The computational complexity of multiple-input multiple-output (MIMO) signal processing algorithms grows exponentially, resulting in increasing computational latency. Conventional approaches to reducing latency are predominantly algorithm-specific, addressing only limited components within the overall MIMO system. In this paper, we propose a novel arithmetic-level variable precision computing (VPC) scheme, introducing a new universal compatible methodology for computational latency reduction. The proposed arithmetic-level VPC dynamically assigns varying levels of computing precision to individual arithmetic operations within an algorithm and employs a Markovian process model with low-complexity computations to minimize additional complexity overhead introduced by VPC. Numerical simulations demonstrate that the proposed scheme significantly outperforms conventional fixed-length computing (FLC), achieving superior performance while maintaining the same level of average computing precision.

Sixth-Generation (6G) networks necessitate intelligent and energy-efficient operations. However, the direct deployment of Large Language Models (LLMs) for real-time 6G control is hindered by significant latency and energy constraints, conflicting with green 6G imperatives. This paper pioneers a collaborative architecture of large-small AI models to address these challenges. In this architecture, resource-intensive LLMs, i.e., large models, are strategically employed offline for comprehensive Wireless Data Knowledge Graph (WDKG) construction, effectively distilling domain knowledge. This WDKG, in turn, enables the development and deployment of lightweight, efficient small models for real-time network tasks by facilitating the generation of optimized feature datasets. To operationalize this, we first introduce a novel multi-agent collaborative LLM framework, bolstered by an enhanced semantic representation method incorporating domain-adaptive embedding fine-tuning and mutual information (MI)-based feature encoding, for automated high-fidelity WDKG construction. Subsequently, we propose the Semantic-Data and Spatio-Temporal (SD-ST) model, which uniquely fuses LLM-extracted semantic information with the spatio-temporal characteristics of wireless network data and WDKG structure. Insights from the SD-ST model guide a WDKG-driven method for generating optimized feature datasets by evaluating node influence and redundancy. Experimental validation demonstrates that these distilled feature datasets lead to substantial reductions in training and inference overhead for the lightweight downstream AI models, offering a tangible pathway towards greener, more efficient, and intelligent 6G networks.

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.

In this paper, we propose a quantized penalty gradient (QPG) detection algorithm for massive multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs). To tackle the challenges of maximum likelihood (ML) detection under discrete constraints, we reformulate the detection problem into an unconstrained optimization by introducing two customized penalty functions that promote alignment between the estimated signals and target constellation set. Based on this, the QPG algorithm is developed to efficiently solve the resulting problem, achieving competitive detection performance with only second-order computational complexity. We further provide a theoretical analysis establishing the Lipschitz continuity of the objective function, which guarantees the monotonic descent property of QPG and ensures its convergence. Moreover, we prove that QPG efficiently finds the local minima with an accessible linear convergence rate, thus leading to an explicit trade-off between detection performance and computational complexity. Finally, simulation results confirm the significant performance gains of QPG over the conventional quantized detectors across various channel conditions, while maintaining low computational complexity.

This paper proposes joint sensing and beamforming solutions tailored for extremely large-scale MIMO (XL-MIMO) near-field systems under both static and dynamic scenarios. For static scenarios, we develop a novel Multi-Layer Reconstruction (MLR) mechanism to address the challenges of large-scale near-field dictionary matrix and coarse range grid spacing, and further propose a sparse sensing algorithm, MLR mechanism based Linear Approximation Variational Bayesian Inference (MLR-LA-VBI), to achieve precise user/target position and radar cross section (RCS) sensing with low pilot overhead. Building upon these sensing results, a beamforming scheme is proposed to optimize radiation patterns. For dynamic scenarios, we exploit near-field Doppler-frequency characteristics to propose the modified MLR-LA-VBI (MMLR-LA-VBI) algorithm for sensing and a predictive beamforming framework, where the former serves as the core module of the latter. Our sensing approach enables full motion status sensing of users/targets from a single echo without requiring prior information of the target motion model. By eliminating echo accumulation and leveraging correlations across consecutive coherent processing intervals (CPIs), it achieves high performance with low computational complexity. Moreover, the proposed predictive beamforming framework naturally inherits the aforementioned advantages of MMLR-LA-VBI, and leverages the sensed full motion status to achieve an efficient and seamless beam tracking scheme with Doppler frequency compensation. In addition, theoretical analysis is conducted to characterize the algorithmic complexity, highlighting the advantages of proposed algorithms in terms of efficiency. Finally, simulations and analyses validate the effectiveness of the proposed algorithms in both static and dynamic scenarios.

Reconfigurable intelligent surface (RIS) plays an essential role in alleviating severe path loss in millimeter wave communication systems. Its performance hinges on the precise modeling of high-dimensional cascaded channels. However, traditional modeling approaches require extensive experience in radio propagation, resulting in complex and inefficient processes. To overcome these limitations, we transform the RIS channel modeling into a channel distribution transport mapping problem and introduce a generative model based on rectified flow. Our approach integrates distance information into a diffusion transformer (DiT) architecture through cross-attention mechanisms, resulting in a conditional DiT capable of synthesizing target channels from distance inputs. We further optimize the rectified flow into a single-step generator via reflow techniques. Building on this framework, we design a generative digital twin (DT) channel model that serves as a high fidelity data generator for downstream tasks. The proposed model acts as a virtual replica of the propagation environment, enabling efficient channel data synthesis for training communication algorithms such as channel state information feedback and channel estimation. Simulation results show that our approach generates channels with minimal distribution discrepancy compared to real channels (a maximum mean discrepancy < 0.01), outperforming existing generative methods. Furthermore, the reflow-driven DT channel model achieves the shortest generation time among all evaluated benchmarks.

In this paper, we study a sampling-based detection strategy for massive multiple-input multiple-output (MIMO) systems, driven by a modified discrete diffusion model formulated as an analytical, non-learning sampling process. Built upon this framework, the proposed discrete diffusion-based sampling (DDS) algorithm improves decoding performance by leveraging residual-dependent sampling, compared to the independent randomized successive interference cancellation (SIC). Specifically, the modified diffusion model incorporates a shortcut perturbation toward the SIC solution, a forward diffusion step to enhance diversity, and step-wise alignment with the perturbed received signal. Within this framework, the DDS algorithm further adopts one-dimensional discrete Gaussian distribution, involving a reformulated discrete Gaussian noise and an explicitly characterized sampling range, but retains computational complexity amenable to practical deployment. Moreover, we theoretically demonstrate an improved expected decoding radius over randomized SIC. Finally, simulation results based on massive MIMO detection are presented to confirm performance gain of the proposed DDS algorithm.

Delay guarantee is essential for wireless communication technology, which is beneficial for real-time data processing. Such guarantee manifests as hard delay constraint, whose resolution facilitates reliable task fulfillment within strict deadline while enabling efficient communication resource scheduling. In this paper, we investigate the problem of transmitting a certain amount of data within a deadline and optimizing the expected total energy in an Orthogonal Frequency Division Multiplexing (OFDM) system. We represent the problem as a finite-horizon stochastic dynamic optimization problem, and aim to derive decision rule for allocating communication transmission rates across subcarriers. We propose a method named Multicarrier Deterministic Approximation Method (MDA). First, to address the complexity of expectation computation, we approximate the expected value function. Subsequently, for the resulting deterministic optimization problem, we introduce auxiliary variable, and adopt low-complexity two-layer optimization framework. For the proposed method, we derive performance upper bound for deterministic parameter with specific value and asymptotic performance upper bound for deterministic parameter with general value. The performance bound theoretically demonstrates the superiority of the proposed method over both the equal rate method and the first slot method. Furthermore, it proves that increasing the deterministic parameter under relaxed delay constraint can achieve enhanced theoretical performance guarantee. Finally, the effectiveness of the proposed method is validated through simulations.

