Accurate radio environment map (REM) construction proves critical for efficient wireless spectrum management. Although conventional 2D REMs have demonstrated effectiveness in wireless network optimization, they inherently overlook vertical signal strength variations, which are vital for UAV operations, particularly in urban landscapes with skyscrapers or diverse terrain features. Current estimation approaches, including ground-based crowdsourcing, random sampling, and predetermined trajectory measurements, show limited capability in generating high-fidelity 3D REMs. This study proposes a joint optimization framework for UAV-enabled adaptive 3D radio mapping, integrating 3D REM construction with adaptive aerial sampling. At the heart of the construction module, a dual-branch encoder-decoder architecture fuses multi-scale features from sparse aerial measurements with building structural data, explicitly modeling obstruction effects through offline pre-training and online refinement to enhance generalization. For adaptive sampling, a diffusion-based trajectory planner dynamically optimizes UAV measurement paths by integrating environmental priors (e.g., building layouts), effectively overcoming the sparse-reward limitations inherent in reinforcement learning methods. Experimental validation demonstrates significant performance improvements across all evaluation metrics. Compared to 2D per-layer estimation methods, our 3D estimator achieves 49% superior structural similarity (SSIM) in construction accuracy, while the feature fusion module yields a 37% reduction in mean squared error (MSE). The diffusion-based planner outperforms reinforcement learning approaches by achieving 45% lower MSE and 18% higher SSIM in resultant map quality after 5,000 step iterations.

The deployment of high-frequency carriers in 5G networks and beyond introduces significant challenges, particularly due to increased path loss and physical limitations of user equipment (UE). In particular, these challenges lead to uplink/downlink coverage imbalances, critically affecting applications that rely on robust uplink capacity such as autonomous driving, telemedicine, and live streaming. While downlink-uplink decoupling (DUDe) and supplementary uplink (SUL) have emerged as promising solutions for uplink enhancement, their flexible cell association mechanisms disrupt conventional handover strategies by introducing multidimensional, decoupled decision-making. To address this, we propose a heterogeneous network architecture that integrates DUDe into SUL, supporting non-co-located deployment of supplementary uplink carriers and enabling dual decoupling at both the base station and carrier levels. Building on this architecture, we introduce Counterfactual Multi-agent fuzzy neural network (COMA-FNN), a multi-agent reinforcement learning (MARL) algorithm based on centralized training with decentralized execution (CTDE). COMA-FNN incorporates fuzzy neural networks to model the complex, nonlinear relationships among multiple communication attributes, improving the precision and adaptability of handover decisions. The algorithm also employs a centralized critic with counterfactual baselines to effectively resolve credit assignment issues among competing agents. To accommodate varying service requirements, COMA-FNN incorporates service-specific reward functions aligned with four 3GPP-standardized service types. These rewards are weighted using the Analytic Hierarchy Process (AHP), enabling the algorithm to support different QoS policies. Simulation results demonstrate that COMA-FNN significantly improves handover efficiency, reduces latency, and enhances throughput, making it a robust solution for intelligent mobility management in decoupled uplink/downlink architectures.

Uplink sensing in cell-free integrated sensing and communication (CF-ISAC) systems provides a promising solution by reusing massive communication signals. This approach offers low system overhead and enables wide-area coverage through densely deployed users and distributed access points (APs). However, due to the tight coupling between communication and sensing, accurate extraction of uplink sensing parameters becomes a critical bottleneck: sensing parameter extraction relies on precise demodulation of uplink communication signals, while high-quality channel estimation for data demodulation, in turn, requires accurate sensing results. To address this challenge, we propose an iterative communication-sensing optimization framework under uplink CF-ISAC architecture. This framework establishes dynamic information feedback among the three core modules of data detection, channel reconstruction and target sensing, achieving the collaborative improvement of communication-sensing performance. Specifically, the target sensing module integrates pilot-based sensing and data signal-enhanced sensing to extract target parameters. The channel reconstruction module maps the sensing results to channel state information (CSI). The data detection module recovers data symbols using the reconstructed CSI and feeds back both demodulated symbols and residual errors to the sensing module, enabling iterative correction of target parameter estimation. The simulation results show that the proposed iterative framework achieves simultaneous suppression in communication bit error rate (BER) and enhancement in sensing accuracy through several iterations, thereby effectively breaking through the traditional performance limits.

Semi-grant-free (SGF) access is regarded as a promising technology for next-generation networks, effectively easing the tension between massive access caused by user growth and limited communication resources. However, how to achieve efficient resource utilization through effective pairing of grant-based (GB) users and grant-free (GF) users in SGF access mechanism while ensuring system reliability is a critical challenge for enhancing overall network performance. This paper proposes a scalable SGF access scheme suitable for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Unlike traditional SGF schemes under centralized cellular systems, this scheme first groups access points (APs) into edge distributed unit (EDU)-centric clusters, then fully leverages the macro-diversity gain and signal power sparsity in the cell-free architecture, and employs a genetic algorithm (GA) to achieve efficient pairing between GB users and GF users. This approach ensures the quality of service (QoS) for GB users while sharing their resources with GF users, optimizing the efficiency of communication resource utilization. Subsequently, closed-form expressions for the signal-to-interference-plus-noise ratio (SINR) and outage probability of GB users and GF users under perfect and imperfect successive interference cancellation (SIC) are derived to evaluate the system reliability. Finally, simulation results demonstrate that the proposed scheme significantly outperforms the other scheduling schemes in terms of outage probability and resource utilization performance, particularly under massive access.

In this paper, we investigate a covert communication system based on a hybrid active and passive reconfigurable intelligent surface (HAPRIS), where each RIS element can be dynamically configured to operate in either active or passive mode. By dynamically configuring the operational mode of each element, the system can effectively harness the high-gain benefits of the active RIS while simultaneously preserving the low power consumption and enhanced covertness properties offered by the passive RIS. Then, we formulate an energy efficiency maximization problem by jointly optimizing the active/passive mode selection vector, transmit beamforming vector and reflect coefficient matrix subject to the power, covertness and hardware constraints. To evaluate the detection performance of the warden, we further derive a tractable expression of the covertness constraint for the multi-antenna warden. To solve the mixed-integer non-convex problem, we propose a penalty dual decomposition (PDD) scheme utilizing the block coordinate descent method to decompose the original problem into several sub-problems, which are subsequently solved via the Lagrangian multiplier method and the interior-point method. In addition, we investigate a comparative scenario considering the position error of warden. Specifically, we first derive an expression for the estimation error of channel state information (CSI) based on the position error. We then propose a successive convex approximation method to obtain an upper bound on the CSI error, enabling us to formulate a worst-case covertness constraint. Finally, we extend the proposed PDD scheme to such a scenario with the CSI error. Simulation results demonstrate the effectiveness of the proposed schemes.

Phase synchronization among distributed transmission reception points (TRPs) is a prerequisite for enabling coherent joint transmission and high-precision sensing in millimeter wave (mmWave) cell-free massive multiple-input and multiple-output (MIMO) systems. This paper proposes a bidirectional calibration scheme and a calibration coefficient estimation method for phase synchronization, and presents a calibration coefficient phase tracking method using unilateral uplink/downlink channel state information (CSI). Furthermore, this paper introduces the use of reciprocity calibration to eliminate non-ideal factors in sensing and leverages sensing results to achieve calibration coefficient phase tracking in dynamic scenarios, thus enabling bidirectional empowerment of both communication and sensing. Simulation results demonstrate that the proposed method can effectively implement reciprocal calibration with lower overhead, enabling coherent collaborative transmission, and resolving non-ideal factors to acquire lower sensing error in sensing applications. Experimental results show that, in the mmWave band, over-the-air (OTA) bidirectional calibration enables coherent collaborative transmission for both collaborative TRPs and collaborative user equipments (UEs), achieving beamforming gain and long-time coherent sensing capabilities.

Optimization of user association in a densely deployed cellular network is usually challenging and even more complicated due to the dynamic nature of user mobility and fluctuation in user counts. While deep reinforcement learning (DRL) emerges as a promising solution, its application in practice is hindered by high trial-and-error costs in real world and unsatisfactory physical network performance during training. Also, existing DRL-based user association methods are typically applicable to scenarios with a fixed number of users due to convergence and compatibility challenges. To address these limitations, we introduce a large vision model (LVM)-enhanced digital twin (DT) for wireless networks and propose a parallel DT-driven DRL method for user association and load balancing in networks with dynamic user counts, distribution, and mobility patterns. To construct this LVM-enhanced DT for DRL training, we develop a zero-shot generative user mobility model, named Map2Traj, based on the diffusion model. Map2Traj estimates user trajectory patterns and spatial distributions solely from street maps. DRL models undergo training in the DT environment, avoiding direct interactions with physical networks. To enhance the generalization ability of DRL models for dynamic scenarios, a parallel DT framework is further established to alleviate strong correlation and non-stationarity in single-environment training and improve data efficiency. Numerical results show that the developed LVM-enhanced DT achieves closely comparable training efficacy to the real environment, and the proposed parallel DT framework even outperforms the single real-world environment in DRL training with nearly 20% gain in terms of cell-edge user performance.

Phase noise (PN) significantly degrades the performance of millimeter-wave (mmWave) wireless communication systems. To mitigate this challenge, we propose the amplitude-phase-sphere block modulation (APSBM) scheme, which integrates amplitude-shift keying, phase-shift keying, and sphere modulation. Exploiting the Wiener random walk characteristic of PN, APSBM carries information on the relative amplitude and phase between consecutive symbols, thereby counteracting the impact of common PN. Immunity to residual PN in the scheme is further enhanced through a three-dimensional spherical constellation and modulation configurations. We also propose flexible detection strategies (joint, parallel, and cascaded) at the receiver to accommodate diverse application scenarios, minimizing the influence of PN while maintaining a low computational burden. Simulation and experimental results demonstrate that in the presence of PN, APSBM outperforms quadrature amplitude modulation (QAM), circular QAM, and spiral modulation, achieving a lower peak-to-average power ratio, a reduced bit error rate, and a higher achievable information rate. The proposed modulation and demodulation scheme is particularly effective under high PN conditions, offering a robust solution for PN mitigation in mmWave wireless communication systems.

In OFDM wireless communications, power amplifier nonlinearity generates significant out-of-band (OOB) emissions and in-band distortion, conventionally requiring large power back-offs to address both issues simultaneously. We propose a forward distortion compensation (FDC) scheme using amplitude-phase-frequency block modulation (APFBM) that decouples OOB emission suppression from in-band distortion management, enabling reliable transmission even under severe power amplifier nonlinearity. At the transmitter, in-band distortion is intentionally introduced to facilitate aggressive OOB emission suppression, while APFBM manages bit mapping and imposes a power-sum constraint on information-carrying subcarriers. At the receiver, this power-sum constraint guides the compensation for in-band distortion, ensuring reliable information recovery. This compensation strategy also relaxes digital pre-distortion (DPD) accuracy requirements, permitting a simpler Sigmoid-based static DPD focused solely on OOB suppression and eliminating feedback circuits. The experimental results show a relative gain of 4.4 dB with a power back-off of 5.2 dB compared to nonlinear transmission based on quadrature amplitude modulation with equivalent processing at 4.9 GHz, while a relative gain of 2.6 dB is achieved at 26.5 GHz. This approach achieves superior energy efficiency and spectral efficiency trade-offs, enhancing coverage and performance in wireless communications.

Cell-free massive multi-input-multi-output (CF-mMIMO) provides significant improvement owing to the distributed architecture. However, it suffers from the constraints including information constraints, and computation resources constraints. In this paper, we propose 4 ranks of available information in CF-mMIMO and aim to find a distributed precoding exploiting randomly accessible side information, which is one-step without iterations and robust against the unreliable fronthaul between distributed central processing units. Quadratic team learning (QTL) is devised which is derived from team theory to handle the distributed underdetermined quadratic programming. The extensive 1440 experiments validate the superiority of QTL and we believe QTL is a definitely excellent choice for CF-mMIMO distributed precoding. To the best of our knowledge, this is the first work utilizing team theory to help the design of artificial intelligence architecture for wireless communications. To prompt the development of QTL, we have open-sourced the implementation code on https://github.com/hzy238221seu/QTL4CF-Precoding.git.

Cell-free massive MIMO (CF-mMIMO) represents the pinnacle of distributed antenna systems, offering superior service to all users. However, the distributed nature of access points leads to fragmented information processing, limiting performance in practical deployments. Additionally, existing studies often overlook the impact of limited fronthaul capacity, which introduces noise and degrades the reliability of shared information. In this work, we implement a practical CF-mMIMO prototype under fifth generation new radio standards and propose a diffusion-based uplink scheme that outperforms conventional distributed cell-free systems without cooperation. Our approach adapts to arbitrary fronthaul topologies by leveraging the law of large numbers. We further analyze the linear effects of fronthaul noise and the correlation of uploaded data, demonstrating that the diffusion uplink excels in Rician fading environments while maintaining robust performance in Rayleigh fading. To the best of our knowledge, this is the first work to employ a diffusion model for mitigating fronthaul non-idealities, enabling distributed cooperative uplink in a real-world CF-mMIMO system.

With the evolution from 5G to 6G, ultra-reliable low-latency communication (URLLC) faces increasingly stringent performance requirements. Lower latency constraints demand shorter channel codeword length, which can severely degrade decoding performance. The massive multiple-input multiple-output (MIMO) system is considered a crucial technology to address this challenge due to its abundant spatial degrees of freedom (DoF). While polar codes are theoretically capacity-achieving in the limit of infinite codeword length, their practical applicability is limited by the latency penalty associated with the long codewords. In this paper, we establish a unified theoretical framework and propose a novel spatiotemporal two-dimensional (2-D) polar coding scheme for massive MIMO systems employing minimum mean square error (MMSE) receivers. The polar transform is jointly applied over both spatial and temporal dimensions to fully exploit the large spatial DoF. By leveraging the near-deterministic signal-to-interference-plus-noise ratio (SINR) property of MMSE detection, the spatial domain is modeled as a set of parallel Gaussian sub-channels. Within this framework, we theoretically analyze the 2-D polarization behavior based on the Gaussian approximation method and show that the proposed scheme asymptotically retains its capacity-achieving property, even under finite blocklength constraints and large spatial DoF. Simulation results further demonstrate that, compared to traditional time-domain polar codes, the proposed 2-D scheme can significantly reduce latency while guaranteeing reliability, or alternatively improve reliability under the same latency constraint—offering a capacity-achieving and latency-efficient channel coding solution for massive MIMO systems in future 6G URLLC scenarios.

The soft-output successive cancellation list (SOSCL) decoder provides a methodology for estimating the aposteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder of polar codes. However, the sequential decoding nature of SCL introduces high decoding latency to SOSCL. In this paper, we incorporate node-based fast decoding into the SO-SCL framework. After addressing the challenge of soft output extraction in special node decoding, we proposed the soft-output fast SCL (SO-FSCL) decoding algorithm, along with its log-domain implementation and hardware-friendly version. The proposed SO-FSCL decoder can be regarded as an addon extension to FSCL decoder, enabling us to autonomously choose whether to output only hard decisions like FSCL or to provide additional soft outputs. Latency and complexity analyses demonstrate that SO-FSCL can significantly reduce, for example, decoding time steps by 81.8% (with unlimited resources), the number of additions by 41.3%, and the number of comparisons by 46.4%. Meanwhile, simulation results indicate that SO-FSCL delivers almost the same soft-output performance as SO-SCL, outperforming other soft-output polar decoders, especially in scenarios involving iterative decoding.

Operating power amplifiers (PAs) at lower input back-off (IBO) levels is an effective way to improve PA efficiency, but often introduces severe nonlinear distortion that degrades transmission performance. Amplitude-phase-time block modulation (APTBM) has recently emerged as an effective solution to this problem. The intrinsic amplitude and phase constraints of each APTBM block can be leveraged to mitigate PA-induced nonlinear distortion via constraint-guided signal reconstruction. However, existing reconstruction methods apply these constraints only heuristically and statistically, limiting the achievable IBO reduction and PA efficiency improvement. This paper addresses this limitation by decomposing the nonlinear distortion into dominant and residual components, and accordingly develops a novel two-stage signal reconstruction algorithm consisting of coarse and fine reconstruction stages. The coarse reconstruction stage eliminates the dominant distortion by jointly exploiting the APTBM block structure and PA nonlinear characteristics. Subsequently, the fine reconstruction stage minimizes the residual distortion by casting it as a nonconvex optimization problem subject to explicit APTBM constraints, for which a closed-form solution is derived. The proposed algorithm is validated through comprehensive numerical simulations and testbed experiments. Results show that, without compromising transmission quality, the proposed algorithm enables an additional IBO reduction of approximately 5 dB in simulations and 2 dB in experiments over baseline methods, yielding relative PA efficiency improvements of 77.8% and 30.9%, respectively.

Cell-free radio access networks (CF-RANs) with network-assisted free-duplex (NA-FD) architecture unify flexible-duplex, hybrid-duplex, and full-duplex operations, but the coupled uplink/downlink interference and centralized processing impose substantial computational and signaling burdens. To address these challenges, this paper studies distributed transceiver design and access point (AP) duplex mode selection under edge distributed unit (EDU)-level information constraints and per- AP power limitations. We develop a partial distributed block coordinate descent (PDBCD) algorithm that decomposes the original sum-rate maximization into three tractable subproblems and iteratively approximates MMSE performance. The proposed design fully leverages EDU computing resources, reducing the computation load at the cloud computing unit (CCU), and significantly lowering fronthaul signaling overhead through an adaptive inter-EDU information sharing mechanism. Furthermore, by integrating a low-complexity greedy search for duplex mode assignment, the algorithm effectively mitigates cross-link interference (CLI) in NA-FD systems. Simulation results show that the proposed scheme improves spectral efficiency compared with conventional duplex mode, and centralized/distributed MMSE baselines, while maintaining strong scalability under practical deployment constraints.

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is regarded as one of the promising technologies for next-generation wireless networks. However, due to its distributed architecture, geographically separated access points (APs) jointly serve a large number of user-equipments (UEs), and there are inevitably discrepancies in the arrival time of transmitted signals. In this paper, we investigate millimeter-wave (mmWave) scalable CF-mMIMO orthogonal frequency division multiplexing (OFDM) systems with asynchronous reception in a wide area coverage scenario, where asynchronous timing offsets may exceed far beyond the cyclic prefix (CP) duration. To address the issue, we propose a novel per-beam timing advance (PBTA) hybrid precoding architecture and derive closed-form expressions of the spectral efficiency (SE) for downlink asynchronous transmission. Both scalable centralized and distributed implementations are taken into account. Furthermore, we formulate the sum rate maximization problem and develop two low-complexity joint beam selection and UE association (BSUA) algorithms considering the impact of asynchronous timing offset. Simulation results demonstrate that asynchronous interference can severely degrade performance in wide-area scenarios, and our proposed PBTA scheme exploits beam-domain synchronization to align signal arrivals, effectively suppressing asynchronous interference and delivering notable performance gains. Additionally, the proposed BSUA algorithms achieve superior SE performance with low computational complexity.

The rapid evolution of the forthcoming sixth-generation (6G) wireless network necessitates seamless integration of artificial intelligence (AI) with wireless communications to support emerging intelligent applications that demand both efficient communication and robust learning performance. This dual requirement calls for a unified framework of integrated learning and communication (ILAC), where AI enhances communication through intelligent signal processing and resource management, while wireless networks facilitate AI model deployment by enabling efficient and reliable data exchanges. However, achieving this integration presents significant challenges in practice. Communication constraints, such as limited bandwidth and fluctuating channels, hinder learning accuracy and convergence. Simultaneously, AI-driven learning dynamics, including model updates and task-driven inference, introduce excessive burdens on communication, necessitating flexible context-aware transmission strategies. This paper provides a comprehensive overview of ILAC design and optimization strategies. We establish corresponding foundational principles, covering system architectures and presenting a unified optimization formulation that closely links learning performance with communication efficiency. We then review recent advancements in ILAC from the strategic perspectives of model and data distributions, computational complexity, and communication overhead. Despite considerable progress, existing ILAC approaches still suffer from high communication overhead, unstable convergence, and scalability challenges. To address these issues, we propose an enhanced ILAC framework with large AI models leveraging hyperdimensional computing (HDC). In particular, utilizing large AI models improves generalization capabilities under dynamic task and network conditions, while HDC provides lightweight high-dimensional representations that reduce both communication and learning costs. Finally, we present a case study on a cost-to-performance optimization problem, where task assignments, model size selection, bandwidth allocation, and transmission power control are jointly optimized, aiming at improving both communication efficiency and inference accuracy with reduced computational cost. Leveraging the Dinkelbach and alternating optimization algorithms, we offer a practical and effective solution to achieve an optimal balance between learning performance and communication constraints.

The vision of 6G communication demands autonomous and resilient networking in environments without fixed infrastructure. Yet most multi-agent reinforcement learning (MARL) approaches focus on isolated stages—exploration, relay formation, or access—under static deployments and centralized control, limiting adaptability. We propose the AI Agent Access (A3) Network, a unified, embodied intelligence-driven framework that transforms multi-agent networking into a dynamic, decentralized, and end-to-end system. Unlike prior schemes, the A3Network integrates exploration, target user access, and backhaul maintenance within a single learning process, while supporting on-demand agent addition during runtime. Its decentralized policies ensure that even a single agent can operate independently with limited observations, while coordinated agents achieve scalable, communication-optimized coverage. By embedding link-level communication metrics into actor–critic learning, the A3Network couples topology formation with robust decision-making. Numerical simulations demonstrate that the A3Network not only balances exploration and communication efficiency but also delivers system-level adaptability absent in existing MARL frameworks, offering a new paradigm for 6G multi-agent networks.

In the 6G era, ultra-reliable and low-latency communications (URLLC) has become a key enabler for mission-critical applications. However, existing approaches predominantly rely on closed-loop communications with hybrid automatic repeat request (HARQ) retransmissions, which struggle to meet the stringent microsecond-level end-to-end (E2E) latency requirements of 6G, particularly during the initial access phase. To bridge this gap, we propose a novel open-loop communication (OLC) framework, integrating grant-free access with frequency diversity under a contention-based transmission paradigm. The framework incorporates a fine-grained resource allocation strategy and detailed mechanisms for collision detection, uplink channel estimation, and signal detection. To evaluate the performance of OLC, we conduct a unified reliability and latency analysis, leveraging the channel hardening property of massive MIMO systems. We derive closed-form approximations for packet loss probability, incorporating factors such as access collisions, channel impairments, and finite blocklength effects. Furthermore, to minimize uplink bandwidth while satisfying reliability and latency constraints, we develop an optimal system parameter configuration method, which is seamlessly integrated with power control in the collision detection process. Simulation results validate the theoretical reliability expressions and demonstrate the superior performance of the proposed OLC framework over conventional closed-loop schemes.

We consider a cell-free massive multiple-input and multiple-output (CF-mMIMO) system, where we derive a deterministic equivalent (DE)-form of the ergodic sum spectral efficiency (SE) based on local partial regularized zero-forcing (LP-RZF) precoding with statistical channel state information (S-CSI) by leveraging large-dimensional random matrix theory. Thanks to this derivation, the previously challenging issue of precoding design based on S-CSI is now resolved, particularly in scenarios where CSI is limited to local information at each access point (AP). Moreover, as the central processing unit (CPU) now only needs to transmit an optimized regularization parameter to the APs, the computational overhead can be reduced, which naturally enhances the system scalability. Driven by these advantages, we then introduce a joint user association, power allocation, and precoding design (i.e., regularization parameter optimization) scheme aimed at maximizing the ergodic sum SE and minimizing the sum power consumption. This is achieved through two optimization problems: one for the ergodic sum SE maximization using weighted minimum mean square error (WMMSE)-based processing and another for the sum power consumption minimization employing a block coordinate descent (BCD)-based algorithm. Numerical results demonstrate the superior performance of the proposed PRO-LPRZF scheme.

The scalable cell-free radio access network (CF-RAN), built on cell-free massive multiple-input multiple-output (CF-mMIMO) networks, achieves a critical trade-off between computational complexity and system performance in cooperative transmission through the rational division of physical-layer functionalities. However, due to its distributed transmission architecture, unavoidable propagation delay differences arise in signal arrival times across different receivers during cooperative transmission, leading to asynchronous reception effects that severely degrade system performance. In this paper, we analyze the specific impacts of asynchronous reception effects in scalable CF-RAN systems, including accumulated phase offset on received signals, as well as inter-carrier interference (ICI) and intersymbol interference (ISI). We investigate channel estimation under non-ideal channel state information (CSI) acquisition caused by non-orthogonal pilot sequences and asynchronous reception effects, deriving closed-form expressions for the achievable uplink and downlink spectral efficiency (SE) in scalable CF-RAN systems under asynchronous conditions. To mitigate asynchronous reception effects, we introduce a beam division multiple access (BDMA) transmission scheme into scalable CF-RAN systems, leveraging large-scale antenna arrays at remote radio units (RRUs) to achieve beam-domain multi-user spatial multiplexing. Building on this framework, we implement per-beam time delay compensation (PBTDC) on RRU antenna arrays to approximate asynchronous received signals as synchronized and derive closed-form expressions for the achievable SE of uplink/downlink in asynchronous scalable CF-RAN systems with PBTDC architecture. Numerical simulations demonstrate that asynchronous reception effects significantly degrade channel estimation and data transmission performance in scalable CF-RAN systems. In contrast, the proposed PBTDC architecture based on BDMA transmission effectively mitigates these adverse effects and substantially enhances system performance.

The fluid antenna system (FAS) enables position reconfigurability. A potential drawback of real-time FAS, however, is that it requires complete channel state information (CSI) for each FAS port at every communication time slot, an approach referred to as ideal-FAS. Recognizing the difficulties of achieving ideal-FAS, we propose a FAS scheme based on incomplete CSI, referred to as semi-blind FAS. This paper first introduces the spatial-temporal framework of FAS, upon which the proposed semi-blind FAS is developed. The proposed semi-blind FAS is lightweight and computationally efficient, scalable to an arbitrary number of ports and time slots, and operates without pre-training or deep learning structures. The scheme effectively exploits incomplete historical CSI to estimate the conditional distribution across all FAS ports at the desired time slot, thereby identifying the statistical optimal port for signal reception. Generally, the key idea of semi-blind FAS is to select the optimal port through conditional distribution analysis, from a statistical perspective, with optimality defined according to the scenario of interest. Inspired by information-theoretic entropy, we further develop the residual entropy power ratio to characterize how physical parameters influence the performance gap between semi-blind FAS and ideal-FAS. Our analysis reveals that estimation performance depends not only on the number of sampled ports and time slots, but also on the specific indices of ports with given CSI at each time slot, i.e., the port sampling strategy. This critical factor has been largely overlooked in existing port estimation studies. Numerical results demonstrate that the proposed semi-blind FAS achieves performance comparable to, and in some cases indistinguishable from, that of ideal-FAS, while requiring significantly fewer port CSI measurements and lower port switching speeds.

An auto-polarization fluid antenna (APFA) is developed for indoor wireless channel sounding and employed to reveal a novel “fluid polarization effect” (FPE) in wireless communications. Unlike conventional fluid antennas (FAs) that are steering their beams/nulls with the aid of external mechanical/electronic actuators, the APFA only relies on the natural swinging of human arms to yield a self-driven polarization switching ability. Compared with the conventional fixed circularly polarized antennas, the wrist-worn, self-driven APFA in indoor wireless channel sounding systems effectively reduces multipath clusters (MPCs), attains smaller path loss exponent (PLE), and consequently yields the FPE. Compared to the fixed circularly polarized case with PLE= 1.62, the measured PLE is reduced by 14% to 1.38, and the system packet error rate (PER) is improved by 76%. It realizes robust anti-multipath fading performance owing to the much-improved FPE. The fluid effect in polarization domain is anticipated to remarkably enhance the anti-multipath fading performance of wearable wireless communication systems. It opens a new horizon to develop self-driven, cost-effective fluid antenna systems (FASs) for universal applications.

Cell-free is considered a promising technology for the future network, which adopts a large number of distributed antennas to provide a uniformly good service. However, current researches under the long-term evolution standard mostly ignore the problem of asynchronous transmission brought by the different transmission delays due to the geographical distance differences, and assume that the system is perfectly synchronized. On the other, these works often do not consider a distributed method with controllable fronthaul overhead compatible with cell-free. To enable an extensive cell-free in the sixth generation, we derive an asynchronous analysis framework and propose a centralized and a distributed downlink precoding method respectively. What is more important, we have verified that cell-free suffers from inter-carrier-interference and inter-symbol-interference under the 5th generation new radio standard. To the best of our knowledge, this is the first work implementing a distributed asynchronous precoding method in an extensive cell-free, and simulation results demonstrate the effectiveness of the proposed two precoding methods, compared to naive precoding ignoring the asynchronous impact.

Cell-free is seen as one of the most important technology for the future wireless communications. In this paper, we adopt a wideband cell-free to implement low-altitude coverage to serve multiple unmanned aerial vehicles (UAVs) in the city playing the core role of low-altitude economy. For practice, distributed computation, asynchronous effects, beam split and imperfect channel state information are considered. We mainly rely on per-beam synchronization (PBS) and discuss different architecture implementations. A wideband asynchronous architecture that reuses the time delay modules exploited in wideband beam split calibration is proposed. In addition, a semi-synchronized path set (SSP-Set) is derived to eliminate asynchronous interference and a geometric scattering graphic convolutional network is used to acquire the (sub)-optimal SSP-Set. Based on these two technologies, a beam-delay alignment transmission (BDAT) scheme is obtained and we implement it with a distributed paradigm. The numerical results demonstrate the proposed BDAT can benefit from the cooperative downlink beamforming and provide a uniformly good service for UAVs.

Efficient resource allocation in hardware-impaired cell-free systems is critical for achieving the stringent requirements of ultra-reliable low-latency communication (URLLC) while maintaining energy efficiency (EE). Traditional optimization-based approaches face scalability issues, while existing learning-based methods struggle to adapt to varying network structures and the complexities of hardware impairments. In this work, we address these challenges by incorporating rate-splitting multiple access (RSMA) into cell-free systems and designing a graph neural network (GNN)-based framework. First, we propose a method leveraging explicit channel state information to optimize precoding for both common and private streams under rate, power, and latency constraints. Next, we further develop an end-to-end approach that bypasses channel estimation by directly using raw pilot signals for joint feature extraction and optimization. Finally, we introduce a pilot-free method that processes distorted message-passing information from real channels, reducing communications overhead while enhancing adaptability to practical conditions. Through extensive simulations, we validate the proposed methods, demonstrating significant improvements in EE, along with insights into their computational complexity and scalability in diverse system configurations.

Cell-free massive multiple-input multiple-output (MIMO) systems, leveraging tight cooperation among wireless access points, exhibit remarkable signal enhancement and interference suppression capabilities, demonstrating significant performance advantages over traditional cellular networks. This paper investigates the performance and deployment optimization of a user-centric scalable cell-free massive MIMO system with imperfect channel information over correlated Rayleigh fading channels. Based on the large-dimensional random matrix theory, this paper presents the deterministic equivalent of the ergodic sum rate for this system when applying the local partial minimum mean square error (LP-MMSE) precoding method, along with its derivative with respect to the channel correlation matrix. Furthermore, utilizing the derivative of the ergodic sum rate, this paper designs a successive convex approximation based deployment optimization method to improve system deployment. Simulation experiments demonstrate that under various parameter settings and large-scale antenna configurations, the deterministic equivalent of the ergodic sum rate accurately approximates the Monte Carlo ergodic sum rate of the system. Furthermore, the deployment optimization algorithm effectively enhances the ergodic sum rate of this system by optimizing the positions of access points.

Future industrial 6G applications will impose stringent requirements on ultra-reliable low-latency communications (URLLC) and precision sensing enabled by integrated sensing and communication (ISAC) techniques, motivating a comprehensive study of the communication–sensing (C–S) trade-off under finite blocklength transmission. Therefore, this paper investigates the fundamental C–S performance limits in a network-assisted full-duplex (NAFD) cell-free ISAC system with URLLC users. To address the theoretical gap in the finite blocklength regime, closed-form upper-bound expressions are derived for key communication metrics, including transmission delay and decoding error probability (DEP), and a Cramér–Rao lower bound (CRLB) framework is established for multi-static sensing. Furthermore, to explicitly characterize the C–S trade-off, the ISAC network availability is evaluated and the Pareto frontier is obtained using the non-dominated sorting genetic algorithm II (NSGA-II). The results demonstrate that increasing the blocklength improves sensing accuracy at the cost of higher communication latency. To address this inherent conflict, this study proposes a DDQN-based finite blocklength optimization (FBLO) algorithm that performs blocklength selection under URLLC and sensing quality-of-service requirements to achieve a favorable C–S trade-off. Simulation results validate that the proposed algorithm achieves near-optimal performance with reduced computational overhead.

To realize the potential of integrated sensing and communication (ISAC) in cell-free (CF) massive MIMO system, an ISAC framework is proposed in this paper. With numbers and locations of scatterers (targets) unknown, based on received downlink data signals from the transmit access point (tAP) antenna, multiple receive access points (rAPs) first estimate delays locally. In each outer iteration, by comparing the estimated delays with the delays of the paths through extracted scatterer locations, selected rAPs search out mismatched estimated delays. Through cooperation, the potential locations of the scatterers forming each candidate location set corresponding to each mismatched delay are obtained, and each set will be evaluated in sequence. Specifically, a joint evaluation algorithm is proposed, where the probability model for the joint evaluation problem is established based on delay and limited angular information. Under the expectation maximization (EM) framework, by fusing information from all the rAPs, the extracted scatterer locations and candidate locations are adjusted, and the evaluation results are estimated, which can be regarded as the global probability of scatterers exist at candidate locations. When the evaluation results of all the locations in a candidate location set are low, the corresponding delay will be discarded, otherwise, the candidate location with the highest evaluation result in the set will be extracted. The proposed framework avoids exhaustive search in different associations of scatterers and estimated delays, and is more flexible than extraction from all the candidate locations based on fixed thresholds. Then, based on a simplified model, the impact of system parameters on delay based location sensing accuracy is revealed with the theoretical analysis. Finally, based on the prototype system with CF radio access network (RAN) architecture, the proposed framework is experimentally validated.

Deterministic communication within the wireless domain is essential for industrial applications. However, the stochastic nature of wireless communication introduces substantial challenges for time-sensitive networking (TSN) services, which require strict end-to-end latency bounds. This paper addresses the challenge of minimizing long-term delay jitter in downlink cell-free multi-user multi-input multi-output orthogonal frequency division multiple access (MU-MIMO OFDMA) systems, subject to heterogeneous delay upper bounds and satisfaction rates. The problem involves time-space-frequency precoding constrained by user-specific delay violation probabilities and transmit power limits. To overcome the limitations of model-driven methods in handling implicit system models and the inefficiency of data-driven approaches in large action spaces, we propose a hybrid solution. Specifically, we decompose the problem into two sub-problems: rate scheduling via a constrained Markov decision process (CMDP), and instantaneous precoding through weighted sum-rate (WSR) maximization. We develop a safety reinforcement learning-based algorithm to optimize rate scheduling by allocating user weights, and a weighted minimum mean squared error (WMMSE) algorithm to solve the WSR maximization. Simulation results demonstrate that our approach effectively reduces jitter while meeting stringent delay-related requirements. In diverse TSN scenarios with heavy loading ratio, our proposed co-driven scheme achieves about 45% reduction in delay jitter compared to earliest deadline first (EDF) scheduling, while realizing user-specific delay satisfactory ratios (99.9%-99.999%).

Next generation wireless network is expected to provide higher rate, more reliable access and lower latency. Cell-free massive multiple input multiple output (CF-mMIMO) has been considered a potential enabler for massive access. In this paper, we primarily investigate massive grant-free random access (GFRA) in CF-mMIMO ultra-reliable and low latency (URLLC) systems. First, we give a massive GFRA model based on CF-mMIMO URLLC system, focusing on analyzing access reliability and access latency. Next, we derive approximated closed-form expression for the decoding error probability and outage probability of active users in the finite block-length regime by leveraging the approximated distribution of the signal to interference plus noise power. Based on this, by comprehensively considering the random access procedure, we derive the expression for access success probability of the attempted access user. We also jointly optimize the pilot length and data block-length by the access success probability to enhance access reliability. Furthermore, by utilizing the macro diversity of the CF-mMIMO system, we propose a scalable user centric-based multiple access points scheme to improve access reliability. In addition, based on access procedure and the associated GFRA protocol, we analyze the components of access latency. Subsequently, considering the access success probability and the number of retransmissions, we analyze and evaluate access latency for massive GFRA in the CF-mMIMO URLLC system. Finally, simulation results demonstrate the rationality and effectiveness of the proposed massive access model for analyzing the access reliability and access latency.

