of optimal transport theory, this cell partitioning problem is proved to be equivalent to a convex optimization problem. Subsequently, a gradient-based algorithm is proposed for optimally partitioning the geographical area based on the users' distribution, hover times, and locations of the UAVs. In the second scenario, given the load requirements of ground users, the minimum average hover time that the UAVs need for completely servicing their ground users is derived. To this end, first, an optimal bandwidth allocation scheme for serving the users is proposed. Then, given this optimal bandwidth allocation, optimal cell partitions associated with the UAVs are derived by exploiting the optimal transport theory. Simulation results show that our proposed cell partitioning approach leads to a significantly higher fairness among the users compared with the classical weighted Voronoi diagram. Furthermore, the results demonstrate that the average hover time of the UAVs can be reduced by 64% by adopting the proposed optimal bandwidth allocation scheme as well as the optimal cell partitioning approach. In addition, our results reveal an inherent tradeoff between the hover time of UAVs and bandwidth efficiency while serving the ground users.

We study the problem of joint load balancing and interference mitigation in heterogeneous networks in which massive multiple-input multiple-output macro cell base station (BS) equipped with a large number of antennas, overlaid with wireless self-backhauled small cells (SCs), is assumed. Self-backhauled SC BSs with full-duplex communication employing regular antenna arrays serve both macro users and SC users by using the wireless backhaul from macro BS in the same frequency band. We formulate the joint load balancing and interference mitigation problem as a network utility maximization subject to wireless backhaul constraints. Subsequently, leveraging the framework of stochastic optimization, the problem is decoupled into dynamic scheduling of macro cell users, backhaul provisioning of SCs, and offloading macro cell users to SCs as a function of interference and backhaul links. Via numerical results, we show the performance gains of our proposed framework under the impact of SCs density, number of BS antennas, and transmit power levels at low and high frequency bands. It is shown that our proposed approach achieves a 5.6 times gain in terms of cell-edge performance as compared with the closed-access baseline in ultra-dense networks with 350 SC BSs per km2.

This work studies the problems of feedback allocation and scheduling for a multichannel downlink cellular network under limited and delayed feedback. We consider a realistic scenario where a fixed and small number F̅ of link states can be reported to the base-station (BS) per time-slot. We study the trade-off between knowing at the BS a small number of accurate link states (i.e. that can be reported within one time-slot) and a larger but outdated number of link states (i.e. number of link states > F̅ that requires more than one slot to be reported). We propose an efficient algorithm that selects the link states that should be reported to the base-station. A novelty here is that this feedback allocation algorithm is performed at the users side. We show that this algorithm combined with the MaxWeight scheduling achieves at least a fraction η of the stability region achieved under the ideal system (i.e. with full and perfect feedback at no cost). We then provide numerical results that show the best aforementioned trade-off under various system setups.

In this paper, a novel approach for providing incentives for caching in small cell networks (SCNs) is proposed based on the economics framework of contract theory. In this model, a mobile network operator (MNO) designs contracts that will be offered to a number of content providers (CPs) to motivate them to cache their content at the MNO's small base stations (SBSs). A practical model in which information about the traffic generated by the CPs' users is not known to the MNO is considered. Under such asymmetric information, the incentive contract between the MNO and each CP is properly designed so as to determine the amount of allocated storage to the CP and the charged price by the MNO. The contracts are derived by the MNO in a way to maximize the global benefit of the CPs and prevent them from using their private information to manipulate the outcome of the caching process. For this interdependent contract model, the closed-form expressions of the price and the allocated storage space to each CP are derived. This proposed mechanism is shown to satisfy the sufficient and necessary conditions for the feasibility of a contract. Moreover, it is shown that the proposed pricing model is budget balanced, enabling the MNO to cover all the caching expenses via the prices charged to the CPs. Simulation results show that none of the CPs will have an incentive to choose a contract designed for CPs with different traffic loads.

We present a multi-user multiple-input multiple-output (MIMO) cognitive radio system consisting of a secondary receiver that deploys spatial multiplexing to decode signals from multiple secondary transmitters, under the presence of primary transmissions. The secondary receiver carries out minimum mean-squared error detection to decode the secondary data streams, while it performs spectrum sensing at the remaining signal to capture the potential presence of primary activity. Assuming Rayleigh fading as well as the realistic cases of channel fading time variation and channel estimation errors, we present novel closed-form expressions for important system measures, namely, the detection and false-alarm probabilities as well as the transmission power of the secondary nodes. The enclosed numerical results verify the accuracy of the presented analysis.

In this paper, the optimal trajectory and deployment of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum energy consumption, a new approach for optimal mobility of the UAVs is proposed. First, given a fixed ground IoT network, the total transmit power of the devices is minimized by properly clustering the IoT devices with each cluster being served by one UAV. Next, to maintain energy-efficient communications in time-varying mobile IoT networks, the optimal trajectories of the UAVs are determined by exploiting the framework of optimal transport theory. Simulation results show that by using the proposed approach, the total transmit power of IoT devices for reliable uplink communications can be reduced by 56% compared to the fixed Voronoi deployment method. Moreover, our results yield the optimal paths that will be used by UAVs to serve the mobile IoT devices with a minimum energy consumption.

Elevation beamforming and Full Dimension MIMO (FD-MIMO) are currently active areas of research and standardization in 3GPP LTE-Advanced. FD-MIMO utilizes an active antenna array system (AAS), that provides the ability of adaptive electronic beam control over the elevation dimension, resulting in a better system performance as compared to the conventional 2D MIMO systems. FD-MIMO is more advantageous when amalgamated with massive MIMO systems, in that it exploits the additional degrees of freedom offered by a large number of antennas in the elevation. To facilitate the evaluation of these systems, a large effort in 3D channel modeling is needed. This paper aims at providing a summary of the recent 3GPP activity around 3D channel modeling. The 3GPP proposed approach to model antenna radiation pattern is compared with the ITU approach. A closed-form expression is then worked out for the spatial correlation function (SCF) for channels constituted by individual antenna elements in the array by exploiting results on spherical harmonics and Legendre polynomials. The proposed expression can be used to obtain correlation coefficients for any arbitrary 3D propagation environment. Simulation results corroborate and study the derived spatial correlation expression. The results are directly applicable to the analysis of future 5G 3D massive MIMO systems.

This article investigates the mobility management of an ultra dense cellular network (UDN) from an energy-efficiency (EE) point of view. Many dormant base stations (BSs) in a UDN do not transmit signals, and thus a received power based handover (HO) approach as in traditional cellular networks is hardly applicable. In addition, the limited front/backhaul capacity compared to a huge number of BSs makes it difficult to implement a centralized HO and power control. For these reasons, a novel user-centric association rule is proposed, which jointly optimizes HO and power control for maximizing EE. The proposed mobility management is able to cope not only with the spatial randomness of user movement but also with temporally correlated wireless channels. The proposed approach is implemented over a HO time window and tractable power con- trol policy by exploiting mean-field game (MFG) and stochastic geometry (SG). Compared to a baseline with a fixed HO interval and transmit power, the proposed approach achieves the 1.2 times higher long-term average EE at a typical active BS.

This article investigates the performance of an ultra-dense network (UDN) from an energy-efficiency (EE) standpoint leveraging the interplay between stochastic geometry (SG) and mean-field game (MFG) theory. In this setting, base stations (BSs) (resp. users) are uniformly distributed over a two-dimensional plane as two independent homogeneous Poisson point processes (PPPs), where users associate to their nearest BSs. The goal of every BS is to maximize its own energy efficiency subject to channel uncertainty, random BS location, and interference levels. Due to the coupling in interference, the problem is solved in the mean-field (MF) regime where each BS interacts with the whole BS population via time- varying MF interference. As a main contribution, the asymptotic convergence of MF interference to zero is rigorously proved in a UDN with multiple transmit antennas. It allows us to derive a closed-form EE representation, yielding a tractable EE optimal power control policy. This proposed power control achieves more than 1.5 times higher EE compared to a fixed power baseline.

Massive MIMO systems, where base stations are equipped with hundreds of antennas, are an attractive way to handle the rapid growth of data traffic. As the number of users increases, the initial access and handover in contemporary networks will be flooded by user collisions. In this work, we propose a random access procedure that resolves collisions and also performs timing, channel, and power estimation by simply utilizing the large number of antennas envisioned in massive MIMO systems and the inherent timing misalignments of uplink signals during network access and handover. Numerical results are used to validate the performance of the proposed solution under different settings. It turns out that the proposed solution can detect the collisions with a probability higher than 90%, while providing reliable timing and channel estimates at the same time. Moreover, numerical results demonstrate that it is robust to overloaded situations.

In this paper, an incentive proactive cache mechanism in cache-enabled small cell networks (SCNs) is proposed, in order to motivate the content providers (CPs) to participate in the caching procedure. A network composed of a single mobile network operator (MNO) and multiple CPs is considered. The MNO aims to define the price it charges the CPs to maximize its revenue while the CPs compete to determine the number of files they cache at the MNO's small base stations (SBSs) to improve the quality of service (QoS) of their users. This problem is formulated as a Stackelberg game where a single MNO is considered as the leader and the multiple CPs willing to cache files are the followers. The followers game is modeled as a non-cooperative game and both the existence and uniqueness of a Nash equilibrium (NE) are proved. The closed-form expression of the NE which corresponds to the amount of storage each CP requests from the MNO is derived. An optimization problem is formulated at the MNO side to determine the optimal price that the MNO should charge the CPs. Simulation results show that at the equilibrium, the MNO and CPs can all achieve a utility that is up to 50% higher than the cases in which the prices and storage quantities are requested arbitrarily.

In this paper, the problem of optimized beam alignment for wearable device-to-device (D2D) communications over millimeter wave (mmW) frequencies is studied. In particular, a noncooperative game is formulated between wearable communication pairs that engage in D2D communications. In this game, wearable devices acting as transmitters autonomously select the directions of their beams so as to maximize the data rate to their receivers. To solve the game, an algorithm based on best response dynamics is proposed that allows the transmitters to reach a Nash equilibrium in a distributed manner. To further improve the performance of mmW D2D communications, a novel quantum game model is formulated to enable the wearable devices to exploit new quantum directions during their beam alignment so as to further enhance their data rate. Simulation results show that the proposed game-theoretic approach improves the performance, in terms of data rate, of about 75% compared to a uniform beam alignment. The results also show that the quantum game model can further yield up to 20% improvement in data rates, relative to the classical game approach.

In this work, we focus on the ergodic sum rate in the downlink of a single-cell large-scale multi-user MIMO system in which the base station employs N antennas to communicate with K single-antenna user equipments. A regularized zero-forcing (RZF) scheme is used for precoding under the assumption that each link forms a spatially correlated MIMO Rician fading channel. The analysis is conducted assuming N and K grow large with a non trivial ratio and perfect channel state information is available at the base station. Recent results from random matrix theory and large system analysis are used to compute an asymptotic expression of the signal-to-interference-plus-noise ratio as a function of the system parameters, the spatial correlation matrix and the Rician factor. Numerical results are used to evaluate the performance gap in the finite system regime under different operating conditions.

We consider a heterogeneous network (HetNet) in which multiple small cell base stations (SBSs) aim to offload a quantity of macro cell user equipments (MUEs) to reduce the energy consumption of the network while guaranteeing the QoS requirements of all UEs. We design an ascending-bid auction mechanism to achieve this goal. Unique and closed form solutions for the demand and supply quantities of offloading MUEs are derived. When the MBS has knowledge about the utilities and strategies of the SBSs, the proposed auction can be formulated as a Stackelberg game where the clinching bid price is obtained in closed form. Numerical results verify the theoretical analysis for different scenarios and show that the proposed auction clinches fast at the unique clinching price, thereby resulting in a win-win solution that improves the energy consumption of the HetNet.