Leveraging inherent advantages of large bandwidth, high carrier frequency, and fine resolution, millimeter-wave (mmWave) technology is poised to play a pivotal role in integrated sensing and communication (ISAC) applications envisioned for sixth-generation (6G) networks. This paper proposes a stochastic geometry-based analytical framework to evaluate the performance of hybrid ISAC networks integrating sub-6 GHz and mmWave base stations (BSs). The framework explicitly incorporates band-specific propagation characteristics. Each mmWave BS is equipped with a large-scale antenna array to compensate for high-frequency propagation loss. Based on received signal power, we propose the maximum received echo signal power (Max-RESP) and maximum received average signal power (Max-RASP) association schemes to ensure that the target and user are associated with the BS providing better link conditions, respectively. Using stochastic geometry and probability theory, we derive analytical expressions for sensing distance accuracy and the communication achievable rate. The analytical results are validated via extensive Monte Carlo simulations. Numerical results show that hybrid sub-6 GHz and mmWave ISAC networks significantly outperform conventional pure sub-6 GHz networks and can approach the performance of pure mmWave networks by appropriately tuning the deployment density ratio. The appropriate density ratio provides practical guidance for balancing cost efficiency with performance enhancement. Moreover, the results reveal a performance bottleneck at higher density ratios, primarily due to the saturation of the signal-to-interference-plus-noise ratio (SINR). These findings highlight the crucial role of selecting an appropriate density ratio in hybrid sub-6GHz and mmWave ISAC networks.

Networked integrated sensing and communication (ISAC) has emerged as a pivotal paradigm for next-generation wireless networks, where dedicated target monitoring terminals (TMTs) can be extensively leveraged for their low-cost flexible deployment and capability to facilitate bistatic and multistatic sensing. Nevertheless, the coordinated beamforming design for networked ISAC tailored for time-of-arrival (ToA)-based multi-TMT localization remains largely unexplored. To address this gap, we present a comprehensive study in this paper. Specifically, we first establish signal models for both communication and localization, and, for the first time, derive a closed-form Cramér-Rao lower bound (CRLB) to quantify the localization performance. Leveraging this CRLB, we formulate two optimization problems focusing on sensing-centric and communication-centric criteria, respectively, to thoroughly investigate the fundamental communication-localization trade-offs. For the sensing-centric problem, we develop a globally optimal algorithm based on semidefinite relaxation (SDR), applicable to scenarios where the number of BS antennas exceeds the total number of communication users. In parallel, for the communication-centric problem, we design a globally optimal algorithm for the single-BS case utilizing bisection search. To address the general cases of both problems, we propose a unified and efficient successive convex approximation (SCA)-based algorithm, which is further extended to multi-target scenarios. Finally, simulation results demonstrate the effectiveness of our proposed algorithms, reveal the intrinsic trade-offs between communication and localization, and further show that deploying more TMTs is more beneficial than deploying more BSs in networked ISAC systems.

Tidal mobile traffic patterns offer opportunities for efficient Cloud Radio Access Network (CRAN) scheduling by leveraging its disaggregated and virtualized baseband processing, where baseband functions form a virtualized network function service chain (VNF-SC, or RAN slice) deployed across metro access, aggregation, and core networks. By dynamically reconfiguring and migrating RAN slices, processing pools can be powered down during low-demand periods to save energy. However, RAN slice migration causes service disruptions and degrades Quality of Service (QoS), making the tradeoff between energy efficiency and QoS a key challenge in CRAN scheduling. Existing approaches, such as heuristic and Deep Reinforcement Learning (DRL)-based methods, have achieved certain optimizations but rely on fixed scheduling intervals, which require provisioning for peak demand within the interval, leading to resource overprovisioning and inefficiency. To enable flexible and adaptive scheduling, we propose threshold-triggered heuristic-assisted DRL (TT-HA-DRL), which employs a threshold-triggered mechanism based on varying service demands and a heuristic-assisted DRL framework for adaptive RAN slice migration. Heuristic algorithms are used for action pruning to optimize the action space, enhancing scheduling performance. Baseline heuristics are incorporated to construct the Normalized Performance Loss as the reward function, enabling a tradeoff among the multiple optimization objectives. Extensive simulations validate the effectiveness and scalability of the proposed TT-HA-DRL. Compared to fixed-interval HA-DRL, our approach achieves reductions of up to 10.8% in power consumption, 12.3% in migration time, and 23.9% in Maximum Frequency Slot Index (MFSI) in a 30-node network. These results confirm TT-HA-DRL's ability for elastic and QoS-guaranteed RAN slice scheduling.

Network slicing has emerged as a promising solution for end-to-end (E2E) resource management and orchestration, enabled by software-defined networking (SDN) and network function virtualization (NFV) technologies. In this paper, we investigate the dynamic E2E optical-wireless network slicing mapping problem in converged optical-wireless access networks. To address user data rate requirements in wireless networks and radio access network (RAN) slicing scheduling in optical networks, we first formulate an E2E optical-wireless network slicing mapping model with its associated constraints. Subsequently, to provide feasible solutions for real-world applications, we propose a dynamic E2E optical-wireless network slicing mapping (D-E2E-OW-NSM) algorithm based on deep reinforcement learning (DRL). To facilitate the decision-making process of the DRL agent, we decompose the intricate E2E optical-wireless network slicing request into several sub-requests, solving them one by one in turn. Simulation results demonstrate that our proposed method reduces the request blocking probability by up to 18.2% in a small-scale network and 11.3% in a large-scale network compared to baseline methods. Our analyses provide valuable insights into the modeling and design of efficient converged optical-wireless access networks for 5G and beyond.

The rising popularity of Internet of things (IoTs) has spurred technological advancements in mobile internet and interconnected systems. While offering flexible connectivity and intelligent applications across various domains, IoT service providers must gather vast amounts of sensitive data from users, which nonetheless concomitantly raises concerns about privacy breaches. Federated learning (FL) has emerged as a promising decentralized training paradigm to tackle this challenge. This work focuses on enhancing the aggregation efficiency of distributed local models by introducing over-the-air computation into the FL framework. Due to radio resource scarcity in large-scale networks, only a subset of users can participate in each training round. This highlights the need for effective user scheduling and model transmission strategies to optimize communication efficiency and inference accuracy. To address this, we propose an integrated approach to user scheduling and receive beam steering, subject to constraints on the number of selected users and transmit power. Leveraging the difference-of-convex technique, we decompose the primal non-convex optimization problem into two sub-problems, yielding an iterative solution. While effective, the computational load of the iterative method hampers its practical implementation. To overcome this, we further propose a low-complexity user scheduling policy based on characteristic analysis of the wireless channel to directly determine the user subset without iteration. Extensive experiments validate the superiority of the proposed method in terms of aggregation error and learning performance over existing approaches.