The belief-selective propagation (BsP) algorithm has recently emerged as a promising approach for massive MIMO detection. However, when applied in MIMO turbo receivers, known for their superior performance compared to separated detection and decoding (SDD) receivers, the BsP-based receiver suffers from significant performance degradation and high processing latency. To overcome these limitations, this paper proposes a fixed-constellation BsP (FCBsP) detector tailored for MIMO turbo receivers. By buildingfixed configuration setsand utilizing theapproximate multi-user interferencefor message updates, the proposed FCBsP detector achieves a better trade-off between error performance and computational complexity compared to the BsP. Furthermore, two unexplored features:information compensation and decoding-first mechanismare proposed to fine-tune the exchanged information and lower the processing latency of the FCBsP-based turbo receiver. Numerical results demonstrate that the proposed FCBsP-based turbo receiver earns about 0.7 and 1.8 dB performance gains over the BsP-based turbo receiver at BLER=10−3in an LDPC-coded 32 × 12 64-QAM MIMO system under Rayleigh and practical channels, respectively.

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.

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising technology for sixth-generation mobile communication systems. Building upon conventional CF-mMIMO, the cell-free radio access network (CF-RAN) architecture distributes physical-layer functionalities among access points (APs), edge distributed units (EDUs), and user-centric distributed units (UCDUs), striking a balance between complexity and performance. However, prior studies on CF-RANs have primarily focused on the physical-layer, while the scalability of the medium access control (MAC) layer remains insufficiently explored. This paper proposes a novel hierarchical scalable CF-RAN architecture that fully exploits the functional potential of distributed UCDUs, enabling a comprehensive decentralized paradigm spanning from the physical-layer to the MAC-layer. Closed-form uplink spectral efficiency (SE) expressions are derived for maximum ratio (MR), distributed full-pilot zero-forcing (FZF), and distributed joint partial zero-forcing (JP-ZF) combining, explicitly accounting for imperfect channel state information and pilot contamination. The analytical insights reveal the improved scalability and how distributed processing and partial information availability affect system performance. To support large-scale deployments, we further develop a structured massive access scheme, including UCDU-EDU deployment, UE-UCDU association, distributed pilot assignment and AP-UE association, centralized refinement, and distributed power control. Simulation results verify the accuracy of the theoretical analysis and demonstrate the superior SE, favorable fairness, and enhanced scalability of the proposed schemes.

In recent years, the unmanned aerial vehicle (UAV) communications have become an important part of the space-air-ground integrated network. Unfortunately, the high mobility, as well as perturbation, of UAV poses a great challenge in aligning narrow high-gain beams between the UAV and base station (BS). To tackle this challenging issue, we propose efficient beam prediction and tracking solutions from the perspective of control in this paper. First of all, for an important and typical flight mode in practice (i.e., the mission flight mode - to assign a series of targets in advance and fly from one target to the next one in turn), we study in depth the underlying control principle and reveal important properties and relationships between beam direction and controlled variables. Then, to exploit the properties and relationships revealed, we propose an efficient learning-based beam prediction and tracking solution. Specifically, we develop an efficient learning model, together with offline training and online inference algorithms. To further reduce the computational complexity, we distinguish two kinds of beam offsets and prove an important property of the mission flight mode, i.e., a multicopter almost keeps fixed attitude and velocity in most part of a flight process, based on which an efficient algorithm is designed. Comprehensive experiment results from open-source software, hardware and real UAV confirm the effectiveness of our control-assisted approach.

The dynamic resource allocation between enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) traffic is a challenging problem. While eMBB strives for high data rates using slots as transmission time intervals, URLLC emphasizes reliability and low latency using mini-slots. Puncturing and superposition schemes are introduced by 3GPP. The existing works considering puncturing and/or superposition overlook the frequency-selective fading and consequently neglect the differences in channel state information of resource blocks (RBs) occupying different sub-carriers. Under the frequency-selective fading, it is crucial to perform RB-specific puncturing and/or superposition by determining which specific RBs will be punctured and/or superposed. In this paper, a deep reinforcement learning (DRL)-based dynamic resource slicing scheme on mini-slot-level timescale for eMBB and URLLC traffic under frequency-selective fading is proposed, considering both puncturing and superposition schemes. In particular, an optimization problem is designed to determine the transmission power allocation ratio of each RB in each mini-slot, where the objective is to maximize the comprehensive performance of eMBB users considering data rate satisfaction, fairness and data rate stability simultaneously under URLLC latency and reliability constraint. Simulation results demonstrate that the proposed DRL-based algorithm employing deep Q-network, with lower complexity, achieves near-optimal performance and outperforms benchmark algorithms.

In this work, we introduce the integration of generalized low-density parity-check (GLDPC) codes with short polar component codes, termed GLDPC codes with polar component codes (GLDPC-PC). A recently proposed soft-input soft-output (SISO) decoder for polar-like codes enables effective iterative belief propagation decoding for GLDPC-PC. This SISO decoder after a post-processing exhibits little performance loss to the optimal SISO decoder when all the variable nodes have relatively low degrees. A three-step method is introduced to design protograph-based GLDPC codes. The constructed GLDPC codes are compared with 5G LDPC codes. They exhibit little performance loss in the waterfall region and possess better error floor with less iterations.

To meet the stringent requirements of latency, reliability and energy efficiency (EE), we introduce rate splitting multiple access (RSMA) into ultra-reliable and low-latency communication (URLLC) IIoT networks, where RSMA has a good flexibility in interference management. By jointly optimizing beamforming design and common rate allocation, we investigate the worst-case EE maximization problem with imperfect channel state information (CSI). Although the problem is nonconvex, we exploit the monotonicity of the problem to develop an optimal solution, where a monotonic optimization framework based on polyblock outer approximation (PA) and boundary searching is proposed to find the optimal points on the Pareto boundary. Simulation results show the convergence of the proposed optimal algorithm, and RSMA can achieve higher EE than space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) in URLLC IIoT scenarios.

Because of the flexible scheduling, improved reliability, enhanced capacity over much wider range, the unmanned aerial vehicle (UAV) communications have become an important part of the space-air-ground integrated network. However, the high mobility and perturbation of UAV impose a challenge on aligning narrow beams between the UAV and another node, such as the base station (BS). Although the position and attitude of UAV have been exploited to develop beam tracking algorithms, they belong to current or past information, which often provide limited performance improvement in the high-mobility scenario. To tackle this challenging issue, we, for the first time, identify a kind of important but ready-made information - the command or control sequence (CCS) provided by the flight control system (FCS). We explain in detail that CCS provides real and direct (rather than estimated) future information for beam prediction. Then, we propose an efficient learning-based algorithm to exploit the information. In particular, we prove theoretically that the convolutional neural network (CNN) is an appropriate choice of the network structure within the nonlinear prediction model. Experiment results from practical real UAVs confirm the effectiveness and superiority of our proposal.

In modern wireless networks, user mobility modeling plays a pivotal role in learning-based network optimization, particularly in tasks such as user association and resource allocation. Traditional random mobility models, e.g., random waypoint and Gauss Markov model, often fail to accurately capture the distribution patterns of users within real-world areas. While trace-based mobility models and advanced learning-based trajectory generation methods offer improvements, they are frequently limited by the scarcity of real-world trajectory data in target areas, primarily due to privacy concerns. This paper introduces Map2Traj, a novel zero-shot trajectory generation method that leverages the diffusion model to capture the intrinsic relationship between street maps and user mobility. With solely the street map of an unobserved area, Map2Traj generates synthetic user trajectories that closely resemble the real-world ones in trajectory pattern and spatial distribution. This enables the creation of high-fidelity individual user channel states and an accurate representation of the overall network user distribution, facilitating effective wireless network optimization. Extensive experiments across multiple regions in Xi'an and Chengdu, China demonstrate the effectiveness of our proposed method for zero-shot trajectory generation. A case study applying Map2Traj to user association and load balancing in wireless networks is also presented to validate its efficacy in network optimization.

As successfully adopted in standard long code scenarios, belief propagation (BP) decoding has been considered a promising universal decoding candidate for next-generation wireless communications. However, when applied to short codes, BP decoding suffers from poor error correction performance due to harmful cycle structures in the Tanner graph. In this paper, we address this issue by designing a structured, sparse parity-check matrix (ssPCM) framework, composed of multiple cycle-free parity-check row blocks (PCRBs). The resulting ssPCMs feature regular row weights and perform better than the state-of-theart 4 -cycle-free row redundant PCMs across Bose-Chaudhuri-Hocquenghem (BCH) codes of length 63.

The bisimulation metric (BSM) is a powerful tool for computing state similarities within a Markov decision process (MDP), revealing that states closer in BSM have more similar optimal value functions. While BSM has been successfully utilized in reinforcement learning (RL) for tasks like state representation learning and policy exploration, its application to multiple-MDP scenarios, such as policy transfer, remains challenging. Prior work has attempted to generalize BSM to pairs of MDPs, but a lack of rigorous analysis of its mathematical properties has limited further theoretical progress. In this work, we formally establish a generalized bisimulation metric (GBSM) between pairs of MDPs, which is rigorously proven with the three fundamental properties: GBSM symmetry, inter-MDP triangle inequality, and the distance bound on identical states. Leveraging these properties, we theoretically analyse policy transfer, state aggregation, and sampling-based estimation in MDPs, obtaining explicit bounds that are strictly tighter than those derived from the standard BSM. Additionally, GBSM provides a closed-form sample complexity for estimation, improving upon existing asymptotic results based on BSM. Numerical results validate our theoretical findings and demonstrate the effectiveness of GBSM in multi-MDP scenarios.

Digital twin (DT)-driven deep reinforcement learning (DRL) has emerged as a promising paradigm for wireless network optimization, offering safe and efficient training environment for policy exploration. However, in theory existing methods can hardly guarantee real-world performance of DTtrained policies before actual deployment. In this paper, we propose the DT bisimulation metric (DT-BSM), a novel metric based on the Wasserstein distance, to quantify the discrepancy between Markov decision processes (MDPs) in both the DT and the corresponding real-world wireless network environment. We prove that for any DT-trained policy, the sub-optimality of its performance (regret) in the real-world deployment is bounded by a weighted sum of the DT-BSM and its sub-optimality within the MDP in the DT, and a modified DT-BSM based on the total variation distance is introduced to avoid the prohibitive calculation complexity of Wasserstein distance for large-scale wireless network scenarios. Numerical experiments validate this first theoretical finding on the provable and calculable performance bounds for DT-driven DRL.

Cell-free is seen as an important implementation for future wireless networks, which eliminates the conventional ‘cell’ concept and enables wide deployment. However, previous works mostly ignore the asynchronous effects in such a large distributed antenna system and assume perfect synchronization which is not practical. In this paper, we proposed a user-centric alignment transmission (UCAT) to settle this problem, which has the analytical beamforming vectors in each access point (AP) being computed locally and fits user-centric cell-free well. With cooperative center processing unit power optimization and AP beamforming computation, an asynchronous downlink method is obtained, and finally, numerical results demonstrate the effectiveness of UCAT.

The soft-output successive cancellation list (SO-SCL) decoder provides a methodology for estimating the a-posteriori probability log-likelihood ratios by only leveraging the conventional SCL decoder for polar codes. However, the sequential nature of SCL decoding leads to a high decoding latency for the SO-SCL decoder. In this paper, we propose a soft-output fast SCL (SO-FSCL) decoder by incorporating node-based fast decoding into the SO-SCL framework. Simulation results demonstrate that the proposed SO-FSCL decoder significantly reduces the decoding latency without loss of performance compared with the SO-SCL decoder.

Abstract With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, divided into three progressive stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies. We compare wireless network large models with conventional large language models (LLMs), and identify key design principles and components for building wireless network architectures. In the final stage, AI as a service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we define the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we analyze the critical challenges faced by the integration of AI and communications in 6G. Finally, we outline promising future research opportunities that are expected to drive the development and refinement of AI and 6G communications.

Abstract Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, nonorthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research directions and new opportunities are highlighted on the way to achieving the DSIN vision.

Enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC) are two critical service types in industrial automation. In a closed-loop control system, device-to-device (D2D) communication is typically employed for direct transmission due to its low-latency requirements. However, this approach does not leverage the large-scale antenna gains of massive MIMO cellular systems. To address this limitation, we introduce a multi-connectivity network that integrates both cellular and D2D links to serve URLLC sensors while accommodating the transmission needs of general eMBB traffic. Since the D2D link serves as the primary link for URLLC transmission in a multi-connectivity setup, we first analyze the packet loss probability components for single-D2D URLLC link. Then, we formulate an optimization problem to maximize the sum channel capacity of eMBB sensors while satisfying URLLC QoS requirements. A sub-optimal power and spectrum allocation scheme is proposed to solve this coexistence problem of single-D2D URLLC and cellular eMBB transmission. For multi-connectivity, we examine the packet loss probability and present two transmission frameworks based on selection combining (SC) and maximal ratio combining (MRC). Simulation results validate the properties of the optimal solution for the relaxed problem and demonstrate the performance gains of multi-connectivity over single-D2D links.

To mitigate the delay caused by large-scale devices access, the grant-free (GF) technology has been widely used in massive Ultrareliable-Low-Latency Communications (mURLLCs). However, the absence of a grant-based scheduling handshake with the base station leads to significant collisions when different users select the same resources, thereby compromising the reliability of the communication. To improve the reliability of the network, we propose a GF scheme assisted by hybrid automatic repeat request (HARQ) in Cell-Free massive Multiple Input-Multiple Output (CF mMIMO) system. Distinct from the traditional HARQ strategies employed in centralized cellular system, the proposed scheme first clusters user devices and access points based on Poisson cluster process (PCP) spatial distribution characteristics, and then fully exploits the macro diversity gain and spatial sparsity of the CF mMIMO system, effectively mitigating the impact of interference on the communication between devices. Subsequently, the HARQ strategy is introduced, and its round-trip delay is analyzed to achieve the maximum number of retransmissions under delay constraints. To further validate the effectiveness of the proposed scheme, the closed-form expression of uplink signal to interference plus noise ratio (SINR) with maximal ratio combining (MRC) receiver is deduced as well as the approximate outage probability expression. Finally, simulation results confirm the precision of the derived expressions and the enhancement of the proposed scheme on system reliability under stringent latency constraints.

Cooperative cell-free (CF) integrated sensing and communication (ISAC) systems emerge as a promising architecture for supporting 6G dynamic Internet of Things (IoT) scenarios. However, the mobility of user equipment (UE) and the inherent non-scalability of CF networks pose critical challenges to the practical deployment of CF ISAC systems. This paper presents a comprehensive mobility management framework for cooperative CF ISAC systems to enhance their deployability and scalability. This framework not only establishes a foundational operation paradigm to obtain the mutual promotion of communication and sensing (C&S) performance in multi-static ISAC, but also employs the dynamic cooperative clustering method and dynamic management mechanism to ensure seamless service for mobile UEs. First, we establish the mathematical signal model of the proposed mobility management framework and conduct the analysis of mobility-aware C&S performance in CF ISAC systems. Subsequently, a distributed, low-complexity initial access scheme is designed to tackle the tightly coupled challenges of access point (AP) clustering and AP mode selection, which can ensure communication reliability and sensing accuracy in static scenarios. Furthermore, to achieve the trade-off between mobility-induced handover loss and per-slot C&S performance, a dynamic access scheme is introduced for dynamic scenarios, comprising the dynamic adaptive hysteresis handover strategy and the kinematic information-based dynamic clustering update algorithm. Theoretical and numerical analyses validate that the proposed access schemes significantly enhance the operability and practicality of CF ISAC systems through low complexity and flexible operations. Meanwhile, simulation results demonstrate that the framework empowers cooperative CF ISAC systems to achieve superior mobility-aware C&S performance, ensuring the stable and efficient system support in 6G dynamic IoT scenarios.

An Internet of Things (IoT) network is a network of devices. In a massive IoT network, devices are often battery-powered, and the battery life determines the network’s lifespan. The goal of this article is thus to examine the inherent relationship between device intrinsic characteristics and network performance (lifespan and capacity). The findings lead to a simple but fundamental view of IoT network longevity and, more significantly, an IoT network longevity principle. This new perception of network longevity alludes to distinct characteristics of IoT devices from those used in existing technologies and systems. These distinctions manifested by the network performance are examined through in-depth theoretical analysis. An exemplary augmentation of characteristics on a practical device demonstrates and reinforces the importance of device characteristics on network performance and the power of the longevit principle.

As a key technology for the evolution to the sixth generation (6G) systems, cell-free massive multiple-input multiple-output (CF-mMIMO) can effectively improve the spectrum efficiency, peak rate, and reliability of wireless communication systems. Starting from the scalable implementation of CF-mMIMO, we study a cell-free RAN (CF-RAN) with distributed cooperative transceivers under the open RAN (ORAN) architecture. Through theoretical analysis and numerical simulation, we investigate the uplink and downlink spectral efficiencies of CF-mMIMO with the distributed transceivers. We then discuss the implementation issues of CF-RAN under ORAN architecture, including time-frequency synchronization and over-the-air reciprocity calibration, low layer splitting, deployment of ORAN radio units (O-RU), and artificial intelligent-based user associations. Finally, we present some representative experimental results for the uplink distributed reception and downlink coherent joint transmission of CF-RAN with commercial off-the-shelf O-RUs.

We investigate hybrid beamforming design for covert millimeter wave multiple-input multiple-output systems with finite-resolution digital-to-analog converters (DACs), which impose practical hardware constraints not yet considered by the existing works and have negative impact on the covertness. Based on the additive quantization noise model, we derive the detection error probability of the warden considering finite-resolution DACs. Aiming at maximizing the sum covert rate (SCR) between the transmitter and legitimate users, we design hybrid beamformers subject to power and covertness constraints. To solve this nonconvex joint optimization problem, we propose an alternating optimization (AO) scheme based on fractional programming, quadratic transformation, and inner majorization-minimization methods to iteratively optimize the analog and digital beamformers. To reduce the computational complexity of the AO scheme, we propose a vector-space based heuristic (VSH) scheme to design the hybrid beamformer. We prove that as the number of antennas grows to be infinity, the SCR in the VSH scheme can approach the channel mutual information. Simulation results show that the AO and VSH schemes outperform the existing schemes and the VSH scheme can be used to obtain an initialization for the AO scheme to speed up its convergence.

The rapid development of generative Artificial Intelligence (AI) continually unveils the potential of Semantic Communication (SemCom). However, current talking-face SemCom systems still encounter challenges such as low bandwidth utilization, semantic ambiguity, and diminished Quality of Experience (QoE). This study introduces a Large Generative Model-assisted Talking-face Semantic Communication (LGM-TSC) System tailored for talking-face video communication. Firstly, we introduce a Generative Semantic Extractor (GSE) at the transmitter based on the FunASR model to convert semantically sparse talking-face videos into text with high information density. Secondly, we establish a private Knowledge Base (KB) based on the Large Language Model (LLM) for semantic disambiguation and correction, complemented by a joint knowledge base-semantic-channel coding scheme. Finally, at the receiver, we propose a Generative Semantic Reconstructor (GSR) that utilizes BERT-VITS2 and SadTalker models to transform text back into a high-QoE talking-face video matching the user’s timbre. Simulation results demonstrate the feasibility and effectiveness of the proposed LGM-TSC system.

Belief propagation (BP) decoding has been recognized for its capacity-approaching performance and high throughput when decoding long low-density parity-check (LDPC) codes. However, the application of BP decoding for short codes is hindered by dense parity-check matrices (PCMs) and prevalent short cycles in the Tanner graph. In this paper, we introduce a general method to extract an optimized sparse PCM for short binary block codes, which removes length-four cycles and enhances the connectivity of short cycles to enable BP decoding with improved performance. Notably, for short binary codes with lengths up to 64, our BP decoding performance approaches the maximum likelihood bound and surpasses the best-reported BP results with reduced computational complexity. Compared with other universal decoding algorithms, BP decoding using our extracted sparse PCMs is competitive in terms of both error-rate performance and computational complexity. These promising results suggest that our method to improve BP decoding for short codes is a step toward a practical universal BP decoder for next-generation communication systems.

In comparison to 4G, 5G wireless needs to support a broader range of applications. Therefore, both low-density parity-check (LDPC) codes and polar codes have been standardized by 5G new radio (NR) to fulfill the requirements of data channel and control channel, respectively. Usually, LDPC/polar decodings are implemented by separate hardware, leading to low area efficiency. Though decoders which can handle both codes have been proposed, how to compromise between throughput and efficiency has always been a persistent dilemma due to the absence of a unified and smooth integration methodology. To this end, by fully utilizing the common parts of graph-theoretic algorithms for both codes, this paper presents a unified decoder (UniDec) which is fully compatible with 5G NR LDPC/polar codes. This UniDec enables three key approaches:1) unified processing nodes for both codes,2) configurable permutation networks with multi-parallelism, and3) flexible scheduling for 5G NR parameter configuration, guaranteeing both high data throughput and area efficiency. Implemented in 40nm CMOS, the UniDec attains a maximum of$33.64\times $throughput and$5.98\times $area efficiency compared to its multi-mode counterparts. Even compared with the state-of-the-art (SOA) dedicated ones, the UniDec still maintains a competitive edge in terms of throughput, energy, and area efficiency. It is noted that this methodology can be generalized to other factor-graph based signal processing algorithms.

In this paper, we investigate a cooperative edge caching problem in the fog radio access networks (F-RANs). In order to obtain the globally optimal caching strategy that minimizes the content transmission delay and maximizes communication efficiency, we propose a multi-agent reinforcement learning based cooperative caching policy with low entropy communications. First, we propose a double deep Q network (DDQN) based caching policy by taking into account the non-deterministic polynomial hard (NP-hard) aspect of this cooperative caching optimization problem. Then, we extend the state transition model of Markov Decision Process (MDP) under the single agent system into the Stochastic Game (SG) one under the multi-agent system. By employing the DDQN in each agent, the agents can learn and make the global decision for caching. For utilizing the cooperation resources of fog access points (F-APs), the interaction of information is introduced to exchange the historical cache records of cooperative F-APs. However, the information in the interaction may require lower entropy in the fiber link. Therefore, the information entropy is largely reduced to improve the communication efficiency by quantifying the information. Finally, due to the non-computable gradient of information entropy, we apply a pseudo gradient descent method to approximate the gradient descent in the local model. Simulation results show that our policy achieves better performance in terms of reducing the transmission delay and improving the cooperation among F-APs compared to the benchmark policies. Additionally, it is demonstrated that the proposed policy improves communication efficiency without compromising the performance of cooperative caching.

A large proportion of the carbon emissions associated with wireless communications stem from electricity consumption during operation. Spectral efficiency (SE) and energy efficiency (EE) are fundamental considerations in wireless communications. However, nonlinear amplification results in a trade-off between these factors. Various techniques have been developed to address this issue, including the frequently-used amplifier linearization. Nonetheless, these approaches have limitations in terms of versatility and complexity, making them impractical for modern broadband multiantenna wireless communications. Here, an amplitude-phase-time block modulation (APTBM) scheme and a corresponding demodulation scheme for resisting amplifier nonlinearity are proposed, establishing a new paradigm for balancing the SE and EE. At the transmitter, the symbol block, consisting of two time-domain consecutive symbols, is used to carry information. Simultaneously, specific amplitude and phase constraints are imposed on the symbols within a block. At the receiver, nonlinearly distorted symbols can be effectively demodulated by utilizing these constraints. Numerical and experimental results show that the proposed APTBM demonstrates excellent nonlinear transmission characteristics compared with conventional offset quadrature amplitude modulation.

Blockchain radio access network (B-RAN) offers a promising solution for trustworthy wireless applications by leveraging blockchain and smart contracts. However, the existing literature falls short in addressing the corresponding modeling and theoretical analysis. In this study, we develop analytical models to characterize the wireless access process in B-RAN, which also sheds light on other blockchain-enabled services. We first construct a two-dimensional queuing model and identify both blockchain scalability and RAN capability as two system bottlenecks, and then introduce the matrix analytic method to reduce complexity and improve efficiency. Furthermore, we build tandem queuing models for obtaining tight latency bounds with closed-form expressions. The performance and complexity of the proposed models are evaluated and compared against existing benchmarks to provide a comprehensive perspective. Finally, we present experimental results from a lab-built B-RAN prototype to demonstrate the efficacy of our models.

Integrated sensing and communication (ISAC) enabled by millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) technologies is envisioned to be a promising candidate for future wireless systems. In this paper, we study the mmWave massive MIMO aided ISAC transceiver design with one-bit digital-to-analog converters (DACs) and one-bit analog-to-digital converters (ADCs) for reduced hardware complexity and power consumption. First, we develop a novel one-bit target detector based on the Bussgang decomposition, which fully exploits the spatial correlation at the receiver side for performance enhancement. Then, we derive the detection probability and the false alarm probability of the proposed detector in closed forms, from which we establish an interesting relationship between the detection performance and a new signal-to-quantization-plus-interference-plus-noise ratio (SQINR) metric. Furthermore, we formulate a one-bit ISAC transceiver optimization problem by incorporating both the communication mean-squared error (MSE) and proposed sensing SQINR metrics into the objective function. To address the complicated discrete optimization, we propose an efficient alternating optimization framework embedded with a majorization-minimization (AOMM) algorithm with guaranteed convergence. Finally, extensive simulations are conducted which confirm the validity of our one-bit detection performance analysis and show that the proposed detector outperforms a recent solution relying on the low input signal-to-noise/interference-to-noise ratio (LIS) assumption. Moreover, the proposed ISAC transceiver design can achieve excellent communication and sensing performances with acceptable complexity.

Combining millimetre-wave (mmWave) communications with an extremely large-scale antenna array (ELAA) presents a promising avenue for meeting the spectral efficiency demands of future sixth-generation (6G) mobile communications. This technology achieves a high data rate and establishes high-gain directional transmission links. However, beam training for mmWave ELAA systems is challenged by excessive pilot overheads as well as insufficient accuracy, as the huge near-field codebook has to be accounted for. In this paper, inspired by the similarity between far-field sub-6 GHz channels and near-field mmWave channels, we propose to leverage sub-6 GHz uplink pilot signals to directly estimate the optimal near-field mmWave codeword, which aims to reduce pilot overhead and bypass the channel estimation. Moreover, we adopt deep learning to perform this dual mapping function, i.e., sub-6 GHz to mmWave, far-field to near-field, and a novel neural network structure called NMBEnet is designed to enhance the precision of beam training. Specifically, when considering the orthogonal frequency division multiplexing (OFDM) communication scenarios with high user density, correlations arise both between signals from different users and between signals from different subcarriers. Accordingly, the convolutional neural network (CNN) module and graph neural network (GNN) module included in the proposed NMBEnet can leverage these two correlations to further enhance the precision of beam training. To better evaluate the performance of the proposed algorithm, we employ state-of-the-art system simulation software to obtain realistic channel data. Simulation results demonstrate the superior performance of the proposed strategy compared to the exhaustive search scheme and existing deep learning-based schemes.

In this paper, we investigate precoder design for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications with orthogonal frequency division multiplexing (OFDM) modulation. We first reveal the effect of the precoder on the effective channel at receivers and formulate the precoder design for a group of subcarriers as a sum-rate maximization problem, where the delay spread of the effective channel is constrained to maintain its smoothness. Then with the beam based channel model and beam domain channel sparsity, the design of space domain precoders for a group of subcarriers are transformed into that of a space-frequency (SF) beam domain vector and the resulting space domain precoder at each subcarrier is beam structured. Efficient calculation for design and implementation of the beam structured precoder (BSP) is proposed. Moreover, effective channel estimation with the BSP is discussed. Simulation results show that the proposed BSP can enhance the effective channel estimation performance and significantly improve the system performance.

Multi-static Integrated Sensing and Communication (ISAC) is a potential technology for future sixth-generation (6G) and cell-free (CF) network is a suitable architecture to integrate it. Current research on multi-static ISAC and full-duplex communication (MIFC) CF systems is scarce, and the adoption of full-duplex (FD) access points (APs) inevitably leads to significant self-interference (SI) and exorbitant deployment costs. Utilizing network-assisted full-duplex (NAFD) technology to implement MIFC CF systems can effectively avoid the above issues. However, in addition to the challenge posed by highly coupled cross-link interference (CLI) and multi-user interference, NAFD-based MIFC CF systems must also address the mutual interference between sensing signals and communication signals. This paper proposes a practical MIFC CF system based on NAFD technology and introduces a four-stage interference management mechanism, which integrates direct interference suppression with indirect interference suppression techniques. Within this mechanism, we initially derive the data transmission estimated channel state information (CSI), the maximum a posteriori ratio test (MAPRT) target detector and inter-AP estimated CSI. Then, we furnish the expressions for communication achievable rate and sensing signal-to-interference-plus-noise ratio (SINR) after direct interference cancellation based on the estimated CSI. Furthermore, a deep learning (DLN)-based joint communication and sensing precoding (JCSP) algorithm is devised for indirect interference suppression. Simulation results demonstrate the effectiveness of the direct interference suppression strategy and DLN-based JCSP algorithm in the proposed interference management mechanism, which can achieve the trade-off between communication and sensing performance.

The proliferation of diverse wireless services has led to emerging technologies for network slicing. Admission control plays a crucial role in achieving service-oriented goals in sliced communication networks through selective acceptance of service requests. To enhance the performance of admission control in intricate contemporary communication networks, deep reinforcement learning (DRL) has been widely adopted to enhance the effectiveness and flexibility of intelligent admission control. However, due to the simulation-to-reality gap, DRL models trained in a simulation environment can face considerable performance degradation when transferred to the deployment environment. Although online DRL tries to avoid such gaps, its expensive trial-and-error cost poses economic and safety concerns for network operators. We propose a cooperative framework integrating digital twin (DT) and online DRL to address this issue. Specifically, a behavior cloning-based DT is established to parameterize a default admission control policy in real networks, and a DT-accelerated online DRL strategy is then developed for further policy optimization. The DT is constructed as a neural network, featuring a customized output layer to address extensive action spaces in queuing systems. Extensive simulations show that the proposed DRL solution facilitates the stability of the online DRL and accelerates the convergence, yielding a resource utilization improvement of up to 26.39% compared to the state-of-the-art DRL model, while maintaining consistent performance with the online DRL method in terms of long-term revenues. Meanwhile, the proposed solution is versatile and adaptable to various DRL-based network optimization tasks.

In the sixth generation (6G), ultra-reliable low-latency communications (URLLC) will be further developed to achieve TKμ extreme connectivity. On the premise of ensuring the same rate and reliability, the spatial domain advantage of multiple-input multiple-output (MIMO) has the potential to further shorten the time-domain code length and is expected to be a key enabler for the realization of TKμ. Different coded MIMO schemes exhibit disparities in exploiting the spatial domain characteristics, so we consider two extreme MIMO coding schemes, namely, time-domain coding in which the codewords on multiple spatial channels are independent of each other, and spatiotemporal coding in which multiple spatial channels are jointly coded. By analyzing the statistical characteristics of information density and utilizing the normal approximation, we provide explicit performance bounds for finite blocklength coded MIMO under time-domain coding and spatiotemporal coding. It is found that, different from the phenomenon in time-domain coding where the performance degrades as the blocklengths decrease, spatiotemporal coding can effectively compensate for the performance loss caused by short blocklengths by improving the spatial degrees of freedom (DoF). These results indicate that spatiotemporal coding can fully exploit the spatial dimension advantages of MIMO systems, enabling extremely low error-rate communication under stringent blocklengths constraint.

The integration of sensing capabilities into communication systems, by sharing physical resources, has a significant potential for reducing spectrum, hardware, and energy costs while inspiring innovative applications. Cooperative networks, in particular, are expected to enhance sensing services by enlarging the coverage area and enriching sensing measurements, thus improving the service availability and accuracy. This paper proposes a cooperative integrated sensing and communication (ISAC) framework by leveraging information-bearing orthogonal frequency division multiplexing (OFDM) signals transmitted by access points (APs). Specifically, we propose a two-stage scheme for target localization, where communication signals are reused as sensing reference signals based on the system information shared at the central processing unit (CPU). In Stage I, we propose a two-dimensional fast Fourier transform (2D-FFT)-based algorithm to measure the ranges of scattered paths induced by targets, through the extraction of delay and Doppler information from the sensing channels between APs. Then, the target locations are estimated in Stage II based on these range measurements. Considering the potential occurrence of ill-conditioned measurements with large error during the extraction of time-frequency information, we propose an efficient algorithm to match the range measurements with the targets while eliminating ill-conditioned measurements, achieving high-accuracy target localization. In addition, based on the transmission configurations defined in the fifth generation (5G) standards, we elucidate the performance trade-offs in both communication and sensing, and extend the proposed sensing scheme for general scenarios. Finally, numerical results confirm the effectiveness of our sensing scheme and the cooperative gain of the ISAC framework.

As a critical component of beyond fifth-generation (B5G) and sixth-generation (6G) mobile communication systems, ultra-reliable and low-latency communication (uRLLC) imposes stringent requirements on both latency and reliability. In recent years, with the evolution of mobile communication networks, centralized and distributed processing schemes for cell-free massive multiple-input multiple-output (CF-mMIMO) have attracted significant attention. This paper investigates the performance of a novel scalable cell-free radio access network (CF-RAN) architecture featuring multiple edge distributed units (EDUs) under the finite block length regime. Closed-form expressions for the upper and lower bounds of the expected sum spectral efficiency (SE) are derived, where centralized and fully distributed deployment can be treated as two special cases, respectively. Furthermore, the spatial distributions of user equipment (UE) and remote radio units (RRUs) are analyzed, revealing that interleaving RRUs deployment associated with the EDUs can enhance SE performance under finite block length constraints with a specified transmission error probability. This paper also compares Monte-Carlo simulation results with multi-RRU clustering-based collaborative processing, validating the accuracy of the space-time exchange theory in the scalable CF-RAN scenario. By deploying scalable EDUs, a practical tradeoff between latency and reliability can be achieved through the spatial degree-of-freedom (DoF), thereby offering a distributed and scalable realization of the space-time exchange theory.

Thanks to its near-optimal performance, breadth-first search detection (BFSD) finds widespread application in small-scale MIMO systems. However, existing BFSD methods struggle to effectively configure the width (number of candidate nodes) for each layer, resulting in prohibitive complexity in large-scale MIMO systems. To address this, we propose two width optimization schemes for BFSD. We introduce a layer-by-layer optimization framework to reduce the design space of width configurations, and a Monte Carlo-assisted method to link width configurations to detection performance. Using this linking scheme in the reduced design space, we formulate the first width optimization scheme given specific performance constraints. Then, we present another scheme that employs a theoretical linking method as an alternative to the Monte Carlo approach. Although slightly less effective, the second scheme has negligible complexity for width optimization, making it well-suited for communication scenarios with time-varying characteristics. In 128×128 MIMO systems, numerical results demonstrate that the optimized BFSD using our first and second schemes can reduce complexity by up to 82% and 65%, respectively, while achieving superior detection performance compared to state-of-the-art BFSD.

One of the primary challenges in ultra-reliable and low-latency communications (URLLC) is to achieve accurate channel estimation and data detection while minimizing latency. Given the small packet size in URLLC, relying solely on pilot-assisted (PA) coherent detection is almost impossible to meet the seemingly contradictory requirements of high channel estimation accuracy, high reliability, low training overhead, and low latency. In this paper, we explore both frequency domain differential modulation (FDDM) and time domain differential modulation (TDDM), enabling non-coherent short packet URLLC with mini-slot structures. The minimum achievable block error rate and the maximum achievable rate for all three modes (i.e., FDDM, TDDM and PA modes) are derived using non-asymptotic information-theoretic bounds. Furthermore, we show that FDDM can more than compensate for the training overhead inadequacy and performance degradation of PA mode in medium-to-high-mobility scenarios, thereby improving the performance of short packet transmission with mini-slot by dynamically activating FDDM. Simulation results validate the feasibility and effectiveness of the proposed low overhead FDDM mini-slot transmission scheme.