This paper presents a cooperative multiple-input multiple-output (MIMO) scheme for a wireless sensor network consisting of inexpensive nodes, organised in clusters and transmitting data towards sinks. The transmission is affected by hardware imperfections, imperfect synchronisation, data correlation among nodes of the same cluster, channel estimation errors and interference among nodes of different clusters. Within this setting, we are interested in determining the number of nodes per cluster that maximises the energy efficiency of the network. The analysis is conducted in the asymptotic regime in which the number N of sensor nodes per cluster grows large without bound. Numerical results are used to validate the asymptotic analysis in the finite system regime and to investigate different configurations. It turns out that the optimum number of sensor nodes per cluster increases with the inter-cluster interference and with the number of sinks.

A dual-hop cognitive (secondary) relaying system incorporating collaborative spectrum sensing to opportunistically switch between data transmission and energy harvesting is introduced. The secondary relays, first scan the wireless channel for a primary network activity, and then convey their reports to a secondary base station (SBS). Afterwards, the SBS, based on these reports and its own estimation, decides cooperatively the presence of primary transmission or not. In the former scenario, all secondary relays start to harvest energy from the transmission of one or more primary nodes. In the latter scenario, the system initiates secondary communication via a best relay selection policy. The performance of the proposed scheme is thoroughly investigated by assuming realistic channel conditions, i.e., non-identical link-distances and outdated channel estimation, while its overall energy consumption is evaluated, indicating the efficiency of the switching approach.

In this paper, the optimal deployment of multiple unmanned aerial vehicles (UAVs) acting as flying base stations is investigated. Considering the downlink scenario, the goal is to minimize the total required transmit power of UAVs while satisfying the users' rate requirements. To this end, the optimal locations of UAVs as well as the cell boundaries of their coverage areas are determined. To find those optimal parameters, the problem is divided into two sub-problems that are solved iteratively. In the first sub-problem, given the cell boundaries corresponding to each UAV, the optimal locations of the UAVs are derived using the facility location framework. In the second sub-problem, the locations of UAVs are assumed to be fixed, and the optimal cell boundaries are obtained using tools from optimal transport theory. The analytical results show that the total required transmit power is significantly reduced by determining the optimal coverage areas for UAVs. These results also show that, moving the UAVs based on users' distribution, and adjusting their altitudes can lead to a minimum power consumption. Finally, it is shown that the proposed deployment approach, can improve the system's power efficiency by a factor of 20 χ compared to the classical Voronoi cell association technique with fixed UAVs locations.

This work focuses on the downlink of a single-cell large-scale MIMO system in which the base station equipped with M antennas serves K single-antenna users. In particular, we are interested in reducing the implementation complexity of the optimal linear precoder (OLP) that minimizes the total power consumption while ensuring target user rates. As most precoding schemes, a major difficulty towards the implementation of OLP is that it requires fast inversions of large matrices at every new channel realizations. To overcome this issue, we aim at designing a linear precoding scheme providing the same performance of OLP but with lower complexity. This is achieved by applying the truncated polynomial expansion (TPE) concept on a per-user basis. To get a further leap in complexity reduction and allow for closed-form expressions of the per-user weighting coefficients, we resort to the asymptotic regime in which M and K grow large with a bounded ratio. Numerical results are used to show that the proposed TPE precoding scheme achieves the same performance of OLP with a significantly lower implementation complexity.

A two-tier heterogeneous cellular network (HCN) with intra-tier and inter-tier dependence is studied. The macro cell deployment follows a Poisson point process (PPP) and two different clustered point processes are used to model the cache-enabled small cells. Under this model, we derive approximate expressions in terms of finite integrals for the average delivery rate considering inter-tier and intra-tier dependence. On top of the fact that cache size drastically improves the performance of small cells in terms of average delivery rate, we show that rate splitting of limited-backhaul induces non-linear performance variations, and therefore has to be adjusted for rate fairness among users of different tiers.

The problem of collaborative distributed hypothesis testing is investigated. In this setting, a binary decision is required about the joint distribution of two arbitrary dependent memoryless processes that are sampled at different physical locations (nodes) in the system. Interactive rate-limited communication is allowed between these nodes. Defining two types of error events, the error exponent for an error of the second type is investigated, under a prescribed probability of error of the first type. A general achievable error exponent, as a function of the total available communication resources, is proposed, for the case of two general hypotheses. The special case of testing against independence is revisited for which it is shown that optimality can be attained, as a special case of the general achievable exponent, provided the constraint over the error probability of the first type goes to zero.

Polarization diversity is generally achieved by using two orthogonal polarizations on the same link, which enables to double the available bandwidth. In this paper, we study the possibility to connect the nodes of a line-of- sight ring topology network with one single channel for all the links, with the condition that the polarization of any link is orthogonal to the polarization of the two adjacent links. The solution proposed in this paper can improve spectrum efficiency by up to 50% in comparison with the widespread polarization multiplexing solution.

Energy efficiency has become an encouragement, and more than this, a requisite for the design of the next-generation wireless communication standards. In this paper, a dual-hop cognitive (secondary) relaying system is considered, incorporating multiple amplify-and-forward relays, a rather cost-effective solution. First, the secondary relays sense the wireless channel, scanning for a primary network activity, and then convey their reports to a secondary base station (SBS). Afterward, the SBS, based on these reports and its own estimation, decides cooperatively the presence of primary transmission or not. In the former scenario, all the secondary nodes start to harvest energy from the transmission of primary nodes. In the latter scenario, the system initiates secondary communication via a best relay selection policy. Performance evaluation of this system is thoroughly investigated, by assuming realistic channel conditions, i.e., non-identical link distances, Rayleigh fading, and outdated channel estimation. The detection and outage probabilities as well as the average harvested energy are derived as new closed-form expressions. In addition, an energy-efficiency optimization problem is analytically formulated and solved, while a necessary condition in terms of power consumption minimization for each secondary node is presented. From a green communication standpoint, it turns out that energy harvesting greatly enhances the resources of secondary nodes, especially when primary activity is densely present.

In this paper, a novel approach for joint power control and user scheduling is proposed for optimizing energy efficiency (EE), in terms of bits per unit energy, in ultra dense small cell networks (UDNs). Due to severe coupling in interference, this problem is formulated as a dynamic stochastic game (DSG) between small cell base stations (SBSs). This game enables capturing the dynamics of both the queues and channel states of the system. To solve this game, assuming a large homogeneous UDN deployment, the problem is cast as a mean-field game (MFG) in which the MFG equilibrium is analyzed with the aid of low-complexity tractable partial differential equations. Exploiting the stochastic nature of the problem, user scheduling is formulated as a stochastic optimization problem and solved using the drift plus penalty (DPP) approach in the framework of Lyapunov optimization. Remarkably, it is shown that by weaving notions from Lyapunov optimization and mean-field theory, the proposed solution yields an equilibrium control policy per SBS, which maximizes the network utility while ensuring users' quality-of-service. Simulation results show that the proposed approach achieves up to 70.7% gains in EE and 99.5% reductions in the network's outage probabilities compared to a baseline model, which focuses on improving EE while attempting to satisfy the users' instantaneous quality-of-service requirements.

Massive MIMO is a promising technique for increasing the spectral efficiency (SE) of cellular networks, by deploying antenna arrays with hundreds or thousands of active elements at the base stations and performing coherent transceiver processing. A common rule-of-thumb is that these systems should have an order of magnitude more antennas M than scheduled users K because the users' channels are likely to be near-orthogonal when M/K 10. However, it has not been proved that this rule-of-thumb actually maximizes the SE. In this paper, we analyze how the optimal number of scheduled users K* depends on M and other system parameters. To this end, new SE expressions are derived to enable efficient system-level analysis with power control, arbitrary pilot reuse, and random user locations. The value of K* in the large-M regime is derived in closed form, while simulations are used to show what happens at finite M, in different interference scenarios, with different pilot reuse factors, and for different processing schemes. Up to half the coherence block should be dedicated to pilots and the optimal M/K is less than 10 in many cases of practical relevance. Interestingly, K* depends strongly on the processing scheme and hence it is unfair to compare different schemes using the same K.

In this paper, the deployment of an unmanned aerial vehicle (UAV) as a flying base station used to provide the fly wireless communications to a given geographical area is analyzed. In particular, the coexistence between the UAV, that is transmitting data in the downlink, and an underlaid device-to-device (D2D) communication network is considered. For this model, a tractable analytical framework for the coverage and rate analysis is derived. Two scenarios are considered: a static UAV and a mobile UAV. In the first scenario, the average coverage probability and the system sum-rate for the users in the area are derived as a function of the UAV altitude and the number of D2D users. In the second scenario, using the disk covering problem, the minimum number of stop points that the UAV needs to visit in order to completely cover the area is computed. Furthermore, considering multiple retransmissions for the UAV and D2D users, the overall outage probability of the D2D users is derived. Simulation and analytical results show that, depending on the density of D2D users, the optimal values for the UAV altitude, which lead to the maximum system sum-rate and coverage probability, exist. Moreover, our results also show that, by enabling the UAV to intelligently move over the target area, the total required transmit power of UAV while covering the entire area, can be minimized. Finally, in order to provide full coverage for the area of interest, the tradeoff between the coverage and delay, in terms of the number of stop points, is discussed.

This paper focuses on a large-scale multi-cell multi-user MIMO system in which L base stations (BSs) of N antennas each communicate with K single-antenna user equipments. We consider the design of the linear precoder that minimizes the total power consumption while ensuring target user rates. Three configurations with different degrees of cooperation among BSs are considered: the coordinated beamforming scheme (only channel state information is shared among BSs), the coordinated multipoint MIMO processing technology or network MIMO (channel state and data cooperation), and a single-cell beamforming scheme (only local channel state information is used for beamforming, while channel state cooperation is needed for power allocation). The analysis is conducted assuming that N and K$ grow large with a non trivial ratio K/N, and imperfect channel state information (modeled by the generic Gauss-Markov formulation form) is available at the BSs. Tools of random matrix theory are used to compute, in explicit form, deterministic approximations for: i) the parameters of the optimal precoder; ii) the powers needed to ensure target rates; and iii) the total transmit power. These results are instrumental to get further insight into the structure of the optimal precoders and also to reduce the implementation complexity in large-scale networks. Numerical results are used to validate the asymptotic analysis in the finite system regime and to make comparisons among the different configurations.

The use of drone small cells (DSCs) which are aerial wireless base stations that can be mounted on flying devices such as unmanned aerial vehicles (UAVs), is emerging as an effective technique for providing wireless services to ground users in a variety of scenarios. The efficient deployment of such DSCs while optimizing the covered area is one of the key design challenges. In this paper, considering the low altitude platform (LAP), the downlink coverage performance of DSCs is investigated. The optimal DSC altitude which leads to a maximum ground coverage and minimum required transmit power for a single DSC is derived. Furthermore, the problem of providing a maximum coverage for a certain geographical area using two DSCs is investigated in two scenarios; interference free and full interference between DSCs. The impact of the distance between DSCs on the coverage area is studied and the optimal distance between DSCs resulting in maximum coverage is derived. Numerical results verify our analytical results on the existence of optimal DSCs altitude/separation distance and provide insights on the optimal deployment of DSCs to supplement wireless network coverage.

In this paper, a novel approach for joint power control and user scheduling is proposed for optimizing energy efficiency (EE), in terms of bits per unit power, in ultra dense small cell networks (UDNs). To address this problem, a dynamic stochastic game (DSG) is formulated between small cell base stations (SBSs). This game enables to capture the dynamics of both the queues and channel states of the system. To solve this game, assuming a large homogeneous UDN deployment, the problem is cast as a mean field game (MFG) in which the MFG equilibrium is analyzed with the aid of low-complexity tractable two partial differential equations. User scheduling is formulated as a stochastic optimization problem and solved using the drift plus penalty (DPP) approach in the framework of Lyapunov optimization. Remarkably, it is shown that by weaving notions from Lyapunov optimization and mean field theory, the proposed solution yields an equilibrium control policy per SBS which maximizes the network utility while ensuring users' quality-of-service. Simulation results show that the proposed approach achieves up to 18.1% gains in EE and 98.2% reductions in the network's outage probabilities compared to a baseline model.