Compressed-sensing (CS)-based channel estimation is a promising technology for future millimeter wave (mmWave) multiple-input–multiple-output (MIMO) systems, enabling significant pilot reduction and improved estimation accuracy. Channel estimators based on matching pursuit (MP) variants offer lower complexity compared with other CS algorithms, but suffer from high latency due to their iterative nature, hindering efficient hardware implementation. To mitigate this issue, this article introduces a turbo pursuit (TP) strategy that relaxes sequential dependencies in MP variants, enabling parallel processing and pipelined implementation. To demonstrate the effectiveness of TP, this article further introduces turbo implicit pursuit (TIP), a hardware-friendly instance of TP that leverages a prioritized gradient descent (GD) strategy for low-complexity least squares (LS) solving. A hardware auto-generator for TIP is then proposed using a formula representation approach, which constructs a parameterized hardware-algorithm design space and enables hardware-algorithm co-optimization. Our optimized$32 \times 4$TIP MIMO channel estimator ASIC in 65-nm CMOS achieves 0.53-$\mu $s latency under 18.75% measurements. Compared with prior implementations of MP variants, this work achieves higher or comparable estimation accuracy with over 18% latency reduction and over 5$\times$higher throughput to area ratio (TAR) for ASICs, and over 90% latency reduction with over 3$\times$higher hardware efficiency for FPGAs.

In this paper, we investigate the problem of jamming detection and channel estimation during multi-user uplink beam training under random pilot jamming attacks in beamspace massive multi-input-multi-output (MIMO) systems. For jamming detection, we distinguish the signals from the jammer and the user by projecting the observation signals onto the pilot space. By using the multiple projected observation vectors corresponding to the unused pilots, we propose a jamming detection scheme based on the locally most powerful test (LMPT) for systems with general channel conditions. Analytical expressions for the probability of detection and false alarms are derived using the second-order statistics and likelihood functions of the projected observation vectors. For the detected jammer along with users, we propose a two-step minimum mean square error (MMSE) channel estimation using the projected observation vectors. As a part of the channel estimation, we develop schemes to estimate the norm and the phase of the inner-product of the legitimate pilot vector and the random jamming pilot vector, which can be obtained using linear MMSE estimation and a bilinear form of the multiple projected observation vectors. From simulations under different system parameters, we observe that the proposed scheme improves the detection probability by more than 25% compared to the baseline at medium-to-high channel correlation level. In addition, the proposed scheme effectively supports zero-forcing precoding, thus enabling reliable data transmission in jamming scenarios, specifically, achieving a symbol error rate (SER) of less than 10−3at a jamming-to-signal ratio of 10 dB and a signal-to-noise ratio of 5 dB.

Extremely large-scale MIMO (XL-MIMO) is regarded as a pivotal enabler for achieving ultra-high spectral efficiency in 6G communications. Near-field channel models, which integrate both line-of-sight (LoS) and non-line-of-sight (NLoS) components, provide accurate characterizations of near-field XL-MIMO channels. However, existing channel estimation schemes encounter severe performance bottlenecks due to the high-dimensional nature of near-field XL-MIMO channels and their structured angular sparsity compared to far-field MIMO systems. To address these challenges, we propose a variance exploding stochastic differential equation (VE-SDE) generator based on an improved diffusion transformer (IDiT) network. The VE-SDE progressively maps the complex XL-MIMO channel distribution to a tractable prior distribution by gradually injecting noise. We utilize the patchify technique to decompose the perturbed angular domain channels into token sequences, which are then processed with diffusion transformer (DiT) blocks, substantially reducing floating-point operations (FLOPs). Additionally, a sparse self-attention mechanism is employed to enhance structured sparsity characterization learning, thereby improving estimation accuracy. Theoretical analysis and numerical experiments show that the VE-SDE generator exhibits strong generalizability and robustness across diverse channel distributions without requiring retraining. Simulation results reveal that the proposed method outperforms state-of-the-art estimation approaches, achieving high-fidelity channel estimation with only 20% pilot density.

Accurate acquisition of channel state information (CSI) is crucial for achieving high-rate communication, yet it introduces significant training overhead and latency, particularly in high-mobility cell-free massive multiple-input multipleoutput (CF-mMIMO) communication systems with unmanned aerial vehicles (UAVs). To address this challenge, we propose a predictive beamforming and resource allocation framework that significantly reduces training overhead while enhancing system throughput. Specifically, a novel frame structure is designed in which uplink training is performed only in the first time slot of each beam tracking frame, while distributed beam tracking and predictive beamforming are applied in all subsequent slots using the extended Kalman filter (EKF) at each access point (AP). Moreover, we develop a centralized information fusion algorithm that exploits cell-free multi-point cooperation to improve estimation accuracy with low fronthaul overhead. Then, we derive the theoretical posterior Cram´er-Rao bound (PCRB) for the fused estimation and show that, under the local linear-Gaussian approximation, the predicted PCRB coincides with the covariance of the fused estimate. We further establish an explicit analytical mapping between the predicted PCRB and the uplink pilot length. Leveraging this theoretical bridge, we formulate a prediction-aware joint optimization problem involving uplink pilot length, downlink AP-user association, and power allocation to actively adapt the training overhead and maximize the effective sum spectral efficiency (SE). A low-complexity iterative algorithm based on fractional programming is proposed to solve this problem. Numerical results demonstrate that the proposed framework achieves a favorable trade-off between signaling overhead and system throughput. Specifically, it reduces the training overhead by 95% with only a 3% decrease in positioning accuracy, incurs only modest additional fronthaul overhead, and improves the effective sum SE by 31% compared to traditional schemes. Furthermore, comprehensive evaluations show that the framework remains effective under multipath fading and higher UAV velocities, and continues to benefit from cooperative gains in expanded network deployments.

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%).

Intelligent reflecting surface (IRS) emerges as a promising technology to enhance wireless communication in recent years. However, the applications of deep learning algorithms within RIS-aided communication systems often suffer performance degradation under extreme conditions owing to a reliance on manual trial-and-error attempts. In this paper, the proposed beamforming neural network architecture search (BNAS) framework automates the design of of neural networks for the joint optimization of precoding vectors and IRS phase shift vectors. To improve robustness and performance, a specialized search space, incorporating two cascading supernets with selectable channel routes, diverse topological connections, and varied operations, is meticulously crafted for beamforming tasks. Meanwhile, the integration of neural tangent kernel theory, supported by alternative optimization guidance and bayesian optimization, not only enhances interpretability but also improves efficiency, thus enabling a more systematic and insightful search process compared to conventional approaches. Extensive numerical simulations confirm the applicability of BNAS, demonstrating superior performance compared to existing deep learning-based methods and traditional algorithms, particularly in challenging scenarios.

This paper investigates an intelligent reflecting surface (IRS)-aided wireless-powered communication network (WPCN) for maximizing the weighted sum rate (WSR). To reduce the complexity of traditional model-driven algorithms and improve convergence in data-driven deep learning approaches, a novel hybrid block coordinate descent (BCD) algorithm motivated by the dilation extraction and context attention (DECA) neural network (NN) is proposed. Specifically, the WSR maximization problem is firstly reformulated as a more tractable form, enabling the BCD algorithm to efficiently optimize the decoupled variables within the constraints. Meanwhile, at each BCD iteration, the DECA NN accelerates IRS phase shift optimization by facilitating the majorization-minimization (MM) algorithm to solve the computationally intensive fractional programming problem. Moreover, by leveraging dilation convolution and high-speed attention mechanisms, the DECA NN significantly outperforms existing deep learning benchmarks in both precision and convergence speed. Numerical results show that the proposed hybrid framework delivers performance comparable to the traditional BCD algorithm with dramatically reduced time consumption, while consistently maintaining robust performance under imperfect CSI and exhibiting strong transferability across diverse communication scenarios.

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.