Pervasive and high-accuracy positioning has become increasingly important as a fundamental enabler for intelligent connected devices in mobile networks. Nevertheless, current wireless networks heavily rely on pure model-driven techniques to achieve positioning functionality, often succumbing to performance deterioration due to hardware impairments in practical scenarios. Here, we reformulate the direction finding or angle-of-arrival (AoA) estimation problem as an image recovery task of the spatial spectrum and propose a new model-driven deep neural network (MoD-DNN) framework. The proposed MoD-DNN scheme comprises three modules: a multi-task autoencoder-based beamformer, a coarray spectrum generation module, and a model-driven deep learning-based spatial spectrum reconstruction module. Our technique enables automatic calibration of angular-dependent phase error, thereby enhancing the resilience of direction-finding precision against realistic system non-idealities. We validate the proposed scheme both using numerical simulations and field tests. The results show that the proposed MoD-DNN framework enables effective spectrum calibration and accurate AoA estimation. To the best of our knowledge, this study marks the first successful demonstration of hybrid data-and-model-driven direction finding utilizing readily available commodity 5G gNodeB.

Guessing random additive noise decoding (GRAND) is establishing itself as a universal method for decoding linear block codes, and ordered reliability bits GRAND (ORBGRAND) is a hardware-friendly variant that processes soft-input information. In this work, we propose an efficient hardware implementation of ORBGRAND that significantly reduces the cost of querying noise sequences with slight frame error rate (FER) performance degradation. Different from logistic weight order (LWO) and improved LWO (iLWO) typically used to generate noise sequences, we introduce a reduced-complexity and hardware-friendly method called shift LWO (sLWO), of which the shift factor can be chosen empirically to trade the FER performance and query complexity well. To effectively generate noise sequences with sLWO, we utilize a hardware-friendly lookup-table (LUT)-aided strategy, which improves throughput as well as area and energy efficiency. To demonstrate the efficacy of our solution, we use synthesis results evaluated on polar codes in a 65-nm CMOS technology. While maintaining similar FER performance, our ORBGRAND implementations achieve 53.6-Gbps average throughput ($1.26\times $higher), 4.2-Mbps worst case throughput ($8.24\times $higher), 2.4-Mbps/mm2 worst case area efficiency ($12\times $higher), and$4.66\times 10 ^{{4}}$pJ/bit worst case energy efficiency ($9.96\times $lower) compared with the synthesized ORBGRAND design with LWO for a (128, 105) polar code and also provide$8.62\times $higher average throughput and$9.4\times $higher average area efficiency but$7.51\times $worse average energy efficiency than the ORBGRAND chip for a (256, 240) polar code, at a target FER of$10^{-7}$.

Expectation propagation (EP) achieves excellent performance with high-order modulation in massive multiple-input multiple-output (MIMO) detection. The soft output of the EP detector can be iteratively combined with turbo soft decoders to enhance error-correction performance. However, the implementation of EP-based iterative detection and decoding (IDD) receivers suffer from an exponential increase in computational complexity as the number of antennas and modulation order grows. In this brief, we propose a simplified EP approximation-based IDD (sEPA-IDD) scheme for hardware implementation. To alleviate the computational burden, a simplified message update scheme is proposed, reducing complexity by 68% without performance degradation. Additionally, a unified design for extrinsic message computation further improves hardware utilization. Finally, we introduce the first unfolded EP-based IDD architecture to boost throughput. Compared with state-of-the-art (SOA) IDD receivers, the sEPA-IDD receiver implemented on 65 nm CMOS delivers a throughput of 3.07 Gb/s with a maximum 0.5 dB gain, achieving 4.03× higher throughput and 6.04× greater area efficiency.

With the advancement of communication technology and the improvement of localization accuracy, cellular networks are gradually evolving from communication to perception-integrated networks. Addressing the research challenges of sensing-assisted communication, we propose, for the first time, the concept of Digital Twin of Channel (DToC). Specifically, we regard user terminal (UT) positions as physical objects, and statistical channel state information (CSI) as virtual digital objects. Observing the change trend of UTs’ statistical CSI caused by the changes of UT’s physical position enables predictive analytics for subsequent communication tasks. Then, we establish the relationship between physical and virtual digital objects using a Diffusion Model (DM) to achieve the DToC. Indeed, the DM can generate the desired objects by gradually denoising from noisy data using neural networks. Furthermore, we propose a conditional DM utilizing UTs’ positions, which completes the task of generating the corresponding statistical CSI under known user-specific position conditions, thus mapping UT positions to statistical CSI. Simulation results demonstrate that our DToC framework outperforms previous statistical CSI estimation methods. Without the need of pilots, our method can simultaneously generate statistical CSIs from a large number of UTs’ positions, achieving satisfactory results.

The antenna weight optimization plays a vital role in improving the key performance indicators (KPIs) of massive multi-input multi-output (MIMO) systems. Due to complicated channel characteristics and numerous parameter combinations, conventional methods suffer from suboptimal performance and unaffordable complexity. Additionally, the implementation of existing intelligent algorithms in the practical system is limited by heavy overhead and potential instability associated with environmental interactions. In this paper, we propose a digital twin assisted optimization framework comprising a conditional generative adversarial network (C-GAN) aided digital twin and a deep reinforcement learning (DRL) based massive MIMO antenna weight optimization algorithm to maximize the KPI of coverage. The C-GAN aided digital twin is built to augment system performance data and offer high-precision KPI pre-validation by fitting the mapping between beamforming schemes and system performance along with the distribution of system performance over user positions. The DRL based optimization algorithm that achieves a better complexity-performance tradeoff is combined with digital twin for reduced training overhead and safe exploration. Simulation results based on quasi deterministic radio channel generator (QuaDRiGa) verify that compared to intelligent optimization methods directly conducted in practical systems, our proposed framework can reduce the overhead while achieving comparable performance by leveraging high-precision pre-validation capabilities of the proposed digital twin.

The Industrial Internet of Things (IIoT) is envisioned as the future paradigm for the next-generation industrial system. To provide low-latency and high-reliable communication for a large number of battery-limited industrial equipments, this paper proposes a cell-free network-assisted full-duplex (NAFD)-enabled short packet communications (SPC) scheme in IIoT networks, where access points (APs) operating in the uplink (UL) mode or downlink (DL) mode simultaneously serve UL sensors and DL actuators on the same frequency resource. Considering the UL-to-DL error propagation as well as the dynamic between latency, reliability and energy efficiency (EE), we take the effective EE as the performance metric and investigate the effective EE maximization problem by jointly optimizing UL power, DL beamforming matrices, data rates and duplex mode selection. The formulated problem is a mixed-integer fractional programming and decomposed into three subproblems. We develop the optimal and suboptimal algorithms for joint power control and beamforming design. A semi-closed-form solution is derived for the optimal data rates, and an efficient duplex selection algorithm is also proposed to find the computationally optimal duplex mode. Simulation results demonstrate the convergence of proposed algorithms, and the flexible cell-free NAFD scheme can achieve better EE performance than benchmark schemes such co-time co-frequency full duplex (CCFD), frequency division duplex (FDD) and greedy algorithm (GA) schemes.

This paper proposes a cooperative integrated sensing and communication (ISAC) scheme for low-altitude sensing scenario, aiming at estimating the parameters of the uncrewed aerial vehicles (UAVs) and enhancing the sensing performance via cooperation. The proposed scheme consists of two stages. In Stage I, we formulate the monostatic parameter estimation problem via using a tensor decomposition model. By leveraging the Vandermonde structure of the factor matrix, a spatial smoothing tensor decomposition scheme is introduced to estimate the UAVs’ parameters. To further reduce the computational complexity, we design a reduced-dimensional (RD) angle of arrival (AoA) estimation algorithm based on generalized Rayleigh quotient (GRQ). In Stage II, the positions and true velocities of the UAVs are determined through the data fusion across the multiple base stations (BSs). Specifically, we first develop a false removing minimum spanning tree (MST)-based data association method to accurately match the BSs’ parameter estimations to the same UAV. Then, a Pareto optimality method and a residual weighting scheme are developed to facilitate the position and velocity estimation, respectively. We further extend our approach to the dual-polarized system. Simulation results validate the effectiveness of the proposed schemes in comparison to conventional techniques.

This paper investigates the joint design of transmit waveform and receive filter for a collocated multipleinput multiple-output (MIMO) radar, where one-bit digitalto-analog converters (DACs) and one-bit analog-to-digital converters (ADCs) are employed to reduce the hardware cost and power consumption. We first derive a novel signal-toquantization-plus-interference-plus-noise ratio (SQINR) metric via a careful theoretical analysis to accurately characterize the one-bit MIMO radar performance. Then, we formulate a onebit transceiver optimization problem by maximizing the SQINR objective subject to binary DAC constraints. To handle the complicated mixed-integer problem, we propose an efficient penalty-based majorization-minimization (PMM) algorithm with guaranteed convergence. As validated via simulations, the proposed algorithm achieves a noticeable performance gain over the existing benchmark scheme based on a low input signal-to-noise/interference-to-noise ratio (LIS) assumption.

In this paper, we quantitatively compare these two effective communication schemes, i.e., digital and analog ones, for wireless federated learning (FL) over resource-constrained networks, highlighting their essential differences as well as their respective application scenarios. We first examine both digital and analog transmission methods, together with a unified and fair comparison scheme under practical constraints. A universal convergence analysis under various imperfections is established for FL performance evaluation in wireless networks. These analytical results reveal that the fundamental difference between the two paradigms lies in whether communication and computation are jointly designed or not. The digital schemes decouple the communication design from specific FL tasks, making it difficult to support simultaneous uplink transmission of massive devices with limited bandwidth. In contrast, the analog communication allows over-the-air computation (AirComp), thus achieving efficient spectrum utilization. However, computation-oriented analog transmission reduces power efficiency, and its performance is sensitive to computational errors. Finally, numerical simulations are conducted to verify these theoretical observations.

As an important component of self-healing technology, outage detection is of great significance for the smooth operation of subsequent fault diagnosis and outage compensation operations. In this paper, we propose an outage detection algorithm that combines a hybrid Generative Adversarial Network (GAN) with an overlap-sensitive Artificial Neural Network (ANN) to solve the data imbalance problem as well as the overlap problem between data classes in outage detection. The proposed algorithm first synthesizes the outage data and adjusts the data distribution by hybrid GAN outage data distribution features. Then, based on the distribution of the samples in the feature space, the K-nearest neighbor algorithm is used to calculate the degree of overlap between the classes of the samples, and the samples are assigned weights accordingly. Finally, the resulting weight set is combined with the calibrated dataset and weighted to train the artificial neural network classifier. Simulation results show that when compared with the existing outage detection algorithm, the proposed algorithm improves the outage detection performance significantly, and can accurately detect multiple classes of outage cells.

The coexistence of Enhanced Mobile Broad-band (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) services is a common scenario in 5G. While eMBB strives for high data rates using slots as transmission time intervals (TTIs), URLLC emphasizes reliability and low latency using mini-slots. Puncturing scheme is introduced by 3GPP, which means puncturing a set of Resource Blocks (RBs) from ongoing eMBB transmissions and reallocating them for URLLC traffic. In this paper, a DRL-based dynamic resource slicing scheme for eMBB and URLLC traffic considering puncturing is proposed, where the data rate, Quality of Service (QoS) satisfaction and rate stability for eMBB users are simultaneously optimized on mini-slot-level timescale, by employing an improved Deep Q-learning (DQN) algorithm. Simulation results demonstrate that the proposed algorithm outperforms the baseline algorithms while ensuring the latency and reliability requirements of URLLC and successfully protects eMBB users under adverse conditions.

In this paper, we investigate a multiuser integrated sensing and communication (ISAC) system with hardware im-pairments at both the base station (BS) transceiver and the users. Specifically, by considering the impact of hardware impairments, we optimize the transmit and receive beamforming at the ISAC BS to maximize the radar output signal-to-interference-plus-noise ratio (SINR) for sensing, under both point and extended target scenarios, subject to the constraints of communication requirement and power limitation. For both scenarios, we first find closed-form optimal radar receive beamforming and then obtain equivalent reformulations with respect to the transmit beamforming. Subsequently, for the resulting problems, a globally optimal solution is obtained for the point target scenario and an iterative solution is proposed for the extended target scenario. Finally, the effectiveness of the proposed methods is evaluated via simulation results.

This paper considers a decentralized massive random access scheme applicable to a user-centric cell-free massive MIMO system. In this system, a large number of user equipment (UEs) select access points (APs) based on the quality of channel, and adjacent APs can achieve lossless data exchange with a finite data volume. Each AP is equipped with an Edge Distributed Unit (EDU) capable of independently performing Maximum Likelihood (ML) estimation of the large-scale fading coefficients (LSFC) for the UEs associated with. Subsequently, the APs collectively assess the activity of associated UEs based on the estimated LSFC vectors obtained from adjacent APs, and employ a heuristic method to identify primary interference sources. On this basis, each AP's EDU uses the approximate message passing - sparse Bayesian learning (AMP-SBL) algorithm for channel estimation (CE). The proposed scheme concentrates on the associated user set, reducing computational complexity and signaling overhead while maintaining accurate performance. Numerical simulations validate the effectiveness and superiority of the approach presented in this paper.

A cell-free radio access network (CF-RAN) is a novel architecture that can effectively solve the scalability problems and lack of collaboration capabilities of other cell-free networks and achieve a better trade-off between performance and complexity. There are multiple edge distributed units (EDUs) in a CF-RAN system and each EDU mounts multiple APs. This paper inves-tigates the spectral efficiency (SE) performance of a downlink CF-RAN with zero-forcing (ZF) beamforming in the presence of pilot reuse, considering power coefficients and compression noise. An asymptotic expression for the downlink signal-to-interference-plus-noise ratio (SINR) of the system can be derived by using large-scale random matrix theory. To improve the performance of the system, we consider the EDU-AP association strategy and propose a heuristic greedy algorithm. Simulation results verify the accuracy of the asymptotic expression and the effectiveness of the proposed association algorithm.

Reconfigurable intelligent surface (RIS) is a promising candidate technology for the upcoming sixth-generation (6G) communication system for its ability to manipulate the wireless communication environment by controlling the coefficients of reflection elements (REs). However, since the RIS usually consists of a large number of passive REs, the pilot overhead for channel estimation in the RIS-aided system is prohibitively high. In this article, the channel estimation problem for an RIS-aided multiuser multiple-input–single-output (MISO) communication system with clustered users is investigated. First, to describe the correlated feature for RIS–user channels, a beam-domain channel model is developed for RIS–user channels. Then, a pilot reuse strategy is put forward to reduce the pilot overhead and decompose the channel estimation problem into several subproblems. Finally, by leveraging the correlated nature of RIS–user channels, an eigenspace projection (EP) algorithm is proposed to solve each subproblem, respectively. Simulation results show that the proposed EP channel estimation scheme can achieve accurate channel estimation with lower pilot overhead than existing schemes.

The demand for higher transmission efficiency and denser user access has been put forth by the next generation of wireless communication systems. To cater to the future communication development, this article focuses on massive random access schemes under the user-centric cell-free massive multiple-input-multiple-output (MIMO) architecture. For uplink transmission, a data frame structure is designed to enable active user detection (AUD), channel estimation (CE), and data transmission. The association between access points (APs) and user equipment (UEs) is presented to facilitate an user-centric cell-free scalable architecture. In this article, a maximum likelihood (ML)-based method is proposed for AUD to obtain the set of active UEs. By setting appropriate thresholds and combining the UE-AP association, accurate active detection results can be obtained. CE can be accomplished with lower computational complexity by utilizing the detected active UE set in AUD module. Specifically, the generalized approximate message passing-based sparse Bayesian learning with Dirichlet process (GAMP-DP-SBL) is adopted as the CE algorithm, leveraging the spatial aggregation and dispersion characteristics of APs to enhance the estimation accuracy. Building upon GAMP-DP-SBL algorithm, a clustered algorithm (GAMP-CDP-SBL) is proposed to reduce the scale of the sensing matrix and improve the accuracy of CE for associated active UEs. Moreover, to enhance system scalability, decentralized AUD and CE algorithms are proposed in this article. Simulation results under various parameter settings and different scenarios exhibit the superior performance of the proposed scheme.

In the low-altitude three-dimensional coverage scenario with unmanned aerial vehicles (UAVs), user data requirements change quickly and the asymmetry of uplink and downlink traffic is hard to address. We utilize cell-free radio access networks (CF-RAN) to support dynamic scenario due to its cooperative capability and scalability, and adopt network-assisted full-duplex (NAFD) to support flexible duplex communication by selecting appropriate node duplex modes according to the traffic in real-time. Considering the scalable minimum mean square error (MMSE) receiver and regularized zero-forcing (RZF) precoding scheme, the closed-form expressions of uplink spectral efficiency with infinite block-length regime and the lower bound of downlink spectral efficiency with finite block-length communication (FBLC) regime are derived. Based on these expressions, we propose a multi-objective optimization problem (MOOP) to maximize and balance the spectral efficiency of upink and downlink by optimizing the duplex mode of remote radio units (RRUs) and solving it with deep Q-learning (DQN) algorithm. Simulation results verify the accuracy of derived expressions, the effectiveness of the proposed optimization algorithm and the superiority of NAFD technique in low-altitude CF-RAN system.

Ultrareliable low-latency communication (URLLC) has been envisioned as the paradigm shift for wireless industrial automation in the coming industry 4.0. Despite the flexibility in conjunction with stringent requirements of latency and reliability, URLLC cannot guarantee the determinism demanded by industrial automation to provide an in-order and low-jitter delivery. Motivated by the evolvements in Rel-17 that strengthen the intrinsic determinism of 5G system, this article studies deterministic URLLC to achieve low-latency and bandwidth-saving scheduling of time-triggered traffic in Industrial Internet of Things (IIoT), instead of using time-sensitive networking (TSN) or hybrid TSN-URLLC. However, due to the queueing congestion and wireless links changes, it is challenging to achieve deterministic scheduling with bounded end-to-end (E2E) latency and low-loss probability. To ensure the determinism and schedulability of URLLC, offset s in gate control lists (GCLs) and radio resources are assigned under constraints of zero congestion, sequential arrival, bounded latency, and ultra reliability. To further improve the latency reduction and bandwidth saving of deterministic URLLC, we find the feasible transmission delays subject to the contradiction on monotonicity of the E2E latency and bandwidth with respect to the transmission delay, then propose a scheduling method to obtain the best solution of three cases by optimizing transmission delay, offset, bandwidth, and the number of subchannel. Simulation results validate the analysis and show the performance gain of the proposed method in three cases.

This article proposes an uplink transmission scheme for cell-free (CF) massive multiple-input-multiple-output (MIMO) systems in high-speed railway communications to mitigate the negative effects of fast time-varying multipath channels with fewer antennas and insufficient angular resolution. To enhance the estimation performance by utilizing the possible similarity of the relatively slow time-varying channel parameters, the Dirichlet process (DP) is introduced to construct the probabilistic model of the doubly selective multiparameter channel estimation problem, which is solved by adopting the expectation-maximization (EM) framework. Furthermore, by introducing successive approximation, an enhanced channel estimation scheme is proposed. Finally, for the data transmission utilizing the orthogonal frequency-division multiplexing (OFDM) modulation, based on estimated channel parameters, a distributed intercarrier interference (ICI) elimination algorithm with local channel quality-based combining is proposed, and the performance of the system under different architectures and ICI elimination algorithms is analyzed. Simulation results show that DP can improve estimation performance by utilizing implicit similarity, the combination of first-order Taylor expansion and successive approximation can effectively improve estimation performance at higher speeds under relatively higher pilot power, and compared to centralized processing, the proposed distributed ICI elimination algorithm with local channel quality-based combining has a relatively small performance loss.

Massive ultrareliable and low-latency communications (mURLLCs) have emerged as a dominating 6G-standard service. Fast uplink grant is an effective means to solve the uplink access in mURLLC due to the advantages of low signaling cost and collision-free. However, as two key challenges in fast uplink grant, active set prediction and optimal scheduling still lead to high resource wastage and low access success probability. Based on the special spatial sparsity of cell-free networks, we propose the spatial-separable nonorthogonal multiple access (SSNOMA). Compared with nonorthogonal multiple access (NOMA), SSNOMA allows multiple users to share same 3-D resources composed of time-frequency resources and pilots, so as to reduce the resource wastage caused by prediction errors. Furthermore, we design an intelligent hierarchical fast uplink grant framework. In this framework, the upper controller is responsible for scheduling users from the predicted active user set to ensure the optimal Quality of Service (QoS) and active probability, while the lower controller strictly controls the allocation of uplink grants among the scheduled users to maximize spectral efficiency. In addition, considering the limited ability to collect information in massive user access, the upper confidence bound (UCB)-based multiarmed bandit (MAB) algorithm is used in the upper layer to schedule fast grant users, while the multiagent deep deterministic policy gradient (MADDPG) is used in the lower layer to perform specific grant allocation. Simulation results show that the proposed SSNOMA-based intelligent hierarchical framework can significantly improve the utilization of limited resources, and track long-term scheduling experience as well as QoS, effectively supporting mURLLC.

This paper proposes a novel localization algorithm using the reconfigurable intelligent surface (RIS) received signal, i.e., RIS information. Compared with BS received signal, i.e., BS information, RIS information offers higher dimension and richer feature set, thereby providing an enhanced capacity to distinguish positions of the mobile users (MUs). Additionally, we address a practical scenario where RIS contains some unknown (number and places) faulty elements that cannot receive signals. Initially, we employ transfer learning to design a two-phase transfer learning (TPTL) algorithm, designed for accurate detection of faulty elements. Then our objective is to regain the information lost from the faulty elements and reconstruct the complete high-dimensional RIS information for localization. To this end, we propose a transfer-enhanced dual-stage (TEDS) algorithm. In Stage I, we integrate the CNN and variational autoencoder (VAE) to obtain the RIS information, which in Stage II, is input to the transferred DenseNet 121 to estimate the location of the MU. To gain more insight, we propose an alternative algorithm named transfer-enhanced direct fingerprint (TEDF) algorithm which only requires the BS information. The comparison between TEDS and TEDF reveals the effectiveness of faulty element detection and the benefits of utilizing the high-dimensional RIS information for localization. Besides, our empirical results demonstrate that the performance of the localization algorithm is dominated by the high-dimensional RIS information and is robust to unoptimized phase shifts and signal-to-noise ratio (SNR).

Extremely large-scale multiple-input multiple-output (XL-MIMO) is capable of supporting extremely high system capacities with large numbers of users. In this work, we build a framework for the analysis and low-complexity design of XL-MIMO in the near field with spatial non-stationarities. Specifically, we first analyze the theoretical performance of discrete-aperture XL-MIMO using an electromagnetic (EM) channel model based on the near-field spherical wavefront. We analytically reveal the impact of the discrete aperture and polarization mismatch on the received power. We also complement the classical Fraunhofer distance based on the considered EM channel model. Our analytical results indicate that a limited part of the XL-array receives the majority of the signal power in the near field, which leads to a notion of visibility region (VR) of a user. Thus, we propose a VR detection algorithm and leverage the acquired VR information to devise a low-complexity symbol detection scheme. Furthermore, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the correctness of the analytical results and the effectiveness of the proposed algorithms. It reveals that our algorithms approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

When faced with challenging antenna configurations or high-order modulations in realistic propagation environments, the Belief Propagation (BP) MIMO detector outperforms its linear counterparts. To mitigate the error floor issue and lower the complexity, a revised BP detector, named the Belief-selective Propagation (BsP) detector, has recently emerged by selectively utilizing trusted incoming messages for updates. Despite those promising potentials, the straightforward hardware implementation of the BsP detector still suffers from high complexity and necessitates further optimization. To bridge the gap between the BsP algorithm and implementation, this paper introduces anapproximatebut implementation-friendly BsP detector called aBsP, based on which the very first BsP hardware is proposed. Two unexplored features:approximate initializationandsimplified message updatessave the complexity (more than$84$%) with acceptable performance penalization. Multi-level optimization techniques involving group-layered message updating, approximate arithmetic circuits, and hybrid-precise quantization are developed to boost the hardware efficiency A$128\times 8$$256$-QAM aBsP MIMO detector ASIC in$40$nm CMOS occupies an area of$0.68$mm$^2$and reaches a throughput of$790.52$Mbps. Benchmarking with the recent arts, this work achieves$1.08\times$area efficiency and$3.34\times$gate efficiency.

Network-assisted full-duplex (NAFD) cell-free (CF) network emerges as a promising solution for enabling massive ultra-reliable and low-latency communications (mURLLC). In the massive Internet-of-Things (mIoT) scenarios where users’ active statuses change, the existing NAFD resource scheduling schemes require frequent invocation of optimization algorithms, causing significant energy loss and operational delays. So they are not conducive to mURLLC. This paper proposes a dual time-scale resource scheduling scheme, which combines the improved long-term AP duplex mode optimization method with the short-term power allocation optimization method to further enhance the mURLLC ability of NAFD CF networks. In the proposed long-term AP duplex mode optimization method, we first derive the closed-form expressions of active users’ time overflow (TO) probability as the service latency indicator. Operating on a superframe as a large time-scale unit, the improved long-term AP duplex mode optimization method initially employs a long-term active user prediction algorithm to forecast active users in an upcoming superframe and then leverages the long-term AP duplex mode optimization algorithm based on multi-agent deep reinforcement learning to achieve optimal long-term AP mode selection which minimizes the TO probability. In the proposed short-term power allocation optimization method, we design a heuristic algorithm to ensure active users in each coherence time can receive high-reliable and low-latency service. Simulation results demonstrate the effectiveness of the proposed scheme. Compared with the short-term AP mode and power joint optimization methods, the dual time-scale resource scheduling scheme achieves similar spectral efficiency and a much lower TO probability, while also avoiding the frequent AP mode optimization and switching, making it more suitable for mURLLC.

Cell-free radio access network (CF-RAN) with network-assisted full-duplex (NAFD) possesses scalability and enables reception and transmission simultaneously, which is suitable for large-scale ultra reliable and low-latency communication (URLLC). Achieving strict requirements of URLLC for each terminal with a fixed infrastructure is challenging, and unmanned aerial vehicles (UAVs) have been considered as promising enablers to handle this issue due to its flexible deployment, low cost and large coverage. In this paper, we investigate a multi-UAV aided CF-RAN with NAFD that use UAVs as aerial access points (APs). We firstly propose a multi-UAV deployment algorithm based on user distribution and quality of service (QoS) requirement. Then, we derive the closed-form expressions for uplink achievable rate with long block length regime and the lower bound of downlink achievable rate with finite block length communication (FBLC) regime. Based on those expressions, we formulate a weighted sum spectral efficiency maximization problem and solve it by Deep Q-Network (DQN) algorithm. Numerical results verify the accuracy of the derived closed-form expressions and illustrate the impact of system parameters on spectral efficiency. The effectiveness of proposed UAV deployment algorithm and the performance advantages of the weighted sum spectral efficiency optimization algorithm are demonstrated.

The statistical gap between existing correlation-based stochastic channel models (CBSMs) and practical propagation is a longstanding issue, especially for massive multiple-input multiple-output (MIMO) transmission. To address this problem, a pervasively correlated channel model (PCCM) applicable to MIMO channels with arbitrary antenna configurations and scenarios is proposed. Unlike the conventional jointly correlated channel model (JCCM) based on an independently and identically distributed (i.i.d.) random matrix, a new random matrix whose elements are independent and nonidentically distributed (i.n.d.) generalized Gamma complex Gaussian mixture (GGCGM) variables with correlated envelopes is used to approximate the real channel statistics better. Moreover, the PCCM can be simplified via the Rayleigh fading assumption for rapid evaluation of channel performance and supports backward compatibility with existing CBSMs under specific assumptions. Demonstrative numerical experiments for MIMO channels are conducted based on the 3GPP TR 38.901 model considering various scenarios, frequencies, and antenna configurations. The channel capacity distributions are obtained based on random samples generated using the geometry-based stochastic channel model (GBSM), the JCCM, and the proposed PCCM. The numerical results show that the PCCM is more flexible than the JCCM with respect to high-order statistics, enabling more accurate estimation of massive MIMO transmission channel performance.

Confronted with the challenges of interruptions and blockages caused by dense obstacles in millimeter-wave (mmWave) communication, we propose to employ a flexible distributed reconfigurable intelligent surface (RIS) assisted massive multiple-input multiple-output (MIMO) system to improve performance in areas with poor coverage. In a scenario featuring multi-antenna user equipments (UEs), we consider the practical additive hardware impairments (AHIs) at transceivers and conduct a comprehensive analysis of their impact on the system performance. Leveraging the pronounced beam directivity inherent in mmWave MIMO, we design phase shifts of RISs and analog beamformers of transceivers to achieve beam alignment. In light of this, we explore a linear minimum mean-square error (LMMSE) equivalent channel estimation method. Furthermore, we derive the closed-form expressions for downlink achievable spectral efficiency (SE) in the presence of AHIs, utilizing statistical channel state information (CSI) and maximum ratio transmission (MRT). Based on the derived closed-form expressions, we propose an efficient power allocation strategy relying on an intelligent algorithm known as primal-dual optimization based deep deterministic policy gradient (PDO-DDPG), which can ensure safe exploration of the agent. Numerical results confirm the accuracy of the derived closed-form expressions, unveil the impact of AHIs on the achievable SE, and verify the effectiveness of the PDO-DDPG based power allocation.

Deep learning (DL) has been increasingly adopted for channel state information (CSI) feedback to harness the performance gains promised by massive multiple-input multiple-output (MIMO). Existing DL-based feedback schemes prioritize the accuracy of CSI reconstruction, which results in substantial memory and computational demands, especially when they are unacceptable for user equipment (UE) with limited resources. In this paper, we propose an asymmetric pooling-based network for more efficient CSI compression, named PoolCsiNet, to reduce the associated overhead of exploiting convolutional neural networks (CNN) for CSI compression at the UE. By leveraging the local information of clustered physical channel models, PoolCsiNet incorporates a low-complexity amplitude-pooling algorithm in its encoder at the UE without requiring any trainable parameters. A corresponding decoder structure is also developed to firstly acquire a coarse CSI and then a lightweight feature refiner is constructed to enhance the coarse CSI reconstruction. The parameter-free encoder and CNN-based refiner constitute a novel asymmetric CSI network architecture. Thanks to the parameter-free design of the encoder, memory demand at the UE is minimized, thereby eliminating the need for joint training and parameter updating. Furthermore, considering the sparsity of indoor wireless channels, a PoolCsiNet+, with a dilated-amplitude-pooling (DAP) module, is further proposed to elevate the CSI reconstruction accuracy of the PoolCsiNet. Thanks to a pooling design tailored for clustered channel models, lossy compression of pooling hardly sacrifices CSI features and can be exploited to eliminate information redundancy in CSI. Experiments demonstrate that both asymmetric PoolCsiNet and PoolCsiNet+ significantly improve the quality of CSI reconstruction up to 4 dB compared with existing DL-based methods, while maintaining a memory-free profile and achieving a sevenfold reduction in computational overhead at the UE.

Network slicing-based communication systems can dynamically and efficiently allocate resources for diversified services. However, due to the limitation of the network interface on channel access and the complexity of the resource allocation, it is challenging to achieve an acceptable solution in the practical system without precise prior knowledge of the dynamics probability model of the service requests. Existing work attempts to solve this problem using deep reinforcement learning (DRL). However, such methods usually require a lot of interaction with the real environment to achieve good results. In this paper, a framework consisting of a digital twin and reinforcement learning agents is present to handle the issue. Specifically, we propose to use the historical data and the neural networks to build a digital twin model to simulate the state variation law of the real environment. Then, we use the data generated by the network slicing environment to calibrate the digital twin so that it is in sync with the real environment. Finally, DRL for slice optimization optimizes its performance in this virtual pre-verification environment. We conducted an exhaustive verification of the proposed digital twin framework to confirm its scalability. Specifically, we propose to use loss landscapes to visualize the generalization of DRL solutions. We explore a distillation-based optimization scheme for lightweight slicing strategies. In addition, we also extend the framework to offline reinforcement learning, where solutions can be used to obtain intelligent decisions based solely on historical data. Numerical simulation experiments show that the proposed digital twin can significantly improve the performance of the slice optimization strategy.

The cloud droplets effective radius is a key feature that plays a critical role in influencing cloud microphysical processes and radiative effects. Accurate quantification of cloud effective radius (CER) is essential for advancing our understanding of cloud microphysics, refining cloud parameterization, and improving future climate prediction. Nonetheless, the accuracy of current CER retrieval algorithms, particularly relying on millimeter-wavelength cloud radar, is often largely affected by assumptions about the cloud droplet number concentration, inappropriate empirical coefficients, attenuated radar reflectivity, and limitations of other auxiliary instruments. In this study, we developed a novel CER retrieval algorithm for single-wavelength radar by leveraging the interconnections between CER, liquid water content (LWC), and cloud radar reflectivity. Unlike the previous studies, we first derive the LWC from a self-consistent method based on cloud liquid water mass absorption instead of empirical relationships. Subsequently, we correct the radar measured reflectivity attenuated by cloud water and perform sensitivity analysis to select an optimal parameter that minimizes the uncertainty associated with the given cloud droplet size distribution (DSD) assumption. Then, the CER is calculated from the retrieved LWC, corrected reflectivity, and the optimal parameter. We compared the frequency distribution, vertical structure, and error fraction of the retrieved CER with aircraft in situ measurements. Our results demonstrate higher consistency with in situ data compared to traditional empirical algorithms. Furthermore, the cloud optical thickness (COT) derived from the CER shows a much better agreement with Moderate Resolution Imaging Spectroradiometer (MODIS) products, which provides additional validation for the efficacy of our method in investigating cloud microphysical properties.

Network slicing (NS) can enable diverse communication services for vertical industries. In radio access network slicing, differential service level agreement (SLA) guaranteeing is an essential resource management task. Benefit from powerful data analysis capabilities, deep learning (DL) is suitable for intelligent resource management under the cases of complex constraints and time-varying states. Thus, DL has been used to manage resources for NS recently. However, the training of these DL-assisted methods is time-consuming and it is difficult to keep high SLA satisfaction rates dynamically. To address this problem, we propose a hierarchical intelligent NS resource configuration method via organically integrating NS preconfiguration models based on deep neural networks and NS reconfiguration models using multiarm bandits. A factory automation system is established to evaluate our proposed methods on different industrial services. Simulation and experimental results demonstrate that our proposed methods outperform benchmarks comprehensively.

Native artificial intelligence (AI) has played a pivotal role in shaping the evolution of 6G networks. It must meet stringent real-time requirements and therefore deploying lightweight AI models is necessary. However, as wireless networks generate a multitude of data fields and only a fraction of them imposes significant impact on the AI models, it is essential to accurately identify a small amount of critical data that significantly impacts communication performance. In this paper, we propose the pervasive multi-level (PML) native AI architecture, which incorporates knowledge graph (KG) into mobile network operations to establish a wireless data KG. Leveraging the wireless data KG, we analyze the relationships among various data fields and provide the on-demand generation of minimal and effective datasets, referred to as feature datasets. Consequently, it not only enhances AI training, inference, and validation processes but also significantly reduces resource wastage and overhead for communication networks. The proposed solution includes a spatio-temporal heterogeneous graph attention neural network model (STREAM) and a feature dataset generation algorithm. Experimental results validate the exceptional capability of STREAM in handling spatio-temporal data and demonstrate that the proposed architecture effectively reduces data scale and computational costs of AI training by almost an order of magnitude.

This paper investigates the performance of a two-timescale transmission design for uplink reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems. We consider the Rician channel model and design the passive beamforming of RISs based on the long-time statistical channel state information (CSI), while the central processing unit (CPU) utilizes the maximum ratio combining (MRC) technology to perform fully centralized processing based on the instantaneous overall channel, which is the superposition of the direct and RIS-reflected channels. Firstly, we derive the closed-form approximate expression of the uplink achievable rate for arbitrary numbers of access point (AP) antennas and RIS reflecting elements, which can be used to obtain energy efficiency through the proposed total power model. Relying on the derived expressions, we theoretically analyze the impact of important system parameters on the rate and draw explicit insights into the benefits of RISs. Then, based on the rate expression under statistical CSI, we optimize the phase shifts of RISs by using the genetic algorithm (GA) to maximize the sum rate and minimum rate of users, respectively. Finally, the numerical results demonstrate the correctness of our expressions and the benefits of deploying large-size RISs into cell-free mMIMO systems. Also, we investigate the optimality and convergence behaviors of the GA to verify its effectiveness. To give a more beneficial analysis, we present numerical results to show the high energy efficiency of the system with the help of RISs. Besides, our results have revealed the benefits of distributed deployment of APs and RISs in the RIS-aided mMIMO system with cell-free networks.