This work focuses on the downlink of a large-scale multi-cell multi-user MIMO system in which L base stations (BSs) of N antennas each communicate with KL single-antenna user equipments. We consider the design of the linear precoder that minimizes the total power consumption while ensuring target user rates. Two configurations with different degrees of cooperation among BSs are considered: the coordinated beamforming scheme (only channel state information is shared between BSs) and the coordinated multipoint MIMO technology (channel state and data cooperation). The analysis is conducted assuming that N and K grow large with a non trivial ratio K/N and imperfect channel state information is available at the BSs. In both configurations, tools of random matrix theory are used to compute, often in closed form, deterministic approximations for: the parameters of the optimal precoder; the powers needed to ensure target rates; and the total transmit power. These results are instrumental to get further insights into the structure of the optimal precoder and also to reduce the complexity of its implementation in large-scale networks. Numerical results are used to validate the asymptotic analysis in the finite system regime and to make comparisons among the two different configurations.

This paper considers a MIMO interference system where interference alignment (IA) technique is adopted to manage the problem of interference. We consider a time division duplex (TDD) system where each transmitter estimates its channel state information (CSI) by probing the receivers. In addition, the transmitters share their local CSI estimate between each other using a backhaul links of limited capacity. A quantization over the backhaul is therefore required to reduce the amount of information to exchange. We study in this paper the impact of this quantization on the system performance and determine the optimal number of transmitter-receiver pairs that maximizes the system throughput.

In this paper we study the downlink of a TDD (Time Division Duplex) single cell system where the Base Station (BS) employs multiple antennas to serve the users taking into account the traffic patterns. The BS chooses each slot the users to be active, and serves them using Zero Forcing (ZF) precoding. This requires the knowledge of the users' channels which is assumed to be performed e.g. via uplink training. Due to the channel acquisition overhead, only a subset of users must be active at each timeslot (depending on traffic patterns and channel states). In this paper, we develop an active user selection strategy where the base station sets a given threshold for the channel gain of the users. Then, only the users that have their channel gain higher than the threshold send their training sequence and are then considered to be served. The base station estimates the channel states of these users and, due to channel reciprocity, uses these channel states to transmit data via ZF precoding. With appropriate signaling and threshold selection, which adapt to the queuing behavior of the users, we prove that our proposed method achieves a larger stability region than the baseline centralized policy where the BS selects the users based on channel statistics and queue lengths. The performance of the threshold-based method is illustrated via simulations, where we can observe a tradeoff between the expansion of the stability region and delay performance.

This paper considers the downlink of a heterogeneous network, where multiple base stations (BSs) can serve the users by non-coherent multiflow beamforming. We assume imperfect channel state information at both BSs and users. The objective is to jointly optimize the precoding, load balancing, and BS operation mode (active or sleep) for improving the energy efficiency of the network. The considered problem is to minimize the weighted total power consumption (both circuit power and dynamic transmit power), while satisfying per-user quality of service constraints and per-BS transmit power constraints. This problem is non-convex, but we prove that for each combination of BS modes, the considered problem has a hidden convexity structure. Thus, the global optimal solution is obtained by an exhaustive search over all possible BS mode combinations. Furthermore, by iterative convex approximations of the non-convex power consumption functions, a heuristic algorithm is proposed to obtain a local optimal solution with low complexity. Simulation results illustrate that our proposed algorithms significantly reduce the total power consumption, compared to the scheme where all BSs are continuously active. This implies that putting a BS into sleep mode by proper load balancing is an important solution for energy savings in heterogeneous networks.

Caching multimedia files at the network edge has been identified as a key technology for enhancing users' quality-of-service (QoS), while reducing redundant transmissions over capacity-constrained backhauls. Nevertheless, in small cell networks, the efficiency of a caching policy depends on the ability of small base stations (SBSs) to anticipate the requests from the user equipments (UEs). In this paper, we propose a collaborative filtering (CF) scheme for estimating the required backhaul usage at each SBS, by mining the cacheability of UEs' file requests. In the proposed approach, each SBS has a two-fold objective: update the bandwidth allocation based on the estimated backhaul utilization, and, given the current bandwidth availability, identify which UEs to service. We formulate the problem as a one-to many matching game between SBSs and UEs, and we propose a novel cache-aware user association algorithm that minimizes the backhaul usage at each SBS, subject to individual QoS requirements. Simulation results, based on real-world service request logs, have shown that the proposed CF-based solution can yield significant gains in terms of backhaul efficiency and cache hit-ratio, reaching up to 25%, with a maximum gap of 9% to an optimal cache-aware association technique.

Hardware non-idealities in wireless transmitter electronics cause distortion that is not captured by conventional linear channel models; in fact, error-vector magnitude (EVM) measurements in conformance testing conceptually reduce their collective effect to an additive noise component at each subcarrier. Motivated by the EVM, the present paper considers a `binoisy' multiple-input multiple-output (MIMO) channel model where the additional non-idealities manifest themselves as an additive distortion noise term at the transmit side. Through this extended MIMO relation, the effects of hardware impairments on the achievable rates of different digital modulation schemes are studied via large system analysis. The numerical results illustrate how tolerable EVM levels depend non-trivially on various factors, including the signal-to-noise ratio, modulation order and the level of asymmetry in antenna array configurations.

This paper considers the problem of energy efficiency maximization in the uplink of a cluster of multiple-antenna coordinated access points. A framework for energy efficiency optimization is developed in which the signal-to-interference-plus-noise ratio takes a more general expression than existing alternatives so as to encompass most 5G candidate technologies. Two energy efficiency optimization problems are formulated, also considering quality-of-service (QoS) constraints: 1) network global energy efficiency maximization; 2) worst-case energy-efficient design. These fractional, non-convex problems are tackled by means of fractional programming coupled with sequential convex optimization, and two low-complexity resource allocation algorithms are designed, which are guaranteed to converge to local optima of the non-convex problems. Numerical results show that the proposed algorithm can efficiently balance between the goals of maximizing the energy efficiency and meeting the QoS constraints. Moreover, it is shown that a small sum-rate reduction allows large energy savings.

This paper characterizes the performance of IA technique taking into account the dynamic traffic pattern and the probing/feedback cost. We consider a TDD system where transmitters acquire their CSI (Channel State Information) by decoding the pilot sequences sent by the receivers. Since global CSI knowledge is required for IA, the transmitters have also to exchange their estimated CSIs over a backhaul of limited capacity. Under this setting, we characterize in this paper the stability region of the system and provide a probing algorithm that achieves the max stability region. In addition, we compare the stability region of IA to the one achieved by a TDMA system where each transmitter applies a simple ZF (Zero Forcing technique).

A distributed hypothesis testing (HT) problem is considered, comprising two nodes and a unidirectional communication link. The receiving node is required to make a decision as to the probability distribution in effect. A binning process is used in order to minimize the probability of error, resulting in a new achievable error-exponent. A sub-class of HT problems with general hypotheses is defined, which contains many interesting and relevant problems. The advantage of the binning strategy in comparison to the non-binning approach is demonstrated by means of a binary symmetric example.

Motivated by the recent interest in 3D beamforming to enhance system performance, we present an information-theoretic channel model for multiple-input multiple-output (MIMO) systems, that can support the elevation dimension. The principle of maximum entropy is used to determine the distribution of the channel matrix consistent with the prior angular information. We provide an explicit expression for the cumulative density function (CDF) of the mutual information in the large number of transmit antennas and paths regime. The derived Gaussian approximation is quite accurate even for realistic system dimensions. The simulation results study the achievable performance through the meticulous selection of the transmit antenna downtilt angles. The results are directly applicable to the analysis of 5G 3D massive MIMO systems.

In this paper, an exact closed-form expression for the Spatial Correlation Function (SCF) is derived for the standardized three-dimensional (3D) multiple-input multiple-output (MIMO) channel. This novel SCF is developed for a uniform linear array of antennas with non-isotropic antenna patterns. The proposed method resorts to the spherical harmonic expansion (SHE) of plane waves and the trigonometric expansion of Legendre and associated Legendre polynomials to obtain a closed-form expression for the SCF for arbitrary angular distributions and antenna patterns. The resulting expression depends on the underlying angular distributions and antenna patterns through the Fourier Series (FS) coefficients of power azimuth and elevation spectrums. The novelty of the proposed method lies in the SCF being valid for any 3D propagation environment. Numerical results validate the proposed analytical expression and study the impact of angular spreads on the correlation. The derived SCF will help evaluate the performance of correlated 3D MIMO channels in the future.

Locally caching contents at the network edge constitutes one of the most disruptive approaches in 5G wireless networks. Reaping the benefits of edge caching hinges on solving a myriad of challenges such as how, what and when to strategically cache contents subject to storage constraints, traffic load, unknown spatio-temporal traffic demands and data sparsity. Motivated by this, we propose a novel transfer learning-based caching procedure carried out at each small cell base station. This is done by exploiting the rich contextual information (i.e., users' content viewing history, social ties, etc.) extracted from device-to-device (D2D) interactions, referred to as source domain. This prior information is incorporated in the so-called target domain where the goal is to optimally cache strategic contents at the small cells as a function of storage, estimated content popularity, traffic load and backhaul capacity. It is shown that the proposed approach overcomes the notorious data sparsity and cold-start problems, yielding significant gains in terms of users' quality-of-experience (QoE) and backhaul offloading, with gains reaching up to 22% in a setting consisting of four small cell base stations.