Cell-free (CF) networks have attracted increasing attention for their effectiveness in mitigating inter-cell interference through cooperative transmission among distributed access points (APs). However, conventional terrestrial CF networks often lack spatial flexibility and struggle to adapt to dynamic environments. To overcome these limitations, we propose a new CF network served by unmanned aerial vehicles (UAVs) equipped with a three-dimensional (3D) rotatable antenna array. Combined with the UAV’s controllable 3D position, the resulting six-dimensional (6D) spatial reconfigurability enables the active beam steering of such aerial APs, thereby enhancing interference mitigation and dynamic user association. However, this design, referred to as 6D aerial rotatable antenna arrays (6DARAs), faces several critical challenges, such as high-dimensional coupled control variables, time-varying user positions, and increased channel state information (CSI) estimation overhead. To address these issues, we develop a two-timescale optimization framework that separates large-timescale 6DARA control (i.e., clustering, position, and rotation) from small-timescale signal processing. At the small-timescale, a closed-form team minimum mean-squared error decoder is derived using local and statistical CSI. At the large-timescale, 6DARA clustering is modeled as a local altruistic game and solved via a concurrent update algorithm, while 6DARA mobility is managed by an enhanced multi-agent reinforcement learning algorithm for efficient position and rotation adaptation under partial observability. Simulation results demonstrate that the proposed network and optimization framework significantly outperform existing baselines in terms of throughput, scalability, and robustness in dynamic environments.

This paper proposes a joint communication and computation scheme (JCCS) for cell-free multiple-input multiple-output (CF-MIMO) network to support federated learning (FL). The JCCS allows users to choose between a model update process or a data offloading process, where the offloaded data and the uploaded model gradient are respectively sent to the distributed processing units (DPUs) deployed on the access points (APs) for further model updates. Moreover, we define a performance metric called iteration error gap as the difference between model errors of adjacent iterations and decouple the total training time minimization problem into the error gap maximization problem within fixed time limit. Base on common machine learning (ML) assumptions, we derive a lower bound of iteration error gap, which is determined by the minimum batch size among all DPUs. An optimization problem aiming to maximize the minimum batch size is then formulated to jointly optimize the time division, power control, and user selection. By employing the block coordinate descent approach, we develop a new algorithm to solve the formulated non-convex mixed integer programming problem. Our simulation results verify the convergence of proposed algorithm and show that it reduces the relative error of a single FL iteration by more than 1.5 dB compared with other baseline schemes. Furthermore, the CF-MIMO network integrated with JCCS achieves a relative error reduction exceeding 1 dB per iteration when compared to collocated MIMO network. In addition, an FL example of handwritten digits classification shows that the JCCS indeed accelerates the convergence of FL model.

As a paradigm shift towards pervasive intelligence, semantic communication (SemCom) has shown great potentials to improve communication efficiency and provide user-centric services by delivering task-oriented semantic meanings. However, the exponential growth in connected devices, data volumes, and communication demands presents significant challenges for practical SemCom design, particularly in resource-constrained wireless networks. In this work, we propose a task-agnostic semantic communication (TASC) framework capable of supporting multimodal data across diverse tasks. To investigate the interplay between communication and intelligent tasks from an information-theoretic perspective, we introduce a distributed multimodal information bottleneck (DMIB) principle, which enables the extraction of minimal sufficient unimodal and multimodal representations by eliminating redundant information while preserving task-relevant semantics. To further reduce the communication overhead, we develop an adaptive semantic feature transmission method under dynamic channel conditions. Then, TASC is trained based on federated meta-learning (FML) to learn a well-initialized model for rapid adaptation and generalization. To gain deep insights, we conduct theoretical analysis and devise resource management to accelerate convergence while minimizing the training latency and energy cost. Moreover, we develop a joint user selection and resource allocation algorithm to address the non-convex problem with theoretical guarantees. Extensive simulation results validate the effectiveness and superiority of the proposed TASC compared to baselines.

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.

This paper investigates the downlink energy efficiency (EE) optimization for cell-free massive multiple input multiple output (CF-mMIMO) systems subject to ultra-reliable and low-latency communication (URLLC) requirements. To achieve superior performance, we jointly consider the impacts of power allocation, access point (AP)-user association, and AP sleep modes under the finite blocklength (FBL) regime, leading to a challenging mixed-integer (MI) non-convex optimization problem. Utilizing a sequential convex approximation (SCA) framework, we first propose the SCA-Relaxation algorithm to convert the original problem into a series of second-order cone programming (SOCP) sub-problems, which can be efficiently addressed via modern convex programming solvers. Moreover, for further reducing computational complexity, we approximate the original problem as a continuous-variable optimization and tackle it via a combination of the Dinkelbach transformation, penalty functions, as well as an accelerated proximal gradient method with adaptive momentum, resulting in the proposed low complexity EE maximization (LCEE-max) algorithm. Besides, the related convergence and complexity analysis of these two algorithms are also presented in detail. Simulation results demonstrate that compared to the state-of-the-art baseline algorithm, the proposed two algorithms achieve the EE improvements of approximately 40% and 30%, respectively, along with a substantial reduction in complexity, thereby enabling efficient and fast resource allocation in CF-mMIMO-enabled URLLC scenarios.

The multiple-input multiple-output dual functional radar communication (MIMO-DFRC) system is a promising platform for future integrated sensing and communication applications. Ensuring reliable performance of both radar and communication functions, the beam selection is a critical technology in MIMO-DFRC systems. However, the beam selection problem is known to be NP-hard, and efficiently addressing it remains an open issue, especially in distributed systems. In this paper, we address the beam selection problem for a MIMO-DFRC system by formulating it as a semi-Markov decision process and propose a novel hierarchical reinforcement learning (HRL) algorithm. In our approach, codebook-based beam selection for transmitting and receiving BS is controlled by an agent deployed in the cloud. Inspired by the mechanism of hierarchical codebook beam training, we employ an option-based policy that enables the agent to explore different layers of the codebook and extract context information across multiple discrete time steps. We utilize an invalid action masking technique to overcome the dynamic action space problem caused by the option-based policy. Simulation results demonstrate that the HRL-based algorithm outperforms existing beam selection methods and achieves remarkable performance even under conditions of a high probability of false alarm and low signal-to-noise ratio. Furthermore, we find that the proposed algorithm exhibits promising capabilities to learn a more efficient policy beyond the full hierarchical codebook training trajectory.

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.

As a revolutionary paradigm for intelligently controlling wireless channels, intelligent reflecting surface (IRS) has emerged as a promising technology for future sixth-generation (6G) wireless communications. While IRS-aided communication systems can achieve attractive high performance gain, existing schemes require plenty of IRS elements to mitigate the “multiplicative fading” effect in cascaded channels, leading to high complexity for real-time beamforming and high signaling overhead for channel estimation. In this paper, the concept of sustainable intelligent element-grouping IRS (IEG-IRS) is proposed to overcome those fundamental bottlenecks. Specifically, based on the statistical channel state information (S-CSI), the proposed grouping strategy intelligently pre-divide the IEG-IRS elements into multiple groups based on the beam-domain grouping method, with each group sharing the common reflection coefficient and being optimized in real time using the instantaneous channel state information (I-CSI). Then, we further analyze the asymptotic performance of the IEG-IRS to reveal the substantial capacity gain in an extremely large-scale IRS (XL-IRS) aided single-user single-input single-output (SU-SISO) system. In particular, when a line-of-sight (LoS) component exists, it demonstrates that the combined cascaded link can be considered as a “deterministic virtual LoS” channel, resulting in a sustainable squared array gain achieved by the IEG-IRS. Finally, we formulate a weighted-sum-rate (WSR) maximization problem for an IEG-IRS-aided multiuser multiple-input single-output (MU-MISO) system and a two-stage algorithm for optimizing the beam-domain grouping strategy and the multi-user active-passive beamforming is proposed. Simulation results validate the superiority of our proposed two-stage algorithm in low pilot overhead conditions and show that in the context of an XL-IRS aided MU-MISO system, the proposed IEG-IRS can achieve a significant WSR gain, thus overcoming this performance drawback associated with high complexity and signaling overhead.