This paper investigates the distributed uplink for the hierarchically backhaul-linked cell-free network with distributed processors to maximize the advantages of jointly serving under the same time and frequency resources. It is validated in previous works that the performance of fully centralized uplink in a cell-free network overwhelms uplink methods without or with limited coordination. On the other hand, a fully centralized uplink requires extremely high costs on backhaul links and computation capacity on the central processing unit (CPU), which is impractical in a widely deployed large cell-free network. To handle the mentioned problems, the relation between centralized uplink and group sliced distributed uplink is revealed, firstly. With the uniform framework compatible with previous fully centralized and fully distributed minimal mean square error (MMSE) equalization, two theorems are derived as group-joint MMSE complementary and the column space equivalence, which indicate the relation between the local optimal and the global optimal and can include conclusions achieved in previous works. Both computation and backhaul signaling overheads are distributed among the whole network. Simulation results demonstrate the excellent performance of proposed methods based on the derived complementary kernel.

Distributed precoding is a meaningful topic in the cell-free massive multiple input multiple output system. This system faces challenges in performance degradation due to the absence of knowledge from other antennas and several realistic constraints brought by the distributed implementation of the communication system such as the presence of phase noise (PN). In this paper, a robust cascaded team minimum mean square error (RCT-MMSE) precoding based on a hierarchical fronthaul structure is exploited to handle distributed and robust precoding including not only PN but signaling noise, sharing cost constraints and channel aging uncertainty. Such RCT-MMSE precoding, characterized by its avoidance of iterations because we derive the analytic expressions, mitigates the need for high fronthaul level instantaneous information exchange. It also demonstrates scalability with distributed computation burden and flexible signaling overhead, which offers an advantageous performance-cost tradeoff. Simulation results demonstrate the effectiveness of RCT-MMSE to combat several practical constraints and provide a flexible distributed precoding framework compared with previous ones.

The development of sixth-generation (6G) communication technologies is confronted with the significant challenge of spectrum resource shortage. To alleviate this issue, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multiple-input multiple-output (MIMO) cognitive radio (CR) system. Specifically, the underlying secondary network in the proposed CR system reuses the same frequency resources occupied by the primary network with the help of the STAR-RIS. The secondary network sum rate maximization problem is first formulated for the STAR-RIS aided MIMO CR system. The adoption of STAR-RIS necessitates an intricate beamforming design for the considered system due to its large number of coupled coefficients. The block coordinate descent method is employed to address the formulated optimization problem. In each iteration, the beamformers at the secondary base station (SBS) are optimized by solving a quadratically constrained quadratic program (QCQP) problem. Concurrently, the STAR-RIS passive beamforming problem is resolved using tailored algorithms designed for the two phase-shift models: 1) For theindependent phase-shift model, a successive convex approximation-based algorithm is proposed; 2) For thecoupled phase-shift model, a penalty dual decomposition-based algorithm is conceived, in which the phase shifts and amplitudes of the STAR-RIS elements are optimized using closed-form solutions. Simulation results show that: 1) The proposed STAR-RIS aided CR communication framework can significantly enhance the sum rate of the secondary system; 2) The coupled phase-shift model results in limited performance degradation compared to the independent phase-shift model.

Ultra-reliable and low-latency communications (URLLC) plays a critical role for the coming era of wireless industrial automation, which increases the flexibility without moderating the stringent requirements of latency and reliability. The task for URLLC is to deliver short packets from sensors to actuators via the central controller reliably and timely, during which large bandwidth is required for ensuring stringent quality-of-service (QoS) metrics. Due to the scarce spectrum resource shared by a large number of devices, and the dynamic channel changes over wireless links, it is challenging to achieve bandwidth-saving URLLC with one single method. Motivated by the observation that groups of devices working in close proximity to each other can form device-to-device (D2D) communications, this paper considers multi-connectivity (MC) together with other cross-layer methods including grant-free access, data replication, broadcasting, and processor-sharing server to minimize the total bandwidth under the QoS constraints of URLLC. With the cross-layer design, we fisrt derive the packet loss probability including the factors of collision due to the contention-based access scheme and decoding error due to the dynamic wireless channel, and hence the overall reliability of MC is provided. Then, we establish a framework to minimize the total bandwidth of MC, where the relationship of collision and packet loss probabilities, the monotonicity of collision and decoding error probabilities, and the relationship of the third type blocklength and size rule are proved to find the optimal resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the considered cross-layer design.

Grant-free random access is a critical enabling technology for massive ultra-reliable and low-latency communications (mURLLC), and user activity detection (UAD), determining which users are active based on received signals, is indispensable in grant-free access. The cell-free massive multiple-input multiple-output (MIMO) system, providing macro diversity and supporting more users, is an architecture suitable for mURLLC. This paper investigates the UAD problem with changeable pilot sequences in cell-free massive MIMO systems, and proposes deep learning based UADFormer, where users are assigned to access points (APs) for detection using user grouping algorithm and transformer-based neural networks (NNs) in APs are employed to output user activity state by exploiting the correlation between received signals and pilot sequences. For users detected by multiple APs, a fusion NN based on transfer learning in the central processing unit is utilized to enhance UAD accuracy. Additionally, considering limited computational and storage resources in APs, sparse attention mechanism is used in NNs to reduce computational complexity and residual attention score is employed to improve NN performance. Simulation results show that UADFormer outperforms other schemes in various evaluation metrics and reduces computational complexity.

Cellular-connected unmanned aerial vehicles (UAVs) is considered as integral components for sixth generation (6G) cellular networks. Cell-free radio access network (CF-RAN) with network-assisted fullduplex (NAFD) possesses global collaborative capabilities and enable uplink and downlink transmission simultaneously, which can be considered as a potential technology for supporting air-ground communication. In this paper, we investigate the performance of cellular-connected UAV in CF-RAN with NAFD. We propose a modified beamforming training scheme to mitigate cross-link interference (CLI) and a location-aware access point (AP) clustering strategy to reduce fronthaul overhead. We derive closed-form expressions for uplink and downlink achievable rates of GUEs and UAVs, respectively. Based on these expressions, we propose an efficient global spectral efficiency optimization scheme by solving a multi-objective optimization problem (MOOP) aiming to maximize the uplink sum rates and downlink sum rates simultaneously with deep Q-network (DQN). Numerical results verify the accuracy of the derived closed-form expressions. The effectiveness of the modified beamforming training scheme and location-aware AP clustering strategy are proved. In addition, the impact of system parameters and the advantages of NAFD system are analyzed. We also illustrate the convergence and benefits of DQN-based optimization scheme on different type of users.

Channel state information (CSI) compression and feedback is a common way of acquiring the CSI at the transmitter in frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems due to the lack of channel reciprocity. However, implicit reciprocity potentially exists in the bi-directional channels of an FDD system because they in fact share physically the same propagation paths. We propose to leverage this implicit reciprocity in FDD mMIMO systems to minimize the feedback overhead and enhance the CSI recovery with uplink channel information at the transmitter. To achieve this, we develop a disentangled representation (DR) learning enabled neural network (NN), named DrCsiNet, to realize the selective CSI compression feedback with the assistance of uplink CSI. The proposed DrCsiNet successfully extracts the information of reciprocity implicitly shared between the downlink and uplink channels in FDD mMIMO, while it simultaneously extracts selective information from the downlink CSI excluding the implicit reciprocity component for compression feedback. We conduct extensive simulations to evaluate the performance of the proposed DrCsiNet against existing methods under various setups. Results demonstrate remarkable performance gains of DrCsiNet for CSI recovery and evidence a strong generalization ability across various network structures. These findings validate the efficacy of disentangling implicit CSI reciprocity embedded in uplink CSI for enhancing the downlink CSI recovery in FDD mMIMO.

This paper investigates the integration of a reconfigurable intelligent surface (RIS) into a secure multiuser massive multiple-input multiple-output (MIMO) system in the presence of transceiver hardware impairments (HWI), imperfect channel state information (CSI), and spatially correlated channels. We first introduce a linear minimum-mean-square error estimation algorithm for the aggregate channel by considering the impact of transceiver HWI and RIS phase-shift errors. Then, we derive a lower bound for the achievable ergodic secrecy rate in the presence of a multi-antenna eavesdropper when artificial noise (AN) is employed at the base station (BS). In addition, the obtained expressions of the ergodic secrecy rate are further simplified in some noteworthy special cases to obtain valuable insights. To counteract the effects of HWI, we present a power allocation optimization strategy between the confidential signals and AN, which admits a fixed-point equation solution. Our analysis reveals that a non-zero ergodic secrecy rate is preserved if the total transmit power decreases no faster than 1/N, whereNis the number of RIS elements. Moreover, the ergodic secrecy rate grows logarithmically with the number of BS antennasMand approaches a certain limit in the asymptotic regimeN→ ∞. Simulation results are provided to verify the derived analytical results. They reveal the impact of key design parameters on the secrecy rate. It is shown that, with the proposed power allocation strategy, the secrecy rate loss due to HWI can be counteracted by increasing the number of low-cost RIS elements.

Reconfigurable intelligent surface (RIS) is regarded as an important enabling technology for the sixth-generation (6G) network. Recently, modulating information in reflection patterns of RIS, referred to as reflection modulation (RM), has been proven in theory to have the potential of achieving higher transmission rate than existing passive beamforming (PBF) schemes of RIS. To fully unlock this potential of RM, we propose a novel superimposed RIS-phase modulation (SRPM) scheme for multiple-input multiple-output (MIMO) systems, where tunable phase offsets are superimposed onto predetermined RIS phases to bear extra information messages. The proposed SRPM establishes a universal framework for RM, which retrieves various existing RM-based schemes as special cases.Moreover, the advantages and applicability of the SRPM in practice is also validated in theory by analytical characterization of its performance in terms of average bit error rate (ABER) and ergodic capacity. To maximize the performance gain, we formulate a general precoding optimization at the base station (BS) for a single-stream case with uncorrelated channels and obtain the optimal SRPM design via the semidefinite relaxation (SDR) technique. Furthermore, to avoid extremely high complexity in maximum likelihood (ML) detection for the SRPM, we propose a sphere decoding (SD)-based layered detection method with near-ML performance and much lower complexity. Numerical results demonstrate the effectiveness of SRPM, precoding optimization, and detection design. It is verified that the proposed SRPM achieves a higher diversity order than that of existing RM-based schemes and outperforms PBF significantly especially when the transmitter is equipped with limited radio-frequency (RF) chains.

To enable wireless federated learning (FL) in communication resource-constrained networks, two communication schemes, i.e., digital and analog ones, are effective solutions. In this paper, we quantitatively compare these two techniques, highlighting their essential differences as well as respectively suitable scenarios. We first examine both digital and analog transmission schemes, together with a unified and fair comparison framework under imbalanced device sampling, strict latency targets, and transmit power constraints. A universal convergence analysis under various imperfections is established for evaluating the performance of FL over wireless networks. These analytical results reveal that the fundamental difference between the digital and analog communications lies in whether communication and computation are jointly designed or not. The digital scheme decouples the communication design from FL computing tasks, making it difficult to support uplink transmission from massive devices with limited bandwidth and hence the performance is mainly communication-limited. In contrast, the analog communication allows over-the-air computation (AirComp) and achieves better spectrum utilization. However, the computation-oriented analog transmission reduces power efficiency, and its performance is sensitive to computation errors from imperfect channel state information (CSI). Furthermore, device sampling for both schemes are optimized and differences in sampling optimization are analyzed. Numerical results verify the theoretical analysis and affirm the superior performance of the sampling optimization.

In order to cope with the demand of explosively increasing service diversity and quality, network slicing has become the key technology of next-generation mobile communication. Mobile edge computing (MEC) can provide multi-dimensional resources and network functions at the edge of the network and reduce the delay of wireless networks. At the same time, non-orthogonal multiple access (NOMA) allows traffic to share resources and improve the spectral efficiency and energy efficiency of wireless networks. In this paper, we propose a hierarchical NOMA-based network slicing architecture in the 6G novel full-spectrum scalable cell-free radio access network with MECs and conduct joint allocation of communication, computing and caching resources at different resource granularity to meet the requirements of latency-critical applications with different latency. In order to realize the hierarchical joint resource allocation to improve system efficiency, we propose to address the optimal computing resource allocation and cache placement problem firstly through conventional optimization methods to reduce the action space and then use the multi-agent deep reinforcement learning algorithm for solving other complex coupling strategies. Simulation results further verify the effectiveness of the proposed intelligent network slicing scheme.

The key performance indicators (KPIs) of the sixth generation (6G) will increase by orders of magnitude compared to the fifth generation (5G), promising extreme connectivity performance with Tbps-scale data rate, Kbps/Hz-scale spectral efficiency (SE) and$\mu \text {s}$-level latency. Cell-free massive MIMO (CF-mMIMO) with rich spatial dimension resources is expected to be a key architecture to realize$\text {TK}\mu $extreme connectivity, but the existing research has not yet given a compact and closed-form approximation to describe the relationship between the spatial dimension and system performance, which makes it difficult to evaluate the KPIs of$\text {TK}\mu $intuitively. This paper derives explicit closed-form expressions for the relationship between system performance and system configuration parameters for finite blocklength CF-mMIMO systems and analyzes the relationship between system performance and spatial dimensions. Based on this, we perform parameter selection and performance evaluation of specific implementations in the three$\text {TK}\mu $KPIs in CF-mMIMO systems. Both theoretical analysis and simulation results show that increasing the spatial degree of freedom (DoF) and deploying antennas more dispersedly can realize latency reduction while guaranteeing the system performance, and the joint collaboration of multi-users and multiple access points (APs) with large DoFs can achieve a continuous increase in SE and data rate.

In this paper, the cooperative edge caching problem is studied in fog radio access networks (F-RANs). Given the non-deterministic polynomial hard (NP-hard) nature of the problem, a dueling deep Q network (Dueling DQN) based caching update algorithm is proposed to make an optimal caching decision by learning the dynamic network environment. In order to protect user data privacy and solve the problem of slow convergence of the single deep reinforcement learning (DRL) model training, we propose a communication-efficient federated deep reinforcement learning (CE-FDRL) method to implement cooperative training of models from multiple fog access points (F-APs) in F-RANs. To address the excessive consumption of communication resources caused by model transmission, we propose to prune and quantize the shared DRL models to reduce the number of transferred model parameters. The communication interval is increased and the communication round is reduced by periodic model aggregation. The global convergence and computational complexity of our proposed method are also analyzed. Simulation results verify that our proposed method can offer better performance in reducing user request delay and improving cache hit rate and the transmitted parameters of our proposed method can drop to 60% compared to the existing benchmark schemes. Our proposed method is also shown to have faster training speed and higher communication efficiency.

One key feature of ultra-reliable low-latency communications (URLLC) in 5G is to support short packet transmission (SPT). However, the pilot overhead in SPT for channel estimation is relatively high, especially in high Doppler environments. In this paper, we advocate the adoption of differential modulation to support ultra-low latency services, which can ease the channel estimation burden and reduce the power and bandwidth overhead incurred in traditional coherent modulation schemes. Specifically, we consider a multi-connectivity (MC) scheme employing differential modulation to enable URLLC services. The popular selection combining and maximal ratio combining schemes are respectively applied to explore the diversity gain in the MC scheme. A first-order autoregressive model is further utilized to characterize the time-varying nature of the channel. Theoretically, the maximum achievable rate and minimum achievable block error rate under ergodic fading channels with PSK inputs and perfect CSI are first derived by using the non-asymptotic information-theoretic bounds. The performance of SPT with differential modulation and MC schemes is then analysed by characterizing the effect of differential modulation and time-varying channels as a reduction in the effective SNR. Simulation results show that differential modulation does offer a significant advantage over the pilot-assisted coherent scheme for SPT, especially in high Doppler environments.

Beam prediction and tracking (BPT) are key technology for millimeter wave communications. Typical techniques include Kalman filtering (KF) and Gaussian process (GP) regression. However, KF requires explicit system dynamics, which is difficult to obtain for complicated scenarios. In contrast, thanks to the data-driven manner, GP regression circumvents this challenging, which, however, suffers from prohibitive computational complexity. To tackle this issue, we propose a novel hybrid model and data driven approach, referred to as data-induced intelligent Kalman filtering (DIIKF). DIIKF learns the system dynamics via the data-driven manner, which can enjoy the advantages of both KF and GP while overcoming their drawbacks. In view that the system dynamics is available, we further propose long-term prediction and design an efficient algorithm. Simulation results show that our method approaches the optimal oracle solution (in terms of effective achievable rate), with the linear complexity order.

In this work, we propose low-complexity designs for XL-MIMO in the near-field with spatial non-stationarities. We first introduce a notion of visibility region (VR) and propose a VR detection algorithm. Then, we exploit the acquired VR information to design a low-complexity detection scheme for XL-MIMO systems. To further reduce the complexity, we propose a graph theory-based user partition algorithm, relying on the VR overlap ratio between different users. Then, partial zero-forcing (PZF) is utilized to eliminate only the interference from users allocated to the same group, which further reduces computational complexity in matrix inversion. Numerical results confirm the effectiveness of the proposed algorithms which approach the performance of conventional whole array (WA)-based designs but with much lower complexity.

In this paper, we investigate the beamforming design for an integrated sensing and communication (ISAC) system involved full-duplex (FD) communications. Specifically, an FD ISAC base station (BS) performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problem is formulated to minimize the total transmit power of the system while ensuring the communication and sensing requirements. The downlink and uplink transmissions are tightly coupled, making the joint optimization challenging. To solve this intractable problem, we first determine the receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power and then suggest an iterative solution to the remaining problem. We demonstrate via numerical results that the optimized FD communication-based ISAC leads to power efficiency improvement compared to conventional ISAC with HD communication.

Turbo codes have been successfully adopted in 4G LTE, which can approach the channel capacity with Bahl-Cocke-Jelinek-Raviv (BCJR) decoding. With the evolution from 4G LTE to 5G NR, there is a demand to design a unified channel decoder that supports both LTE Turbo codes and NR low-density parity-check (LDPC) codes. One solution is to employ belief propagation (BP) decoding on the bipartite Tanner graph for both codes. However, although MacKay pointed out that Turbo codes have a sparse parity-check matrix, the existence of 4-cycles in such a matrix severely deteriorates the performance of BP decoding. In this paper, we propose two polynomial-based methods to optimize the parity-check matrix of Turbo codes by improving the sparsity while also removing 4-cycles and even 6-cycles compared to the original matrix. Simulation results show that the improved BP decoding for Turbo codes halves the error-correction performance gap between the original BP decoding and BCJR decoding, which is a promising step towards the unified channel decoder design based on the BP algorithm.

Graph neural networks have achieved state-of-the-art performance on collaborative filtering (NeuGCFs). The success of NeuGCFs is mainly attributed to the stacking of message aggregation layers. Recent studies have shown that the effectiveness of existing NeuGCFs largely relies on the selection of optimal aggregation steps, which makes the performance on various recommendation scenarios unsatisfactory. To tackle this, we for the first time propose a framework to achieve personalized aggregation step assignment on NeuGCF. First, each user is endowed with a learnable unit to measure the aggregation degree at each aggregation stage. Second, the aggregation degree is used to determine whether the aggregation process should stop after the current stage. Third, the learnable unit is jointly trained with the NeuGCF by bi-level optimization. Finally, we term this new framework AutoGCF, implementing it on the state-of-the-art NeuGCF model - Light-GCN. Empirical experiments on five datasets demonstrate the effectiveness of AutoGCF, and highlight that AutoGCF can adaptively choose optimal steps on different datasets.

In this paper, the computation offloading problem in reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC)-enabled cell-free radio access network (CF-RAN) is investigated. To minimize the average task execution delay, we propose to formulate a joint optimization problem of computation offloading and RIS phase shifts. Considering the non-deterministic polynomial hard (NP-hard) property of this problem and time-varying network environment, we further propose a meta reinforcement learning (meta-RL)-based computation offloading policy, which can adapt to new environment quickly with only a few gradient updates. By aggregating powerful decision-making ability of conventional RL and rapid environment learning ability of meta-learning, our proposed policy can find the optimal strategy in very fast speed. Simulation results show that our proposed meta-RL-based computation offloading policy reduces the average task execution delay by 25% compared to the considered two state-of-the-art benchmark policies.

The explosive growth of data results in more scarce spectrum resources. It is important to optimize the system performance under limited resources. In this paper, we investigate the weighted throughput (WPT) maximization for cell-free (CF) multiuser (MU) MIMO multicarrier (MC) systems through resource allocation (RA) in finite blocklength regime (FBL) while ensuring the quality of service (QoS) of each user under the constraints of total power consumption. Since the channels vary in different subcarriers and inter-user interference strengths, the WPT can be maximized by scheduling the best users in each time-frequency (TF) resource and advanced beamforming design (BF). With this motivation, we propose a joint user scheduling (US) and BF algorithm to address an mixed integer nonlinear programming (MINLP) problem. Numerical results demonstrate that the proposed RA scheme outperforms the comparison schemes. And the CF system in our scenario is capable of achieving higher spectral efficiency (SE) than the centralized antenna systems (CAS).

In this paper, we investigate the channel estimation for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We revisit the information geometry approach (IGA) for massive MIMO-OFDM channel estimation. By using the constant magnitude property of the entries of the measurement matrix and the asymptotic analysis, we find that the second-order natural parameters (SONPs) of the distributions on all the auxiliary manifolds (AMs) are equivalent to each other at each iteration of IGA, and the first-order natural parameters (FONPs) of the distributions on all the AMs are asymptotically equivalent to each other at the fixed point. Motivated by these results, we simplify the iterative process of IGA and propose a simplified IGA for massive MIMO-OFDM channel estimation. It is proved that at the fixed point, the a posteriori mean obtained by the simplified IGA is asymptotically optimal. The simplified IGA allows efficient implementation with fast Fourier transformation (FFT). Simulations confirm that the simplified IGA can achieve near the optimal performance with low complexity in a limited number of iterations.

We investigate the downlink secrecy performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems in the presence of a multi-antenna eavesdropper. We first derive a tight closed-form expression for characterizing the lower bound of the achievable ergodic secrecy rate under spatially correlated channels, taking into account low-resolution digital-to-analog converters (DACs) and RIS phase noise. Subsequently, building upon the derived results, we optimize the power allocation among the information signal and artificial noise in closed form and the RIS phase shifts by developing a projected gradient ascent algorithm, which requires only statistical channel state information of the aggregated channel with low implementational complexity. All theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments. Our results reveal that low-resolution DAC can be beneficial with equal power allocation when the number of RIS elements is small. Besides, the detrimental influence attributed to low-resolution DACs gains prominence as the number of RIS elements grows substantially.

With the widespread use of massive multi-input-multi-output (MIMO) technology in current wireless networks, network optimization faces much higher costs due to the significantly increased angular space. Digital twin (DT), as a promising tool to enhance the effectiveness and efficiency of performance evaluation, still faces many challenges for massive MIMO optimization, where the complex channel characteristics and the system performance uncertainty over randomly distributed user equipment (UE) position both make it difficult to obtain an explicit relationship expression between the beamforming parameters at the base station (BS) and the system performance. In this article, we propose a conditional generative adversarial network (C-GAN) based digital twin network (DTN), which can fit the mapping from the beamforming to the system performance and match the distribution of system performance under a certain beamforming configuration over different UE position simultaneously. Moreover, it provides a generalized way for pre-validation of different key performance indicators (KPIs) and further raises the accuracy via data augmentation. QuaDRiGa based simulations validate the effectiveness of our proposed method in system performance modeling and KPI prediction.

This paper considers a cell-free (CF) massive multiple-input multiple-output (MIMO) with network-assisted full-duplex (NAFD) system for the ultra-reliable and low-latency (URLLC) communications, jointly optimizing duplex mode selection and transceiver design. To reduce the cross-link interference (CLI) and improve the weighted URLLC sum-rate of downlink and uplink users, we propose an optimization problem to maximize the achievable sum rates for the cell-free massive MIMO with NAFD system under a finite blocklength, limited by data rates and transmission power constraints. Then, a concave-convex procedure (CCCP) algorithm is used to solve this optimization problem. Simulation results show that the proposed algorithm has better performance than the traditional co-frequency co-time full-duplex (CCFD) and half-duplex (HD) schemes.

Abstract The authors propose a secure intelligent reflecting surface (IRS) assisted transmission system in the presence of a ground eavesdropper, where the source in the ground transmits confidential information to unmanned aerial vehicle (UAV), IRS is deployed to promote the transmission rate of the source to UAV. Closed‐form expression for secure transmission non‐outage probability is derived, and secure transmission performance is analyzed. Simulation validates the correctness of the derivation. Compared with benchmarks, results show that the IRS can improve the secure transmission of ground to UAV.

Massive ultrareliable and low-latency communications (mURLLC) is an emerging and dominate traffic service in 6G. To reduce the signaling overhead and access delay, grant-free random access (GFRA) is widely used in mURLLC. As the first step in GFRA, active user detection (AUD) is aimed to identify the set of active users accurately and timely. Conventional AUD schemes relying on iterative computations over massive users bring redundant computing overload and processing delay, which seriously affect the system scalability in the mURLLC scenario. Considering the near-real-time requirement of mURLLC, we propose a scalable deep learning-based AUD approach utilizing similar channel sparsity in cell-free (CF) massive multiple-input–multiple-output (mMIMO) systems. By exploiting the distributed computing unit, i.e., space expansion unit (SEU), we design an SEU-assisted CF mMIMO to improve the scalability of the traditional centralized CF computing architecture. In the proposed system, all access points (APs) are divided into several clusters, and the SEU in each cluster provides a reliable distributed AUD scheme through a 1-D convolutional network (1-D CNN). In addition, a transfer learning-based ensemble model is established at the CPU to achieve a better global detection decision. Simulation results demonstrate the superiority of our scalable deep learning-based approach, and reveal that through the transfer learning-based model fusion at the CPU, our proposed scalable SEU-assisted approach can obtain success probability close to that of the centralized CF computing scheme with less access delay. In addition, our scheme requires fewer pilots than other compressed sensing-based schemes.

In this article, we consider an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and relay communication network consisting of a base station (BS), a UAV, and multiple ground users. The UAV acts as both a wireless power transmission source and an uplink communication relay. Specifically, an entire transmission period of the considered system is divided into two stages. In the first stage, the UAV transfers the power to the ground users along a well-optimized flight trajectory and meanwhile, the users transmit data to the UAV using the harvested energy. Subsequently, in the second stage, the UAV flies to the vicinity of the BS and forwards the data to the BS. For the purpose of minimizing the energy consumed by the UAV, we jointly optimize the time durations of the two stages, the UAV’s transmit powers for WPT and data forwarding, as well as its flight trajectory, subject to the constraints of the Quality of Service (QoS), the information forwarding, the energy causality, and the mobility of the UAV. The involved optimization problem is nonconvex and highly intractable. To this end, we propose an efficient alternating algorithm to iteratively solve the two subproblems with respect to the time durations of the two stages and the UAV’s transmit powers and trajectory, respectively. The first subproblem has a closed-form optimal solution and the second subproblem is handled by addressing a surrogate convex problem based on the technique of successive convex approximation. Finally, the simulation results confirm the superiority of our proposed algorithm.

Recently, blockchain radio access network (B-RAN) arises as an innovative paradigm for the sixth-generation (6G) wireless communications to build cooperative trust, aggregate wireless resources, and schedule inter- and intra-network tasks among independent network entities. It establishes an open platform based on blockchain to provide diverse wireless services and applications, such as radio access, Internet of Things (IoT), and mobile-edge computing, via trusted interactions with enhanced security and efficiency. As a distinctive feature, B-RAN enables secure and efficient resource sharing and trading by aggregating, pooling, and coordinating resources from multiple resource hosts and owners across subnetworks. Therefore, an implementable architecture along with various functional modules shall be delicately designed. This work aims to establish a unified architecture with enhanced efficiency, security, compatibility, and flexibility for resource sharing and trading in B-RAN. Specifically, we develop a six-layer architecture that incorporates a number of novel features, such as enhanced blockchain structures, secure interaction methods, efficient service mechanisms, and scalable transaction patterns. We design a number of pluggable functional modules in each layer to support diverse functions, services, and applications of resource sharing and trading. Finally, we implement a practical prototype based on the layered architecture for resource-limited devices. Multiple experiments are presented to verify the performance of the proposed architecture from different aspects.

The angle of arrival (AoA) is a vital parameter for geometry-based 3-D indoor positioning. Considering a wireless local area network (WLAN) as an example, a conventional antenna array designed for multiple-input–multiple-output (MIMO) communication is not suitable for AoA estimation due to the angle ambiguity introduced by the large interelement spacing (IES). Consequently, the integration of positioning and communication functions using the same radio frequency (RF) frontend and the array would be highly valuable. A nonuniform array (NUA) is proposed to solve this challenging problem. The NUA is optimized to achieve unambiguous angle estimation while maintaining high-capacity WLAN MIMO communication. Moreover, based on the estimated MIMO channel state information (CSI), an improved trilinear parallel factor (TPF) decomposition method is proposed to accurately estimate AoAs with low complexity. Simulated and experimental results show that the proposed NUA-based scheme can achieve decimeter-level 3-D positioning accuracy.

Downlink coherent multiuser transmission is an essential technique for cell-free massive multiple-input multiple-output (MIMO) systems, and the availability of channel state information (CSI) at the transmitter is a basic requirement. To avoid CSI feedback in a time-division duplex system, the uplink channel parameters should be calibrated to obtain the downlink CSI due to the radio frequency circuit mismatch of the transceiver. In this paper, a design of a reference signal for over-the-air reciprocity calibration is proposed. The frequency domain generated reference signals can make full use of the flexible frame structure of the fifth-generation (5G) new radio, which can be completely transparent to commercial off-the-shelf (COTS) remote radio units (RRU) and commercial user equipments. To further obtain the calibration of multiple RRUs, an interleaved RRU grouping with a genetic algorithm is proposed, and an averaged Argos calibration algorithm is also presented. We develop a cell-free massive MIMO prototype system with COTS RRUs, demonstrate the statistical characteristics of the calibration error and the effectiveness of the calibration algorithm, and evaluate the impact of the calibration delay on the different cooperative transmission schemes.

Beamforming design has been widely investigated for integrated sensing and communication (ISAC) systems with full-duplex (FD) sensing and half-duplex (HD) communication, where the base station (BS) transmits and receives radar sensing signals simultaneously while the integrated communication operates in either downlink or uplink. To achieve higher spectral efficiency, in this paper, we extend existing ISAC beamforming design to a general case by considering the FD capability for both radar and communication. Specifically, we consider an FD ISAC system, where the BS performs target detection and communicates with multiple downlink users and uplink users reusing the same time and frequency resources. We jointly optimize the downlink dual-functional transmit signal and the uplink receive beamformers at the BS and the transmit power at the uplink users. The problems are formulated under two criteria: power consumption minimization and sum rate maximization. The downlink and uplink transmissions are tightly coupled due to both the desired target echo and the undesired interference received at the BS, making the problems challenging. To handle these issues in both cases, we first determine the optimal receive beamformers in closed forms with respect to the BS transmit beamforming and the user transmit power. Subsequently, we invoke these results to obtain equivalent optimization problems and propose iterative algorithms to solve them. In addition, we consider a special case under the power minimization criterion and propose an alternative low complexity design. Numerical results demonstrate that the optimized FD communication-based ISAC brings tremendous improvements in terms of both power efficiency and spectral efficiency compared to the conventional ISAC with HD communication.

Ultra-reliable and low-latency communications (URLLC) is firstly proposed in 5G networks, and expected to support applications with the most stringent quality-of-service (QoS). However, since the wireless channels vary dynamically, the transmit power for ensuring the QoS requirements of URLLC may be very high, which conflicts with the power limitation of a real system. To fulfil the successful URLLC transmission with finite transmit power, we propose an energy-efficient packet delivery mechanism incorparated with frequency-hopping and proactive dropping in this paper. To reduce uplink outage probability, frequency-hopping provides more chances for transmission so that the failure hardly occurs. To avoid downlink outage from queue clearing, proactive dropping controls overall reliability by introducing an extra error component. With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements. Via theoretical analysis (e.g., the convexity with respect to bandwidth, the independence of bandwidth allocation, the convexity of antenna configuration with inactive constraints), the simplication of finding the global optimal solution for resource allocation is addressed. A three-step method is then proposed to find the optimal solution for resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the proposed packet delivery mechanism.

As an important part of beyond the fifth-generation (B5G) and the sixth-generation (6G) mobile communication systems, ultra-reliable and low latency communications (uRLLC) puts forward strict requirements for delay and reliability (e.g., 99.9999% reliability and$500 \mu \text{s}$latency). At present, the evaluation measures of delay and reliability are usually based on infinite block length and rely on long-term statistics, which cannot meet the requirement of low latency. The cell-free system, with a very large number of distributed antennas, has the characteristics of macro-diversity and spatial sparsity, which can further enhance the performance of uRLLC. In this paper, the downlink multidevice cell-free system with hard deadlines is considered and analyzed in the finite block length (FBL) regime. The communication’s delay and reliability are described based on two instantaneous evaluation measures: transmission error (TE) and time overflow (TO) probability. From the perspective of information theory, this paper analyzes the analytic expression of TE probability for a single device and the performance impact of FBL on the traditional channel capacity analysis. Considering the multidevice TO probability in a cell-free system, the closed-form expressions of upper and lower bounds are derived and compared with the gamma approximation results. This paper further provides three methods, namely, transmission rate selection, device grouping and space division multiplexing, to balance the delay and reliability of the system and analyzes the performance.

Expectation propagation (EP) achieves near-optimal performance for large-scale multiple-input multiple-output (L-MIMO) detection, however, at the expense of unaffordable matrix inversions. To tackle the issue, several low-complexity EP detectors have been proposed. However, they all fail to exploit the properties of channel matrices, thus resulting in unsatisfactory performance in non-ideal scenarios. To this end, in this paper, a block-diagonal Neumann-series-based expectation propagation approximation (BD-NS-EPA) algorithm is proposed, which is applicable for both ideal uncorrelated channels and the correlated channels with multiple-antenna user equipment system. First, a block-diagonal-based Neumann iteration is employed, which skillfully exerts the main information of the channels while reducing computational cost. An adjustable sorting message updating scheme then is introduced to reduce the update of redundant nodes during iterations. Numerical results show that, for$128\times 32$MIMO with the non-ideal channel, the proposed algorithm exhibits 0.3 dB away from the original EP when bit error-rate (BER)$=10^{-3}$, at the cost of mere 3% normalized complexity. The implementation results on SMIC 65-nm CMOS technology suggest that the proposed detector can achieve 1.252 Gbps/W and 0.275 Mbps/kGE hardware efficiency, further demonstrating that the proposed detectors can achieve a good trade-off between error-rate performance and hardware efficiency.

In this paper, we investigate the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs). In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based regressor to model the content request pattern. Firstly, the relationship between content features and popularity is captured by our proposed model. Then, we utilize Bayesian learning to train the model parameters, which is robust to overfitting. However, Bayesian methods are usually unable to find a closed-form expression of the posterior distribution. To tackle this issue, we apply a stochastic variance reduced gradient Hamiltonian Monte Carlo (SVRG-HMC) method to approximate the posterior distribution. To utilize the computing resource of fog access points (F-APs) and also reduce the communication overhead, we propose a quantized federated learning (FL) framework combining with Bayesian learning. The proposed quantized federated Bayesian learning framework allows each F-AP to send gradients to the cloud server after quantizing and encoding. It can achieve a tradeoff between prediction accuracy and communication overhead effectively. Simulation results show that the performance of our proposed policy outperforms the considered baseline policies.