Welcome to the special issue of IET Communications on device-to-device (D2D) communications. As more and more new mobile multimedia rich services are becoming available to larger audiences, there is an ever increasing demand for higher data rate wireless access. Wireless D2D communication is a promising technology to improve user experience and optimise resource utilisation in cellular networks. These benefits are achieved by enabling two or more mobile devices in the proximity of each other to establish a direct local link bypassing the base station or the access point. Significant progress has been made since the introduction of this technology a few years ago. On the other hand, many research challenges still exist for such a new wireless communication paradigm, for example, how to share resources dynamically (e.g. spectrum and energy) between cellular communication and ad hoc D2D communication to accommodate larger volumes of traffic, how to ensure Quality of Service (QoS) to end users, etc. Other challenges include: discovery of services for D2D communication; radio resource allocation and resource management; self-organisation of direct links; proximity-based offloading, and capacity enhancement as well as performance evaluation. The future D2D networks cannot operate efficiently unless these and other relevant challenges are properly addressed. The purpose of this special issue is to address research advances that enable D2D communication in cellular networks, and to report on the state-of-the-art contributions in this area. We are grateful to the overwhelming interest and support from the community, as we received a large body of excellent contributions. Out of the twenty-eight submissions to this special issue, we have selected nine outstanding papers that we believe represent the state-of-art in the device to device communication area. The papers are organised into three groups: two papers on channel coding and signal processing algorithms, seven papers on resource management and scheduling, and five papers on new services and applications. The first group addresses channel coding and signal processing algorithms for the physical layer. The first of these two papers, written by Xiang et al. presents an overview of the state-of-the-art D2D communication on channel measurements and modeling. The future trends and research directions are also comprehensively discussed. The work in this paper will facilitate system design and optimisation in channel-oriented D2D communication. The second paper, by Zhang et al. presents a sub-block tracking based equalisation technique for orthogonal frequency division multiplexing (OFDM) based D2D communications in high mobility environments. By partitioning an OFDM block into several sub-blocks and rendering that the channel response of each sub-block is time-invariant, one can equalise the partitioned sub-block by a single tap. Both theoretical analysis and simulation results demonstrate that the merits of the proposed scheme in high mobility D2D environments. The second group mainly investigates multiple access control (MAC) and network layer resource allocation and scheduling issues. The first paper in this group, written by Dai et al. studies the spectrum sharing problems in heterogeneous D2D networks where different D2D users coexist with the cellular users. A scheme called spectrum partition based D2D transmission is proposed to improve the spectrum efficiency of the D2D and cellular networks. Simulation results are reported to show evident performance gains of the proposed scheme. The second paper, written by Wang et al. exploits historical social interaction information of mobile users for a D2D link setup and resource allocation. It develops a contact time model to characterise the D2D links such that only those D2D links with sufficiently long contact time may be considered for a D2D link setup and resource allocation. This work demonstrates that sociality-aware allocation, compared to sociality-unaware schemes, can achieve better performance. The third paper, by Zheng et al. studies dynamic resource allocation for D2D communication. The authors propose a service time prediction based dynamic resource allocation mechanism. The simulation results show that the proposed mechanism can significantly improve the system performance. The paper, by Kang et al. focuses on link scheduling in D2D networks. The authors propose distributed link scheduling schemes based on a recently proposed D2D communication technology, FlashLinQ. To deal with the inefficiency of resource reuse in FlashLinQ, two new link scheduling schemes based on binary matrix on-off interference map are designed. The performance enhancement over conventional schemes is demonstrated through simulations. Another paper, by Zhou et al. considers the network-controlled D2D multicast with network coding. It proposes a user-specific bit mapping algorithm to make different information with a different equivalent coding rate before performing network-coded. A corresponding user-specific link adaptation scheme is proposed to adaptively choose an optimal modulation and coding scheme for D2D multicast. Numerical results show that the user-specific link adaptation scheme can improve the capacity performance of network controlled D2D multicast. The sixth paper of this group, by Zhou et al. considers the Spectral Efficiency (SE) and the Energy Efficiency (EE) problems in D2D networks. The target is to maximise each User Equipment's EE in an interference-limited environment subject to its specific QoS and maximum transmission power constraints (i.e., EE). The problem is formulated and solved within the framework of non-cooperative game. The tradeoff between EE and SE is analysed and closed-form expressions are also derived. The seventh paper, written by Chen et al. concentrates on user-centric relay assisted D2D communications. A Vickrey-Clarke-Groves auction based relay allocation mechanism (ARM) is proposed. The work is also extended to a general case and a general ARM. Extensive simulation results show the efficiency and effectiveness of the proposed mechanisms. The third group explores new services and applications of D2D communication. The first paper, written by Chu et al. investigates robust secrecy rate optimisation for multiple input and single output (MISO) secrecy channel with multiple D2D communications. Two robust secrecy rate optimisation problems, one for robust power minimisation and the other for robust secrecy rate maximisation are discussed. Simulation results are provided to validate the performance gains. The second paper, by Li et al. proposes a new model for analysing the multi-hop delay of safety-related message broadcasting in V2V communications, taking into account actual traffic factors. A new scheme is proposed to reduce multi-hop delay by tracking the optimal one-hop transmission range and it was validated by simulations using realistic vehicular traces. The third paper, by Sun et al. reviews the standardisation progress of D2D in 3GPP Release 12. Three scenarios, in-coverage, partial-coverage and out-of-coverage, are defined. For these scenarios, channel models are obtained by an amendment to existing channel models. Centralised, distributed and hybrid synchronisation procedures are introduced. The design aspects of D2D discovery and communication are discussed in detail. Possible future work toward post releases is also briefly outlined. The fourth paper, by Xi et al. proposes an efficient hybrid data collection scheme for the machine nodes in hierarchical smart building networks. These concerned nodes can form clusters via distributed methods. The corresponding resource allocation scheme can be realised easily. This cooperative scheme can reduce the signaling overhead and meanwhile enhance the delay or security performance. The last paper, written by Kwak et al. designs a service-oriented networking platform to offer dynamic networking services in supporting mobile group communications among connected smart devices. The proposed platform adopts a session initiation protocol (SIP) protocol as the service signaling protocol that provides high extensibility and compatibility. Also, the quality of end-to-end service is guaranteed by a virtue smart delivery scheme in the platform. In summary, this special issue offers a comprehensive review of recent advances in the area of wireless D2D communication and networks, exposes and addresses the research challenges in physical, MAC as well as application layers. The special issue will serve as a good reference for the academic community for future research ideas, and in the meantime provide valuable concepts and directions for future standardisation and commercialisation in the industry. Finally, we would like to thank all the authors who have submitted their papers for consideration for publication in this issue. We are grateful to the anonymous reviewers who spent much of their precious time in reviewing all the submissions. Their timely reviews and comments greatly helped us select the best papers for inclusion in this special issue. We would also like to thank the devoted staff of IET Communications for their professional support, and particularly express our gratitude to the Editor-in-Chief, Professor Sherman Shen, for his advice, patience, and encouragement from the beginning until the final stage. Lingyang Song received his PhD from the University of York, UK, in 2007, where he received the K.M. Stott Prize for excellent research. He worked as a research fellow at the University of Oslo, Norway, and Harvard University, until rejoining Philips Research UK in March 2008. In May 2009, he joined the School of Electronics Engineering and Computer Science, Peking University, China, as a full professor. His main research interests include MIMO, cognitive and cooperative communications, physical layer security, and wireless ad hoc/sensor networks. He published extensively and wrote 3 text books. He is the recipient of 2012 IEEE Asia Pacific (AP) Young Researcher Award, and received 7 best paper awards including IEEE WCNC, ICC and Globecom. He is currently on the Editorial Board of IEEE Transactions on Wireless Communications, China Communications, and Journal of Network and Computer Applications. He is a senior member of IEEE, and an IEEE ComSoc distinguished lecturer since 2015. Mérouane Debbah entered the Ecole Normale Supérieure de Cachan (France) in 1996 where he received his M.Sc and Ph.D. degrees respectively. He worked for Motorola Labs (Saclay, France) from 1999–2002 and the Vienna Research Center for Telecommunications (Vienna, Austria) until 2003. From 2003 to 2007, he joined the Mobile Communications department of the Institut Eurecom (Sophia Antipolis, France) as an Assistant Professor. Since 2007, he is a Full Professor at Supelec (Gif-sur-Yvette, France). From 2007 to 2014, he was director of the Alcatel-Lucent Chair on Flexible Radio. Since 2014, he is Vice-President of the Huawei France R&D center and director of the Mathematical and Algorithmic Sciences Lab. His research interests are in information theory, signal processing and wireless communications. He is an Associate Editor in Chief of the journal Random Matrix: Theory and Applications and was an associate and senior area editor for IEEE Transactions on Signal Processing respectively in 2011-2013 and 2013-2014. Mérouane Debbah is a recipient of the ERC grant MORE (Advanced Mathematical Tools for Complex Network Engineering). He is a IEEE Fellow, a WWRF Fellow and a member of the academic senate of Paris-Saclay. He is the recipient of the Mario Boella award in 2005, the 2007 IEEE GLOBECOM best paper award, the Wi-Opt 2009 best paper award, the 2010 Newcom++ best paper award, the WUN CogCom Best Paper 2012 and 2013 Award, the 2014 WCNC best paper award as well as the Valuetools 2007, Valuetools 2008, CrownCom2009, Valuetools 2012 and SAM 2014 best student paper awards. In 2011, he received the IEEE Glavieux Prize Award and in 2012, the Qualcomm Innovation Prize Award. Rong Yu received his Ph.D. degree from Tsinghua University, China, in 2007. After that, he worked in the School of Electronic and Information Engineering of South China University of Technology (SCUT). In 2010, he joined the Institute of Intelligent Information Processing at Guangdong University of Technology (GDUT), where he is now a full professor. His research interest mainly focuses on wireless communications and mobile computing. Dr. Yu is currently serving as the deputy secretary general of the Internet of Things (IoT) Industry Alliance, Guangdong, China, and the deputy head of the IoT Engineering Center, Guangdong, China. Frank Y. Li holds a Ph.D. degree from the Norwegian University of Science and Technology (NTNU). He worked as a Senior Researcher at UniK - University Graduate Center, University of Oslo before joining the Department of Information and Communication Technology, University of Agder (UiA) in August 2007 where he is currently a Professor. Dr. Li's research interests include MAC mechanisms and routing protocols in 4G and beyond mobile systems and wireless networks, mesh and ad hoc networks; wireless sensor network; D2D communication; cooperative communication; cognitive radio networks; green wireless communications; QoS, resource management and traffic engineering in wired and wireless IP-based networks; analysis, simulation and performance evaluation of communication protocols and networks. Jianzhong (Charlie) Zhang: Charlie Zhang is currently senior director and head of Wireless Communications Lab with Samsung Research America at Dallas, where he leads technology development, prototyping and standardisation for Beyond 4G and 5G wireless systems. From Aug 2009 to Aug 2013, he served as the Vice Chairman of the 3GPP RAN1 working group and led development of LTE and LTE-Advanced technologies such as 3D channel modeling, UL-MIMO and CoMP, Carrier Aggregation for TD-LTE, etc. Before joining Samsung, he was with Motorola from 2006 to 2007 working on 3GPP HSPA standards, and with Nokia Research Center from 2001 to 2006 working on IEEE 802.16e (WiMAX) standard and EDGE/CDMA receiver algorithms. He received his Ph.D. degree from University of Wisconsin, Madison.

We consider a small cell network (SCN) consisting of N cells, with the small cell base stations (SCBSs) equipped with Nt≥ 1 antennas each, serving K single antenna user terminals (UTs) per cell. Under this set up, we address the following question: given certain time average quality of service (QoS) targets for the UTs, what is the minimum transmit power expenditure with which they can be met? Our motivation to consider time average QoS constraint comes from the fact that modern wireless applications such as file sharing, multi-media etc. allow some flexibility in terms of their delay tolerance. Time average QoS constraints can lead to greater transmit power savings as compared to instantaneous QoS constraints since it provides the flexibility to dynamically allocate resources over the fading channel states. We formulate the problem as a stochastic optimization problem whose solution is the design of the downlink beamforming vectors during each time slot. We solve this problem using the approach of Lyapunov optimization and characterize the performance of the proposed algorithm. With this algorithm as the reference, we present two main contributions that incorporate practical design considerations in SCNs. First, we analyze the impact of delays incurred in information exchange between the SCBSs. Second, we impose channel state information (CSI) feedback constraints, and formulate a joint CSI feedback and beamforming strategy. In both cases, we provide performance bounds of the algorithm in terms of satisfying the QoS constraints and the time average power expenditure. Our simulation results show that solving the problem with time average QoS constraints provide greater savings in the transmit power as compared to the instantaneous QoS constraints.

International audience

This paper studies the problem of feedback resource allocation in multiple-input-single-output (MISO) channels utilizing partial channel state information (CSI) feedback. Considering low/moderate signal-to-noise ratios (SNRs), the optimal quantizers and the feedback bit allocation maximizing the throughput are obtained in the asymptotic case where the number of feedback bits increases. Moreover, the results are utilized to derive the optimal retransmission rates in the automatic repeat request (ARQ) protocols and joint CSI-ARQ schemes are proposed for the MISO setups. We show that uniform channel amplitude quantization is asymptotically optimal in terms of throughput. Also, the optimal retransmission rates of the incremental redundancy (INR) ARQ protocols follow an arithmetic progression in the exponential domain. Under certain conditions, a MISO system using quantized CSI can be mapped to a MISO or a SISO (S: single) setup using ARQ or joint CSI-ARQ feedback in the sense that they lead to the same throughput. Finally, to maximize the throughput, the optimal number of channel direction quantization bits should be (M-1) times the number of amplitude quantization bits, where M is the number of transmit antennas.

Hardware impairments in radio-frequency components of a wireless system cause unavoidable distortions to transmission that are not captured by the conventional linear channel model. In this paper, a “binoisy” single-user multiple-input multiple-output (SU-MIMO) relation is considered where the additional distortions are modeled via an additive noise term at the transmit side. Through this extended SU-MIMO channel model, the effects of transceiver hardware impairments on the achievable rate of multi-antenna point-to-point systems are studied. Channel input distributions encompassing practical discrete modulation schemes, such as, QAM and PSK, as well as Gaussian signaling are covered. In addition, the impact of mismatched detection and decoding when the receiver has insufficient information about the non-idealities is investigated. The numerical results show that for realistic system parameters, the effects of transmit-side noise and mismatched decoding become significant only at high modulation orders.