Beamforming enhances signal strength and quality by focusing energy in specific directions. This capability is particularly crucial in cell-free integrated sensing and communication (ISAC) systems, where multiple distributed access points (APs) collaborate to provide both communication and sensing services. In this work, we first derive the distribution of joint target detection probabilities across multiple receiving APs under false alarm rate constraints, and then formulate the beam selection procedure as a Markov decision process (MDP). We establish a deep reinforcement learning (DRL) framework, in which reward shaping and sinusoidal embedding are introduced to facilitate agent learning. To eliminate the high costs and associated risks of real-time agent-environment interactions, we further propose a novel digital twin (DT)-assisted offline DRL approach. Different from traditional online DRL, a conditional generative adversarial network (cGAN)-based DT module, operating as a replica of the real world, is meticulously designed to generate virtual state-action transition pairs and enrich data diversity, enabling offline adjustment of the agent’s policy. Additionally, we address the out-of-distribution issue by incorporating an extra penalty term into the loss function design. The convergency of agent-DT interaction and the upper bound of the Q-error function are theoretically derived. Numerical results demonstrate the remarkable performance of our proposed approach, which significantly reduces online interaction overhead while maintaining effective beam selection across diverse conditions including strict false alarm control, low signal-to-noise ratios, and high target velocities.

Accurate sensing channel estimation is fundamental to high-performance integrated sensing and communication (ISAC), as it supplies critical information for target detection and localization. Despite extensive research, most existing approaches rely on the unrealistic assumption of ideal hardware conditions. However, hardware impairments are often inevitable due to the use of cost-efficient circuit components. This highlights the necessity for robust estimation techniques that remain reliable under imperfect conditions. To this end, we propose a self-supervised model-fusion network (SMF-Net) tailored for sensing channel estimation in hardware-impaired ISAC systems. To suppress distortions induced by hardware non-idealities, we design a two-stage cascaded convolutional neural network that leverages the spectral bias of neural networks, i.e., their tendency to learn high response frequency details in shallow layers and low frequency information in deep layers, to better separate different types of distortions present in corrupted channel estimates. By analyzing domain-specific features of the distorted channel components, we introduce a hybrid-domain denoising strategy that effectively exploits spatial correlations and angular sparsity inherent in the channel model. Furthermore, the framework is trained in a self-supervised manner, obviating the need for clean channel labels. Theoretical analysis validates the effectiveness of the proposed method and demonstrates that the self-supervised training strategy can match the performance of its supervised counterpart given a sufficiently large training set. Numerical results confirm the superiority of the proposed SMF-Net across various challenging scenarios, including severe nonlinear distortions, low transmission power, and limited training data.

AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators. While there is a growing consensus that intelligent network self-driving holds the key, it heavily relies on expert experience and knowledge extracted from network data. In an effort to facilitate convenient analytics and utilization of wireless big data, we introduce the concept of knowledge graphs into the field of mobile networks, giving rise to what we term as wireless data knowledge graphs (WDKGs). However, the heterogeneous and dynamic nature of communication networks renders manual WDKG construction both prohibitively costly and error-prone, presenting a fundamental challenge. In this context, we propose an unsupervised data-and-model driven graph structure learning (DMGSL) framework, aimed at automating WDKG refinement and updating. Tackling WDKG heterogeneity involves stratifying the network into homogeneous layers and refining it at a finer granularity. Furthermore, to capture WDKG dynamics effectively, we segment the network into static snapshots based on the coherence time and harness the power of recurrent neural networks to incorporate historical information. Extensive experiments conducted on the established WDKG demonstrate the superiority of the DMGSL over the baselines, particularly in terms of node classification accuracy.

Deterministic low-latency communication is critical for emerging applications such as industrial automation and autonomous driving. Cell-free (CF) is a promising network architecture for deterministic delay communications, owing to its user-centric cooperative transmission. In this paper, we address the joint optimization problem of resource allocation and beam selection for deterministic delay in the CF network. Specifically, we propose a delay-aware hierarchical distributed deep reinforcement learning (DRL) framework that enables distributed decision-making and improves scalability in the CF network. This framework incorporates a safe reinforcement learning (RL) algorithm to effectively address the delay constraint. Simulation results demonstrate that the proposed scheme improves spectral efficiency and reduces the delay violation ratio, achieving a delay violation ratio of 10−4at a system load of around 90%, an order of magnitude lower than the 10−3achieved by the modified largest weighted delay first (MLWDF) scheme.

In unmanned aerial vehicle (UAV) communications, beam tracking faces significant challenges due to persistent variations in mobile users’ positions and flight attitudes. Fixed tracking periods result in either unnecessary or insufficient overhead depending on whether the UAV’s relative motion is static or dynamic. To address these challenges, this paper proposes a two-level beam tracking scheme enabled by data-driven beam prediction. In the upper layer, a time-series prediction method is used to adaptively adjust the tracking interval by forecasting confidence intervals of future communication quality. In the lower layer, a threshold-based probing beam selection method is implemented, which selects probing beams whose conditional likelihoods exceeding a predefined threshold. Simulation results demonstrate that the total beam training overhead drop 43.37 %, while the average outage probability decreases from 6.229 % to 0.095 %.

In this paper, a two-stage intelligent scheduler is proposed to minimize the packet-level delay jitter while guaranteeing delay bound. Firstly, Lyapunov technology is employed to transform the delay-violation constraint into a sequential slot-level queue stability problem. Secondly, a hierarchical scheme is proposed to solve the resource allocation between multiple base stations and users, where the multi-agent reinforcement learning (MARL) gives the user priority and the number of scheduled packets, while the underlying scheduler allocates the resource. Our proposed scheme achieves lower delay jitter and delay violation rate than the Round-Robin Earliest Deadline First algorithm and MARL with delay violation penalty.

The emergence of extremely large-scale MIMO (XLMIMO) has made target sensing in near-field environments crucial. However, the vast number of antennas and the big size of near-field dictionary matrix (DM) result in substantial pilot overhead for beam training algorithms and significant computational complexity for subspace algorithms. Moreover, there is few of work capable of accurately obtaining information beyond target location, such as radar cross-section (RCS). To this end, we propose a high-precision, low-pilot-overhead off-grid compressive sensing (CS) algorithm capable of jointly estimating target's location and RCS-the Multi-Layer Reconstruction Linear Approximation Variational Bayesian Inference (MLR-LA-VBI) algorithm. Specifically, the entire algorithm is divided into two phases. In the first phase, we propose the Multi-Layer Reconstruction (MLR) mechanism to reconstruct a surrogate DM. In the second phase, based on the surrogate DM, thus proposing the MLR-LA-VBI algorithm for joint estimation of target location and RCS. The final simulation results verify the superior performance of the proposed algorithm.

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.

This paper proposes a cluster-based low-complexity codebook design scheme for hierarchical near-field beam training in extremely large-scale MIMO(XL-MIMO), referred to as the cluster hierarchical beam training (CHB). Specifically, to reduce the codebook dimensionality while preserving essential angle and distance information, the proposed CHB scheme employs a cluster-based approach to identify cluster centers as new polar-domain sampling points. By doing so, CHB significantly reduces the complexity of codeword generation. The generated codewords are then applied to hierarchical beam training, thereby further decreasing the associated training overhead. Finally, simulation results confirm that CHB reduces complexity while offering comparable or even superior performance to other codebook-based near-field beam training schemes.