In this paper, we consider the robust beamforming design in a reconfigurable intelligent surface (RIS)-aided cell-free (CF) system considering the channel state information (CSI) uncertainties of both the direct channels and cascaded channels at the transmitter with capacity-limited backhaul. We jointly optimize the precoding at the access points (APs) and the phase shifts at multiple RISs to maximize the worst-case sum rate of the CF system subject to the constraints of maximum transmit power of APs, unit-modulus phase shifts, limited backhaul capacity, and bounded CSI errors. By applying a series of transformations, the non-smoothness and semi-infinite constraints are tackled in a low-complexity manner that facilitates the design of an alternating optimization (AO)-based iterative algorithm. The proposed algorithm divides the considered problem into two subproblems. For the RIS phase shifts optimization subproblem, we exploit the penalty convex-concave procedure (P-CCP) to obtain a stationary solution and achieve effective initialization. For precoding optimization subproblem, successive convex approximation (SCA) is adopted with a convergence guarantee to a Karush-Kuhn-Tucker (KKT) solution. Numerical results demonstrate the effectiveness of the proposed robust beamforming design, which achieves superior performance with low complexity. Moreover, the importance of RIS phase shift optimization for robustness and the advantages of distributed RISs in the CF system are further highlighted.

It is supposed that ultra-reliable low latency communication (uRLLC) would continue to evolve in the future sixth generation (6G) network, to provide enhanced capability towards extreme connectivity, with the aid of well established multiple-input multiple-output (MIMO) technology. Since the latency constraint can be represented equivalently by the blocklength of a codeword, channel coding theory at a finite blocklength plays an important role in theoretic analysis of uRLLC. Based on Polyanskiy’s and Yang’s asymptotic results on maximal achievable rate, we first derive the proximate closed-form expressions for the expectation and variance of channel dispersion. Then, the upper bound of average maximal achievable rate is obtained for massive MIMO systems under ideal independent and identically distributed fading channels. Since almost all the fundamental parameters, including the spatial degree-of-freedom (DoF), are considered, this expression can be viewed as a performance bound of the spatiotemporal two-dimension channel coding to some extent. Moreover, it is shown by simulation and analysis, as the DoF goes to infinity, MIMO systems reveal a nature of deterministic transmission, since the average maximal achievable coding rate per antenna can be achieved at each transmission. In this case, the inversely proportional law observed therein implies that the blocklength in the time domain can be further shortened at the expense of spatial DoF. This exchangeability of space and time, to support a given coding rate, paves a solid and feasible road for us to further reduce latency in 6G uRLLC.

Due to the ability of feature extraction, deep learning (DL)-based methods have been recently applied to channel state information (CSI) compression feedback in massive multiple-input multiple-output (MIMO) systems. Existing DL-based CSI compression methods are usually effective in extracting a certain type of features in the CSI. However, the CSI usually contains two types of propagation features, i.g., non-line-of-sight (NLOS) propagation-path feature and dominant propagation-path feature, especially in channel environments with rich scatterers. To fully extract the both propagation features and learn a dual-feature representation for CSI, this paper proposes a dual-feature-fusion neural network (NN), referred to as DuffinNet. The proposed DuffinNet adopts a parallel structure with a convolutional neural network (CNN) and an attention-empowered neural network (ANN) to respectively extract different features in the CSI, and then explores their interplay by a fusion NN. Built upon this proposed DuffinNet, a new encoder-decoder framework is developed, referred to as Duffin-CsiNet, for improving the end-to-end performance of CSI compression and reconstruction. To facilitate the application of Duffin-CsiNet in practice, this paper also presents a two-stage approach for codeword quantization of the CSI feedback. Besides, a transfer learning-based strategy is introduced to improve the generalization of Duffin-CsiNet, which enables the network to be applied to new propagation environments. Simulation results illustrate that the proposed Duffin-CsiNet noticeably outperforms the existing DL-based methods in terms of reconstruction performance, encoder complexity, and network convergence, validating the effectiveness of the proposed dual-feature fusion design.

Compared to the linear MIMO detectors, the Belief Propagation (BP) detector has shown greater capabilities in achieving near-optimal performance and better nature to iteratively cooperate with channel decoders. Aiming at real applications, recent works mainly fall into the category of reducing the complexity by simplified calculations, at the expense of performance sacrifice. However, the complexity is still unsatisfactory with exponentially increasing complexity or required exponentiation operations. Furthermore, the state-of-the-art (SOA) BP detectors persistently encounter error floor in high signal-to-noise ratio (SNR) region, which becomes even worse with calculation approximation. This work aims at a revised BP detector, named Belief-selective Propagation (BsP) detector by selectively utilizing the trusted incoming messages with sufficiently large a priori probabilities for updates. Two proposed strategies: symbol-based truncation (ST) and edge-based simplification (ES) squeeze the complexity (orders lower than the BP detector), while greatly relieving the error floor issue over a wide range of antenna and modulation combinations. For the 256-QAM$128 \times 64$uplink massive multiuser MIMO (MU-MIMO) system, the$\mathcal {B}(1,1)$BsP detector achieves more than 1dB performance gain (@$\text {BER}=10^{-4}$) with lower complexity than the state-of-the-art (SOA) BP detector. Trade-off between performance and complexity towards different application requirements can be conveniently obtained by tuning the parameters of the ST and ES strategies.

In this paper, a channel estimation problem for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with hybrid structures is studied. Firstly, a beamspace multiple signal classification (MUSIC) algorithm for mmWave wideband channels is proposed to simultaneously estimate the angles of arrival (AOAs), angles of departure (AODs) and transmission delays. Since the traditional spectral peak search method has high complexity, a multi-spectral peak search method is skillfully designed to search for multiple spectral peaks on the MUSIC spatial spectrum more quickly and accurately. Then, the proposed channel estimator is extended to more actual systems equipped with uniform planar arrays (UPAs). Finally, the Cramér–Rao bound (CRB) results of these channel parameters are derived for evaluating the performance of the proposed channel parameter estimator. Simulation results demonstrate that the proposed channel estimator has greatly high channel estimation accuracy.

Channel-state-information-based localization in 5G networks has been a promising way to obtain highly accurate positions compared to previous communication networks. However, there is no unified and effective platform to support the research on 5G localization algorithms. This paper releases a link-level simulator for 5G localization, which can depict realistic physical behaviors of the 5G positioning signal transmission. Specifically, we first develop a simulation architecture considering more elaborate parameter configuration and physical-layer processing. The architecture supports the link modeling at sub-6GHz and millimeter-wave (mmWave) frequency bands. Subsequently, the critical physical-layer components that determine the localization performance are designed and integrated. In particular, a lightweight new-radio channel model and hardware impairment functions that significantly limit the parameter estimation accuracy are developed. Finally, we present three application cases to evaluate the simulator, i.e. two-dimensional mobile terminal localization, mmWave beam sweeping, and beamforming-based angle estimation. The numerical results in the application cases present the performance diversity of localization algorithms in various impairment conditions.

The trend of developing distributed multiple-input multiple-output (MIMO) cooperation has been growing for future wireless networks due to its potential of capacity improvement through network-level precoding. In massive MIMO applications, the overhead of channel state information (CSI) exchange among distributed transmitters is too large to make it possible in practical implementations. In this paper, we consider a cooperative multicell massive MIMO network with distributed regularized zero-forcing (RZF) precoding at each base station (BS), where a novel CSI exchange scheme is devised to reduce the interactive overhead. As a key finding of this work, we theoretically prove that it suffices to share the Gram matrix of local CSI among the cooperative BSs in order to achieve the same performance as a centralized cooperative MIMO network using the RZF precoding with global CSI sharing. The CSI exchange from each BS is thus reduced to a symmetric matrix that has a much smaller size than the full CSI and the amount of CSI exchange does NOT grow with the large number of antennas in massive MIMO. Specifically, based on the exchanged Gram matrices, we derive a decentralized RZF precoding design at each BS and develop both the optimal and suboptimal cooperative power allocation strategies, which achieve different performance and complexity tradeoffs. A virtual centralized power allocation is accomplished at each BS and the performance achieved by the proposed decentralized precoding is the same as the centralized benchmark scheme with full CSI exchange. These superiorities of the proposed schemes are verified through simulation results.

In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs) is investigated. To minimize the content transmission delay, we formulate the cooperative caching optimization problem to find the globally optimal caching strategy. By considering the non-deterministic polynomial hard (NP-hard) property of this problem, a Multi Agent Reinforcement Learning (MARL)-based cooperative caching scheme is proposed. Our proposed scheme applies a double deep Q-network (DDQN) in every fog access point (F-AP), and introduces the communication process in a multi-agent system. Every F-AP records the historical caching strategies of its associated F-APs as the observations of communication procedure. By exchanging the observations, F-APs can leverage the cooperation and make the globally optimal caching strategy. Simulation results show that the proposed MARL-based cooperative caching scheme has remarkable performance compared with the benchmark schemes in minimizing the content transmission delay.

In this paper, the cooperative caching problem in fog radio access networks (F-RANs) is investigated to jointly optimize the transmission delay and energy consumption. Exploiting the potential social relationships among fog access points (F-APs), we firstly propose a clustering scheme based on hedonic coalition game (HCG) to improve the potential cooperation gain. Then, considering that the optimization problem is non-deterministic polynomial hard (NP-hard), we further propose an improved firefly algorithm (FA) based cooperative caching scheme, which utilizes a mutation strategy based on local content popularity to avoid pre-mature convergence. Simulation results show that our proposed scheme can effectively reduce the content transmission delay and energy consumption in comparison with the baselines.

Compared with the bit-wise successive cancellation list (SCL) decoding of polar codes, the node-based Fast SCL decoding significantly reduces the decoding latency by identifying special constituent codes and decoding these in parallel. To further reduce the latency of current Fast SCL decoders, we first propose a fast sequence repetition (SR) node-based SCL (Fast SR-SCL) decoding algorithm, which only involves one type of node in the SCL decoding tree. Furthermore, we employ the adaptive path splitting (APS) strategy to terminate the path splitting in the SR node early, without degrading the error-correcting performance. Numerical results show that for 5G uplink codes with a length of 1024 and rates of 1/4, 1/2, and 3/4, our decoder can deliver the same decoding performance while reducing the average latency by 34.5%, 38.0%, and 39.6% compared with the state-of-the-art Fast SCL decoder for a list size L = 8.

In this paper, we investigate maximum distance separable (MDS) codes based group coded caching in fog radio access networks (F-RANs). The goal is to minimize the average fronthaul rate under nonuniform file popularity. Firstly, an MDS codes and file grouping based coded placement scheme is proposed to provide coded packets and allocate more cache to the most popular files simultaneously. Next, a fog access point (F-AP) grouping based coded delivery scheme is proposed to meet the requests for files from different groups. Furthermore, a closed-form expression of the average fronthaul rate is derived. Finally, the parameters related to the proposed coded caching scheme are optimized to fully utilize the gains brought by MDS codes and file grouping. Simulation results show that our proposed scheme obtains significant performance improvement over several existing caching schemes in terms of fronthaul rate reduction.

In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. Based on clustered federated learning, we propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users. For local users, the content popularity is predicted by learning the hidden representations of local users and contents. Initial features of local users and contents are generated by incorporating neighbor information with self information. Then, dual-channel neural network (DCNN) model is introduced to learn the hidden representations by producing deep latent features from initial features. For mobile users, the content popularity is predicted via user preference learning. In order to distinguish regional variations of content popularity, clustered federated learning (CFL) is employed, which enables fog access points (F-APs) with similar regional types to benefit from one another and provides a more specialized DCNN model for each F-AP. Simulation results show that our proposed policy achieves significant performance improvement over the traditional policies.

In this paper, we investigate the problem of optimizing the overall system secrecy spectral efficiency of cell-Free massive multiple-input multiple-output (CF-mMIMO) with network-assisted full-duplexing (NAFD) system, considering resisting interception by superimposing artificial noise (AN) on the transmitted signal. The access points (APs) are selected by binary mode selection vectors as transmitting or receiving APs flexibly to serve both uplink and downlink users simultaneously. Since optimization variables are tightly coupled, a double-loop strategy is developed to solve the non-covex combinatorial optimization problem. The outer loop is constructed according to greedy search for duplex mode selection, while the inner loop based on the proposed successive convex approximation (SCA) algorithm aims to optimize the transceivers and AN. Simulation results show that the proposed solution is superior to the fixed-mode duplex scheme in terms of secure spectral efficiency and able to achieve similar performance to that of the optimal exhaustive search scheme.

Abstract The evolution of the fifth-generation (5G) new radio (NR) has progressed swiftly since the third generation partnership project (3GPP) standardized the first NR version (Release 15) in mid-2018. Nowadays, the world’s leading carriers are competing to provide various commercial services over 5G networks. Looking ahead to 2025 and beyond, it is expected that over 6.5 million 5G base stations will be installed to offer services to over 58% of the world’s population via over 100 billion 5G connections. Following the rapid development of 5G, an increasing number of commercialization use cases will drive the 5G network to continuously improve performance and expand capabilities. Hence, it is the right time to consider a well-defined framework and standardization for 5G NR evolution (5G-Advanced) to support commercialization between 2025 and 2030. First, this study addresses the key driving forces, requirements, usage scenarios, and capabilities of 5G-Advanced; then, it highlights the main technological challenges and introduces the top 10 promising technological directions in detail. Finally, other fascinating technological directions in 5G-Advanced are shortly mentioned.

With the wide application of Internet of Things (IoT) devices, security attacks against their firmware often occur, which has attracted more attention from the research community. Firmware is an important part of IoT devices, and attacks against them is one of the main means to destroy them. Therefore, firmware security is considered a core of the overall devices’ security. At present, most of the firmware vulnerabilities have a small number of related samples, so it is difficult to use machine learning methods to generate detectors for some of them. Therefore, based on the collected data of related firmware vulnerabilities, this article proposes a firmware vulnerability homology detection method based on the clonal selection algorithm. We design the numerical and structural characteristics of vulnerability functions, train a detector for each function separately, and improve the recall rate of vulnerability detection. Compared with existing machine learning methods, this method only depends on the affinity between the objective function and the detector, which avoids the requirement of a large number of sample data sets. Finally, relevant experiments are carried out to verify the effectiveness of the method.

Non-orthogonal multiple access (NOMA) has been considered as a promising technology for future wireless communications. In most of the existing NOMA schemes, the ideal information rate based on Shannon capacity is used as the performance metric, assuming perfect successive interference cancellation (SIC) and Gaussian transmit signals without considering practical modulations. The implicit assumptions and the resulting schemes may lead to suboptimal performance in practical NOMA systems. In this paper, we consider multi-user multi-channel NOMA systems using practical quadrature amplitude modulation (QAM) with imperfect SIC. We aim to maximize a more practical performance metric, namely theeffective throughput, which takes into account the data rate and error performance. To achieve this goal, we derive both the exact and approximate expressions of the effective throughput. We also formulate a joint resource optimization problem of the power allocation, channel assignment, and modulation selection to maximize the effective throughput. We develop an efficient power allocation solution by proposing a closed-form power allocation within channels and a waterfilling-form power budget allocation among channels. We also develop efficient channel assignment and modulation selection methods with the aid of matching theory and machine learning, respectively. Consequently, we provide an efficient joint resource allocation algorithm via iterative optimization to maximize the effective throughput. Numerical results are presented to verify the superiority of the proposed NOMA scheme over orthogonal multiple access (OMA) and other NOMA schemes.

Fifth-generation (5G) wireless communications confront explosive growth in mobile data demand and massive intensive user equipment (UE) connections. The optimization of base station (BS) array orientations in large-scale networks can provide high potential performance gain to meet these requirements. However, traditional schemes are highly dependent on experience and difficult in implementation due to their demand on repeated drive tests and large amounts of data samples. In this paper, via exploiting UE locations and channel direction information, we propose an intelligent network optimization framework to maximize the long-term network rate performance. For the augmentation of limited drive test data, a deep Gaussian process regression (DGPR) model is designed to construct a scenario-specific channel modeling. In addition, via a domain-knowledge driven fusion of convolutional neural network (CNN) and multi-layer perceptron (MLP), we propose a multi-branch deep neural network (DNN) to accurately map BS array orientations and concise channel measurements to the network performance. Finally, based on the scenario-specific modeling, an efficient gradient search approach is proposed to optimize BS array orientations via neural network (NN) backpropagation. Both simulations and field tests in 5G experimental networks validate the effectiveness and efficiency of our proposed framework, especially with limited drive test data.

We consider the channel estimation and environment mapping problems in multiple-input multiple-output orthogonal frequency division multiplexing systems empowered by intelligent reconfigurable surfaces (IRSs). In order to acquire more in-depth environmental information, as well as, to flexibly take into account existing real-life infrastructure, we propose a novel three-dimensional conformal IRS architecture consisting of reflective unit cells distributed on curved surfaces. We model the training signal as a third-order canonical polyadic tensor and construct a tensor factorization problem. Given specific conditions on the allocated temporal-frequency training resources, we develop four channel estimation approaches, i.e., least squares, direct, wideband direct and wideband subspace methods, by leveraging tensor techniques and nonlinear system solvers. By fully exploiting the characteristics of conformal IRSs, we propose two decoupling modes to precisely recover the multipath parameters without ambiguities, which cannot be supported by the traditional IRS planar topologies. We implement scatterer mapping and user positioning tasks based on precise parameter estimates. Simulation results indicate that the proposed conformal IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of environment mapping.

As the requirements related to resolution or the data rate increase, most angle-of-arrival (AoA) estimation problems can be assumed to be under a broadband signal model. In this paper, AoA estimation using a nonuniform planar array (NUPA), which aims to resolve more signals and reduce mutual coupling, is considered under a broadband signal model. The analysis is conducted in both the space and frequency domains, and a broadband co-array is proposed to improve the performance of the AoA estimation. The broadband co-array is generated as follows: First, the received broadband signal is decomposed into several narrowband signals, and the samples from different frequency bins are transformed to space-domain samples to generate a virtual array. Then, the second-order statistics of the virtual array are used to generate the broadband co-array. The virtual array can decrease the number of element pairs with small interelement spacing to reduce mutual coupling in AoA estimations. With the help of the virtual array, the following broadband co-array can greatly increase the degrees of freedom, resulting in more resolvable signals and lower Cramer-Rao bounds of AoA estimation. An optimization method based on sparse recovery is proposed to locate the array. The simulation results confirm the AoA estimation performance achieved by the designed NUPA.

In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based onfederated learning(FL). Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users’ context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters by using the stochastic variance reduced gradient (SVRG) algorithm. Finally, FL based model integration is proposed to learn the global popularity prediction model based on local models using the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only can predict content popularity accurately, but also can significantly reduce computational complexity. Moreover, we theoretically analyze the convergence bound of our proposed FL based model integration algorithm. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to existing policies.

In this paper, we investigate maximum-distance separable (MDS) codes and weighted graph based coded caching in fog radio access networks (F-RANs). In the placement phase, the redundant MDS based coded placement scheme is used to provide redundant coded packets and homogeneous cached contents. The redundant coded packets can be used to construct multicast opportunities for similar requests. In the delivery phase, the weighted graph based coded delivery scheme is conducted based on homogeneous cached contents, which can induce considerable multicast opportunities. By integrating the above two schemes, a joint MDS codes and weighted graph based coded caching policy is proposed to minimize the fronthaul load. Finally, we theoretically analyze the performance of the proposed policy by deriving the lower and upper bounds of the fronthaul load. Simulation results show that our proposed policy can provide 44% savings in the fronthaul load compared to the MDS-based uncoded delivery policy.

We consider the channel estimation problem and the channel-based wireless applications in multiple-input multiple-output orthogonal frequency division multiplexing systems assisted by intelligent reconfigurable surfaces (IRSs). To obtain the necessary channel parameters, i.e., angles, delays and gains, for environment mapping and user localization, we propose a novel twin-IRS structure consisting of two IRS planes with a relative spatial rotation. We model the training signal from the user equipment to the base station via IRSs as a third-order canonical polyadic tensor with a maximal tensor rank equal to the number of IRS unit cells. We present four designs of IRS training coefficients, i.e., random, structured, grouping and sparse patterns, and analyze the corresponding uniqueness conditions of channel estimation. We extract the cascaded channel parameters by leveraging array signal processing and atomic norm denoising techniques. Based on the characteristics of the twin-IRS structures, we formulate a nonlinear equation system to exactly recover the multipath parameters by two efficient decoupling modes. We realize environment mapping and user localization based on the estimated channel parameters. Simulation results indicate that the proposed twin-IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of user positioning.

We propose a joint channel estimation and data detection (JED) algorithm for densely-populated cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems, which reduces the channel training overhead caused by the presence of hundreds of simultaneously transmitting user equipments (UEs). Our algorithm iteratively solves a relaxed version of a maximum a-posteriori JED problem and simultaneously exploits the sparsity of cell-free massive MU-MIMO channels as well as the boundedness of QAM constellations. In order to improve the performance and convergence of the algorithm, we propose methods that permute the access point and UE indices to form so-called virtual cells, which leads to better initial solutions. We assess the performance of our algorithm in terms of root-mean-squared-symbol error, bit error rate, and mutual information, and we demonstrate that JED significantly reduces the pilot overhead compared to orthogonal training, which enables reliable communication with short packets to a large number of UEs.

In order to fully exploit the advantages of massive multiple-input multiple-output (mMIMO), it is critical for the transmitter to accurately acquire the channel state information (CSI). Deep learning (DL)-based methods have been proposed for CSI compression and feedback to the transmitter. Although most existing DL-based methods consider the CSI matrix as an image, structural features of the CSI image are rarely exploited in neural network design. As such, we propose a model of self-information that dynamically measures the amount of information contained in each patch of a CSI image from the perspective of structural features. Then, by applying the self-information model, we propose a model-and-data-driven network for CSI compression and feedback, namely IdasNet. The IdasNet includes the design of a module of self-information deletion and selection (IDAS), an encoder of informative feature compression (IFC), and a decoder of informative feature recovery (IFR). In particular, the model-driven module of IDAS pre-compresses the CSI image by removing informative redundancy in terms of the self-information. The encoder of IFC then conducts feature compression to the pre-compressed CSI image and generates a feature codeword which contains two components, i.e., codeword values and position indices of the codeword values. Subsequently, the IFR decoder decouples the codeword values as well as position indices to recover the CSI image. Experimental results verify that the proposed IdasNet noticeably outperforms existing DL-based networks under various compression ratios while it has the number of network parameters reduced by orders-of-magnitude compared with various existing methods.

In this paper, the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs) is investigated. In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based Poisson regressor to model the content request pattern. Firstly, the relationship between content features and popularity is captured by our developed model. Then, we utilize Bayesian learning to learn the model parameters, which are robust to over-fitting. However, Bayesian methods are usually unable to find a closed-form expression of the posterior distribution. To tackle this issue, we apply a Stochastic Variance Reduced Gradient Hamiltonian Monte Carlo (SVRG-HMC) to approximate the posterior distribution. Two types of predictive content popularity are formulated for the requests of existing contents and newly-added contents. Simulation results show that the performance of our proposed policy outperforms the policy based on other Monte Carlo based method.

This paper studies the secure computation offloading for multi-user multi-server mobile edge computing (MEC)-enabled internet of things (IoT). A novel jamming signal scheme is designed to interfere with the decoding process at the Eve, but not impair the uplink task offloading from users to APs. Considering offloading latency and secrecy constraints, this paper studies the joint optimization of communication and computation resource allocation, as well as partial offloading ratio to maximize the total secrecy offloading data (TSOD) during the whole offloading process. The considered problem is nonconvex, and we resort to block coordinate descent (BCD) method to decompose it into three subproblems. An efficient iterative algorithm is proposed to achieve a locally optimal solution to power allocation subproblem. Then the optimal computation resource allocation and offloading ratio are derived in closed forms. Simulation results demonstrate that the proposed algorithm converges fast and achieves higher TSOD than some heuristics.

This is the demonstration description for a cloud- based cell-free distributed massive MIMO system, which is based on our already published work. Distributed massive MIMO antennas will result in design and implementation challenges regarding synchronization, calibration, real-time baseband processing, and so on. The contributors propose a cloud-based cellfree distributed massive MIMO system, which cannot only meet 5G NR requirements but also can be easily extended to different application scales. For this demostration, a 128 × 128 distributed MU-MIMO system with frequency 100 [email protected] GHz is given. Test results show that 10.185 Gbps throughput and more than 100 bps/Hz spectrum utilization can be obtained. On-site applications such as HD video transmission and virtual reality (VR) are offered for visitor experiences and interacts.

Soft-output decoder is of great importance to be applied in iterative receivers, of which belief propagation (BP) algorithm has been widely studied for 5G low-density parity- check (LDPC) and polar codes. However, for polar codes, BP decoding suffers from high computational complexity and unsatisfactory convergence. To this end, soft cancellation (SCAN) polar decoder has recently drawn attention from academia and can be further improved by using the bit-flipping strategy. Limited by the serial nature of message propagation, the SCAN flip (SCANF) decoder cannot meet a high throughput. In this paper, we accelerate the decoding speed by the fast processing mechanism, conducting Fast-SCANF decoder. The corresponding hardware architecture is designed with memory optimization and implemented by TSMC 40nm technology, delivering a 2.1 Gbps throughput and 65 pJ/b energy. To the knowledge of authors, this is the first SCANF hardware decoder.

This paper focuses on the channel estimation in cell-free hybrid massive multiple-input multiple-output (MIMO). Efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all access points (APs), which may enable the CPU to infer the private information of user locations. We therefore develop privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, a joint differentially private noisy matrix completion algorithm based on Frank-Wolfe iteration is presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we characterize the scaling laws of the estimation error in terms of data payload size. Simulation results demonstrate the tradeoff between privacy and channel estimation performance, and show that the estimation error can be mitigated by increasing the payload size while keeping the pilot size fixed.

We propose a novel soft-output joint channel estimation and data detection (JED) algorithm for multiuser (MU) multiple-input multiple-output (MIMO) wireless communication systems. Our algorithm approximately solves a maximum a-posteriori JED optimization problem using deep unfolding and generates soft-output information for the transmitted bits in every iteration. The parameters of the unfolded algorithm are computed by a hyper-network that is trained with a binary cross entropy (BCE) loss. We evaluate the performance of our algorithm in a coded MU-MIMO system with 8 basestation antennas and 4 user equipments and compare it to state-of-the-art algorithms separate channel estimation from soft-output data detection. Our results demonstrate that our JED algorithm outperforms such data detectors with as few as 10 iterations.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

This paper presents a passive vector-modulated phase shifter (VMPS). The passive X-type attenuator consisting of digitally controlled transistor-array units is employed to perform the phase-invertible gain tuning, and thus enables phase shift in all four quadrants. The Wilkinson-like power combiner is utilized to sum up the quadrature signals and avoid impedance mismatches. Analysis proves that the proposed passive VMPS can provide consistent phase-shift performance for bidirectional operation. The proof-of-concept VMPS is implemented in 40-nm CMOS technology and occupies a core chip area of 0.15 mm2. Measured results prove that it can provide consistent bidirectional 6-bit phase-shift operation, with accurate phase tuning (i.e., RMS phase error <; 24°) and low gain error (i.e., ±0.6 dB) over the whole 70-90 GHz band.

Belief propagation (BP) decoding has natural advantages in throughput for polar codes to meet high-speed and low-latency requirements. The soft outputs of BP decoding can be utilized further for joint detection and decoding in the baseband communication system. However, its error-correction performance is not comparable with the successive cancellation list (SCL) decoding. Belief propagation flip (BPF) decoding is recently proposed to improve the error-correction performance of BP decoding and indicates the potential to compete with SCL decoding. In this paper, we propose an advanced BPF (A-BPF) scheme that reduces the decoding latency with the help of one critical bit and improves the error-correction performance by the proposed joint detection criterion. To improve area efficiency in the hardware level, an optimized sorting network is proposed and applied for the A-BPF decoder. The decoder is implemented on 65 nm CMOS technology for length-1024 and rate-1/2 polar codes, and the results show that the proposed decoder can achieve a close frame error rate performance to the SCL decoder with four lists and deliver a throughput of 5.17 Gb/s at Eb/N0= 4.0 dB.

Sparse code multiple access (SCMA) is a promising technology for the development of wireless communication, which supports a large number of overloading users and enjoys high spectral efficiency. However, conventional SCMA decoders suffer very high complexity in implementations. Changing the updating scheme is a superior approach to reduce complexity, which guarantees the updated information immediately join in the following message propagating of the current iteration and accelerates the decoding convergence. In this paper, a row-layered message passing algorithm (MPA) is proposed, which offers a good trade-off between the hardware complexity and the bit error rate (BER) performance. Simulation results show that the proposed decoder saves 66.7% computation complexity compared with the original MPA with the similar BER performance. Pipelining and folding technology are adopted in VLSI implementations. The synthesis results with 45-nm CMOS technology show that the proposed decoder can achieve higher hardware efficiency and throughput under a high frequency than the existing decoders, achieving 1777.78 Mb/s throughput with 1.112 mm2area consumption.

For massive multiple-input multiple-output (MIMO) systems, linear minimum mean-square error (MMSE) detection has been shown to achieve near-optimal performance but suffers from excessively high complexity due to the large-scale matrix inversion. Being matrix inversion free, detection algorithms based on theGauss–Seidel(GS) method have been proved more efficient than conventionalNeumannseries expansion-based ones. In this paper, an efficient GS-based soft-output data detector for massive MIMO and a corresponding VLSI architecture are proposed. To accelerate the convergence of the GS method, a new initial solution is proposed. Several optimizations on the VLSI architecture level are proposed to further reduce the processing latency and area. Our reference implementation results on a Xilinx Virtex-7 XC7VX690T FPGA for a 128 base-station antenna and eight user massive MIMO system show that our GS-based data detector achieves a throughput of 732 Mb/s with close-to-MMSE error-rate performance. Our implementation results demonstrate that the proposed solution has advantages over the existing designs in terms of complexity and efficiency, especially under challenging propagation conditions.

Large-scale millimeter-wave (mm-Wave) integrated phased array is the key technology to enable broadband 5G and satellite communications. This paper details the design considerations, challenges and trade-offs of mm-Wave integrated phased arrays based on bulk CMOS and multi-layer hybrid PCB technologies. Both technologies attain high yield and low cost for mass production. Important beamforming building blocks are addressed and compared in detail. Demonstrators of integrated phased arrays are presented from circuit to board levels. The 1024- and 4096-element integrated phased arrays achieve the EIRP of 72.5 and 84.0 dBm respectively. Finally, relevant phased-array transceivers and antennas from the recent literature are discussed.

This article presents a Ku-band power amplifier with a series-shunt LC notch filter in 65-nm CMOS. The notch filter is integrated into the inter-stage matching network to attenuate the receiver-band noise, thereby reducing transmitter-to-receiver interference. A comprehensive analysis of the series and the shunt notch filters, as well as the position to apply the notch filter is discussed. Besides, a systematic method of optimizing passive devices in the notch filter is proposed to further improve network Q and minimize the influence on the power amplifier. Fabricated in 65-nm CMOS technology, the power amplifier prototype delivers a measured gain of 21.9 dB with 3-dB bandwidth from 13.7 GHz to 16.7 GHz at the nominal state. At 14.2 GHz, it can offer a saturated output power of 14.5 dBm with peak power added efficiency of 24.1%. The notch frequency is adjustable from 10.3 to 11.9 GHz to offer the best attenuation at the receiver band. From 10 to 12 GHz, a maximal attenuation of 30 dB is achieved. The design occupies a core area of 0.35×0.85 mm2.

The clustered delay line channel model, in which each cluster consists of many rays, is widely used for link-level evaluations in mobile communications. Multiple parameters of each ray, including the delay, amplitude, cross-polarization ratio (XPR), initial phases of four polarization combinations, and the azimuth and elevation angles of arrival and departure, must be known and are measured using a channel sounder. The number of rays in every cluster is usually greater than the number of elements in the antenna array of the channel sounder, which represents a challenging issue in multipolarized channel measurements. Based on the broadband extended array response of an electromagnetic vector antenna array, a new subspace estimation method is proposed to resolve a large number of rays. The inter-element spacing of the array can be greater than half the carrier wavelength, which reduces inter-element coupling and simplifies the array design, especially for millimeter-wave bands. The delay of each cluster is first estimated using the reference antenna element. Next, two-dimensional angles of every ray are estimated using the classic rank-deficient multiple signal classification algorithm. Finally, the initial phases, XPR, and amplitude of every ray are estimated. Simulation results validate the proposed method.

In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.

In this article, delay and energy efficiency (EE) are investigated in fog radio access networks (F-RANs) with hybrid caching. With multiple caching and transmission strategies, hybrid caching offers great flexibility for file placement and file fetching. By using tools from stochastic geometry, we firstly derive tractable expressions of delay for coded cached, non-partitioned cached and uncached files. Then, we derive tractable expressions of EE by jointly considering power consumed in circuits, transmissions and fronthaul links. To balance delay and EE, the corresponding multi-objective optimization problem is formulated to obtain the optimal hybrid caching strategy. Furthermore, considering the NP-hard complexity of the problem, we first theoretically analyze the optimal structure of the caching result. Then, we convert the original problem into a classification problem. We further propose a gradual-replacement greedy algorithm to obtain a near optimal hybrid caching strategy, which ensures high accuracy with low complexity. Numerical results show a significant performance gain of the proposed near optimal hybrid caching strategy over baselines and flexibility in delay-sensitive and EE-sensitive scenarios.

Network-assisted full-duplex (NAFD) distributed massive multiple-input multiple-output (MIMO) systems enable simultaneous uplink and downlink communications by dynamically allocating the numbers of uplink and downlink remote antenna units (RAUs), which potentially improve the spectral efficiency in wireless communications. In such systems, channel state information (CSI) plays a critical role in uplink reception and downlink transmission, as well as the cross link interference cancelation caused by downlink RAUs to uplink RAUs. Moreover, downlink terminals need to estimate CSI to reliably decode the received signals due to the reduced channel hardening effect. However, high training overhead makes it generally impossible to directly estimate CSI. This paper proposes to estimate effective CSI (inner products of beamforming and channel vectors) instead based on beamforming training scheme. Under this scheme, we derive closed-form expressions for uplink and downlink achievable rates with different receivers and beamforming. Given these expressions, we propose an efficient power allocation scheme which is only dependent on slowly varying large-scale fading from the perspective of multi-objective optimization. Numerical results verify the accuracy of the derived closed-form expressions and effectiveness of beamforming training based CSI estimation. Moreover, trade-off regions between the considered optimization objectives under various system parameters offer numerous flexibilities for system optimization.

We consider the channel estimation problem in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems assisted by intelligent reconfigurable surfaces (IRSs). To avoid the inherent estimation ambiguities of the two-hop channels from mobile stations (MS) to the base station (BS), we adopt a hybrid IRS architecture composed of passive reflectors and active sensors, and establish two independent subproblems of estimating the MS-to-IRS and BS-to-IRS channels. By leveraging the sparse characteristics of high-frequency propagation, we model the training signals as multi-dimensional canonical polyadic decomposition (CPD) tensors with missing fibers or slices. We develop algebraic algorithms to solve the tensor completion problems and recover channel multipath parameters, i.e., angles of arrival, time delays and path gains. Our methods require neither random initialization nor iterative operations, and for these reasons they can perform robustly with a low computational complexity. Moreover, we investigate the uniqueness condition of CPD tensor completion, which can be utilized to inform both the physical design of hybrid IRSs and the time-frequency resource allocation of training strategies. Simulation results indicate that the proposed schemes outperform the traditional counterparts in terms of accuracy, robustness and complexity, especially for the case of low-complexity IRSs with limited number of active sensing elements.

In this paper, we investigate the problem of sum rate maximization by guaranteeing the quality of service (QoS) of both uplink and downlink in cell-free with network-assisted full Duplexing (NAFD), where the users operate in half-duplex mode and access points (APs) operate in either full-duplex mode or half-duplex mode. In the considered network, the central processor unit (CPU) sends the compressed beamformed signals to the transmit-APs (T-APs) over the downlink fronthaul, and the T-APs forward the signals to downlink users. At the same time, the receive-APs (R-APs) compress the signals transmitted by uplink users and forward them to the CPU via uplink fronthaul. We aim to maximize the spectral efficiency and the number of users that should be admitted by the network, where users’ requirements of both downlink and uplink signal-to-interference-plus-noise (SINR) constraints, fronthaul capacity constraints, energy harvesting constraints and simultaneous wireless information and power transfer (SWIPT) ratio design are considered. A successive convex approximation-based algorithm is proposed to solve the highly coupled problem, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) conditions. We conduct a comprehensive comparison between the NAFD scheme and the traditional co-frequency co-time full duplex (CCFD) scheme and time division duplex (TDD) scheme and offer some valuable opinions about the system design.