In this paper, the problem of feedback and active user selection in multiple-input single-output (MISO) wireless systems such that the system's stability region is as big as possible is examined. The focus is on a system in a Rayleigh fading environment where zero forcing precoding is used to serve all active users in every slot. Acquisition of the channel states is done via uplink training in time division duplexing mode by the active users. Clearly, only a subset of users can perform uplink training and the selection of this subset is a challenging and interesting problem especially in MISO systems. The stability regions of a baseline centralized scheme and two novel decentralized policies are examined analytically. In the decentralized schemes, the transmitter broadcasts periodically the queue state information and the users contend for the channel in a carrier sense multiple access-based manner with parameters based on the outdated queue state information and real-time channel state information. We show that, using infrequent signaling between the base station and the users, the decentralized policies outperform the centralized policy. In addition, a threshold-based user selection and training scheme for discrete-time contention is proposed. The results of this paper imply that, as far as stability is concerned, the users must be involved in the active user selection and feedback/training decision. This should be leveraged in future communication systems.

Massive multiple-input multiple-output (MIMO) systems are cellular networks where the base stations (BSs) are equipped with unconventionally many antennas, deployed on co-located or distributed arrays. Huge spatial degrees-of-freedom are achieved by coherent processing over these massive arrays, which provide strong signal gains, resilience to imperfect channel knowledge, and low interference. This comes at the price of more infrastructure; the hardware cost and circuit power consumption scale linearly/affinely with the number of BS antennas N. Hence, the key to cost-efficient deployment of large arrays is low-cost antenna branches with low circuit power, in contrast to today's conventional expensive and power-hungry BS antenna branches. Such low-cost transceivers are prone to hardware imperfections, but it has been conjectured that the huge degrees-of-freedom would bring robustness to such imperfections. We prove this claim for a generalized uplink system with multiplicative phase-drifts, additive distortion noise, and noise amplification. Specifically, we derive closed-form expressions for the user rates and a scaling law that shows how fast the hardware imperfections can increase with N while maintaining high rates. The connection between this scaling law and the power consumption of different transceiver circuits is rigorously exemplified. This reveals that one can make √N the circuit power increase as N, instead of linearly, by careful circuit-aware system design.

Assume that a multi-user multiple-input multiple-output (MIMO) system is designed from scratch to uniformly cover a given area with maximal energy efficiency (EE). What are the optimal number of antennas, active users, and transmit power? The aim of this paper is to answer this fundamental question. We consider jointly the uplink and downlink with different processing schemes at the base station and propose a new realistic power consumption model that reveals how the above parameters affect the EE. Closed-form expressions for the EE-optimal value of each parameter, when the other two are fixed, are provided for zero-forcing (ZF) processing in single-cell scenarios. These expressions prove how the parameters interact. For example, in sharp contrast to common belief, the transmit power is found to increase (not to decrease) with the number of antennas. This implies that energy-efficient systems can operate in high signal-to-noise ratio regimes in which interference-suppressing signal processing is mandatory. Numerical and analytical results show that the maximal EE is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve a relatively large number of users using ZF processing. The numerical results show the same behavior under imperfect channel state information and in symmetric multi-cell scenarios.

This paper considers a downlink heterogeneous network, where different types of multiantenna base stations (BSs) communicate with a number of single-antenna users. Multiple BSs can serve the users by spatial multiflow transmission techniques. Assuming imperfect channel state information at both BSs and users, the precoding, load balancing, and BS operation mode are jointly optimized for improving the network energy efficiency. We minimize the weighted total power consumption while satisfying quality-of-service constraints at the users. This problem is nonconvex, but we prove that for each BS mode combination, the considered problem has a hidden convexity structure. Thus, the optimal solution is obtained by an exhaustive search over all possible BS mode combinations. Furthermore, by iterative convex approximations of the nonconvex objective function, a heuristic algorithm is proposed to obtain a suboptimal solution of low complexity. We show that although multicell joint transmission is allowed, in most cases, it is optimal for each user to be served by a single BS. The optimal BS association condition is parameterized, which reveals how it is impacted by different system parameters. Simulation results indicate that putting a BS into sleep mode by proper load balancing is an important solution for energy savings.

Multiple-input-multiple-output (MIMO) systems of current LTE releases are capable of adaptation in the azimuth only. Recently, the trend is to enhance system performance by exploiting the channel's degrees of freedom in the elevation, which necessitates the characterization of 3D channels. We present an information-theoretic channel model for MIMO systems that supports the elevation dimension. The model is based on the principle of maximum entropy, which enables us to determine the distribution of the channel matrix consistent with the prior information on the angles. Based on this model, we provide analytical expression for the cumulative density function (CDF) of the mutual information (MI) for systems with a single receive and finite number of transmit antennas in the general signal-to-interference-plus-noise-ratio (SINR) regime. The result is extended to systems with finite receive antennas in the low SINR regime. A Gaussian approximation to the asymptotic behavior of MI distribution is derived for the large number of transmit antennas and paths regime. We corroborate our analysis with simulations that study the performance gains realizable through meticulous selection of the transmit antenna downtilt angles, confirming the potential of elevation beamforming to enhance system performance. The results are directly applicable to the analysis of 5G 3D-Massive MIMO-systems.

In this work, we consider the downlink of a single-cell multi-user MIMO system in which the base station (BS) makes use of N antennas to communicate with K single-antenna user equipments (UEs). The UEs move around in the cell according to a random walk mobility model. We aim at determining the energy consumption distribution when different linear precoding techniques are used at the BS to guarantee target rates within a finite time interval T. The analysis is conducted in the asymptotic regime where N and K grow large with fixed ratio under the assumption of perfect channel state information (CSI). Both recent and standard results from large system analysis are used to provide concise formulae for the asymptotic transmit powers and beamforming vectors for all considered schemes. These results are eventually used to provide a deterministic approximation of the energy consumption and to study its fluctuations around this value in the form of a central limit theorem. Closed-form expressions for the asymptotic means and variances are given. Numerical results are used to validate the accuracy of the theoretical analysis and to make comparisons. We show how the results can be used to approximate the probability that a battery-powered BS runs out of energy and also to design the cell radius for minimizing the energy consumption per unit area. The imperfect CSI case is also briefly considered.

In this work, we consider a network composed of several single-antenna transmitter-receiver pairs in which each pair aims at selfishly minimizing the power required to achieve a given signal-to-interference-plus-noise ratio. This is obtained modeling the transmitter-receiver pairs as rational agents that engage in a non-cooperative game. Capitalizing on the well-known results on the existence and structure of the generalized Nash equilibrium (GNE) point of the underlying game, a low complexity, iterative and distributed algorithm is derived to let each terminal reach the GNE using only a limited feedback in the form of link-layer acknowledgements (ACK) or negative acknowledgements (NACK). Numerical results are used to prove that the proposed solution is able to achieve convergence in a scalable and adaptive manner under different operating conditions.

We consider the downlink of a single-cell multi-user MIMO system in which the base station makes use of N antennas to communicate with K single-antenna user equipments (UEs) randomly positioned in the coverage area. In particular, we focus on the problem of designing the optimal linear precoding for minimizing the total power consumption while satisfying a set of target signal-to-interference-plus-noise ratios (SINRs). To gain insights into the structure of the optimal solution and reduce the computational complexity for its evaluation, we analyze the asymptotic regime where N and K grow large with a given ratio and make use of recent results from large system analysis to compute the asymptotic solution. Then, we concentrate on the asymptotically design of heuristic linear precoding techniques. Interestingly, it turns out that the regularized zero-forcing (RZF) precoder is equivalent to the optimal one when the ratio between the SINR requirement and the average channel attenuation is the same for all UEs. If this condition does not hold true but only the same SINR constraint is imposed for all UEs, then the RZF can be modified to still achieve optimality if statistical information of the UE positions is available at the BS. Numerical results are used to evaluate the performance gap in the finite system regime and to make comparisons among the precoding techniques.

In a large network of agents, we consider a distributed convex optimization problem where each agent has a private convex cost function and a set of local variables. We provide an algorithm to carry out a multi-area decentralized optimization in an asynchronous fashion, obtained by applying random Gauss-Seidel iterations on the Douglas-Rachford splitting operator. As an application, a direct-current linear optimal power flow model is implemented and simulations results confirm the convergence of the proposed algorithm.

Massive multiple-input multiple-output (MIMO) systems are cellular networks where the base stations (BSs) are equipped with unconventionally many antennas. Such large antenna arrays offer huge spatial degrees-of-freedom for transmission optimization; in particular, great signal gains, resilience to imperfect channel knowledge, and small inter-user interference are all achievable without extensive inter-cell coordination. The key to cost-efficient deployment of large arrays is the use of hardware-constrained base stations with low-cost antenna elements, as compared to today's expensive and power-hungry BSs. Low-cost transceivers are prone to hardware imperfections, but it has been conjectured that the excessive degrees-of-freedom of massive MIMO would bring robustness to such imperfections. We herein prove this claim for an uplink channel with multiplicative phase-drift, additive distortion noise, and noise amplification. Specifically, we derive a closed-form scaling law that shows how fast the imperfections increase with the number of antennas.

In this work, we consider the downlink of a single-cell multi-user multiple-input multiple-output system in which zero-forcing precoding is used at the base station (BS) to serve a certain number of user equipments (UEs). A fixed data rate is guaranteed at each UE. The UEs move around in the cell according to a Brownian motion, thus the path losses change over time and the energy consumption fluctuates accordingly. We aim at determining the distribution of the energy consumption. To this end, we analyze the asymptotic regime where the number of antennas at the BS and the number of UEs grow large with a given ratio. It turns out that the energy consumption is asymptotically a Gaussian random variable whose mean and variance are derived analytically. These results can, for example, be used to approximate the probability that a battery-powered BS runs out of energy within a certain time period.

Radio-frequency (RF) impairments in the transceiver hardware of communication systems (e.g., phase noise (PN), high power amplifier (HPA) nonlinearities, or in-phase/quadrature-phase (I/Q) imbalance) can severely degrade the performance of traditional multiple-input multiple-output (MIMO) systems. Although calibration algorithms can partially compensate these impairments, the remaining distortion still has substantial impact. Despite this, most prior works have not analyzed this type of distortion. In this paper, we investigate the impact of residual transceiver hardware impairments on the MIMO system performance. In particular, we consider a transceiver impairment model, which has been experimentally validated, and derive analytical ergodic capacity expressions for both exact and high signal-to-noise ratios (SNRs). We demonstrate that the capacity saturates in the high-SNR regime, thereby creating a finite capacity ceiling. We also present a linear approximation for the ergodic capacity in the low-SNR regime, and show that impairments have only a second-order impact on the capacity. Furthermore, we analyze the effect of transceiver impairments on large-scale MIMO systems; interestingly, we prove that if one increases the number of antennas at one side only, the capacity behaves similar to the finite-dimensional case. On the contrary, if the number of antennas on both sides increases with a fixed ratio, the capacity ceiling vanishes; thus, impairments cause only a bounded offset in the capacity compared to the ideal transceiver hardware case.

In this paper, the problem of uplink user association in small cell networks, which involves interactions between users, small cell base stations, and macro-cell stations, having often conflicting objectives, is considered. The problem is formulated as a college admissions game with transfers in which a number of colleges, i.e., small cell and macro-cell stations seek to recruit a number of students, i.e., users. In this game, the users and access points (small cells and macro-cells) rank one another based on preference functions that capture the users' need to optimize their utilities which are functions of packet success rate (PSR) and delay as well as the small cells' incentive to extend the macro-cell coverage (e.g., via cell biasing/range expansion) while maintaining the users' quality-of-service. A distributed algorithm that combines notions from matching theory and coalitional games is proposed to solve the game. The convergence of the algorithm is shown and the properties of the resulting assignments are discussed. Simulation results show that the proposed approach yields a performance improvement, in terms of the average utility per user, reaching up to 23% relative to a conventional, best-PSR algorithm.