Cell-free massive multiple-input multiple-output (CF-mMIMO) networks improve spectral efficiency via coordinated transmission and flexible beam selection. However, the resource allocation in such networks presents a high-dimensional optimization challenge due to the distributed architecture with multiple access points and antennas. To address this, we first formulate a beam selection problem, and then propose an efficient Q-value mixing (QMIX)-based algorithm. Furthermore, recognizing the inherent limitations of deep reinforcement learning (DRL) in practical applications, such as costly training, risky exploration phases, and suboptimal convergence speeds, we design a data-driven digital twin (DT) framework to optimize the DRL training phase. Simulation results show that our approach achieves accelerated convergence and enhanced stability compared to conventional methods. DT-based pre-training establishes a robust performance lower bound prior to real-system deployment.

With the rapid development of 5 G, managing resources to achieve high throughput and low latency has become an increasingly prominent challenge. In this paper, we propose an alternating deep reinforcement learning (ADRL) framework for dynamic resource allocation in cell-free systems, aiming to enhance the sum-rate and reduce delay violation probability by optimizing the association between access points (APs) and user equipments (UEs), along with subcarrier allocation, beamforming, and power control. To achieve efficient resource management while simplifying decision-making, the ADRL framework employs diverse deep reinforcement learning (DRL) networks to alternately optimize specific resources at different time granularities. Simulation results demonstrate improvements in both throughput and delay performance, highlighting the potential of the proposed framework in advancing industrial wireless communication systems.

In this paper, we study the joint detection and angle estimation problem for beamspace multiple-input multipleoutput (MIMO) systems with multiple random jammers. An iterative low-complexity generalized likelihood ratio test (GLRT) is proposed by transforming the composite multiple hypothesis test on the projected vector into a series of binary hypothesis tests based on the spatial covariance matrix. In each iteration, the detector implicitly inhibits the mainlobe effects of the previously detected jammers by utilizing the estimated angles and average jamming-to-signal ratios. This enables the detection of a new potential jammer and the identification of its corresponding spatial covariance. Simulation results demonstrate that the proposed method outperforms existing benchmarks by suppressing sidelobes of the detected jammers and interference from irrelevant angles, especially in medium-to-high jamming-to-noise ratio scenarios.

This paper addresses the compressive sensing (CS)-based frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) downlink channel estimation problem in dynamic scenarios. We propose a multipath angle of arrival (AoA) and angle of departure (AoD)-based shared dictionary learning (MASDL) algorithm, where the discriminative and shared features in the angular domain are exploited via supervised dictionary learning, enhancing the generalization ability of the model. Simulation results show that the proposed algorithm achieves better normalized mean square error (NMSE) performance and substantially reduces the pilot overhead compared with other channel estimation schemes.

Cloud-edge collaboration has become a pivotal approach in edge computing, especially for applications needing real-time inference. This holds particular significance in scenarios where continuous and concurrent inference tasks have stringent latency demands. This paper considers a scenario where dynamically arriving tasks can be partially offloaded to a server with parallel computing for inference. We build a dynamic model for each task's inference process and formulate the problem of minimizing the long-term delay violation rate. To address the strategy problem of joint splitting and batch scheduling for multiple users, we adopt a sequential decision-making approach which effectively lowers the computational complexity. Meanwhile, we innovatively devise a wireless resource pool and a batch scheduling linked list to characterize the system's overall computing and communication resources. On this basis, we can evaluate different task splitting and batch scheduling strategies to make more effective decisions. Simulation results demonstrate a significant improvement in latency guarantees with millisecondlevel execution efficiency and robust scalability across diverse user scales, outperforming baseline methods under heterogeneous workloads.

Radio map, or pathloss map prediction, is a crucial method for wireless network modeling and management. By leveraging deep learning to construct pathloss patterns from geographical maps, an accurate digital replica of the transmission environment could be established with less computational overhead and lower prediction error compared to traditional model-driven techniques. While existing state-of-the-art (SOTA) methods predominantly rely on convolutional architectures, this paper introduces a hybrid transformer-convolution model, termed RM-Transformer, to enhance the accuracy of radio map prediction. The proposed model features a multi-scale transformer-based encoder for efficient feature extraction and a convolution-based decoder for precise pixel-level image reconstruction. Simulation results demonstrate that the proposed scheme significantly improves prediction accuracy, and over a 30% reduction in root mean square error (RMSE) is achieved compared to typical SOTA approaches.

Multi-timeslot multi-user communication scenarios necessitate achieving a balance among system efficiency, user fairness, and reliability under jamming attacks. Millimeter-wave (mmWave) technology can achieve high data rates, but its weak penetration capability and security vulnerabilities make the practical anti-jamming schemes critical to mitigate the attacks. Due to the time-varying nature of wireless channels, statistical quality of service (QoS) provisioning is critical for supporting real-time wireless communication. In this paper, we design a practical anti-jamming strategy for a multi-timeslot multi-user mm Wave system. By jointly designing beamforming and user scheduling, we formulate a cumulative sum-rate maximization problem subject to statistical QoS provisioning. To address the system causality and channel state uncertainty, we introduce residual performance vectors and a penalty function, which transforms the original problem into finite time domain dynamic programming. The problem is subsequently discretized to reduce computational complexity. The proposed algorithm achieves 15% and 22% performance gains over the proportional fair and greedy subcarrier allocation schemes, and approaches the ideal programming algorithm.

Ultra-reliable and low-latency communication (URLLC) with statistical quality-of-service (QoS) guarantees has garnered increasing attention. Cell-free massive multiple-input multiple-output (CF-mMIMO) emerges as a promising network architecture for such applications. This paper analyzes the reliability and latency-constrained performance in CF-mMIMO uplink transmission. First, we derive the closed-form expression for the signal-to-plus-noise ratio (SNR) distribution with maximum ratio combining (MRC) under independent non-identically distributed (i.n.i.d.) Rayleigh fading channels, precisely characterizing large-scale fading effects. Subsequently, we develop closed-form approximations for both decoding error probability and effective capacity. Finally, the QoS exponent is obtained by solving the effective bandwidth-effective capacity equation under Poisson traffic arrivals, thereby providing the delay violation probability expression. Monte Carlo simulations validate the theoretical results and demonstrate that the derived results achieve accurate approximations for both decoding error probabilities and delay violation probabilities. Furthermore, they highlight significant enhancements in reliability and latency assurance enabled by CF-mMIMO architectures.

The quality of channel state information (CSI) feedback is critical for maximizing the spectral efficiency of massive multiple-input multiple-output systems. With multiple antenna arrays, the overhead of direct CSI feedback in frequency division duplex mode is usually large, and many CSI compression techniques have been proposed to alleviate this problem. Deep learning (DL) has achieved tremendous strides in CSI feedback. However, most current DL-based CSI compression methods utilize fully connected layers to achieve dimensionality reduction, which may be suboptimal for network optimization and result in noteworthy information loss and reduced CSI reconstruction accuracy. In this paper, we propose a novel recursive discretization compression framework with a selective state space model for CSI feedback, namely CsiMamba-RDC. The framework employs improved residual vector quantization to recursively refine CSI representation, reducing information loss and storage overhead. Additionally, we present an encoder-decoder model leveraging a selective state space model to extract diverse channel features.

In this paper, we investigate the hybrid traffic scheduling problem in 5G-TSN integrated systems, to achieve the endogenous deterministic communication for 5G. A novel strategy namely time-slot orchestration is proposed, and a latencyand jitter-aware traffic scheduling problem with the objective of maximizing the resource utilization is further formulated. To the best of our knowledge, it is the first time for such a problem being studied in the context of 5G-TSN integration. To solve this complex combinatorial optimization problem, a low-complexity heuristic scheduling algorithm is elaborately designed and extensively evaluated. Experimental results show that, compared with the optimal method derived from the genetic algorithm (GA) and the method based on the classical earliest deadline first (EDF) scheduling, the proposed method can efficiently enhance the endogenous deterministic communication capability of 5G by orders of magnitude, especially under high loads.