We consider a cell-free hybrid massive multiple-input multiple-output (MIMO) system with K users and M access points (APs), each with Naantennas and Nraradio frequency (RF) chains. When Ka, efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all APs, which may enable the CPU to infer the private information of user locations. We therefore develop and analyze privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, two joint differentially private noisy matrix completion algorithms based respectively on Frank-Wolfe iteration and singular value decomposition are presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we show that the estimation error can be mitigated while maintaining the same privacy level by increasing the payload size with fixed pilot size; and the scaling laws of both the privacy-induced and privacy-independent error components in terms of payload size are characterized. Simulation results are provided to further demonstrate the tradeoff between privacy and channel estimation performance.

Millimeter wave communications, equipped with large-scale antenna arrays, are able to provide Gbps data rates by exploring abundant spectrum resources. However, the use of a large number of antennas along with narrow beams causes a large overhead in obtaining channel state information (CSI) via beam training, especially for fast-changing channels. To reduce beam training overhead, in this paper we develop an interactive learning design paradigm (ILDP) that makes full use of domain knowledge of wireless communications (WCs) and adaptive learning ability of machine learning (ML). Specifically, the ILDP is fulfilled via deep reinforcement learning (DRL), which yields DRL-ILDP, and consists of communication model (CM) module and adaptive learning (AL) module, which work in an interactive manner. Then, we exploit the DRL-ILDP to design efficient beam training algorithms for both multi-user and user-centric cooperative communications. The proposed DRL-ILDP based algorithms enjoy three folds of advantages. Firstly, ILDP takes full advantages of the existing WC models and methods. Secondly, ILDP integrates powerful ML elements, which facilitates extracting interested statistical and probabilistic information from environments. Thirdly, via the interaction between the CM and AL modules, the algorithms are able to collect samples and extract information in real-time and sufficiently adapt to the ever-changing environments. Simulation results demonstrate the effectiveness and superiority of the designed algorithms.

We propose a legal full‐duplex unmanned aerial vehicle (UAV) surveillance system in the presence of the ground‐to‐ground suspicious link with antisurveillance technology. UAV performs passive surveillance and active jamming simultaneously, and the suspicious source with multiantenna employs artificial noise to avoid being monitored. In order to ensure effective surveilling, we adopt two beamforming schemes, namely, maximum ratio transmission (MRT)/receiving zero‐forcing (RZF) and transmitting zero‐forcing (TZF)/maximum ratio combing (MRC), for MIMO UAV. For the two beamforming schemes, we derive the surveilling nonoutage probability in a closed‐form expression and analyze the surveilling performance under different system environments. Monte Carlo (MC) simulation validates the correctness of the formula.

In this paper, the cooperative caching problem in fog radio access networks (F-RANs) is investigated. To minimize the content request delay, we formulate the hierarchical cooperative caching optimization problem to find the optimal caching policy. Considering the non-deterministic polynomial hard (NP-hard) property of this problem, we propose a brain storm optimization (BSO) approach which utilizes the penaltybased fitness function in individuals evaluation to meet the storage capacity constraint and the genetic algorithm (GA) in new individuals generation to meet the integer constraint, respectively. To further reduce the computational complexity, we propose to implement the convergent operation in the objective space via individuals classification. Simulation results show that our proposed BSO-based hierarchical cooperative caching policy achieves remarkable performance in minimizing the content request delay.

For the scenarios with high throughput requirements, the belief propagation (BP) decoding is one of the most promising decoding strategies for polar codes. By pruning the redundant variable nodes (VNs) and check nodes (CNs) in the original factor graph, the graph is condensed to a sparse bipartite graph which is similar to the graph for low-density parity-check (LDPC) codes. In this paper, we introduce the bit-flipping scheme into the LDPC-like BP (L-BP) decoding and propose two methods to identify the error-prone VNs. By additional decoding attempts, the L-BP flip (L-BPF) decoding improves the error-rate performance with a similar average complexity for high Eb=N0values. The simulation results show that the L-BPF decoding achieves 0:25 dB gain compared with the L-BP decoding.

In this paper, the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction with low complexity, we propose a novel context-aware popularity prediction policy based on federated learning. Firstly, user preference learning is applied by considering that users prefer to request the contents they are interested in. Then, users' context information is utilized to cluster users efficiently by adaptive context space partitioning. After that, we formulate a popularity prediction optimization problem to learn the local model parameters using the stochastic variance reduced gradient (SVRG) algorithm. Finally, federated learning based model integration is proposed to construct the global popularity prediction model based on local models by combining the distributed approximate Newton (DANE) algorithm with SVRG. Our proposed popularity prediction policy not only predicts content popularity accurately, but also significantly reduces computational complexity. Simulation results show that our proposed policy increases the cache hit rate by up to 21.5 % compared to the traditional policies.

In a multiuser massive multiple-input multipleoutput (MIMO) system, hybrid analog-and-digital structure is widely applied due to the high cost of deploying a large number of radio-frequency (RF) chains to drive the large antenna array. Phase shifter network is a common way of accomplishing the analog component. However, phase shifters impaired by hardware constrains can seriously degrade the performance of the system. This paper investigates the influence of imperfect phase shifters on the uplink achievable rate of the system with fullyconnected and sub-connected architectures. We derive a tractable expression for the uplink achievable sum rate. The proposed studies show that massive antennas are able to compensate for the performance degradation caused by imperfect phase shifters in the hybrid massive MIMO system. Our analytical results are verified by extensive simulations.

This paper studies transceiver design for a large-scale distributed antenna system (L-DAS) with network assisted full duplexing (NAFD), where all the users and remote antenna units (RAUs) operate in either half-duplex (HD) or full-duplex (FD) mode. In the considered network, transmitting-RAUs (TRAUs) transmit information to downlink users (DUs) while receiving-RAUs (R-RAUs) receive signal from uplink users (UUs). All the T-RAUs and R-RAUs are connected to the central processor (CP) via high-speed backhaul links. T-RAUs obtain DUs' data from the CP via downlink backhaul (D-backhaul), and forward the data to DUs by sparse beamforming. Meanwhile, R-RAUs detect the signal transmited by UUs, and forward the signal to the CP via uplink backhaul (U-backhaul). We aim to maximize the the spectral efficiency subject to quality of service (QoS) constraints and backhaul constraints. Since various design parameters, such as the downlink sparse beamformers, the uplink transmit power, and the receiver, are tightly coupled together in both the subject function and the constraints, the solution of the problem is challenging. By converting the object function to the difference between two convex functions (D.C.) structure with semi definite relax (SDR), an iterative SDR-block coordinate descent (SDR-BCD) method is proposed. Simulation results show that the proposed algorithm yield a higher spectral efficiency (SE) gain compared with the traditional time-division duple (TDD) scheme.

Device‐to‐device (D2D) communication provides the communication of the users in the vicinity and thereby decreases end‐to‐end delay and power consumption. More importantly, D2D communication enables offloading the traffic load of the base station (BS), and it is very suitable for caching, especially in ultra dense networks (UDNs). In this paper, a joint optimisation problem of collaborative caching and sleeping strategy based on energy‐delay tradeoff for D2D‐aided UDN is investigated. To solve the joint optimization problem, we decompose it into sleeping sub‐problem and collaborative caching sub‐problem. For sleeping subproblem, the optimal sleeping ratio is first derived, then, a delay‐aware sleeping strategy is proposed to obtain the local optimal sleeping scheme. For the collaborative caching subproblem, the suboptimal solution is obtained by distributed iterative method. In each iteration step, the suboptimal solution is derived by solving the combinatorial optimisation problem under Karush‐Kuhn‐Tucker conditions. Simulation results show that the proposed algorithms can coverage to the global solution. It also demonstrate that increasing caching capacity shortened mean delay, and benefitting from collaborative caching, delay performance and energy saving can be improved significantly. Moreover, it can be seen that combining the sleeping strategy with collaborative caching further reduced energy consumption by a considerable amount.

Massive multiple-input-multiple-output (MIMO) system inevitably faces the hardware cost and energy efficiency problem due to its large number of antennas at the base station (BS). The use of low-resolution analog-to-digital converters (ADCs), e.g., typical 1-bit ADCs, can effectively reduce the system cost. In this paper, we consider a massive MIMO uplink with ternary/three level ADCs. The design of typical linear combiner based detectors is given along with their analytical symbol-error-rate (SER) performance results. Analytical and simulation results show that 1) ternary ADCs can effectively compensate the SER performance gap between 1-bit and full-resolution ADCs; 2) optimal design of ternary ADCs for SER minimization can be referred to the existing design for quantization error minimization; 3) ternary ADCs perform better than 2-bit ADCs in energy efficiency. Thus, for some low-cost scenarios where implementing 2-bit ADCs for each antenna in massive MIMO may be even unaffordable, ternary ADC can be a good choice.

In this letter, for outage performance orientated large intelligent surfaces (LISs)-assisted point to point wireless systems with severely blocked direct link and Rayleigh fading channels,we first propose a jointly interleaved training and transmission design. Then a semi-closed form expression is derived for the average training overhead. And it is shown to be upper bounded by the minimum between the LIS size and a value explicitly dependent on the target receiver signal-to-noise-ratio (SNR). The upper bound gives the condition on the target SNR for achieving overhead saving compared to the full CSI scheme. And the overhead saving increases linearly with the LIS size for constant target SNR. Non-negligible overhead saving is still available even though one increases the target SNR with larger LIS, e.g., as the square of the LIS size for fully exploiting the beamforming gain. Finally, we indicate the impact of practical phase quantization on the training and feedback overhead. Simulations verify these results and show that the proposed scheme can significantly reduce the training overhead without performance loss compared to the full CSI scheme.

Physical unclonable function (PUF) is a technique to produce secret keys or complete authentication in integrated circuits (ICs) by exploiting the uncontrollable randomness due to manufacturing process variations. For better PUF applications, efficient analysis of different designs is important. In this article, a mathematical model to analyze the performance of typical strong PUF designs is proposed and applied to arbiter PUF, ring oscillator (RO) PUF, and duty cycle (DC) PUF. For better reliability, a new PUF design, DC multiplexer (DC MUX) PUF proposed in our previous work is analyzed. The proposed model indicates that DC MUX PUF achieves 2% higher reliability than arbiter PUF under environment influences. It also shows that DC PUF achieves 10% higher reliability than RO PUF. For verification, the aforementioned four PUF designs are testified using HSPICE. As our model analysis indicates, for reliability DC MUX PUF outperforms arbiter PUF, and DC PUF outperforms RO PUF. For randomness, DC MUX PUF and DC PUF outperform arbiter PUF and RO PUF, respectively. For security, LR attacks on DC MUX PUF and arbiter PUF are performed. The training time for DC MUX PUF is 40 000 times of arbiter PUF.

Convolutional neural networks (CNNs) have gained great success in various fields, such as computer vision and natural language processing. Besides, with the breakthrough in unsupervised learning, generative adversarial network (GAN) is recently utilized to generate virtual data from limited data sets. The generative model of GAN has impressive applications, such as style transfer and image super-resolution. However, the promising performance of CNN and GAN comes at the cost of prohibitive computation complexity. The convolution (CONV) in CNN and the transposed CONV (TCONV) in GAN are the two operations that dominant the overall complexity. The prior works exploit the fast algorithms, Winograd and fast Fourier transform (FFT), to reduce the complexity of spatial CONV. However, Winograd only supports fixed filter size while FFT has high transform overhead. Moreover, very few works apply fast algorithms to accelerate GAN models. In this article, a reconfigurable and low-complexity accelerator on ASIC for both CNN and GAN is proposed to address these problems. First, by exploiting Fermat number transform (FNT), we propose two FNT-based fast algorithms to reduce the complexity of CONV and TCONV computations, respectively. Then the architectures of the FNT-based accelerator are presented to implement the proposed fast algorithms. The methodology to determine the design parameters and optimize the dataflow is also described for obtaining maximum performance and optimal efficiency. Moreover, we implement the proposed accelerator on 65 nm 1P9M technology and evaluate it on various CNN and GAN models. The post-layout results show that our design achieves a throughput of 288.0 GOP/s on VGG-16 with 25.11 GOP/s/mm2area efficiency, which is superior to the state-of-the-art CNN accelerators. Furthermore, at least $1.7\times $ speedup over the existing accelerators is obtained on GAN. The resulting energy efficiency is $275.3\times $ and $12.5\times $ of CPU and GPU.

In this paper, the edge caching optimization problem in fog radio access networks (F-RANs) is investigated. Taking into account time-variant user requests and ultra-dense deployment of fog access points (F-APs), we propose a distributed edge caching scheme to jointly minimize the request service delay and fronthaul traffic load. Considering the interactive relationship among F-APs, we model the optimization problem as a stochastic differential game (SDG) which captures the dynamics of F-AP states. To address both the intractability problem of the SDG and the caching capacity constraint, we propose to solve the optimization problem in a distributive manner. Firstly, a mean field game (MFG) is converted from the original SDG by exploiting the ultra-dense property of F-RANs, and the states of all F-APs are characterized by a mean field distribution. Then, an iterative algorithm is developed that enables each F-AP to obtain the mean field equilibrium and caching control without extra information exchange with other F-APs. Secondly, a fractional knapsack problem is formulated based on the mean field equilibrium, and a greedy algorithm is developed that enables each F-AP to obtain the final caching policy subject to the caching capacity constraint. Simulation results show that the proposed scheme outperforms the baselines.

We consider the channel estimation problem in millimeter wave (mmWave) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with hybrid analog-digital architectures. Leveraging the spatial- and frequency-wideband (dual-wideband) effects in massive MIMO scenarios, we derive a spatial-frequency channel model with dual-wideband effects that incorporates the multipath parameters, i.e., time delay, complex gain, angle of departure/arrival. We adopt a successive beam training scheme and formulate the training OFDM signal as a third-order low-rank tensor fitting a canonical polyadic (CP) model with factor matrices containing the channel parameters. Exploiting the Vandermonde nature of factor matrices, we propose a structured CP decomposition-based channel estimation strategy aided by the spatial smoothing method, where two dedicated algorithms with particular tensor modeling and parameter recovery operations are developed. The proposed scheme leverages standard linear algebra, and, hence, avoids the random initialization problem and iterative procedure. An analysis of the uniqueness condition of CP decomposition is also pursued. Simulation results indicate that the proposed strategy achieves enhanced estimation performance, which outperforms the traditional approaches in terms of accuracy, robustness and complexity.

Although uplink (UL) downlink (DL) decoupling (DUDe) brings significant gains in the UL throughput of decoupled user equipments (DeUEs) in heterogeneous networks, channel estimation and DL performance of DeUEs are worse than the coupled UEs due to the DUDe property and the cell edge effect. To address these fundamental problems, we propose a transmission scheme with data-aided (DA) minimum mean square error (MMSE) channel estimator and zero-forcing (ZF) interference nulling (IN) precoding for a two-tier multi-cell HetNet with DUDe. We first present a method to estimate the bit error rate (BER) of UL data, then, derive the form of DA MMSE estimator, which utilizes decoded UL data, estimated BER and known UL training sequences to jointly estimate the DL channels of DeUEs. ZF IN precoding uses the estimated channels of DeUEs to cancel the nearest DL interference without any cooperation and message transmission. Also, we derive a tight approximation to the achievable DL rate of DeUEs and analyze the benefits of the DA estimator and ZF IN precoding. Our simulations show that DA MMSE estimator outperforms the conventional MMSE counterpart, while the proposed scheme improves the DL performance of both DeUEs and macro UEs, though the rate gain may be degraded by pilot contamination and inter-cell interference.

Since the inherent serial nature of successive cancellation list (SCL) decoding results in a long latency, belief propagation (BP) decoding for polar codes has drawn attention for high-throughput applications. However, its error correction performance is inferior to that of SCL decoding. Therefore, the bit-flipping strategy has been recently applied to BP decoding, which can approach the SCL decoding performance through multiple additional decoding attempts. The original BP flip (BPF) decoding suffers from an inaccurate identification of erroneous bits by a fixed flip set (FS), which has been improved by the generalized BPF (GBPF) decoding. In this article, the GBPF decoding is extended to support multiple bits being flipped in one decoding attempt. In addition, for two types of decoding errors: detected errors and undetected errors, we propose two novel methods to more effectively identify erroneous bits. For detected errors, the concept of loop sets is defined and a loopbased identification method is introduced based on the study of error patterns of BP decoding. On the other hand, a method to generate a more accurate fixed FS is proposed for undetected errors, which considers the bit error distribution under BP decoding. Combining the two methods, the GBPF with merged sets (GBPF-MS) decoding can achieve the SCL-8 performance and outperforms the state-of-the-art BPF, BP list, and SC flip (SCF) decoding, for polar codes with length 1024 and information rate 1/2. Implemented by 40nm CMOS technology, the proposed GBPF-MS decoder with ten flips exhibits an average throughput of 4.19 Gbps at 2.5 dB, which is 1.6× and 1.72× faster than the state-of-the-art SCL-4 and SCF decoders, respectively.

In this paper, the spectral efficiency of network assisted full-duplex communications (NAFD) in cell-free (CF) massive multiple-input multiple-output (MIMO) network with imperfect channel state information is investigated under spatial correlated channels. Based on large dimensional random matrix theory, the deterministic equivalents for the uplink sum-rate with minimum-mean-square-error receiver as well as the downlink sum-rate with zero-forcing and regularized zero-forcing beamforming are presented. Numerical results show that under various environmental settings, the deterministic equivalents are accurate in both a large-scale system and system with a finite number of antennas. It is also shown that with the downlink-to-uplink interference cancellation, the uplink spectral efficiency of CF massive MIMO with NAFD could be improved. The spectral efficiencies of NAFD with different duplex configurations such as in-band full-duplex, and half-duplex are compared. With the same total numbers of transmit and receive antennas, NAFD with half-duplex remote antenna units offers a higher spectral efficiency. To alleviate the uplink-to-downlink interference, a novel genetic algorithm based user scheduling strategy (GAS) is proposed. Simulation results show that the achievable downlink sum-rate by using the GAS is greatly improved compared to that by using the random user scheduling.

This paper studies the energy-efficient power allocation problem for physical-layer security in multi-user (MU) distributed massive multiple-input multiple-output (MIMO) systems. A new metric called global average secrecy energy efficiency (GASEE) is proposed to measure the MU secrecy energy efficiency (SEE) with a single eavesdropper (Eve). We first derive closed-form expressions for the signal to interference-plus-noise ratios (SINRs) of legitimate users and the Eve with pilot contamination. Under a power consumption model that incorporates transmit power, backhaul power, remote antenna unit (RAU) circuit and signal processing power, and with transmit power constraints as well as SINR constraints for both users and the Eve, the GASEE maximization problem is formulated as a joint optimization of power allocation, RAU clustering, RAU selection and artificial noise (AN) selection. The formulated problem is a mixed integer nonlinear program (MINLP), which is solved by a double-loop procedure. In the outer loop, the denominator of objective is approximated as a linear function. In the inner loop, an efficient algorithm is proposed to find a near-optimal solution to the approximated problem by solving a sequence of sub-problems. Simulation results demonstrate that the proposed algorithm converges fast and achieves a higher GASEE than some heuristics.

Millimeter wave (mmwave) communications have attracted increasing attention thanks to the abundant spectrum resource. The short wave-length of mmwave signals facilitates exploiting large antenna arrays to achieve large array gains and combat the large path-loss. However, the use of large antenna arrays along with narrow beams leads to a large overhead in beam training for obtaining channel state information, especially in dynamic environments. To reduce the overhead of beam training, in this paper we formulate the problem of beam alignment and tracking (BA/T) as a stochastic bandit problem. In particular, to sense the change of the environments, the actions are designed based on the offset of successive beam indexes (i.e., beam index difference), which measures the rate of change of the envir-onments. Then, we propose two efficient BA/T algorithms based on the stochastic bandit learning. To reveal useful insights, the performance of effective achievable rate is further analyzed for the proposed BA/T algorithms. The analytical results show that the algorithms can sense the change of the environments and adjust beam training strategies intelligently. In addition, they do not require any priori knowledge of dynamic channel modeling, and thus are applicable to a variety of complicated scenarios. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

Though belief propagation (BP) polar decoders can achieve higher throughput than successive-cancellation (SC)-based decoders, and how to efficiently generate different belief propagation decoders (BPDs) which can meet various design specifications remains challenging. To this end, an autogeneration, which can translate the generation formula of BPDs to efficient hardware implementations, has been proposed in this article. For different requirements, two BPD architectures have been given: 1) low-cost decoder (Type-I) and 2) high-throughput decoder (Type-II). The autogeneration of them can support different code rates, code lengths, and parallelisms. Synthesis results show that Type-I and Type-II provide higher throughput and hardware efficiency than the state-of-the-art (SOA) SC decoders. Moreover, compared to the SOA BPDs, both Type-I and Type-II achieve similar even better energy- and area-efficiency with a comparable throughput, for fully parallel configuration. With the autogeneration, we are able to obtain the design space regarding different design metrics, such as area efficiency, energy efficiency, and power density, within which the design optimization under given design constraints can be conducted.

In this article, the edge caching policy in fog radio access networks (F-RANs) is optimized via deep learning. Considering that it is hard for fog access points (F-APs) to collect sufficient data of massive content features, our proposed edge caching policy only utilizes the number of requests and user location. In an offline phase, we propose to learn the corresponding popularity prediction model for every content popularity trend class and user location prediction models to make the popularity prediction accurate, adaptive and targeted. Moreover, we develop a loss function to avoid overfitting and increase sensitivity to high popularity for popularity prediction models. In an online phase, we propose a reactive caching scheme to react to user requests. In order to guarantee that classification can improve the popularity prediction accuracy in both phases, deep learning and k-Nearest Neighbor (kNN) are combined to classify popularity trends. Besides, a joint proactive-reactive caching policy is proposed to maximize the cache hit rate. The proposed policy is able to promptly track the various popularity trends with spatial-temporal popularity, trend and user dynamics with a low computational complexity. Extensive performance evaluation results show that the cache hit rate of our proposed policy approaches that of the optimal policy.

Millimeter wave (mmwave) communication has attracted increasing attention owing to its abundant spectrum resource. The short wave-length of mmwave signals facilitates exploiting large antenna arrays to achieve large array gains and combat large path-loss. However, the use of large antenna arrays and narrow beams leads to a large overhead in beam training for obtaining channel state information, especially in dynamic environments. To reduce the overhead of beam training, in this paper we propose an environment sensing based beam training algorithm via deep reinforcement learning. The proposed algorithm can sense the change of the environment and learn required latent probability information from the environment, and intelligently trains beams with a low overhead. In addition, the proposed algorithm does not require any priori knowledge of dynamic channel modeling, and thus is applicable to a variety of complicated scenarios. Simulation results demonstrate the effectiveness and superiority of the proposed intelligent beam training algorithm.

In this paper, the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs) is investigated. In order to make the popularity prediction accurate and adaptive, we propose to learn the corresponding popularity prediction model for every content class from the preprocessed popularity series by training a simplified bidirectional long short-term memory (Bi-LSTM) network, and further use it to help build a content classifier in terms of content popularity trend in the training phase. Then, content popularity can be predicted by the right prediction model with respect to the corresponding content class in the predicting phase. Considering that it is hard to collect enough data about numerous content features through F-APs, we propose to only use the number of requests. Our proposed content popularity prediction policy offers a high prediction accuracy with low computational complexity by transferring the high complexity tasks from the predicting phase to the training phase. Simulation results show that the cache hit rate of our proposed policy approaches the optimal performance.

In this paper, the cooperative edge caching problem in fog radio access networks (F-RANs) is investigated to minimize the average download delay. Considering the non-linear and coupled multi-variable nature of the original optimizing problem, we transform it into an equivalent integer linear programming problem with decoupled variables. Then, we decomposed the transformed problem into two subproblems which can be solved separately by each fog access point (F-AP). Considering the non-deterministic polynomial hard (NP-hard) nature of the two decomposed subproblems, we propose an improved pigeon inspired optimization (PIO) based cooperative edge caching scheme, which utilizes Cauchy perturbation and self-adaptive factor to avoid pre-mature convergence and achieve a better search performance, respectively. Our proposed scheme not only allows F-APs to make cache decisions with low computational complexity, but also has very low message passing overhead. Simulation results show that our proposed scheme can greatly decrease the average download delay.

For massive multiple-input multiple-output (MIMO) detection, belief propagation (BP) based on graphical models has become a popular detection algorithm since it provides a good tradeoff between performance and complexity. To further lower the complexity of BP detection, an efficient BP detection based on channel hardening (BP-CH) is proposed. In this paper, the comparison in terms of both performance and complexity between proposed BP-CH and general BP is firstly investigated exhaustively. Simulation results have shown that the proposed BP-CH achieves similar performance behavior as general BP while keeping lower computational complexity. Additionally, an folded hardware architecture for proposed BPCH detector is designed to improve the implementation efficiency. Meanwhile, VLSI implementation results have verified the great advantage of BP-CH regarding hardware overhead, especially for scenarios with large system loading factor.

In this paper, we study the low-complexity iterative soft-input soft-output (SISO) detection algorithm in a large-scale distributed multiple-input multiple-output (MIMO) system. The uplink interference suppression matrix is designed to decompose the received multi-user signal into independent single-user receptions. An improved minimum-mean-square-error iterative soft decision interference cancellation (MMSE-ISDIC) based on eigenvalue decomposition (EVD-MMSE-ISDIC) is given to perform low-complexity detection of the decomposed signals. Furthermore, two iteration schemes are given to improve receiving performance, which are iterative detection and decoding (IDD) scheme and iterative detection (ID) scheme. While IDD utilizes the external information generated by the decoder for iterative detection, the output information of the detector is directly exploited with ID. In particular, the performance of the schemes is evaluated in a 128×128 (16 remote antenna units (RAUs) and 16 users, each equipped with 8 antennas) large-scale distributed MIMO prototyping system, which is also a cell-free massive MIMO. The experimental results show that the proposed iterative receiver greatly outperforms the linear MMSE receiver, since it reduces the average number of error blocks of the system significantly.

Multi-resolution codebook based hierarchical beam training is an attractive solution to the heavy overhead of millimeter-wave (mmWave) beam alignment. However, most existing codebooks suffer from either undesired main-lobefluctuation or high hardware-complexity. To address these issues, this paper proposes a closed-form phase-shifted discrete Fourier transformation (PS-DFT/CF) codebook design for mmWave links with hybrid structures. The proposed codebook is of hybrid analog/digital architecture. Analog components are fixed-size DFT vectors, which can be readily implemented with radiofrequency (RF) phase shifters. Digital components at baseband select unitary subbeams shaped by the analog components and tackle the impact of the phase difference between adjacent subbeams, such that multi-resolution flat beam patterns can be synthesized. Moreover, the closed-form PS-DFT codebook design enables joint transceiver design for mmWave beam alignment. Numerical results verify that the proposed PS-DFT/CF codebook approaches the performance of the ideal PS-DFT counterpart.

The deployment of millimeter-wave (mmWave) wireless communication systems in urban macrocell scenarios with large coverage and cost-efficient transmission schemes is investigated with a focus on space-time propagation characteristics. First, channel measurement campaigns are conducted at two 5G main candidate frequency bands of 28 GHz and 39 GHz in a central business district and a residential area, based on our new-designed time domain channel sounder, which can support directional scanning sounding with less time consumption. Next, using a reasonable data preprocessing method, small-scale channel characteristics across line-of-sight (LoS) and non-LoS (NLoS) links are analyzed via power delay angular profiles, root mean square delay spread, and azimuth and elevation angular spread. Measurement and analysis results show that space-time propagation parameters are layout-related and have further implications on the mmWave system design.

Distributed phased arrays based multiple-input multiple-output (DPA-MIMO) is a recently proposed highly reconfigurable architecture enabling both spatial multiplexing and beamforming in millimeter-wave (mmWave) systems. In this work, we focus on coping with the hybrid precoding for the wideband DPA-MIMO system with orthogonal frequency division multiplexing (OFDM) modulation. More specifically, we propose an alternating direction method of multipliers (ADMM) enabled hybrid precoding approach based on an alternating optimization framework, abbreviated to ADMM-AltMin, for such cooperative array-of-subarrays structures. Simulation results show that the proposed ADMM-AltMin method achieves favourable performance with practical quantization of phase shifters taken into account.

In this paper, the distributed edge caching problem in fog radio access networks (F-RANs) is investigated. By considering the unknown spatio-temporal content popularity and user preference, a user request model based on hidden Markov process is proposed to characterize the fluctuant spatio-temporal traffic demands in F-RANs. Then, the Q-learning method based on the reinforcement learning (RL) framework is put forth to seek the optimal caching policy in a distributed manner, which enables fog access points (F-APs) to learn and track the potential dynamic process without extra communications cost. Furthermore, we propose a more efficient Q-learning method with value function approximation (Q-VFA-learning) to reduce complexity and accelerate convergence. Simulation results show that the performance of our proposed method is superior to those of the traditional methods.

In this paper, a large-scale distributed antenna system (DAS) with network-assisted full duplexing (NAFD) is investigated. Our model assumes that imperfect channel state information (CSI) is available and channels have independent spatial correlations. Based on large dimensional random matrix theory (RMT), the deterministic equivalent expressions for the ergodic uplink (UL) sum-rate with minimum-mean-square-error (MMSE) receiver as well as the ergodic downlink (DL) sum-rate with regularized zero-forcing (RZF) beamforming are derived. With the cancellation of downlink-to- uplink interference, the spectral efficiency of NAFD system is further improved. Numerical simulations indicate that under various environment settings, the deterministic equivalent results are accurate in both large-scale system and system with finite number of antennas.

To enhance downlink (DL) performance of decoupled user equipments (DeUEs) in decoupled heterogeneous networks, a transmission scheme with data-aided (DA) channel estimation and zero-forcing (ZF) interference-nulling (IN) precoding is proposed. In the scheme, DL base station (BS) uses decoded uplink (UL) data and estimated UL bit error ratio (BER) combined with known training sequences to perform DA channel estimation and refined estimated channels of DeUEs are then used in DL precoding for better DL performance. Also, ZF IN precoding is adopted at UL BSs of DeUEs, who pose the strongest interference on their serving DeUEs in DL, by leveraging estimated channels of DeUes in UL without any message transferring. The closed-form achievable DL rate of DeUEs is derived and analyzed, which shows that DL rate can be improved remarkably by DA method and ZF IN precoding although there exists rate upper bounds for co-channel interference from other BSs.

In this study, cooperative caching is investigated in fog radio access networks. To maximise the offloaded traffic, a cooperative caching optimisation problem is formulated. By analysing the relationship between clustering and cooperation and utilising the solutions of the knapsack problems, the above challenging optimisation problem is transformed into a clustering subproblem and a content placement subproblem. To further reduce complexity, the authors propose an effective graph‐based approach to solve the two subproblems. In the graph‐based clustering approach, a node graph and a weighted graph are constructed. By setting the weights of the vertices of the weighted graph to be the incremental offloaded traffics of their corresponding complete subgraphs, the objective cluster sets can be readily obtained by using an effective greedy algorithm to search for the max‐weight independent subset. In the graph‐based content placement approach, a redundancy graph is constructed by removing the edges in the complete subgraphs of the node graph corresponding to the obtained cluster sets. Furthermore, they enhance the caching decisions to ensure each duplicate file is cached only once. Compared with traditional approximate solutions, their proposed graph‐based approach has lower complexity. Simulation results show remarkable improvements in terms of offloaded traffic by using the proposed approach.

This study investigates the joint power allocation and energy cooperation problem in a multiuser downlink distributed antenna system with hybrid energy supply. The authors focus on the energy efficiency (EE) maximization problem for three different types of precoding, zero‐forcing (ZF), general beamforming method and conjugate‐beamforming. For ZF precoding, they apply fractional programming and reform the optimization problem to a convex one. An iterative algorithm to deal with the fractional objective function is presented. Whereas the problem is non‐convex for general beamforming with user interference, they take maximum ratio transmission as an example and adopt a set of transformation and approximation based on the difference of convex (DC) programming. Furthermore, for conjugate‐beamforming, they first transform the quadratic signal to interference plus noise power ratio into a trackable form and then DC programming is applied. A two‐loop algorithm for the general beamforming and conjugate‐beamforming is presented with fractional programming in the inner loop and DC programming in the outer loop. Simulations show that the proposed algorithm improved EE significantly and indicate that energy cooperation can contribute to a higher EE. It also reveals that ZF achieves better EE with small noise variance while conjugate beamforming in high noise circumstance.

In this paper, the edge caching problem in fog radio access network (F-RAN) is investigated. By maximizing the overall cache hit rate, the edge caching optimization problem is formulated to find the optimal policy. Content popularity in terms of time and space is considered from the perspective of regional users. We propose an online content popularity prediction algorithm by leveraging the content features and user preferences, and an offline user preference learning algorithm by using the online gradient descent (OGD) method and the follow the (proximally) regularized leader (FTRL-Proximal) method. Our proposed edge caching policy not only can promptly predict the future content popularity in an online fashion with low complexity, but also can track the content popularity with spatial and temporal popularity dynamic in time without delay. Furthermore, we design two learning-based edge caching architectures. Moreover, we theoretically derive the upper bound of the popularity prediction error, the lower bound of the cache hit rate, and the regret bound of the overall cache hit rate of our proposed edge caching policy. Simulation results show that the overall cache hit rate of our proposed policy is superior to those of the traditional policies and asymptotically approaches the optimal performance.

Hybrid transceiver designs for millimeter wave massive multiple-input multiple-output systems are feasible candidates to reduce the volume of radio frequency (RF) chains, decomposing the signal processing into the analog and digital domains. The existing schemes heavily depend on the singular value decomposition to obtain subchannels with uneven power gains, causing bit error rate (BER) performance degradation. In this paper, we propose a hybrid transceiver design based on the uniform channel decomposition (UCD), yielding subchannels with identical gains to improve the BER performance. Inspired by the UCD concept, we derive an equivalent optimization problem and propose two schemes, namely, phase-extraction and iterative update, to determine the RF beamformers, yielding an effective baseband channel with the greatest possible geometric mean of singular values. We apply the UCD with a minimum mean squared error criterion to complete the baseband beamforming. Finally, we combine the hybrid UCD beamforming with the vertical-Bell Labs layered space-time and dirty paper coding, to eliminate the inter-subchannel interference. An asymptotic analysis of the scheme performance is also pursued. The simulation results show that the proposed hybrid scheme outperforms the conventional schemes on the transmission BER, which achieves a spectral efficiency close to that of the fully-digital counterpart.

In this paper, we investigate a multi-cell millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) network with low-precision analog-to-digital converters (ADCs) at the base station. Each cell serves multiple users and each user is equipped with multiple antennas but driven by a single RF chain. We first introduce a channel estimation strategy for the mmWave massive MIMO network and analyze the achievable rate with imperfect channel state information. Then, we derive an insightful lower bound for the achievable rate, which becomes tight with a growing number of users. The bound clearly demonstrates the impacts of the number of antennas and the ADC precision, especially for a single-cell mmWave network at low signal-to-noise ratio. It characterizes the tradeoff among various system parameters. Our analytical results are finally confirmed by extensive computer simulations.

In this paper, a joint transmitter and receiver design for pattern division multiple access (PDMA) is proposed. At the transmitter, pattern mapping utilizes power allocation to improve the overall sum rate, and beam allocation to enhance the access connectivity. At the receiver, hybrid detection utilizes a spatial filter to suppress the inter-beam interference caused by beam-domain multiplexing, and successive interference cancellation to remove the intra-beam interference caused by power-domain multiplexing. Furthermore, we propose a PDMA joint design approach to optimize pattern mapping based on both the power domain and beam domain. The optimization of power allocation is achieved by maximizing the overall sum rate, and the corresponding optimization problem is shown to be convex theoretically. The optimization of beam allocation is achieved by minimizing the maximum of the inner product of any two beam allocation vectors, and an effective dimension reduction method is proposed through the analysis of pattern structure and proper mathematical manipulations. Simulation results show that the proposed PDMA approach outperforms the orthogonal multiple access and power-domain non-orthogonal multiple access approaches even without any optimization of pattern mapping, and the optimization of beam allocation yields a significant performance improvement than the optimization of power allocation.