In this paper we study the stability region of the 2-user MISO broadcast channel where the transmitter employs Zero Forcing precoding when both users are scheduled, taking into account the time overheads needed for uplink channel training. We show that, with proper signalling design, combining a decentralized policy with the baseline centralized one for user selection can increase the stability region of the system.

Macroscopic mobility of users is important to determine the performance and energy efficiency of a wireless network, because of the temporal correlations it introduces in the consumed power and throughput. In this work, we introduce a methodology that allows to compute the long time statistics of such metrics in a network. After describing the general approach, we consider a specific example of the uplink channel of a mobile user in the vicinity of a base station equipped with a large number of antennas (the so called “massive MIMO” base station). To guarantee a fixed signal-to-noise ratio and rate, the user inverts the pathloss channel power, while moving around in the cell. To calculate the long time distribution of the corresponding consumed energy, we assume that its movement follows a Brownian motion, and then map the problem to the solution of the minimum eigenvalue of a partial differential equation, which can be solved either analytically, or numerically very fast. The single-user throughput is also treated. We then present some results and discuss how they can be generalized if the mobility model is assumed to be a Levy random walk. A roadmap to use this methodology is eventually given to extend results to a multiple user set-up with multiple base stations.

The objective of this study is to analyze a new disruptive deployment of wireless sensors in order to cope with the explosive demand for bandwidth while taking into account energy consumption considerations. The work is grounded on the idea of massive network densification by drastically increasing the number of sensors in a given area in a Time Division Duplex (TDD) mode. Using ideas from the recent Massive MIMO technology (more than 400 antennas, without any modification of the network infrastructure), we transpose the idea to a massive deployment of sensors and show the benefits of such an infrastructure. This research is expected to provide the optimal deployment of massive wireless sensor networks in terms of cost/performance/complexity/energy efficiency trade-off and define the next generation wireless Machine-to-Machine (M2M) communication.

Due to large numbers of interferers, in-band interference is a bottleneck that future wireless heterogeneous networks have to deal with. Recent advances in information theory have shown that interference does not have to be avoided or strongly limited, so that reliable transmissions can occur. In this paper, we propose a novel Radio Resource Management (RRM) approach, capable to exploit those recent advances, by methodically coupling and processing in-band interferers. To do so, we first reuse the design of a previous two interference regime classifier. Then, we propose an algorithm which forms coalitions of interferers over the available spectral resources, in such a way that the spectral efficiency (SE) of the system is maximized. Simulations results show that our proposed RRM algorithm strongly limits the undesired effects of the traditional tradeoff between in-band interference and overall system performance (SE).

Assume that a multi-user multiple-input multiple-output (MIMO) communication system must be designed to cover a given area with maximal energy efficiency (bits/Joule). What are the optimal values for the number of antennas, active users, and transmit power? By using a new model that describes how these three parameters affect the total energy efficiency of the system, this work provides closed-form expressions for their optimal values and interactions. In sharp contrast to common belief, the transmit power is found to increase (not decrease) with the number of antennas. This implies that energy efficient systems can operate at high signal-to-noise ratio (SNR) regimes in which the use of interference-suppressing precoding schemes is essential. Numerical results show that the maximal energy efficiency is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve relatively many users using interference-suppressing regularized zero-forcing precoding.

Performance of heterogeneous network is strongly limited by the interference due to multiple access points operating in the same geographical area, with overlapping service coverage. The common understanding is that interference, classically processed as additive noise, compromises the transmission and therefore must be ideally avoided or at least strongly limited. However, recent investigations in the domain of information theory and successive interference cancellation (SIC) techniques have proved that interference may not necessarily be treated as an opponent, but may become an ally. In this paper, we propose a novel interference aware resource management algorithm, where the system may only control its interference perception. In a system consisting of a couple of downlink users and access points with overlapping coverage, we aim to define the most spectral-efficient way to process interference at each receiver. Based on a 3-regimes interference classifier, both users in the system may either treat interference as noise, orthogonalize transmissions so that interference may be avoided, or cancel interference out of the received signal via SIC-based techniques. Our study shows that, when aiming at maximizing total spectral efficiency, ignoring or avoiding interference is not always the best option. Based on our theoritical study, we propose an interference classification algorithm, with only 2 admissible regimes for each user. Finally, we assess its notable performance improvement by simulation results.

This paper considers the mode selection problem for network-assisted device-to-device (D2D) communications with multiple antennas at the base station. We study transmission in both dedicated and shared frequency bands. Given the type of resources (i.e., dedicated or shared), the user equipment (UE) decides to transmit in the conventional cellular mode or directly to its corresponding receiver in the D2D mode. We formulate this problem under two different objectives. The first problem is to maximize the quality-of-service (QoS) given a transmit power, and the second problem is to minimize the transmit power given a QoS requirement. We derive closed-form results for the optimal decision and show that the two problem formulations behave differently. Taking a geometrical approach, we study the area around the transmitter UE where the receiving UE should be to have D2D mode optimality, and how it is affected by the transmit power, QoS, and the number of base station antennas.

In this paper, we address the caching problem in small cell networks from a game theoretic point of view. In particular, we formulate the caching problem as a many-to-many matching game between small base stations and service providers' servers. The servers store a set of videos and aim to cache these videos at the small base stations in order to reduce the experienced delay by the end-users. On the other hand, small base stations cache the videos according to their local popularity, so as to reduce the load on the backhaul links. We propose a new matching algorithm for the many-to-many problem and prove that it reaches a pairwise stable outcome. Simulation results show that the number of satisfied requests by the small base stations in the proposed caching algorithm can reach up to three times the satisfaction of a random caching policy. Moreover, the expected download time of all the videos can be reduced significantly.

Predictive small cells networks and proactive resource allocation are considered as one of the key mechanisms for increasing the long-term energy-efficiency of communication networks. Learning techniques exploit repetitive patterns in human behavior to predict some future transmission contexts of the network. In this paper, we target to improve the energy efficiency of delay-tolerant transmissions by enabling flexibility in resource allocation with prediction-based strategies. We study the performance, in terms of energy efficiency of several scenarios of future knowledge ranging from zero to perfect knowledge of the future context, but also partial knowledge scenarios (short-term predictions, long-term statistics or partial knowledge). An iterative process, approaching the optimal strategies in each scenario, is described. In some cases, closed-form expressions of the optimal strategies to be implemented can be obtained and the performance in each scenario is computed. Our analytical and numerical results assess the potential benefit of exploiting the knowledge of the future in the case of a delay-tolerant transmission and show how the system may benefit from a provided piece of information about the future transmission context.

Anticipating multimedia file requests via caching at the small cell base stations (SBSs) of a cellular network has emerged as a promising technique for optimizing the quality of service (QoS) of wireless user equipments (UEs). However, developing efficient caching strategies must properly account for specific small cell constraints, such as backhaul congestion and limited storage capacity. In this paper, we address the problem of devising a user-cell association, in which the SBSs exploit caching capabilities to overcome the backhaul capacity limitations and enhance the users' QoS. In the proposed approach, the SBSs individually decide on which UEs to service based on both content availability and on the data rates they can deliver, given the interference and backhaul capacity limitations. We formulate the problem as a one-to-many matching game between SBSs and UEs. To solve this game, we propose a distributed algorithm, based on the deferred acceptance scheme, that enables the players (i.e., UEs and SBSs) to self-organize into a stable matching, in a reasonable number of algorithm iterations. Simulation results show that the proposed cell association scheme yields significant gains, reaching up to 21% improvement compared to a traditional cell association techniques with no caching considerations.

In this paper, we explore the physical limits of successful information transfer in a point-to-point communication system. In interest of studying the fundamental limits imposed by physics on communication systems in general, we model a simple generic system that enables us to make some basic inquiries about the energy efficiency in information transfer and processing. We use ideas from thermodynamics such as Szilard engine to represent information bits. We further use ideas from electromagnetic theory for transfer of information, and information theory to define the energy efficiency metric. We find the upper limit of this efficiency and conditions at which it can be achieved.

Three dimensional (3D) beamforming (also elevation beamforming) is now gaining interest among researchers in wireless communication. The reason can be attributed to its potential for enabling a variety of strategies such as sector or user specific elevation beamforming and cell-splitting. Since these techniques cannot be directly supported by current LTE releases, the 3GPP is now working on defining the required technical specifications. In particular, a large effort is currently being made to get accurate 3D channel models that support the elevation dimension. This step is necessary as it will evaluate the potential of 3D and full dimensional (FD) beamforming techniques to benefit from the richness of real channels. This work aims at presenting the on-going 3GPP study item “study on 3D-channel model for elevation beamforming and FD-MIMO studies for LTE” and positioning it with respect to previous standardization works.

In this paper, we perform transmit antenna selection to improve the energy efficiency of large scale multiple antenna systems. We derive a good approximation of the distribution of the mutual information in this antenna selection system. It shows that channel hardening phenomenon is still retained as full complexity with antenna selection. Then, we use this closed-form expression to assess the energy efficiency performance. Specifically, we evaluate the performance of the energy efficiency in two different cases: 1) the circuit power consumption is comparable to or even dominates the transmit power, and 2) the circuit power can be ignored due to relatively much higher transmit power. The theoretical analysis indicates that there exists an optimal number of selected antennas to maximize the energy efficiency in the first case, whereas in the second case, the energy efficiency is maximized when all the available antennas are used. Based on these conclusions, two simple but efficient antenna selection algorithms are proposed to obtain the maximum energy efficiency. All the analytical results are verified through computer simulations.

In this paper, we consider a decode-and-forward-based wireless multihop network with a single source node, a single destination node, and N intermediate nodes. To increase the spectral efficiency and energy efficiency of the system, we propose a cooperative multihop communication protocol with spatial reuse, in which interference is treated as noise or can be canceled. The performance of a spatial-reused space-time-coded cooperative multihop network is analyzed over Rayleigh fading channels. In particular, the exact closed-form expression for the outage probability at the nth receiving node is derived when there are multiple interference sources over non-i.i.d. Rayleigh fading channels. Furthermore, the outage probability expressions are derived when nodes are equipped with more than one antenna. In addition, to reduce the effect of interference on multihop transmission, we propose a simple power control scheme that is only dependent on the statistical knowledge of channels. In the second approach for managing the interference, linear interference cancelation schemes are employed for both noncooperative and cooperative spatial-reused multihop transmissions. Finally, the analytic results were confirmed by simulations. Simulation results show that the spatial-reused multihop transmission outperforms the interference-free multihop transmission in terms of energy efficiency in low- and medium-signal-to-noise scenarios.

The use of large-scale antenna arrays can bring substantial improvements in energy and/or spectral efficiency to wireless systems due to the greatly improved spatial resolution and array gain. Recent works in the field of massive multiple-input multiple-output (MIMO) show that the user channels decorrelate when the number of antennas at the base stations (BSs) increases, thus strong signal gains are achievable with little interuser interference. Since these results rely on asymptotics, it is important to investigate whether the conventional system models are reasonable in this asymptotic regime. This paper considers a new system model that incorporates general transceiver hardware impairments at both the BSs (equipped with large antenna arrays) and the single-antenna user equipments (UEs). As opposed to the conventional case of ideal hardware, we show that hardware impairments create finite ceilings on the channel estimation accuracy and on the downlink/uplink capacity of each UE. Surprisingly, the capacity is mainly limited by the hardware at the UE, while the impact of impairments in the large-scale arrays vanishes asymptotically and interuser interference (in particular, pilot contamination) becomes negligible. Furthermore, we prove that the huge degrees of freedom offered by massive MIMO can be used to reduce the transmit power and/or to tolerate larger hardware impairments, which allows for the use of inexpensive and energy-efficient antenna elements.