In this paper, we consider real-time beamforming design for dynamic wireless environments with different channel state information (CSI) distributions in cell-free systems. Specifically, a sum-rate maximization optimization problem for different CSI distributions is built to model the beamforming design of dynamic wireless environments in cell-free systems. To efficiently solve the optimization problem, we propose a high-generalization network (HGNet). By preserving invariant features and discarding sensitive features for different CSI distributions, HGNet effectively improves the generalization performance of beamforming design for dynamic wireless environments in cell-free systems. Numerical results demonstrate that HGNet achieves a higher sum rate with a lower reflection time for different CSI distributions, thus realizing real-time beamforming design for dynamic wireless environments in cell-free systems.

High-dimensional time series data are becoming more widespread in many domains, including large-scale wireless networks for communication. However, because of its high dimensionality, label scarcity, and complicated temporal connections, anomaly detection in such data is difficult. This work proposes a Bayesian network conditional normalizing flow model for multivariate time series anomaly detection, called Graphormer-based Bayesian Network Conditional Normalizing Flow (GBNCNF), based on a graph Transformer (Graphormer) to convert the spatial and temporal dependencies of high-dimensional time series into simple evaluable conditional densities. It models the causal links between numerous time series using a Bayesian network, and it obtains representations of the interdependencies between different time series by combining LSTM modules with Graphormer modules. These representations are introduced as conditional information into the normalizing flow for density estimation, and data corresponding to low density are judged as anomalies. Experiments are conducted on two real datasets and show that our method detects anomalies more accurately than baseline methods, accurately captures the correlations between sensors, and allows users to infer the root causes of detected anomalies.

Network slicing is a key enabler for 6G mobile networks. Guaranteeing the service level agreement with the smallest amount of radio resources is a challenging problem in network slicing scenarios due to random traffic patterns and the channel environment. To this end, we propose a novel deep reinforcement learning algorithm named CGDSAC based on the conditional generative diffusion model to achieve the optimization objective while capturing the underlying environment distribution. Subsequently, we further design a digital twin (DT) enhanced version of CGDSAC named CGDSAC-DT, to address issues that CGDSAC is unsafe or has lower performance than the default strategy in the early training stages, and converges slowly. Numerical results show that our proposed method can solve the issues encountered and outperform the baseline algorithm regarding performance metrics.

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.

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.

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.

Extremely large-scale antenna arrays (ELAAs) introduce a new communication paradigm called near-field communications, where users are likely to operate in the near-field region of the base-stations (BSs). In such a region, beam training needs to search both the angle and distance dimensions, leading to a prolonged training process and a coverage hole (dead zone). To cope with this issue, we developed a beam depth-based codebook and training scheme for near-field ELAA systems. As the performance of codebook design is mainly dictated by the array configurations, we study the codebook design considering uniform linear, circular and planar antenna arrays. Specifically, we first offer an integrated model to characterize the near-field channel for the considered array configurations. Then, we propose a novel codebook design guideline by maximizing the overlap depth between the near-field codeword (beam) coverage and near-field region, where the energy spread effect is exploited to obtain the optimal focusing point to improve the beam gain inside the dead zone. Based on this guideline, we respectively construct the beam depth based on two-stage and hierarchical codebooks as well as the corresponding beam training schemes. Numerical simulations show that the proposed codebook based beam training schemes can potentially reduce beam training overhead while improving the success rate and beam gain inside the dead zone.

Integrating wireless sensing capabilities into base stations (BSs) has become a widespread trend in the future beyond fifth-generation (B5G)/sixth-generation (6G) wireless networks. In this paper, we investigate intelligent reflecting surface (IRS) enabled wireless localization, in which an IRS is deployed to assist a BS in locating a target in its non-line-of-sight (NLoS) region. In particular, we consider the case where the BS-IRS channel state information (CSI) is unknown. Specifically, we first propose a separate BS-IRS channel estimation scheme in which the BS operates in full-duplex mode (FDM), i.e., a portion of the BS antennas send downlink pilot signals to the IRS, while the remaining BS antennas receive the uplink pilot signals reflected by the IRS. However, we can only obtain an incomplete BS-IRS channel matrix based on our developed iterative coordinate descent-based channel estimation algorithm due to the “sign ambiguity issue”. Then, we employ the multiple hypotheses testing framework to perform target localization based on the incomplete estimated channel, in which the probability of each hypothesis is updated using Bayesian inference at each cycle. Moreover, we formulate a joint BS transmit waveform and IRS phase shifts optimization problem to improve the target localization performance by maximizing the weighted sum distance between each two hypotheses. However, the objective function is essentially a quartic function of the IRS phase shift vector, thus motivating us to resort to the penalty-based method to tackle this challenge. Simulation results validate the effectiveness of our proposed target localization scheme and show that the scheme’s performance can be further improved by finely designing the BS transmit waveform and IRS phase shifts intending to maximize the weighted sum distance between different hypotheses.

As a key technology in beyond 5G and future 6G communications, RAN slicing can realize differentiated service level agreement (SLA) guarantees. In this paper, we investigate the multi-slice multi-user RAN slicing. A dynamic bandwidth allocation scheme for RAN slicing is proposed based on hierarchical intelligence, where bandwidth pre-allocation and hyper-parameter tuning for MAC layer schedulers are jointly optimized to maximize the system utility, i.e., the weighted sum of spectrum efficiency (SE) and SLA satisfaction ratio (SSR) of different slices. The problem is formulated as a twin-time scale Markov decision process (MDP), where the bandwidth pre-allocation and the scheduler parameter tuning are performed on a long-term scale (e.g., seconds) and a short-term scale (e.g., 100 ms), respectively, for which we propose a hierarchical twin-time scale Dueling deep Q learning network (TTS-DDQN) algorithm. A new reward-clipping mechanism is proposed to get a better trade-off between stabilized training and higher system utility. In order to improve the robustness to time-varying traffic patterns and non-stationary dynamic environments, we further propose a traffic-aware module for more efficient sampling of the experience pool, and a variational adversarial inverse reinforcement learning (VAIRL) module for reward automation design. Extensive simulations show that the traffic-aware TTS-DDQN in stationary scenarios and the VAIRL module embedded TTS-DDQN in non-stationary scenarios outperform existing typical DQN-based algorithms, hard slicing and non-slicing, etc.

Mobile edge computing (MEC) has emerged as a disruptive paradigm that facilitates effective offloading from clouds and enables processing tasks near users. With the surge of mobile data traffic and escalating demands for responsive wireless services, it becomes imperative to enhance trust, security, and efficiency within MEC environments. Blockchain technology, renowned for its immutability, transparency, and security, has proven to be a compelling solution for securing data, enhancing supervision, and fostering trusted collaborations among heterogeneous MEC stakeholders. Despite these advantages, integrating blockchain with MEC also introduces a substantial efficiency bottleneck. Specifically, the blockchain consensus process can result in the aging of critical MEC system information, causing users to perform suboptimal service decisions, thus risking a degradation in service performance. To analyze this bottleneck, we first explore a blockchain-based MEC model that ensures secure task processing across diverse stakeholders. We employ the practical Byzantine fault tolerance (PBFT) consensus to validate and share service statuses, providing critical on-chain references for users to select their preferred target MEC servers. We then introduce the age-of-information (AoI) as a metric of freshness to characterize the aging of status reports, identifying variable consensus delay as a key factor affecting AoI. We reveal the critical impact of AoI on MEC service performance through analysis, challenging the notion that a lower AoI always leads to better performance. Finally, we validate our analysis and findings through comprehensive simulations.