As an improved version of successive cancellation (SC) polar decoder, an SC stack (SCS) decoder has been proposed for performance improvement. However, the existing SCS polar decoder suffers a lot from high time complexity at low signal-to-noise ratio (SNR) region and space complexity compared with the SC decoder. To this end, two improved decoders are proposed to reduce time and space complexity in both low and high SNR regions. The first one is the segmented cyclic redundancy check (CRC)-aided SCS (SCA-SCS) decoder, which is based on segmented parity checkers. The second one is the adaptive SCS (ASCS) decoder, which has the flexibility of stack depth and searching width. Furthermore, a channel condition estimator is proposed to select appropriate decision criteria for different SNR scenarios. Results have shown that for the polar code of length 1024 and rate 1/2, two improved SCS decoders can perform better than the traditional SCS decoder. The proposed SCA-SCS decoder and the ASCS decoder can achieve 10.8% and 11.42% time complexity reduction and 31.68% and 60.85% space complexity reduction on average over binary-input additive white Gaussian noise channels (BI-AWGNCs), respectively. Efficient parallel hardware architecture of the SCS polar decoder is first proposed and implemented with 90- and 65-nm technologies. Results have verified its advantages over the state of the art (SOA).

This paper aims to provide a partial discrete Fourier transform (DFT) pilot sequence assisted joint channel estimation and user activity detection scheme for massive connectivity, in which a large number of devices with sporadic transmission communicate with a base station (BS) in the uplink. The joint channel estimation and device detection problem can be formulated as a compressed sensing single measurement vector or multiple measurement vector (MMV) problem depending on whether the BS is equipped with single or large number of antennas. Due to high hardware cost and power consumption in massive multiple-input multiple-output (MIMO) systems, a mixed analog-to-digital converter (ADC) architecture is considered. In order to accommodate a large number of simultaneously transmitting devices, the joint channel estimation and active user detection are formulated as an MMV problem for the massive connectivity scenario; and the proposed GTurbo-MMV algorithm can precisely estimate the channel state information and detect active devices with relatively low overhead. Furthermore, we study the state evolution (SE) for the MMV problem to obtain achievable bounds on channel estimation and device detection performance, in which both the missing and false detection probabilities can be made tend to zero in the massive MIMO regime. The simulation results confirm the theoretical accuracy of our analysis.

In this paper, power control in the uplink for two-tier small-cell networks is investigated. We formulate the power control problem as a Stackelberg game, where the macrocell user equipment (MUE) acts as the leader and the small-cell user equipment (SUE) acts as the follower. To reduce the cross-tier and cotier interferences and the power consumption of both the MUE and SUE, we propose optimizing not only the transmit rate but also the transmit power. The corresponding optimization problems are solved through a two-layer iteration. In the inner iteration, the SUE items (SUEs) compete with each other, and their optimal transmit powers are obtained through iterative computations. In the outer iteration, the optimal transmit power of the MUE is obtained in a closed form based on the transmit powers of the SUEs through proper mathematical manipulations. We prove the convergence of the proposed power control scheme, and we also theoretically show the existence and uniqueness of the Stackelberg equilibrium (SE) in the formulated Stackelberg game. The simulation results show that the proposed power control scheme provides considerable improvements, particularly for the MUE.

For polar codes, both successive cancellation list (SCL) decoding and list sphere decoding (LSD) aim to balance performance and complexity. The same list structure but different decoding schedules of SCL and LSD can lead to a combination of both schemes. In this paper, an efficient joint list decoder with SCL and LSD (JLSCD) is proposed to reduce time complexity. We apply SCL and LSD schemes simultaneously but independently, then merge them at the middle point of the decoding. Numerical results have demonstrated JLSCD scheme's advantage in complexity. FPGA implementation of JLSCD decoder is also given in this paper.

Polar code is increasing its popularity recently for its capacity-achieving property for B-DMCs. However, when designing decoders for polar code, it has always been an inevitable concern for us to balance the decoding performance and the hardware consumption. In this paper, we propose an approximate belief propagation (BP) decoder for polar code for the first time. By introducing the approximate computation schemes, we reduced the critical path delay (CPD) and the hardware consumption of the conventional BP decoders. Simulation results show that the proposed approximate BP decoder achieves nearly the same decoding performance as the conventional one. Advantages of the proposed decoder has been verified by FPGA implementation.

Recently, low-precision weight method has been considered as a promising scheme to efficiently implement inference of deep convolutional neural networks (DCNN). But it suffers from expensive retraining cost and accuracy degradation. In this paper, a low-bit and retraining-free quantization method, which enables DCNNs to deal inference with only shift and add operations, is proposed. The efficiency is demonstrated in terms of power consumption and chip area. Huffman coding is adopted for further compression. Then by exploring two-level systolic, an efficient hardware accelerator is introduced with respect to the given quantization strategy. Experiment results show that our method achieves higher accuracy than other low-precision networks without retraining process on ImageNet. 5× to 8× compression is obtained on popular models compared to full-precision counterparts. Furthermore, hardware implementation indicates good reduction of slices whereas maintaining throughput.

The design of measurement matrices is an important part in compressed sensing (CS). Random matrices superior to incoherence are considered to be optimal measurement matrices to achieve successful recovery. However, they are deficient in memory cost. Structure matrices like circulant matrices are preferred for low-memory cost. Nevertheless, their recovery performance is greatly damaged because of element coherence. In this paper, a new method called different-spaced selection & different-spaced flipping (DSS & DSF) is proposed to modify structure matrices. Based on circulant matrices, regular extraction and symbol flipping imposed on columns of measurement matrices can increase randomness to a large scale. As a result, not only near optimal recovery but also much less memory cost can be achieved. Compared with Gaussian random matrices, the memory cost can be reduced to 4% when measurement matrices based on circulant matrices are in 128 × 512 dimensions. An efficient hardware design and VLSI implementation are also presented at the end of this paper.

This paper presents a design methodology to implement basic arithmetics, including addition, subtraction, multiplication and division, with molecular reactions using analog signals. Simpler than conventional digital logic, our design can still implement the same functionality. Two kinds of designs are contained, one is based on synchronous sequential logic, and the other basis is an asynchronous one. The feasibility of our method is validated via simulations of chemical kinetics.

This paper offers an implementation of a sinusoid and its pulse-width modulation (PWM) based on chemical reaction networks (CRNs). The generation of a sinusoid is given under the guidance of ordinary differential equations (ODEs) combined with the non- negative concentration of chemical molecules. The accuracy of the sinusoid is verified by listing the ODEs. Comparing the concentrations of the sampled sinusoid and sawtooth wave, the target PWM could be finally synthesized. This process requires a sampler and a comparator. Based on the obtained PWM, nearly any arbitrary analog signal could be converted to a digital one.

This paper proposes a CRN-based implementation approach for low-density parity-check (LDPC) decoding based on belief propagation (BP). Since the belief (probability) can be naturally mapped to molecule concentration, LDPC decoding can be realized with CRNs instead of silicon based hardware. Theoretical analysis and numerical simulations have demonstrated the feasibility of the proposed approach. Note that, we do not try to substitute the silicon-based LDPC decoder with CRN- based one for high-speed applications. We show that this method can be generalized for other BP-based algorithms and is suitable for large-scale, bio- interface, and latency-insensitive applications.

Chemical reaction networks(CRNs) have been used as a formal language for constructing and analysing molecular systems. Theoretical analysis and experiments have demonstrated that CRNs could be physically implemented with DNA strand displacement reactions. This paper proposes a method of synthesizing Mealy machine with molecular reactions, which focuses on deriving CRN from state diagram of any Mealy machine. All components of the proposed design could be experimentally implemented by DNA reactions. Therefore, the proposed CRN-based Mealy machine has potential applications in \(vitro\) and in \(vivo\) biotechnology.

With low-density parity-check (LDPC) code and polar code selected as the standard codes for 5G eMBB scenario, one challenge is how to improve the hardware efficiency when both decoders are required by one system. Since LDPC and polar codes can be decoded with belief propagation (BP) algorithms, this similarity allows us to design a reconfigurable decoder, which can decode both codes at the cost of only one decoder. Numerical and implementation results are also given in this paper to show that the proposed decoder achieves higher hardware efficiency than stand-alone LDPC or polar decoder, without harming the error performance.

Sparse-code multiple-access (SCMA) is an effective non-orthogonal multiple-access (NOMA) technique. Existing detectors such as deterministic message passing algorithm (DMPA) are one-dimensional and require precise channel estimation. This paper proposes a blind detector from a two-dimensional perspective. Main work involves pattern construction and pre-filtering with different image techniques. In this paper, a 4 × 4 Sudoku template is applied for the pattern construction of one-dimensional SCMA signals. Total variation based on first order differential operator is adopted for global pre-filtering of DMPA. Image training is adopted in DMPA to further reduce the environment noise. The output signal of both pre-filtering methods are detect through DMPA with constant noise density N0. Numerical results show that two-dimensional blind detection can well compensate the performance when channel estimation of DMPA is not perfect. A general hardware architecture of the detecting method is also proposed in this paper.

In this paper, we investigate asynchronous coded caching in fog radio access networks (F-RAN). To minimize the fronthaul load, the encoding set collapsing rule and encoding set partition method are proposed to establish the relationship between the coded-multicasting contents in asynchronous and synchronous coded caching. Furthermore, a decentralized asynchronous coded caching scheme is proposed, which provides asynchronous and synchronous transmission methods for different delay requirements. The simulation results show that our proposed scheme creates considerable coded-multicasting opportunities in asynchronous request scenarios.

In this paper, we investigate the energy-delay tradeoff (EDT) problem to reduce energy consumption while ensure packet delay performance for wireless-backhauling ultra-dense network (UDN). We formulate the EDT problem as a cost minimization problem to find the optimal base stations (BS) sleeping ratio for sleeping mechanism. To solve the problem, a dynamic gradient descent iteration algorithm is used to obtain a locally optimal sleeping ratio. We prove it can converge to the global optimal BS sleeping ratio. The impacts of BS sleeping ratio on EC and packet delay are investigated and validated by simulation and numerical results.

As a promising technology for 5G networks, device-to-device (D2D) communication can improve spectrum utilization by sharing the resources of cellular users (CUs). However, this is at the cost of generating interference to the CUs. While most existing works focused on eliminating or suppressing the interference between the D2D links and the CUs, such interference could in fact be beneficial for improving the security of cellular communication. Specifically, D2D links may, in return for reusing cellular resources to achieve high spectral efficiency, act as friendly jammers and help the CUs against malicious wiretapping. To reach this win-win situation, D2D resource management has to be designed from a physical layer security perspective. In this paper, we consider the joint optimization of power allocation and channel assignment of the D2D links and the CUs with the aim to provide security to the CUs and improve the spectral efficiency of the D2D links simultaneously. We focus on the challenging downlink resource sharing problem and investigate both single-channel and multi-channel D2D communications. The resulting resource management design problems turn out to be difficult nonlinear mixed integer problems. Nevertheless, by exploiting the inherent properties of the formulated optimization problems, we are able to analytically characterize the optimal power allocation of the CUs and D2D links, and develop efficient methods for joint optimization of their channel assignments. Simulation results show that the proposed resource management policies outperform several baseline schemes and can indeed achieve the desired twofold objective.

In this paper, a home Internet-of-Things system is analyzed by dividing it into four layers, i.e., the node layer, gateway layer, service layer, and open layer. The gateway layer, which supports a variety of wireless technologies and is the core of home wireless networks access unit, together with the node layer constitutes the home wireless network. A gateway prototype following the proposed architecture has been implemented. A testbed of an interference-aware wireless network which includes the gateway prototype has also been created for testing its user interaction performances. The experimental results show that both Wi-Fi and Bluetooth have an impact on the ZigBee communication. Considering the complex scene of home and building, ZigBee multihop communications are set to reduce the packet loss probability. In addition, an event-level-based transmission control strategy is proposed, in which the packet loss probability of wireless network is reduced by controlling the transmission priority of different levels of monitoring events, and optimizing the ZigBee wireless network channel occupancy.

A novel unified framework of geometry-based stochastic models for the fifth generation (5G) wireless communication systems is proposed in this paper. The proposed general 5G channel model aims at capturing small-scale fading channel characteristics of key 5G communication scenarios, such as massive multiple-input multiple-output, high-speed train, vehicle-to-vehicle, and millimeter wave communications. It is a 3-D non-stationary channel model based on the WINNER II and Saleh-Valenzuela channel models considering array-time cluster evolution. Moreover, it can easily be reduced to various simplified channel models by properly adjusting model parameters. Statistical properties of the proposed general 5G small-scale fading channel model are investigated to demonstrate its capability of capturing channel characteristics of various scenarios, with excellent fitting to some corresponding channel measurements.

Uplink/downlink (UL/DL) decoupling promises more flexible cell association and higher throughput in heterogeneous networks (HetNets), however, it hampers the acquisition of DL channel state information (CSI) in time-division-duplex systems due to different base stations (BSs) connected in UL/DL. In this paper, we propose a novel data-aided (DA) channel estimation scheme to address this problem by utilizing decoded UL data to exploit CSI from received UL data signal in decoupled HetNets where a massive multiple-input multiple-output BS and dense small cell BSs are deployed. We analytically estimate bit error ratio (BER) performance of UL decoded data, which are used to derive an approximated normalized mean square error (NMSE) expression of the DA minimum mean square error (MMSE) estimator. Compared with the conventional least square and MMSE, it is shown that NMSE performances of all estimators are determined by their signal-to-noise ratio (SNR)-like terms and there is an increment consisting of UL data power, UL data length, and BER values in the SNR-like term of DA method, which suggests DA method outperforms the conventional ones in any scenarios. Higher UL data power, longer UL data length, and better BER performance lead to more accurate estimated channels with DA method. Numerical results verify that the analytical BER and NMSE results are close to the simulated ones and a remarkable gain in both NMSE and DL rate can be achieved by DA method in multiple scenarios with different modulations.

Generally, separate detection and decoding (SDD) scheme is usually adopted by multiple-input and multiple-output (MIMO) systems. In this paper, a novel approach which combines detection and decoding jointly using K-best detection and polar codes is proposed for the first time. Since the generation matrix of polar codes is triangular, polar codes could well adapt to the structure of K-best searching tree. Moreover, the property of polarization could reduce the latency of the proposed joint detection and decoding (JDD) scheme. Based on the joint optimization, the system model is given. For successive cancellation list (SCL) polar decoding, numerical results show that the performance of the proposed JDD is superior to the state-of-the-art SDD. At the frame error rate (FER) of 10-4, JDD outperforms SDD by approximately 2.5 dB for (256,128) polar coded 4×4 16-QAM MIMO system. Furthermore, for half rate polar codes, the proposed JDD could reduce 50% complexity compared to SDD. Results indicate that JDD shows superiorities in both performance and complexity. In addition, the corresponding hardware architectures are also given to demonstrate JDD's advantages and implementation feasibilities.

Non-orthogonal multiple access (NOMA) enables user multiplexing in the power domain via successive interference cancellation (SIC). The key to achieve the full benefit of NOMA is resource allocation, including power allocation and channel assignment for all users, which leads to difficult mixed integer programs. In the literature, the optimal power allocation has only been investigated for users on a single channel (or in one group), while the joint optimization of power allocation and channel assignment generally requires an exhaustive research. In this paper, we investigate resource allocation in downlink NOMA systems. We analytically characterize the optimal power allocation in closed-form for sum rate maximization with weights or quality of service (QoS) constraints. Furthermore, we also propose a low-complexity efficient method to jointly optimize channel assignment and power allocation in NOMA systems by incorporating the matching algorithm with the optimal multichannel power allocation. Simulation results show that the joint resource optimization using our optimal power allocation yields better performance than the existing schemes.

To address the inherent limits of silicon-based technologies, the research on synthesizing various logic functions with chemical reaction networks (CRNs) has emerged in large numbers. However, in order to properly synthesize a given sequential logic, the difficulties lie in constructing a clock signal with an arbitrary duty cycle of M/N. Therefore, this paper is dedicated to putting forward a CRN-based design methodology, which can generate clock signals with an arbitrary duty cycle. Only unimolecular or bimolecular reactions are employed, which makes the real DNA strand displacement reactions successfully compiled from formal CRNs. First, a clock signal with 1/2 duty cycle is constructed. Then, the systematic design flow to construct an arbitrary M/N duty cycle clock is explained in details. Conditions are different when N is odd or even. All the proposed design methods come along with a theoretical basis and a numerical validation.

With better spectral efficiency, multiple-input and multiple-output (MIMO) systems have drawn increasing attentions. Due to its near-optimal performance, K-best algorithm has been widely adopted for MIMO detection. To the best knowledge of the authors, this paper first proposes a joint detection and decoding (JDD) method for MIMO with low-density parity-check (LDPC) codes. By pruning the searching tree of K-best detection with LDPC coding constraint, the proposed JDD scheme benefits from both reduced tree-search complexity and improved performance compared to its uncoded MIMO counterpart. Numerical results of 16-QAM MIMO with (8, 2) LDPC code and 64-QAM MIMO with (18, 6) LDPC code have shown that, the proposed JDD scheme's performance is evidently superior over separated detection and decoding (SDD) scheme. More specifically, for the latter case with 12 antennas, JDD shows nearly 10 dB performance improvement than SDD when BER = 10-3. Hardware architecture and complexity analysis are also given in this paper to demonstrate JDD's advantages.

Path metric sorting unit of successive cancellation list (SCL) decoders for polar codes is the main concern in this paper. After reviewing existing sorting units in SCL decoders, we propose 2 new sorting schemes namely quick select (QS) based selection algorithm and simplified bitonic sorter (SBT), which exploit the special data dependency of path metrics in log-likelihood ratio based SCL decoding. Theoretical analysis shows that for the list size of L ≤ 8, QS-based selection algorithm has lower delay than existing schemes. FPGA implementation based on Artix7 Family shows that for the list size of L ≥ 16, SBT has the same delay while the hardware reduction is over 40%.

In this paper, pattern division multiple access with large-scale antenna array (LSA-PDMA) is proposed as a novel non-orthogonal multiple access (NOMA) scheme. In the proposed scheme, pattern is designed in both beam domain and power domain in a joint manner. At the transmitter, pattern mapping utilizes power allocation to improve the system sum rate and beam allocation to enhance the access connectivity and realize the integration of LSA into multiple access spontaneously. At the receiver, hybrid detection of spatial filter (SF) and successive interference cancellation (SIC) is employed to separate the superposed multiple-domain signals. Furthermore, we formulate the sum rate maximization problem to obtain the optimal pattern mapping policy, and the optimization problem is proved to be convex through proper mathematical manipulations. Simulation results show that the proposed LSA-PDMA scheme achieves significant performance gain on system sum rate compared to both the orthogonal multiple access scheme and the power-domain NOMA scheme.

Indoor positioning for visible light communication (VLC) has gained significant attentions recently with the popularity of light-emitting diodes (LEDs). In this paper, we consider a typical application of VLC by proposing a three-dimensional positioning scheme for a target terminal equipped with multiple photodiodes (PDs). Given the relative coordinates between the target terminal and receiving PDs along with positions of fixed transmitting LEDs, precise location estimation of the terminal device can be achieved via measuring received signal strength (RSS) through line-of-sight (LoS) channels. Moreover, multipath reflections from interior walls are considered as a major interference in non-LoS environment. It is discovered that the positioning error increases linearly with respect to the reflection coefficient of walls, which also verified by simulation results. The positioning error is achieved in millimeter scale under an ideal condition and in decimeter scale with multipath reflections.

To cope with the growing trend of asymmetric data traffic, the bidirectional dynamic networks (BDNs) dynamically allocate the number of uplink and downlink remote radio heads (RRHs), which facilitates simultaneous uplink and downlink communications. In this study, the authors derive the asymptotic approximations of the achievable uplink and downlink rates using maximum ratio transmission precoder and maximum ratio combination receiver, as the RRH antenna number ( M ) approaches infinity. Considering an optical fibre connected backhaul network, a practical power consumption model is presented to study the system energy efficiency (EE). Based on the asymptotic analysis, they exploit the power scaling laws that both the uplink and downlink powers should scale down to 1/ M to maintain a desirable uplink or downlink rate. Numerical results verify that when M is large, the BDN system outperforms the dynamic time division duplex system in both the spectral efficiency and EE.

Deployment of small cell base stations (SBSs) overlaying the coverage area of a macrocell BS (MBS) results in a two-tier hierarchical small cell network. Cross-tier and inter-tier interference not only jeopardize primary macrocell communication but also limit the spectral efficiency of small cell communication. This paper focuses on distributed interference management for downlink small cell networks. We address the optimization of transmit strategies from both the game theoretical and the network utility maximization (NUM) perspectives and show that they can be unified in a generalized Nash equilibrium problem (GNEP) framework. Specifically, the small cell network design is first formulated as a GNEP, where the SBSs and MBS compete for the spectral resources by maximizing their own rates while satisfying global quality of service (QoS) constraints. We analyze the GNEP via variational inequality theory and propose distributed algorithms, which only require the broadcasting of some pricing information, to achieve a generalized Nash equilibrium (GNE). Then, we also consider a nonconvex NUM problem that aims to maximize the sum rate of all BSs subject to global QoS constraints. We establish the connection between the NUM problem and a penalized GNEP and show that its stationary solution can be obtained via a fixed point iteration of the GNE. We propose GNEP-based distributed algorithms that achieve a stationary solution of the NUM problem at the expense of additional signaling overhead and complexity. The convergence of the proposed algorithms is proved and guaranteed for properly chosen algorithm parameters. The proposed GNEP framework can scale from a QoS constrained game to an NUM design for small cell networks by trading off signaling overhead and complexity.

Non-orthogonal multiple access (NOMA) enables power-domain multiplexing via successive interference cancellation (SIC) and has been viewed as a promising technology for 5G communication. The full benefit of NOMA depends on resource allocation, including power allocation and channel assignment, for all users, which, however, leads to mixed integer programs. In the literature, the optimal power allocation has only been found in some special cases, while the joint optimization of power allocation and channel assignment generally requires exhaustive search. In this paper, we investigate resource allocation in downlink NOMA systems. As the main contribution, we analytically characterize the optimal power allocation with given channel assignment over multiple channels under different performance criteria. Specifically, we consider the maximin fairness, weighted sum rate maximization, sum rate maximization with quality of service (QoS) constraints, and energy efficiency maximization with weights or QoS constraints in NOMA systems. We also take explicitly into account the order constraints on the powers of the users on each channel, which are often ignored in the existing works, and show that they have a significant impact on SIC in NOMA systems. Then, we provide the optimal power allocation for the considered criteria in closed or semi-closed form. We also propose a low-complexity efficient method to jointly optimize channel assignment and power allocation in NOMA systems by incorporating the matching algorithm with the optimal power allocation. Simulation results show that the joint resource optimization using our optimal power allocation yields better performance than the existing schemes.

This letter investigates the optimal power allocation for weighted sum rate maximization (WSRM) in nonorthogonal multiple access (NOMA) systems with power order and quality-of-service (QoS) constraints. We show that the NOMA WSRM problem is a convex problem under some condition of the user weights. The optimal solution to the WSRM problem without QoS constraints is analytically characterized in two cases. Then, we investigate the feasibility of the WSRM problem with QoS constraints. We further show that the power order constraint can be omitted without loss of any optimality under some mild condition of the QoS thresholds, which enables us to derive an analytical expression of the optimal power allocation.

Massive multiple-input multiple-output (MIMO) systems are expected to operate in the frequency-division duplex (FDD) mode, which is feasible in the channel environment with limited scattering. Since accurate channel estimation is critical for gaining unprecedented capacity, we investigate beam-domain channel estimation and feedback for FDD massive MIMO systems. In particular, we focus on the threshold-based method for channel estimation, where an enhanced estimator is proposed to exploit the common support among all beam-domain channels. For threshold-based estimation, we derive its closed-form mean-squared error (MSE) expression, and obtain an optimal threshold as a function of sparsity, noise variance, and channel variance, and a simplified threshold, which is a function of noise variance only. For the enhanced estimator, we present a threshold to identify the common support, with which an algorithm is designed to improve the estimation accuracy. As for channel feedback, we suggest to feed back only significant elements (above the given threshold) in the beam domain. Numerical results validate our derived MSE expression and demonstrate the superior performance of proposed threshold-based estimators.

Machine-type communication (MTC) is the key technology to support data transfer among devices (sensors and actuators) in Internet of Things (IoT). However, MTC, especially when applied to massive low-power IoT (mIoT), poses some unique and serious challenges due to the low-cost and low-power nature of an mIoT device. One of the most challenging issues is providing a robust way for an mIoT device to acquire the network under a large frequency offset/error (due to the use of a low-cost crystal oscillator) and a low operating SNR (due to the extended coverage). We address the issues in the existing mIoT system acquisition, particularly the initial synchronization waveform detection, and derive a new synchronization waveform that is more robust in an mIoT environment. The mathematical approach provides a useful analytical insight into the design of the synchronization signal waveform for the 5G mIoT system.

For polar codes, cyclic redundancy check (CRC)aided successive cancellation list (CA-SCL) decoder has attracted increasing attention from both academia and industry. In this paper, a hardware efficient and low-latency CA-SCL polar decoder based on distributed sorting is first proposed. For path metric (PM) sorting of each level, a distributed sorting (DS) algorithm is proposed to reduce the comparison complexity from (L2) to (L) (L denotes list size), together with the latency from kL2to kL (k is a coefficient independent of L). Employing folding technique, the N-bit folding polar decoder can be implemented based on the basic √N-bit polar decoder. In addition, pipelining technique is employed to refine the timing issue resulting from folding. The CRC is performed for 2L candidate paths serially to reduce hardware cost. According to demo of (1024, 512) code on Altera Stratix V FPGA, the proposed CA-SCL decoders with L = 2 and adjustable L = 2, 4 consume 9% and 50% board resources, respectively. Decoding latencies (in terms of clock cycles) are 2, 528 and 4, 064, respectively. For L = 2 and 4, we can achieve the frame error rate (FER) of 10-2at the signal noise ratio (SNR) of 2.36 dB and 2.06 dB, respectively. Compared with the floating point results, the performance degradation is negligible. Thus, the proposed design is suitable and adjustable for different real-life scenarios.

This paper investigates the performance of bidirectional dynamic networks (BDN) with massive multiple input multiple output (MIMO) systems. In BDN, dynamic allocation of the number of uplink and downlink remote radio heads (RRHs) is proposed, which offers a flexible solution to balance the data traffic asymmetry without requiring the time synchronization. Intuitively, the interference between the downlink and uplink RRHs is one of the main challenges in BDN. However, we prove that the massive MIMO strategy can effectively reduce a certain portion of the downlink-to-uplink interference. We derive the approximations of the achievable uplink and downlink rates using a maximum ratio transmission (MRT) precoder and a maximum ratio combination (MRC) receiver. Based on the asymptotic analysis, we exploit the power scaling laws that both the uplink and downlink power should scale down to 1/M (M is the antenna number) to ensure a desirable uplink or downlink rate. Furthermore, simulations show that BDN outperforms traditional time division duplex (TDD) systems in terms of the spectral efficiency.

In this paper, the successive cancellation (SC) heap polar decoding scheme is firstly proposed to reduce the complexity. Unlike SC list decoder which keepsLsame length paths, SC heap decoding stores different length paths in a heap and always decodes the global optimal path in the root. It has been strictly proved that SC heap decoding is superior to SC stack decoding because the time complexity of inserting new paths is only related to the height of the heap. Proposed SC heap decoding is a dynamic decoding scheme with robustness in various scenarios. Numerical results with binary-input additive white Gaussian noise channel (BI-AWGNC) show that SC heap decoding reduces 63.53% decoding complexity compared with SC list decoding on the same performance at SNR of 2.5 dB. A low-complexity hardware architecture for proposed SC heap decoder is also designed.

In this paper, a low-complexity stochastic belief propagation (BP) detector for large-scale MIMO is first proposed. Its efficient hardware architecture, with parallel pipeline, is presented in detail. Thanks to the stochastic approach, all arithmetic operations of the detector are implemented with simple logic structures. Several approaches which can potentially improve the detection performance are exploited. Simulation results have demonstrated that the stochastic BP detector can achieve similar detection performance compared with deterministic one for 32 × 32 MIMO system with 4-quadrature amplitude modulation (4-QAM). With the increase of antenna number, the detection performance improves at the linear expense of complexity and latency. Therefore, the proposed stochastic BP detector is suitable for large-scale MIMO system applications with good balance of detection performance and implementation complexity.

In massive multiple-input multiple-output (MIMO) uplink, the minimum mean square error (MMSE) algorithm is near-optimal and linear, but still suffers from high-complexity of matrix inversion. Based on Gauss-Seidel (GS) method, an efficient architecture for massive MIMO soft-output detection is proposed in this paper. To further accelerate the convergence rate of the conventional GS method with acceptable overhead complexity, a truncated Neumann series of the first 2 terms, is employed for initialization. The architecture can meet various application requirements by flexibly adjusting the number of iterations. FPGA implementation for a 128 × 8 MIMO demonstrates its advantages in both hardware efficiency and flexibility.

As well known, the near-optimal K-best detection is popular in multiple-input and multiple-output (MIMO) systems. In this paper, we first propose the joint approaches of K-best detection and polar decoding. For joint detection and decoding (JDD) approach, both hard and soft decisions are considered. The simplified successive cancellation (SSC) decoding is exploited for hard decision, and the successive cancellation list (SCL) decoding is used as soft decision. The system setup for JDD is als o introduced, in which the modulation points across several channels are considered together. Simulation results have demonstrated the performance advantage of the JDD algorithms over the separated ones. For 1/2-rate polar codes, JDD schemes show 50% complexity reduction compared to the separated ones. Furthermore, by employing SSC hard decoding, the JDD algorithm is promising for high-throughput and low-complexity application s.

Due to its inherent higher parallelism over successive cancellation (SC) polar decoder, belief propagation (BP) polar decoder becomes more favorable for high throughput applications. However, most existing BP decoders suffer from low utilization. In this paper, a new updating scheme, in which both left-to-right and right-to-left messages are considered identical, is first proposed for memory reduction. By revealing the similarity between BP polar decoder and fast Fourier transform (FFT) processor, both feed-forward and feed-back pipelined BP polar decoders are proposed along with detailed processing schedules. Implementation results have shown that both proposed pipelined BP decoders achieve more than 99.8% arithmetic logical units (ALUTs) reduction and 3.40% registers & block memory reduction, together with more than 7.45% speed-up, compared to the conventional fully parallel one. The proposed design approaches can be generalized with folding technique to achieve the required balance between area and speed flexibly.

In this study, we propose a generalized turbo signal recovery algorithm to estimate a signal from quantized measurements, in which the sensing matrix is a row-orthogonal matrix, such as the partial discrete Fourier transform matrix. The state evolution of the proposed algorithm is derived and is shown to be consistent with that obtained with the replica method. Numerical experiments illustrate the excellent agreement of the proposed algorithm with theoretical state evolution.

Wireless Network Virtualization (WNV) enables high resource utilization, inter-slice isolation and customizable resource allocation, and has attracted much interest. However, most of works on WNV did not consider the business model between Infrastructure Providers (InPs) and Service Providers (SPs) nor did they focus on the isolation and customization issues. This paper proposes a WNV framework for LTE where hypervisor does not have direct access to user information, and Resource Block (RB) allocation problem in this framework is investigated by adopting the Vickrey-Clarke-Groves (VCG) mechanism to model auction games between InPs and Mobile Virtual Network Operators (MVNOs) where MVNOs have the incentive to bid truthfully to compete for RBs. A unified utility function is utilized to enable MVNOs to determine their own scheduling strategies by setting the parameters in it. The RB allocation problem is formulated to divide the total RB set into different subsets to maximize the summation of reporting valuations of all MVNOs. Due to its complexity, a heuristic algorithm is proposed. Simulation results show that the intra-slice customization and inter-slice isolation of virtual networks are satisfied in our model.

Self-healing is an interesting topic in SON (Self- Organizing Networks). In this paper, we investigate cell outage detection problem, and propose an improved TCM (Transductive confidence machines) based automatic cell outage detection algorithm. By incorporating a hypothesis test with the Neyman-Pearson criterion to improve the detection accuracy, the improved TCM can effectively detect cell outage using normal data for training. The simulation results demonstrate that the proposed scheme has lower false alarm rate while ensuring high detection accuracy than the traditional outage detection methods.

Scaling up the conventional multiple-input multiple- output (MIMO) by orders of magnitude, massive MIMO technique brings huge improvement in spectrum efficiency and energy efficiency. However, under time- division duplex (TDD) operation, the reciprocity calibration need to be necessarily investigated, since the mismatches of the transceiver radio frequency (RF) circuits at both sides of the link will make the whole communication channels non-reciprocal. In this paper, by utilizing the strong mutual coupling between adjacent antennas, a new calibration algorithm called adjacent mutual coupling method (AdjMC) is proposed for massive MIMO systems with zero forcing (ZF) precoding. Exploiting this method, the base station (BS) can perform the reciprocity calibration without the involvement of user equipments (UEs). Theoretical analysis and simulation results show that, comparing with the several methods in the least squares (LS) framework, the AdjMC method significantly reduces the complexity and achieves the high performance.

When both access points (APs) and user equipments (UEs) have multiple antennas, the block diagonalization (BD) precoding is preferred for the distributed large-scale multiple-input multiple-output (MIMO) systems. In time division duplexing (TDD) operation, the APs can exploit the estimated uplink channel for downlink joint precoding transmission to simultaneously serve multiple UEs, due to the principle of channel reciprocity. However, the non-symmetric hardware radio frequency (RF) circuits at both sides of the link disable the channel reciprocity and result in a system performance loss. Based on a low complex BD precoding method, this paper designs a scheme for both the downlink and the uplink transmissions with the partial calibration. Theoretical analysis and simulation results show that the inter-stream interference (ISI) can be canceled out at the UEs through the minimum mean square error (MMSE) receiver in the downlink transmission. While in the uplink transmission, the APs can distinguish each data stream of every UE via the maximal ratio combining (MRC) receiver. Besides, the interference suppression precoding matrix at the APs can be used for both the downlink and uplink transmissions, which need to be calculated only once.

Channel coding for the next generation communication system needs to support higher data throughput and transmission rate. Due to the inherent parallel feature, low density parity check (LDPC) codes comply with the target. In this paper, we propose a complete channel coding scheme based on structured LDPC codes. In our design, the correlation among different parity check matrices for different code rates is established, meanwhile the correlation between adjacent rows is established, then matrix design should be restricted by these two type defined correlations in order to make great progress in the support of multiple code rates and layered decoding algorithm. Our scheme includes the definition of the four matrices and five constraint features of these matrices, also the benefits of these features are analyzed. Simulation result shows that the performance of the proposed scheme is the same as or a little better than that of 11ad LDPC codes. The proposed LDPC scheme obviously decreases route complexity and increases decoder throughput. So, this scheme is very suitable for high speed decoder with more than 1Gbps throughput in various future communication systems.

5G sets stringent requirement on delay, which means large packets have to be segmented into shorter ones. However, the gain of turbo coding can be severely degraded due to short code size. In this paper, a novel coding and encoding scheme is proposed to obtain high coding gain under short size. The idea is to encode multiple short code blocks cooperatively to obtain a long code size. In the process of encoding, an XOR operation is conducted on certain parts of previously encoded blocks to introduce correlation. The mechanism of successive interference cancelation (SIC) receiver is applied in the decoding procedure and each code block utilizes the extrinsic information from the correlated parts from other code blocks. The scheme incurs no extra delays at reduced complexity. Simulation results show the scheme can yield equivalent or better performance than using traditional turbo code on long code block.

In this paper, energy efficient power allocation for downlink massive MIMO systems is investigated.A constrained non-convex optimization problem is formulated to maximize the energy efficiency (EE), which takes into account the quality of service (QoS) requirements.By exploiting the properties of fractional programming and the lower bound of the user data rate, the non-convex optimization problem is transformed into a convex optimization problem.The Lagrangian dual function method is utilized to convert the constrained convex problem into an unconstrained convex one.Due to the multi-variable coupling problem caused by the intra-user interference, it is intractable to derive an explicit solution to the above optimization problem.Exploiting the standard interference function, we propose an implicit iterative algorithm to solve the unconstrained convex optimization problem and obtain the optimal power allocation scheme.Simulation results show that the proposed iterative algorithm converges in just a few iterations, and demonstrate the impact of the number of users and the number of antennas on the EE.