In this work, we address the problem of power allocation for interfering transmitter-receiver pairs so that the probability that each queue length exceeds a specified threshold is fixed at a desired value. One application is satisfying QoS requirements in a dense cellular network. We deal with this problem using heavy traffic approximation techniques which lead to an asymptotic model of a (controlled) stochastic differential equation. The proposed power control strategy consists of allocating most of the power according to the states of the channel and a smaller fraction according to the queue lengths, for which we find a closed-form expression. We first consider a scenario where all channel realizations and queue lengths are known instantaneously to every transmitter. Then, the algorithm is extended to the case where only local SINR feedback is available and when queue length information is shared with delays among the transmitters. These models and results are also extended to the case where the transmitters are equipped with multiple antennas. Finally, the applicability in practical system settings are discussed and simulation results are provided to illustrate the performance of the proposed method.

The deployment of underlay small base stations (SBSs) is expected to significantly boost the spectrum efficiency and the coverage of next-generation cellular networks. However, the coexistence of SBSs underlaid to a macro-cellular network faces important challenges, notably in terms of spectrum sharing and interference management. In this paper, we propose a novel game-theoretic model that enables the SBSs to optimize their transmission rates by making decisions on the resource occupation jointly in the frequency and spatial domains. This procedure, known as interference draining, is performed among cooperative SBSs and allows to drastically reduce the interference experienced by both macro- and small cell users. At the macrocell side, we consider a modified water-filling policy for the power allocation that allows each macrocell user (MUE) to focus the transmissions on the degrees of freedom over which the MUE experiences the best channel and interference conditions. This approach not only represents an effective way to decrease the received interference at the MUEs but also grants the SBS tier additional transmission opportunities and allows for a more agile interference management. Simulation results show that the proposed approach yields significant gains at both macrocell and small cell tiers, in terms of average achievable rate per user, reaching up to 37%, relative to the non-cooperative case, for a network with 150 MUEs and 200 SBSs.

In this paper, a novel game-theoretic model of the complex interactions between network service providers (NSPs) and users in heterogeneous small-cell networks is investigated. In this game, the NSPs selfishly aim at maximizing their profit while, simultaneously, the users seek to optimize their chosen service's quality-price tradeoff. A Stackelberg formulation in which the NSPs act as leaders and the users as followers is proposed. The users' interactions are modeled as a general nonatomic game. The existence of a Wardrop equilibrium (WE) in the users' game is proven, and its expression as a solution of a fixed-point equation is provided (irrespective of the number of NSPs, services offered, pricing policies, and QoS functions). Moreover, a set of sufficient conditions that ensure the uniqueness of the WE is provided. Notably, the uniqueness of the equilibrium for the particular case of congestion games is shown. An algorithm approximating these equilibria is provided and its convergence to an ε-WE is proven. The existence of Nash equilibria for the leaders' game is shown and illustrated via numerical simulations.

In this paper, the problem of channel selection and power control is jointly analyzed in the context of multiple-channel clustered ad-hoc networks, i.e., decentralized networks in which radio devices are arranged into groups (clusters) and each cluster is managed by a central controller (CC). This problem is modeled by game in normal form in which the corresponding utility functions are designed for making some of the Nash equilibria (NE) to coincide with the solutions to a global network optimization problem. In order to ensure that the network operates in the equilibria that are globally optimal, a learning algorithm based on the paradigm of trial and error learning is proposed. These results are presented in the most general form and therefore, they can also be seen as a framework for designing both games and learning algorithms with which decentralized networks can operate at global optimal points using only their available local knowledge. The pertinence of the game design and the learning algorithm are highlighted using specific scenarios in decentralized clustered ad hoc networks. Numerical results confirm the relevance of using appropriate utility functions and trial and error learning for enhancing the performance of decentralized networks.

Recently, it was shown that transceiver hardware impairments have a detrimental impact on the performance of communication systems, especially for high-rate systems. The vast majority of technical contributions in the area of relaying assume ideal transceiver hardware. This paper quantifies the impact of transceiver hardware impairments in dual-hop Amplify-and-Forward (AF) relaying, both for fixed and variable gain relays. The outage probability (OP) in this practical scenario is a function of the instantaneous end-to-end signal-to-noise-and-distortion ratio (SNDR). This paper derives closed-form expressions for the exact and asymptotic OPs under Rayleigh fading, accounting for hardware impairments at both the transmitter and the relay. The performance loss is small at low spectral efficiency, but can otherwise be very substantial. In particular, it turns out that for high signal-to-noise ratio (SNR), the instantaneous end-to-end SNDR converges to a deterministic constant, called the SNDR ceiling, which is inversely proportional to the level of impairments. This stands in stark contrast to the ideal hardware case for which the end-to-end SNDR grows without bound in the high SNR regime.

In this paper, we study the performance of regularized channel inversion precoding in MISO broadcast channels with confidential messages under imperfect channel state information at the transmitter (CSIT). We obtain an approximation for the achievable secrecy sum-rate which is almost surely exact as the number of transmit antennas and the number of users grow to infinity in a fixed ratio. Simulations prove this anaylsis accurate even for finite-size systems. For FDD systems, we determine how the CSIT error must scale with the SNR, and we derive the number of feedback bits required to ensure a constant high-SNR rate gap to the case with perfect CSIT. For TDD systems, we study the optimum amount of channel training that maximizes the high-SNR secrecy sum-rate.

This paper presents a new synthesis method for Hybrid Filter Banks A/D converters (HFB-ADC). As most of the classical methods minimizes a SDR criterion, which is too restrictive, this method minimizes the SNR criterion. Unlike the few methods minimizing also the SNR, this one does not involve any optimization process.

Having multiple secondary users (SUs) can be exploited to achieve multiuser diversity and improve the throughput of the underlay secondary network. In the cognitive setting, satisfying the interference constraint is essential, and thus, a scheduling scheme is considered where some SUs are preselected based on the low interference power. From this subset, the SU that yields the highest throughput is selected for transmission. This scheduling scheme helps to lower the interference power while giving good throughput. For an independent but not identically distributed Nakagami-m fading channel, we obtain exact closed-form expressions of the capacity of this scheduling scheme. Furthermore, the scheduling time of SUs is characterized and closed-form expressions for the mean time after which a SU is selected for transmission are obtained. Numerical simulations are performed to corroborate the derived analytical results. Our results show that at low interference threshold, increasing transmit power of the SUs is not beneficial and results in reduced capacity. Furthermore, the channel idle time (i.e. time that no user is utilizing the channel) reduces with increasing the number of SUs.

We propose a time division duplex (TDD) based network architecture where a macrocell tier with a “massive” multiple-input multiple-output (MIMO) base station (BS) is overlaid with a dense tier of small cells (SCs). In this context, the TDD protocol and the resulting channel reciprocity have two compelling advantages. First, a large number of BS antennas can be deployed without incurring a prohibitive overhead for channel training. Second, the BS can estimate the interference covariance matrix from the SC tier which can be leveraged for downlink precoding. In particular, the BS designs its precoding vectors to transmit independent data streams to its users while being orthogonal to the subspace spanned by the strongest interference directions; thereby minimizing the sum interference imposed on the SCs. In other words, the BS “sacrifices” some of its antennas for interference cancellation while the TDD protocol allows for an implicit coordination across both tiers. Simulation results suggest that, given a sufficiently large number of BS antennas, the proposed scheme can significantly improve the sum-rate of the SC tier at the price of a small macro performance loss.

In this paper, a decentralized iterative algorithm, namely the optimal dynamic learning (ODL) algorithm, is analysed. The ability of this algorithm of achieving a Pareto optimal working point exploiting only a minimal amount of information is shown. The algorithm performance is analysed in a clustered ad hoc network, where radio devices are assumed to operate above a minimal signal to interference plus noise ratio (SINR) threshold while minimizing the global power consumption. Sufficient analytical conditions for ODL to converge to the desired working point are provided, moreover through numerical simulations the ability of the algorithm to configure an interference limited network is shown. The performances of ODL and of a Nash equilibrium reaching algorithm are numerically compared, and their performance as a function of available resources is studied. The gain of ODL is shown to be larger when the amount of available radio resources is scarce.

As future small cell base stations (SCBSs) are set to be multi-mode capable (i.e., transmitting on both licensed and unlicensed bands), a cost-effective integration of both technologies coping with peak data demands is crucial. Using tools from reinforcement learning, a distributed cross-system traffic steering framework is proposed whereby SCBSs leverage WiFi, to autonomously optimize their long-term performance over the licensed spectrum band, as a function of the traffic load and users' heterogeneous Quality of Service (QoS) requirements. The proposed traffic steering solution is validated in a Long-Term Evolution (LTE) simulator augmented with WiFi hotspots. Remarkably, it is shown that the proposed cross-system learning-based approach outperforms several benchmark algorithms and traffic steering policies, with gains reaching up to 200% when using a traffic-aware scheduler as compared to the classical proportional fair (PF) scheduler.

In this work we address the problem of channel probing in a multicarrier downlink wireless network where in order to collect CSI feedback from each user at a channel, a fraction of the available time for transmission is used. This means that the time left to transmit is getting smaller. We study the aspect of stability of such a system and we find a randomized algorithm which can guarantee an expansion of the stability region with respect to full probing and prior works. In addition, we investigate a special case of a probing scheme that does not require knowledge of the statistics of the channels and can still enlarge the stability region of the system. Simulations show the performance of the proposed scheme.

In this work we study the problem of channel state feedback and user scheduling in a single cell downlink wireless network employing multiple orthogonal parallel channels. The aspect of the system we are focusing on is stability. For user scheduling for stability as a performance measure, both the queue and channel states need to be known by the base station. However channel states can be known only via feedback from the receivers. In order to collect CQI feedback from each user at one channel, a fraction of the available time for transmission is used. This means that the time left to transmit is getting smaller. We present a joint feedback and scheduling algorithm which can guarantee an expansion of the stability region with respect to prior works. We also provide expressions regarding the distribution of the time needed to be devoted for feedback at each channel in some special cases. The proposed algorithm does not need knowledge of the statistics of the channels and traffic patterns. Simulations illustrate the operation of the proposed scheme.

We consider a decode-and-forward based wireless multihop network with a single source node, a single destination node, and N intermediate nodes. To increase the spectral efficiency and energy efficiency of the system, we propose a cooperative multihop communication with spatial reuse, in which interference is treated as noise. The performance of spatial-reused space-time coded cooperative multihop network is analyzed over Rayleigh fading channels. More specifically, the exact closed-form expression for the outage probability at the nth receiving node is derived when there are multiple interferences over non-i.i.d. Rayleigh fading channels. In addition, we propose a simple power control scheme which is only dependent on the statistical knowledge of channels. Finally, the analytic results were confirmed by simulations. It is shown by simulations that the spatial-reused multihop transmission outperforms the interference-free multihop transmission in terms of energy efficiency in low and medium SNR scenarios.

This paper considers pilot-based channel estimation in large-scale multiple-input multiple-output (MIMO) communication systems, also known as “massive MIMO”. Unlike previous works on this topic, which mainly considered the impact of inter-cell disturbance due to pilot reuse (so-called pilot contamination), we are concerned with the computational complexity. The conventional minimum mean square error (MMSE) and minimum variance unbiased (MVU) channel estimators rely on inverting covariance matrices, which has cubic complexity in the multiplication of number of antennas at each side. Since this is extremely expensive when there are hundreds of antennas, we propose to approximate the inversion by an L-order matrix polynomial. A set of low-complexity Bayesian channel estimators, coined Polynomial ExpAnsion CHannel (PEACH) estimators, are introduced. The coefficients of the polynomials are optimized to yield small mean square error (MSE). We show numerically that near-optimal performance is achieved with low polynomial orders. In practice, the order L can be selected to balance between complexity and MSE. Interestingly, pilot contamination is beneficial to the PEACH estimators in the sense that smaller L can be used to achieve near-optimal MSEs.