Because of the existence of channel noise, channel coding serves as an indispensable part of mobile communication system and the essential guarantee for the reliable, accurate, and effective transmission of information. As one of the most competitive channel code candidates for the 5th generation (5G) mobile communication, polar codes are the first codes which can provably achieve the symmetric capacity of binary-input discrete memoryless channels (B-DMCs). In this paper, the segmented CRC- aided successive cancellation list (SCA-SCL) polar decoding scheme is proposed for better tradeoff of performance and complexity. Numerical results on binary-input additive white Gaussian noise channel (BI-AWGNC) have shown that, at SNR of 0.5 dB, this approach successfully provides as high as 41.65% complexity reduction and similar decoding performance compared to state-of-the-art ones.

We study the Enhanced Inter-Cell Interference Coordination (eICIC) for co-channel deployments of pico cells in macro cells. In particular, we consider a non- uniform topology where the number of pico cells within the coverage area of each macro cell is different. To alleviate the interference caused by the co-channel deployment, the macro cells employ low-power almost- blank subframe. We consider the joint problem of power and resource allocation,where the goal is to maximize the proportional fairness utility of the system. A convergent iterative algorithm is proposed and simulation results demonstrate that our proposed algorithm improves both the system throughput and user fairness compared to existing schemes.

This paper considers the optimal energy efficient association for HetNets by applying almost blank subframes (ABSs) techniques. Aiming at maximizing energy efficiency (EE) without the quality of service (QoS) constraints, we obtain a closed-form optimal solution, which indicates that the optimal choice of blank resource block (RB) fraction for EE maximization is binary without individual user QoS requirements. We further incorporate QoS constraints and equivalently transform the corresponding complicated fractional EE optimization problem to a single linear program (LP) by introducing some new auxiliary variables. Finally, we confirm the validity of the assumption that a user is served by at most one BS in a given RB both in theory and by numerical results.

In this paper, energy efficient power allocation for the uplink of a multi-cell massive MIMO system is investigated. With the simplified power consumption model, the problem of power allocation is formulated as a constrained Markov decision process (CMDP) framework with infinite-horizon expected discounted total reward, which takes into account different quality of service (QoS) requirements for each user terminal (UT). We propose an offline solution containing the value iteration and Q-learning algorithms, which can obtain the global optimum power allocation policy. Simulation results show that our proposed policy performs very close to the ergodic optimal policy.

In this paper, energy efficient power control for the uplink two-tier networks where a macrocell tier with a massive multiple-input multiple-output (MIMO) base station is overlaid with a small cell tier is investigated. We propose a distributed energy efficient power control algorithm which allows each user in the two-tier network taking individual decisions to optimize its own energy efficiency (EE) for the multi-user and multi-cell scenario. The distributed power control algorithm is implemented by decoupling the EE optimization problem into two steps. In the first step, we propose to assign the users on the same resource into the same group and each group can optimize its own EE, respectively. In the second step, multiple power control games based on evolutionary game theory (EGT) are formulated for each group, which allows each user optimizing its own EE. In the EGT-based power control games, each player selects a strategy giving a higher payoff than the average payoff, which can improve the fairness among the users. The proposed algorithm has a linear complexity with respect to the number of subcarriers and the number of cells in comparison with the brute force approach which has an exponential complexity. Simulation results show the remarkable improvements in terms of fairness by using the proposed algorithm.

This paper considers the uplink of a multicell multiuser multiple‐input‐multiple‐output (MIMO) time‐division duplexing system, where K mobile users in the target cell send their uncoded M‐ary phase shift keying (M‐PSK) signals to the target base station (BS) equipped with a linear minimum mean square error (MMSE) receiver in the presence of receiver antenna correlations. By employing the equivalent channel model, the uplink is analysed in terms of symbol error rate (SER), giving approximated closed‐form expressions. The complicated functions of the SER can be simplified for the case where all the eigenvalues of the correlation matrix of the target BS are identical or distinct. It is proved that in the high signal to noise ratio (SNR) region, the SER is independent of the correlations between the BS antennas. And due to the existence of pilot contamination and intercell interference, the SER tends to a constant whose value is depended on the number of mobile users in each cell and the interference strength between the cells as well. Finally computer simulations are conducted to show that our approximated expressions have good performance in low SNR even for not large K.

In this paper, spatial channel pairing (SCP) is introduced to coherent combining at the relay in relay networks. Closed-form solution to optimal coherent combining is derived. Given coherent combining, the approximate SCP solution is presented. Finally, an alternating iterative structure is developed. Simulation results and analysis show that, given the symbol error rate and data rate, the proposed alternating iterative structure achieves signal-to-noise ratio gains over existing schemes in maximum ratio combining (MRC) plus matched filter, MRC plus antenna selection, and distributed space-time block coding due to the use of SCP and iterative structure.

Massive multiple-input multiple-output (MIMO) is a promising technique to greatly increase the spectral efficiency and may be adopted by the next generation mobile communication systems. Base stations (BSs) equipped with large-scale antennas can serve multiple users simultaneously by exploiting the downlink precoding in time division duplex (TDD) mode. However, channel state information (CSI) of uplink transmissions cannot be simply used for downlink precoding, because the gain mismatches of the transceiver radio frequency (RF) circuits disable the channel reciprocity. In this paper, we focus on antenna calibration for massive MIMO systems with maximal ratio transmit (MRT) precoding to solve the channel nonreciprocity problem. A new calibration method, called mutual coupling calibration, is proposed by using the effect of mutual coupling between adjacent antennas. By exploiting this method, the BS can perform the calibration without extra hardware circuit and users' involvement. We also build up the model of calibration error and derive the closed-form expressions of the ergodic sum-rates for evaluating the impact of calibration error on system performance. Simulation results verify the high calibration accuracy of the proposed method and show the significant improvement of system performance by performing antenna calibration.

The large-scale multiple-input multiple-output (MIMO) system with full-duplex relay is a promising candidate for future mobile communication systems, where accurate channel estimation is very challenging due to interference. Here, we address two channel estimation problems for such systems: individual estimation, where both the base station (BS) and the relay estimate their respective channels, and cascaded estimation, where only the BS estimates the cascaded two-hop channel. For the BS, we propose an estimator that exploits the sparsity and slowly varying nature of the channel in the beam domain. Then, we analyze the probability of correctly distinguishing the desired and interfering direct-link channels. For the relay, we present an estimator that simultaneously estimates both the source-to-relay and self-interference channels based on the expectation–maximization algorithm. The performance of this estimator is also analyzed. Numerical results demonstrate the excellent performance of the proposed channel estimators and corroborate the analysis results

In this paper, the downlink spectral efficiency of multi-cell multi-user large-scale distributed antenna systems (DASs) is studied in the presence of pilot contamination. Based on the properties of Gamma distribution, the closed-form expression is derived for the downlink achievable rate with maximum ratio transmission (MRT). The ultimate rate is also given when the ratio of the total number of base station (BS) antennas to the number of users goes to infinity. Finally, the results are validated via numerical simulations. It is shown that the closed-form expression is very accurate, and the downlink achievable rate of large-scale DAS is much larger than that of co-located massive multiple-input multiple-output (MIMO) with the same antenna configuration.

In this paper, we study the performance of ultradensely distributed antenna system in multi-floor buildings with high user density. To reduce the pilot overhead, we consider multi-floor pilot reuse. We derive the closed-form approximations of the sum-rate for the system using linear receivers, including the linear minimum-mean-squared-error receiver and the maximal ratio combining (MRC) receiver. We demonstrate the spectral efficiency per unit volume of the system and show that the ultradensely distributed antenna system is a promising way to achieve the spectral efficiency target of 5G.

In this paper, we first reveal the similarity of polar encoder and fast Fourier transform (FFT) processor. Based on this, both feed-forward and feed-back pipelined implementations of polar encoder are proposed. It is pointed out that the feedback part of SC polar decoder is nothing but a simplified version of polar encoder and therefore can be pipelined implemented also. Moreover, a general approach which uniformly constructs most pipelined polar encoders via folding transformation is proposed. Implementation results have shown that both proposed pipelined polar encoder architectures achieve more than 98.3% complexity reduction and more than 9.86% speed-up compared to the conventional implementation.

In this paper, we study the large-scale multi-user distributed antenna system (DAS) for hot-spot coverage in the future 5G wireless network. Firstly, the multi- user system model of large- scale DAS is introduced and a simple random pilot reuse scheme is presented to reduce the overhead of pilot. Then, the sum-rate of system is derived, and its asymptotical performance is studied when the number of antennas goes to infinity. Moreover, an attractive method for large-scale DAS using matrix sparsification is presented to simplify the signal processing, which is of great significance for the future research. Finally, we study the detection bit error ratio (BER) and area spectral efficiency of large-scale DAS.

In this paper, a resource allocation algorithm for maximizing energy efficiency (EE) is studied in multi-cell orthogonal frequency division multiple access (OFDMA) wireless networks. The resource allocation is designed based on imperfect channel state information (CSI). We formulate the resource allocation problem as a mixed non-convex probabilistic optimization problem. The user scheduling, data rate adaptation and power allocation are jointly designed to maximize the system EE, under the maximum transmitted power constraint and the outage probability constraint. An iterative algorithm is proposed in which the EE keeps improving until algorithm convergence. In each iteration, the energy-efficient power allocation optimization problem is solved by a lower bound problem and a parameterized transformation. Numerical results illustrate the convergence and the effectiveness of the proposed algorithm.

We consider the downlink multi-user multi-cell massive MIMO systems, assuming that the number of antennas at base station (BS) and the number of users are large. Our system model accounts for channel estimation, pilot contamination, and uniformly random user location distribution. We derive the approximation of area spectral efficiency (ASE) with regularized zero-forcing (RZF) precoding technique which are proven to be accurate via simulation results. With a realistic power consumption model considering not only transmit power but also the fundamental power for operating the circuit at transmitter and receiver, we analyze the performance of area energy efficiency (AEE). Finally, based on the proposed power consumption model, we determine the optimal number of antennas at BS aimed at maximizing AEE when transmit power is given.

In this study, the downlink spectral efficiency of multi‐cell multi‐user large‐scale distributed antenna systems (DASs) with pilot contamination is studied in a composite correlated Rayleigh fading channel. Firstly, under a physical channel model, the equivalent channel model of large‐scale DAS with pilot contamination is given. Secondly, based on the equivalent channel model, the closed‐form expression is derived for the downlink achievable rate with maximum ratio transmission, and the ultimate rate is also given when the ratio of the total number of base station antennas to the number of users goes to infinity. Thirdly, the results are validated via numerical simulations. It is shown that the closed‐form expression is very accurate, and the downlink achievable rate of the large‐scale DAS is much larger than that of the co‐located massive multiple‐input multiple‐output (MIMO) with the same antenna configuration.

In this paper, we investigate robust beamforming to improve the energy efficiency (EE) of wireless networks when only imperfect or partial channel state information (CSI) is available at the transmitter. Due to CSI quantization errors and/or limited feedback information, CSI imperfections can be well modeled by a bounded uncertainty region. We focus on the worst case robust beamforming strategy to optimize the EE of downlink transmission under the deterministic bounded channel model, which merely assumes a maximal channel error magnitude. We start with a single-user single-cell MIMO system and obtain a closed-form design for robust beamforming. For a multicell network, robust beamforming is in a nonconvex fractional form, and the solution cannot be directly extended from the single-cell scenario. To solve this problem efficiently, we resort to a lower bound, instead of the primal problem, and cast it as a semidefinite program (SDP). The robustness and efficiency of the proposed beamforming design are confirmed by computer simulation results.

We investigate the sum-rate-optimal precoding for the uplink of the multi-cell large-scale MIMO systems. Specifically, we focus on transmitter precoder design based only on the statistical channel state information (CSI). We first consider the partial cooperation system, where only the CSI is shared among cells, and its base station (BS) decodes its in-cell users by treating out-cell signals as colored Gaussian noise. We derive the ergodic sum rate and its deterministic approximation for the regime where both the number of total user antennas and the number of BS antennas are large. We obtain the necessary conditions to maximize the deterministic approximation of the sum rate under the transmit power constraint, based on which a gradient search algorithm is proposed to find the local optimal precoders. Furthermore, a super cell system is also considered, where users from all cells are jointly decoded. The deterministic approximation to the sum rate and optimal precoder design for such systems are given. Numerical experiments show that the proposed precoders achieve significant performance gains over systems with no precoding. Compared with the existing precoding methods that require perfect CSI, the proposed precoders achieve similar sum rates and require only the statistical CSI.

We study the power allocation for incremental redundancy (IR) hybrid automatic repeat request (HARQ) in block fading channel where causal channel state information is known at both the transmitter and the receiver. In IR-HARQ, the traffic is assigned into consecutive transmission rounds, and at each round, the instantaneous mutual information that is required for successful decoding is upper bounded by the data rate that is to be delivered. We propose an HARQ power allocation method that maximizes the average of incremental mutual information at each round, and its throughput quickly converges to the ergodic capacity as the number of retransmissions increases. The numerical results show that the proposed power allocation method achieves almost the full channel capacity with moderate average transmission delay and that it maintains good throughput under stringent delay requirement.

In ultra dense heterogeneous networks (HetNets), the interference becomes more serious since multiple small cells coexist in the coverage area of the macrocells and share the same spectrum. An efficient interference coordination method is adjusting the transmit powers of all cells in a cooperative way. However, under practical serving cell selection rules in which serving cells of users alter with the variation of cell powers, finding optimal cell powers becomes a difficult problem. In this paper, an improved modified particle swarm optimization (MPSO) is proposed to tackle this difficult problem caused by the altering of serving cell. Local search and multi-restart process are introduced to guarantee the local and then global optimality, and the convergence conditions and global optimality are proved by mathematical deduction to guide the selection of the parameters. Simulations show that the proposed algorithm can significantly improve system throughput compared with existing algorithms which do not consider the alteration of serving cells. By improving MPSO, the proposed algorithm can spend less iteration time to achieve higher system throughput, and exhibit similar performance as exhaustive search in both system throughput and the signal to interference plus noise ratio (SINR) of users.

With the rapidly increasing demand for high speed data transmission and a growing number of terminals in one cell, massive multiple-input multiple-output (MIMO) has been shown promising owing to its high spectrum efficiency. Although massive MIMO can efficiently improve the system performance, usage of orthogonal pilots and growing terminals cause large resource consumption especially when the coherence interval is short. This paper presents a semi-orthogonal pilot design with simultaneous data and pilot transmission. In the proposed technique, we exploit the asymptotic channel orthogonality in massive MIMO systems, with which the mutual interference between data and pilot can be mitigated by successive interference cancellation (SIC). Theoretical analysis and simulation results show that the proposed pilot design can achieve a significant performance improvement with reduced pilot resource consumption compared with the popular orthogonal pilots.

This paper aims at designing an efficient hardware architecture for list successive cancellation (SC) polar decoder. Previous literatures have shown that, compared to conventional SC decoder, list SC decoder has the ability to approach the performance of maximum likelihood (ML) decoder. However, the efficient implementation of list SC decoder has not been proposed yet. To tackle this issue, first we propose a sub-optimal version of list SC decoding. Then different selections of list size L are evaluated. By introducing the pre-computation technique, the hardware architecture for a list SC decoder with L = 2 is proposed. Comparison results have shown that, for a rate-½ (1024, 512) polar code, the proposed decoder can achieve near-optimal decoding performance with less hardware cost and latency than the decoder with conventional design approach. We believe that the design approach presented in this paper will facilitate practical applications of list SC polar decoder.

The recently proposed single block-row quasi-cyclic low-density parity-check (QC-LDPC) codes are favorable for high-speed applications. However, conventional decoder design methods are not suitable for this kind of codes. To tackle this issue, this paper aims at designing efficient column-layered single block-row QC-LDPC decoder architecture without affecting the decoding performance. Moreover, the simplified version which only requires single minimum value is also proposed for further hardware reduction. Results show that, for the rate-0.9006 (1640, 1477) single block-row QC-LDPC code, the proposed two designs achieves significant advantages in both hardware and latency over their row-layered counterpart.

The large-scale multiple-input multiple-output (MIMO) system achieves high spectral efficiency, and its performance is ultimately limited by pilot contamination. Since the channel energy usually concentrates in a significant angle region, some mean-square-error (MSE)-based schedule methods have been proposed to mitigate the contamination, but these methods cannot guarantee the quality of service (QoS) for each served terminal. In order to overcome this deficiency, we derive an asymptotic achievable rate for each served terminal under the spatially correlated channel. Further, we analyze how the achievable rate is affected by the degree of channel overlap in the angle region. Based on the analysis result, we design a low-complexity schedule algorithm to guarantee the QoS and find the optimal number of served terminals. Numerical results show that the proposed schedule algorithm outperforms the MSE-based one in terms of QoS and fairness, and can maximize the number of served terminals.

In this paper, we discuss three different design criteria for a distributed antenna system (DAS). They are maximizing throughput under the constraint of the overall transmit power, minimizing the overall transmit power while guaranteeing the minimum spectral efficiency (SE) requirements, and maximizing energy efficiency (EE) under the constraints of minimum SE requirements and overall transmit power. We use sub-gradient iteration approach to solve the first two optimization problems and exploit fractional programming method to deal with the third one. Based on these design criteria, three power allocation algorithms are developed for the downlink multi-user DAS. Depending on application enviroments, we can use the first and second criteria to achieve the highest throughput and to save the most energy, respectively, while we can balance throughput and energy consumption using the third criterion.

The spectral efficiency of single‐cell multi‐user large‐scale distributed antenna system (DAS) is studied. Firstly, the closed‐form expressions are derived for the uplink achievable rate with maximum ratio combining and the downlink achievable rate with maximum ratio transmission. Secondly, the limiting rates are also given when the ratio of the total number of base station (BS) antennas to the number of users goes to infinity. Thirdly, the results are validated via numerical simulations. It is shown that the closed‐form expressions are very accurate with respect to the simulation results over a wide range of the ratio of the total number of BS antennas to the number of users, both the uplink and downlink achievable rates of the large‐scale DAS are much larger than that of the co‐located massive multiple‐input multiple‐output with the same antenna configuration and the theoretical results get more and more close to the limiting rates with increasing the ratio of the total number of BS antennas to the number of users.

The 15 papers in Part II of this special issue focus on the spectrum and energy efficient design of wireless communication networks.

Two new differential flip-flops using neuron-MOS transistors are presented, including one-latch single edge-triggered(IL-SET) flip-flop and one-latch double edge-triggered(IL-DET) flip-flop. In the new differential flip-flops, a pair of n-channel neuron-MOS transistors is used to replace the nMOS logic tree in the conventional differential flip-flops. The construction of the circuits has been simplified by employing the neuron-MOS transistors. In the proposed configurations, the edge-triggering operations are achieved by a narrow pulse produced by two input gates of multiple-input neuron-MOS transistors receiving a clock and a delayed clock, respectively. In comparison with neuron-MOS-based differential master-slave flip-flop, the IL-SET configuration has reduced transistor count and lower power consumption. The HSPICE simulation using TSMC 0.35μm 2-ploy 4-metal CMOS technology validated the effectiveness of the proposed approach.

In this paper, the uplink sum-rate of the multi-cell multi-user massive MIMO system is studied under correlated Rayleigh fading channels. First, by considering pilot contamination effect, the equivalent system model of the massive MIMO is given with MMSE channel estimation. Then, the lower bound of the sum-rate is derived, and its asymptotical performance is studied when the base station antenna number goes to infinity. The result is general and is very accurate under the physical channel models.

The intensive downlink (DL) inter-cell interference created by cell range expansion (CRE) of picocells is an urgent problem needing to be solved under macro-pico heterogeneous network (HetNet) scenario. A promising approach is taking advantage of additional degree of freedom brought by carrier aggregation (CA). However, poorly arranged carrier configurations or interference coordination schemes can lead to a degradation of system overall performance. In this paper, we propose a novel dynamic interference coordination scheme based on carrier aggregation to alleviate the DL interference from macrocells to users located in the cell range expansion area. Novel carrier configuration pattern and dynamic power control scheme based on price algorithm are applied to mitigate detrimental interference to picocell-edge users and improve the availability of macrocells. Simulation results show that the proposed scheme can boost the picocell-edge throughput significantly while ameliorating the overall system throughput.

In this paper, we propose an optimal scheme for a distributed antenna system (DAS) to maximize energy efficiency (EE) under a constraint of overall transmit power of each remote access unit (RAU). We exploit the multicriteria optimization method to systematically investigate the relationship between EE and spectral efficiency (SE). Using the weighted sum method, we first convert the multicriteria optimization function, which is extremely complex, into a simpler single objective optimization function. Then an optimal algorithm is developed to allocate the available power to tradeoff EE and SE effectively. Furthermore, we also illustrate the effectiveness of the proposed method and demonstrate there is a tradeoff between energy-efficient and spectral-efficient transmission through computer simulation of a downlink multiuser DAS.

In this paper, we investigate the energy efficiency problem in macro-relay network. Considering users with different locations and rate requirements, our objective is to serve all users with strict rate guarantee and meanwhile minimize the total energy consumption of the network. First, we formulate an integer optimization problem with the variables as stationuser connections. Then we analyze the complexity of the optimal brute-force search (BFS) method and propose a practical algorithm, i.e., serving station selection for energy minimization (SSSEM). The proposed SSSEM algorithm dynamically alters user's serving station according to both the radio frequency energy and the circuit energy, thus minimizing the total energy consumption of the network. Extensive simulations are conducted and the results show that, compared with existing schemes, the proposed SSSEM algorithm can significantly reduce the total energy consumption of the network, and meanwhile achieve the optimum of BFS.

The spectral efficiency of multi-cell multi-user distributed antenna system with imperfect channel state information (CSI) is studied in this paper. First, considering the pilot contamination and minimum mean-squared-error (MMSE) channel estimation, the equivalent channel model is presented and the sum-rate of the uplink transmission is derived. Under a physical channel model, the equivalent channel model can be seen as a special case of the joint correlated MIMO channel model proposed by Gao [1]. Then, the closed-form approximation of the spectral efficiency is derived. The spectral efficiency of the system with large number of antennas is also studied. Finally, the theoretical results are compared with Monte-Carlo simulation, and the performances of DAS and co-located massive MIMO are also compared. It is shown that similar to the systems with perfect CSIs, DAS still has large performance gains compared with massive MIMO.

Coordinated multipoint has been widely introduced to enhance the capacity, especially that of users at cell edge. Recently energy efficient transmission techniques are increasingly important due to the increase of energy consumption in wireless communication systems. In this paper, an energy efficient cooperative transmission algorithm using coordinated beamforming is proposed for multi-cell MIMO networks. We formulate the problem as a non-convex optimization problem in a fractional form. Then we transform the problem into an equivalent form and propose an iterative algorithm to solve it. In each iteration, a simplified zero-forcing coordinated beamforming using beam tracing and an optimal power allocation algorithm for maximizing energy efficiency are derived. Simulation results demonstrate that the proposed algorithm has a better energy efficiency performance than the traditional capacity maximizing method. With the lower complexity, the proposed method using beam tracing approaches the energy efficiency performance of the subspace decomposition method in slowly varying channels.

This paper addresses the analog feedback of channel state information (CSI) in multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) based wideband wireless communication systems. In MIMO-OFDM systems, the amount of CSI to be fed back grows prohibitively with the number of subcarriers. Aiming at reducing the feedback overhead, we design efficient analog feedback schemes. In our approach, the user equipment (UE) samples the CSI so that only a small fraction of the CSI needs to be fed back to the base station (BS). Based on the sampled feedback information, the BS reconstructs full CSI using some interpolation method. Low complexity clustering/interpolation based schemes are first considered, but these schemes only provide limited feedback quality. To improve the performance, we analyze the sparsity of the time-domain channel impulse response and propose a time-domain filtering scheme based on the sparsity property. Simulation results show that the time-domain filtering scheme efficiently decreases the interpolation distortion and feedback error, and achieves much better performance than the clustering/interpolation based schemes.

Energy efficiency(EE) has caught more and more attention in future wireless communications due to steadily rising energy costs and environmental concerns. In this paper, we propose an EE scheme with proportional fairness for the downlink multiuser distributed antenna systems (DAS). Our aim is to maximize EE, subject to constraints on overall transmit power of each remote access unit (RAU), bit-error rate (BER), and proportional data rates. We exploit multi-criteria optimization method to systematically investigate the relationship between EE and spectral efficiency (SE). Using the weighted sum method, we first convert the multi-criteria optimization problem, which is extremely complex, into a simpler single objective optimization problem. Then an optimal algorithm is developed to allocate the available power to balance the tradeoff between EE and SE. We also demonstrate the effectiveness of the proposed scheme and illustrate the fundamental tradeoff between energy- and spectral-efficient transmission through computer simulation.

Distributed multi-input multi-output (D-MIMO) system is a promising system to greatly improve the spectral efficiency and power efficiency of the cellular system. The performance analysis of the spectral efficiency of D-MIMO system is a fundamental problem for both theoretical study and technique evaluation. In this paper, the theoretical performances of spectral efficiency for D-MIMO system and traditional collocated MIMO (C-MIMO) system are studied and compared. First, a composite D-MIMO channel including path loss, shadow fading and multipath fading is given. Conditioned on the desired user position, by using the tight bounds and central limit theory, the analytical approximations of the mean and the cumulative distribution function (CDF) of the mutual information (MI) are derived for C-MIMO and D-MIMO channels at both high signal to noise ratio (SNR) and low SNR. Assuming that the users are randomly distributed in the cell, the CDFs of the spectral efficiency are also given for the C-MIMO and D-MIMO cellular system, and the closed-form expressions for the mean spectral efficiency, mean outage spectral efficiency are derived. Finally, the theoretical comparisons between C-MIMO and D-MIMO for large number of antennas are given, and simulation results are presented which validate the analytical results.

For practical bi-directional relaying, symbols transmitted by two sources cannot arrive at the relay with perfect symbol and frame alignments and the asynchronous multiple-access channel (MAC) should be seriously considered. In this paper, we consider the Low-Density Parity-Check (LDPC)-coded BPSK signalling over the general asynchronous MAC with both frame and symbol misalignments. For the symbol-asynchronous MAC, we present a formal log-domain generalized sum-product-algorithm (Log-G-SPA) for efficient decoding. When the frame-asynchronism is encountered at the relay, we propose an original approach by employing the cyclic LDPC codes and the simple cyclic-redundancy-check (CRC) coding technique. Simulation results demonstrate the effectiveness of the proposed approach.

The fifteen papers in this special issue are devoted to the topic of spectrum and energy efficiency of wireless and mobile communications networks.

Zadoff-Chu (ZC) sequences have been used as random access sequences in modern wireless communication systems, replacing the conventional pseudo-random-noise (PN) sequences due to their superior autocorrelation properties. An analytical framework quantifying the ZC sequence's performance and its fundamental limitation as a random access sequencein the presence of frequency offset between the transmitter and the receiver is introduced. We show that a ZC sequence's perfect autocorrelation properties can be severely impaired by the frequency offset thereby limiting the overall performance of the random access signals formed from these sequences. First, we derive the autocorrelation function of these random access sequences as a function of the frequency offset. Next, we introduce the concept of critical frequency offsets and the spectrum associated with a ZC sequence set to characterize the frequency offset properties of the random access signals. Finally, we demonstrate that the frequency offset immunity of a ZC sequence set can be controlled by shaping the spectrum of the ZC sequence set.

In this paper, we propose a novel opportunistic scheduling algorithm in multi-cell cooperation scenario, i.e., joint multi-user diversity (MUD) exploitation and inter-cell interference (ICI) mitigation based scheduling (MEIMS). Our algorithm jointly considers the intended channel condition of the scheduled user from its serving cell and the orthogonality between that and the corresponding interference channels to concurrently scheduled users in neighboring cells so as to exploit MUD and mitigate ICI simultaneously. The performance of our algorithm is evaluated through simulation. Results show that, our scheme can significantly enhance the received signal to interference plus noise ratio (SINR) with relatively better fairness guarantee, thus achieves the largest throughput and utility comparing to several well-known scheduling algorithms.

In this paper, we compare EE of the distributed MIMO (D-MIMO) and co-located MIMO (C-MIMO) in the uplink cellular systems since mobile stations are battery powered. The total energy consumption includes both the circuit energy consumption and the transmission energy. We get the closed-form expression for EE of D-MIMO and C-MIMO systems. What's more, an optimization algorithm is proposed to get the optimal EE values while satisfying given spectral efficiency (SE) requirement for both D-MIMO and C-MIMO systems. Simulation results show that the D-MIMO systems are more energy efficient than C-MIMO systems in composite fading channel, and the optimal EE value can be obtained by the proposed algorithm while satisfying given SE requirement.

In this paper, we propose a method to connect a group of wireless sensors to LTE/LTE-A system by using the cellular users as mobile relays, with the basic principle of maintaining the normal traffic between the cellular users and eNodeB during the sensor data collection. The cellular spectrum is re-used on sensor-to-mobile relay link without employing additional frequency resources. Multi- carrier CDMA methods are suspected to be utilized by sensor nodes, where data of each sensor is spread to the whole re-used cellular spectrum to create a low data rate transmission. In order to avoid additional complex receiver on cellular terminals, the simple amplify-and-forward relaying scheme is used at mobile relays. Since eNodeB can observe the cellular traffic components in the re-used spectrum exactly, it has the capability of canceling them in the received overlapped signals, and detecting the sensor data by using advanced multi-user detection methods. Finally, the end-to-end outage probability is analyzed and a closed-form approximation is derived. The numerical results show that the approximate closed-form equation is significant and that the proposed scheme can be used to collect sensor data in LTE/LTE-A networks.

3GPP LTE networks can provide a higher capacity by adopting advanced physical layer techniques and serve users with different Quality of Service (QoS) requirements. However, unbalanced user distributions and strong inter-cell interference (ICI) still deteriorate network performances severely. Since fractional frequency reuse (FFR) technique is recommended to mitigate ICI, we investigate the load balancing problem in a 3GPP LTE multi-cell FFR network with heterogenous services in this paper. Firstly we formulate a multi-objective optimization problem, whose objectives are intra- and inter-cell load balancing index for users with QoS requirements and total utility function for users without QoS requirements. Then we analyze the complexity of the problem and propose a practical algorithm which includes QoS aware intra- and inter-cell handover and call admission control. Extensive simulations are conducted, the results show that our algorithm can lead to significantly better performances, i.e., a lower new call blocking rate for users with QoS requirements, a larger utility for users without QoS requirements at the cost of a bit degradation of total throughput.

In this paper, we propose a novel opportunistic scheduling algorithm, joint useful and interference channel based scheduling (JUICS), in coordinated multi-point (CoMP) transmission scenario. Our scheme jointly considers the useful channel condition of the scheduled user from its serving cell and the orthogonality between that and the corresponding interference channels to concurrently scheduled users in neighboring CoMP cells, thus to exploit multi-user diversity (MUD) and mitigate inter-cell interference (ICI) simultaneously. The performance of the proposed algorithm is evaluated through simulation in terms of the cumulative distribution performance of the received signal to interference plus noise ratio (SINR) and that of the scheduled times of all CoMP users. Results show that, with limited complexity and overhead, our scheme can significantly enhance the received SINR with relatively better fairness guarantee, thus to achieve the largest throughput and utility comparing to several well-known scheduling algorithms.

IEEE 802.16 can provide wireless broadband access with its support to both single-hop and multi-hop mesh modes. It defines several traffic/service categories and offers differentiated quality of service assurance through scheduling algorithms. However, it is not quite clear how well an IEEE 802.16 network could support real-time services such as video streaming and voice over Internet protocol services, especially in its mesh-mode operation. This study aims to analyse delay and throughput properties of an IEEE 802.16 mesh network for evaluating the performance of various real-time applications. The authors establish an analytical model to calculate delay and throughput of IEEE 802.16 distributed scheduling schemes. The proposed model helps to investigate how delay and throughput vary in terms of network parameters in order to optimise the system design via proper parameter configuration. Extensive simulations have been conducted to demonstrate the accuracy of the model.

Uplink–downlink duality has emerged as an attractive approach to optimise the downlink beamforming problem with fixed cooperation clusters where either all base stations serve all terminals or each base station serves only its own terminals. Although easily implementable for co-located base stations, the performance is still limited by out-of-cluster interference. To address these concerns, this study establishes an uplink–downlink duality for the multi-cell multi-user system with dynamic cooperation clusters where each base station has responsibility for the interference leaked to a set of terminals while only serving a subset of them with data. The multi-cell downlink problem of minimising the total transmit power subject to individual signal-to-interference-and-noise ratio requirements under per-base station power constraints is solved via a dual uplink problem. Conditions for beamforming optimality and the optimal downlink beamforming design are derived using Lagrange duality theory. The convergence behaviour of proposed algorithm is shown. The percentage of power saved by proposed algorithm is calculated subject to the user-specific SINR value achieved by zero-forcing (ZF), maximum-ratio transmit (MRT), virtual SINR (VSINR) and layered virtual SINR (LVSINR) under different cooperation scenarios and the sum rate performance is compared.

In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the proposed model framework and using a typical cooperative MIMO communication environment as an example, we derive a novel geometry-based stochastic model (GBSM) applicable to multiple wireless propagation scenarios. The proposed GBSM is the first cooperative MIMO channel model that has the ability to investigate the impact of the local scattering density (LSD) on channel characteristics. From the derived GBSM, the corresponding multi-link spatial correlation functions are derived and numerically analyzed in detail.

In this paper, we investigate call admission control (CAC) with opportunistic scheduling and propose a novel CAC algorithm for users with quality of service (QoS) requirements. Our main contribution is threefold. First, we verify that, compared with several other scheduling schemes, cumulative distributed function based scheduling (CS) makes the best tradeoff between efficiency and fairness in full-load scenario and exploits the best opportunism with absolutely fair resource allocation. Then we deduce and validate the multi-user diversity gain (MDG) of CS, which determines its long-term average performance and is used for estimation of resource occupation in CAC algorithm design. After that, we use opportunistic round robin (ORR) method to calculate the statistical low performance bound of CS, and propose CS/ORR based CAC (COCAC) algorithm, which guarantees the heterogeneous minimum rate requirement (MRRs) of both new access users and existing ones. Finally, we evaluate the performance of the proposed COCAC algorithm via simulation. Results show that COCAC can significantly reduce new call block probability, effectively make use of system resources, as well as strictly guarantee all users' MRRs.

In this paper, we investigate load balancing problem in 3GPP Long Term Evolution (LTE) networks and propose our solution which considers users with different Quality-of-Service (QoS) requirements. Load unbalance among neighboring cells often yields negative impacts on user experience and network performance, and it has mainly been considered for only data services without QoS guarantee. However, 3GPP LTE network aims to support multi-class services with different QoS requirements, on which the influence of load unbalance is quite different. For those with minimum rate requirements, it may result in high block probability, while for others without rate requirements, the throughput of boundary users may be degraded. In this paper, we incorporate all the differences into a network utility maximization framework and formulate it as a multi-objective optimization problem. The objectives in the problem are load balancing index of services with QoS requirements and the total utility of other services, and the constraints are physical resource limits and QoS demands. Then we analyze the complexity of the problem, and propose our solution, which includes a QoS guaranteed hybrid scheduling scheme, handover of users with and without QoS requirements, and a call admission control algorithm. Extensive simulation is conducted and the results show that the proposed framework leads to significantly better load balancing, and thus the decrease in call block probability of users with QoS requirements, and the increase in throughput of boundary best effort users.

Cognitive radio has been suggested as an efficient method for secondary users to promote the efficient utilization of spectrum. Meanwhile, Cooperative relay allows different users or nodes to share resources and to create collaboration through distributed transmission in a wireless networks. The combination of Cognitive radio with cooperative communication could significantly improve the system performance in cognitive radio ad-hoc networks (CRAHNs). In this paper, we discuss how to use cooperative relay to increase the transmission rate in CRAHNs. We first give a new distributed relay selection algorithm, it considers several aspects including channel gain, channel available probability and spectrum heterogeneity of secondary nodes. A cooperative MAC protocol, Cooper-MAC, is then proposed for CRAHNs which enables secondary users to negotiate channels and relays. Simulation results demonstrate the effectiveness of the Cooper-MAC.

The total energy minimization in wireless multihop networks using two-way network coding is investigated in this paper. The problem is first formulated as a linear programming problem, then decomposed into two sub-problems using the La grangian decomposition, and finally solved with the subgradient method. After that, the backpressure based algorithm is proposed to solve the problem in a distributed manner. The performance of the algorithm is evaluated first in a simple topology for analysis and then in a random topology with different number of flows for practical consideration. Simulation results show that the convergence time increases as the number of nodes in the network, and the energy cost per packet can be saved up to 30% by using two-way network coding.