In this contribution we propose a method to increase the energy efficiency of orthogonal frequency division multiplexing (OFDM)-based femtocells. This is accomplished with no impact to the current power consumption, radio frequency (RF) circuitry, link adaptation strategies, bandwidth and transmit power. The proposed technique recycles redundant resources of OFDM transmissions (e.g., guard bands and cyclic prefixes), introduced to combat frequency selectivity. We borrow the underlying idea from a technique called cognitive interference alignment (CIA). Interestingly, our novel approach does not suffer from the same issues inherent to CIA, such as synchronization at the primary receiver and channel knowledge related complications. Nevertheless, it introduces a new issue related to the interference from the OFDM signal, which prompted the adoption of an adequate linear receiver at the femtocell user equipment. Numerical findings demonstrate that spectral efficiency gains are achieved, improving the energy efficiency of the femtocell by up to 20% for the simulated scenario.

Although small cell networks are environmentally friendly and can potentially improve the coverage and capacity of cellular layers, it is imperative to control the interference in such networks before overlaying them in a macrocell network on a large-scale basis. In recent work, we developed the joint admission and power control algorithm for two-tier small cell networks in which the number of small cell users that can be admitted at their quality-of-service (QoS) constraints is maximized without violating the macrocell users' QoS constraints. The QoS metric adopted is outage probability. In this paper, we investigate the distributed implementation of the joint admission and power control problem where the small cells can determine jointly their admissibility and transmit powers autonomously.

In this paper, we study the performance of regularized channel inversion (RCI) precoding in large MISO broadcast channels with confidential messages (BCC). We obtain a deterministic approximation for the achievable secrecy sum-rate which is almost surely exact as the number of transmit antennas M and the number of users K grow to infinity in a fixed ratio β=K/M. We derive the optimal regularization parameter ξ and the optimal network load β that maximize the per-antenna secrecy sum-rate. We then propose a linear precoder based on RCI and power reduction (RCI-PR) that significantly increases the high-SNR secrecy sum-rate for 1<;β<;2. Our proposed precoder achieves a per-user secrecy rate which has the same high-SNR scaling factor as both the following upper bounds: (i) the rate of the optimum RCI precoder without secrecy requirements, and (ii) the secrecy capacity of a single-user system without interference. Furthermore, we obtain a deterministic approximation for the secrecy sum-rate achievable by RCI precoding in the presence of channel state information (CSI) error. We also analyze the performance of our proposed RCI-PR precoder with CSI error, and we determine how the error must scale with the SNR in order to maintain a given rate gap to the case with perfect CSI.

We consider the uplink (UL) and downlink (DL) of non-cooperative multi-cellular time-division duplexing (TDD) systems, assuming that the number N of antennas per base station (BS) and the number K of user terminals (UTs) per cell are large. Our system model accounts for channel estimation, pilot contamination, and an arbitrary path loss and antenna correlation for each link. We derive approximations of achievable rates with several linear precoders and detectors which are proven to be asymptotically tight, but accurate for realistic system dimensions, as shown by simulations. It is known from previous work assuming uncorrelated channels, that as N→∞ while K is fixed, the system performance is limited by pilot contamination, the simplest precoders/detectors, i.e., eigenbeamforming (BF) and matched filter (MF), are optimal, and the transmit power can be made arbitrarily small. We analyze to which extent these conclusions hold in the more realistic setting where N is not extremely large compared to K. In particular, we derive how many antennas per UT are needed to achieve η% of the ultimate performance limit with infinitely many antennas and how many more antennas are needed with MF and BF to achieve the performance of minimum mean-square error (MMSE) detection and regularized zero-forcing (RZF), respectively.

In this work, the problem of cross-tier interference in a two-tiered (macro-cell and cognitive small-cells) network, under the complete spectrum sharing paradigm, is studied. A new orthogonal precoder transmit scheme for the small base stations, called multi-user Vandermonde-subspace frequency division multiplexing (MU-VFDM), is proposed. MU-VFDM allows several cognitive small base stations to coexist with legacy macro-cell receivers, by nulling the small- to macro-cell cross-tier interference, without any cooperation between the two tiers. This cleverly designed cascaded precoder structure, not only cancels the cross-tier interference, but avoids the co-tier interference for the small-cell network. The achievable sum-rate of the small-cell network, satisfying the interference cancelation requirements, is evaluated for perfect and imperfect channel state information at the transmitter. Simulation results for the cascaded MU-VFDM precoder show a comparable performance to that of state-of-the-art dirty paper coding technique, for the case of a dense cellular layout. Finally, a comparison between MU-VFDM and a standard complete spectrum separation strategy is proposed. Promising gains in terms of achievable sum-rate are shown for the two-tiered network w.r.t. the traditional bandwidth management approach.

Physical transceivers have hardware impairments that create distortions which degrade the performance of communication systems. The vast majority of technical contributions in the area of relaying neglect hardware impairments and, thus, assume ideal hardware. Such approximations make sense in low-rate systems, but can lead to very misleading results when analyzing future high-rate systems. This paper quantifies the impact of hardware impairments on dual-hop relaying, for both amplify-and-forward and decode-and-forward protocols. The outage probability (OP) in these practical scenarios is a function of the effective end-to-end signal-to-noise-and-distortion ratio (SNDR). This paper derives new closed-form expressions for the exact and asymptotic OPs, accounting for hardware impairments at the source, relay, and destination. A similar analysis for the ergodic capacity is also pursued, resulting in new upper bounds. We assume that both hops are subject to independent but non-identically distributed Nakagami-m fading. This paper validates that the performance loss is small at low rates, but otherwise can be very substantial. In particular, it is proved that for high signal-to-noise ratio (SNR), the end-to-end SNDR converges to a deterministic constant, coined the SNDR ceiling, which is inversely proportional to the level of impairments. This stands in contrast to the ideal hardware case in which the end-to-end SNDR grows without bound in the high-SNR regime. Finally, we provide fundamental design guidelines for selecting hardware that satisfies the requirements of a practical relaying system.

Vandermonde-subspace frequency division multiplexing (VFDM) is an overlay spectrum sharing technique for cognitive radio. VFDM makes use of a precoder based on a Vandermonde structure to transmit information over a secondary system, while keeping an orthogonal frequency division multiplexing (OFDM)-based primary system interference-free. To do so, VFDM exploits frequency selectivity and the use of cyclic prefixes by the primary system. Herein, a global view of VFDM is presented, including also practical aspects such as linear receivers and the impact of channel estimation. We show that VFDM provides a spectral efficiency increase of up to 1 bps/Hz over cognitive radio systems based on unused band detection. We also present some key design parameters for its future implementation and a feasible channel estimation protocol. Finally we show that, even when some of the theoretical assumptions are relaxed, VFDM provides non-negligible rates while protecting the primary system.

In this paper, the problem of fast point-to-point multiple-input-multiple-output channel mutual information estimation is addressed, in the situation where the receiver undergoes unknown colored interference, whereas the channel with the transmitter is perfectly known. The considered scenario assumes that the estimation is based on a few channel use observations during a short sensing period. Using large dimensional random matrix theory, an estimator referred to as G-estimator is derived. This estimator is proved to be consistent as the number of antennas and observations grow large and its asymptotic performance is analyzed. In particular, the G-estimator satisfies a central limit theorem with asymptotic Gaussian fluctuations. Simulations are provided which strongly support the theoretical results, even for small system dimensions.

This paper provides a central limit theorem for a consistent estimator of population eigenvalues with large multiplicities based on sample covariance matrices. The focus is on limited sample size situations, whereby the number of available observations is comparable in magnitude to the observation dimension. An exact expression as well as an empirical, asymptotically accurate, approximation of the limiting variance is derived. Simulations are performed that corroborate the theoretical claims.

The use of small cells serviced by low-power base stations such as femtocells is envisioned to improve the spectrum efficiency and the coverage of next-generation mobile wireless networks. However, one of the major challenges in femtocell deployments is managing interference. In this paper, we propose a novel cooperative solution that enables femtocells to improve their achievable data rates, by suppressing intratier interference using the concept of interference alignment (IA). We model this cooperative behavior among the femtocells as a coalitional game in partition form and we propose a distributed algorithm for the coalition formation. The proposed algorithm allows the femtocell base stations to independently decide on whether to cooperate or not, while maximizing a utility function capturing both the gains and costs from cooperation. Using the proposed algorithm, the femtocells can self-organize into a stable network partition composed of disjoint femtocell coalitions and which constitutes the recursive core of the game. Inside every coalition, cooperative femtocells use advanced IA techniques to improve their downlink transmission rate. Simulation results show that the proposed coalition formation algorithm yields significant gains, in terms of average payoff per femtocell, reaching up to 30 percent relative to the noncooperative case for a network of N=300 femtocells.

In this paper, we study opportunistic relay selection in cooperative networks with secrecy constraints, where a number of eavesdropper nodes may overhear the source message. To deal with this problem, we consider three opportunistic relay selection schemes. The first scheme tries to reduce the overheard information at the eavesdroppers by choosing the relay having the lowest instantaneous signal-to-noise ratio (SNR) to them. The second scheme is conventional selection relaying that seeks the relay having the highest SNR to the destination. In the third scheme, we consider the ratio between the SNR of a relay and the maximum among the corresponding SNRs to the eavesdroppers, and then select the optimal one to forward the signal to the destination. The system performance in terms of probability of non-zero achievable secrecy rate, secrecy outage probability and achievable secrecy rate of the three schemes are analyzed and confirmed by Monte Carlo simulations.

We consider coordination in a multi-user multiple input single output cellular system. In contrast with existing base station cooperation methods that rely on sharing CSI with or without user data to manage interference, we propose to share user data only. We consider a system where blind interference alignment (BIA) is applied to serve multiple users in each cell. We apply interference coordination through data sharing to mitigate other-cell interference at the cell-edge users. While BIA mitigates intra-cell interference in MU-MISO systems, it does not address the problem of inter-cell interference. We apply interference coordination through data sharing to mitigate inter-cell interference at the cell-edge users. We propose a new cooperative BIA scheme that takes into account the users whose data is being shared between adjacent base stations. We derive the achievable sum rate with interference mitigation and we compare it to achievable rates with the original BIA strategy. Numerical results show that the achievable sum rate of the cell-edge users with data sharing decreases with increasing number of served users in each cell and increasing number of antennas at the base stations.

This article proposes a large system approximation of the ergodic sum-rate (SR) for cellular multi-user multiple-input multiple-output uplink systems. The considered system has various degrees of freedom, such as clusters of base stations (BSs) performing cooperative multi-point processing, randomly distributed user terminals (UTs), and supports arbitrarily configurable antenna gain patterns at the BSs. The approximation is provably tight in the limiting case of a large number of single antenna UTs and antennas at the BSs. Simulation results suggest that the asymptotic analysis is accurate for small system dimensions. Our deterministic SR approximation result is applied to numerically study and optimize the effects of antenna tilting in an exemplary sectorized 3D small cell network topology. Significant SR gains are observed with optimal tilt angles and we provide new insights on the optimal parameterization of cellular networks, along with a discussion of several non-trivial effects.

In the context of multidimensional signals, the linear Wiener receiver is frequently encountered in wireless communication and in array processing; it is in fact the linear receiver that achieves the lowest level of interference. In this contribution, we focus on the study of the associated Signal-to-interference plus noise ratio (SINR) at its output in the context of Ricean multiple-input multiple-output (MIMO) channels. The case of Ricean channels, which induces non-centered random variables, can be encountered in several practical environments and has not been studied so far, as it raises substantial technical issues. With the help of large random matrix theory, which has shown to be fruitful to successfully address several problems in wireless communications, we study the behaviour of the SINR, together with its fluctuations via a central limit theorem. As realistic models also involve non-Gaussian random variables, we relax the Gaussian assumption. This results in an extra term involving the fourth cumulant in the expression of the variance.