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.

Broadcast beam design and power control are essential for enhancing the network coverage, improving the quality of service (QoS) and reducing the energy consumption in massive multiple-input-multiple-output (MIMO) communications. To improve the broadcasting performance and decrease the power consumption in dynamic scenarios with varying user numbers and distributions, we leverage deep reinforcement learning (DRL) to jointly optimize the beam selection and power control policy, and propose a multi-agent DRL (MA-DRL) framework to address the extremely high action dimension brought by the non-convex combinational multi-objective optimization problem. To reduce the cost of performance fluctuations during the exploration of DRL, we construct a data-driven surrogate model (SM) as a virtual environment for the initial training phase, while using an empirical baseline scheme to ensure acceptable real-time performance. Simulation results demonstrate that the SM-enabled MA-DRL approach not only enhances the coverage and reduces the power consumption, but also enables safe exploration and rapid adaptation to varying user numbers and distributions. Moreover, since the optimization algorithm can interact with the SM much more quickly than the real network, a faster convergence speed can be achieved with the help of the SM.

Network slicing is essential for the sixth-generation mobile networks. Minimizing radio resources while guaranteeing service level agreement (SLA) remains challenging due to random traffic patterns and channel conditions, making policy security enforcement and underlying traffic distribution inference critical research goals. In this paper, to facilitate the design of policy agents for radio resource management, we first design a high-fidelity conditional generative diffusion model (CGDM)-driven digital twin network (DTN) to provide closed-loop interaction and pre-validation capabilities. The DTN consists of a safety-bound coarse correction method to enhance strategy SLA compliance, a model market for agent warm-up and decision-level pre-validation, and a virtual interaction environment for high-fidelity agent pre-optimization. Then, a CGDM-driven safe reinforcement learning agent based on constrained multi-agent Markov decision process, termed CGD safe actor-critic (CGDSAC), is proposed to manage inter-slice radio resources. CGDSAC balances safety and strategy quality via Lagrangian primal-dual optimization and a behavior cloning objective targeting DTN-corrected strategies, while capturing latent traffic patterns. Furthermore, CGDSAC comprehensively leverages policy warm-up, decision-level pre-validation, and policy-level pre-optimization capabilities of DTN to resolve early inferior performance, SLA jitter, and slow convergence. Numerical results confirm that the built DTN exhibits good fidelity. Under fixed slices and stable traffic pattern, the DTN-enhanced approaches outperform the best baseline with an average SLA violation relative reduction of 71.3% and an average resource block utilization relative degradation of 10.9%, achieve about convergence speed enhancement of 80% compared to native CGDSAC, and is capable of adapting to the scenarios of dynamic number of slices and varying traffic patterns through knowledge transfer and pre-validation.

To improve channel estimation (CE) for reconfigurable intelligent surface (RIS)-assisted systems in time-varying mmWave channels, this paper proposes a two-stage adaptive CE scheme. This is the first attempt to develop a CE scheme without assumptions on specific timescales for channel variations. In the first stage, the adaptive scheme incorporates the estimation of partial channel state information and a channel status check process. The introduced check process can monitor the changing status of the channels and provide information for the second stage. In the second stage, based on the results from the check process, the adaptive scheme adaptively selects from two proposed candidate CE algorithms: Two-Phase orthogonal matching pursuit (TP-OMP) and Structured-Shift OMP (SS-OMP). Simulation results show that both TP-OMP and SS-OMP can reduce pilot overhead by around 33%, and respectively lower the computational complexity of existing works by about 55% and 65%. Additionally, the check process obtains an accuracy rate of approximately 92% so that the proposed CE scheme can maintain stable CE performance in time-varying channels.

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.

The deployment of extremely large-scale antenna array (ELAA) brings higher spectral efficiency and spatial degree of freedom, but triggers issues on near-field channel estimation. Inspired by the success of deep learning (DL) in far-field channel estimation, this paper proposes a novel spatial-attention-based method to reconstruct extremely large-scale MIMO (XL-MIMO) channel. Initially, the spatial antenna correlation in near-field channels is drawn as the expectation over spatial region, different from only over spatial angle in far-field channels. The spatial antenna correlation implies that the near-field channel exhibits spatial nonstationarity, that the inter-antenna correlation vary with the antenna index and spatial regions and reveals the weakness of the widely applied convolutional neural network (CNN) with fixed receptive field. Subsequently, we develop a multi-scale spatial attention network (MsSAN) with low computational cost to enhance near-field MIMO channel estimation. In MsSAN, the channel is refined to subchannels of different scales layer by layer and each subchannel is treated as a whole and the spatial attention (SA) map is calculated by the sum of dot products of inter-subchannel so that the complexity grows linearly with channel size. Simulation results are presented to validate the proposed MsSAN with low computational cost outperforms others in terms of near-field channel reconstruction.

Knowledge discovered from telecom data can facilitate proactive understanding of network dynamics and user behaviors, which in turn empowers service providers to optimize cellular traffic scheduling and resource allocation. Nevertheless, the telecom industry still heavily relies on manual expert intervention. Existing studies have been focused on exhaustively exploring the spatial-temporal correlations. However, they often overlook the underlying characteristics of cellular traffic, which are shaped by the sporadic and bursty nature of telecom services. Additionally, concept drift creates substantial obstacles to maintaining satisfactory accuracy in continuous cellular forecasting tasks. To resolve these problems, we put forward an online cellular traffic prediction method grounded in Multi-Grained Spatial-Temporal feature Complementarity (MGSTC). The proposed method is devised to achieve high-precision predictions in practical continuous forecasting scenarios. Concretely, MGSTC segments historical data into chunks and employs the coarse-grained temporal attention to offer a trend reference for the prediction horizon. Subsequently, fine-grained spatial attention is utilized to capture detailed correlations among network elements, which enables localized refinement of the established trend. The complementarity of these multi-grained spatial-temporal features facilitates the efficient transmission of valuable information. To accommodate continuous forecasting needs, we implement an online learning strategy that can detect concept drift in real-time and promptly switch to the appropriate parameter update stage. Experiments carried out on four real-world datasets demonstrate that MGSTC outperforms eleven state-of-the-art baselines consistently.

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.

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.

In this paper, we consider real-time beamforming design for dynamic wireless environments with varying channels and different numbers of access points (APs) and users in cell-free systems. Specifically, a sum spectral efficiency (SE) maximization optimization problem is formulated for the beamforming design in dynamic wireless environments of cell-free systems. To efficiently solve it, a high-generalization network (HGNet) is proposed to adapt to the changing numbers of APs and users. Then, a high-generalization beamforming module is also designed in HGNet to extract the valuable features for the varying channels, and we theoretically prove that such a high-generalization beamforming module is able to reduce the upper bound of the generalization error. Subsequently, by online adaptively updating about 3% of the parameters of HGNet, an online adaptive updating (OAU) algorithm is proposed to enable the online adaptive real-time beamforming design for improving the sum SE. Numerical results demonstrate that the proposed HGNet with OAU algorithm achieves a higher sum SE with a lower computational cost on the order of milliseconds.

In this paper, we consider robust joint access point (AP) clustering and beamforming design with imperfect channel state information (CSI) in cell-free systems. Specifically, we jointly optimize AP clustering and beamforming with imperfect CSI to simultaneously maximize the worst-case sum rate and minimize the number of AP clustering under power constraint and the discrete constraint of AP clustering. Through transformations, the intractable simultaneous optimization of continuous and discrete variables is reduced to optimizing only the sparsity of the continuous variables, facilitating a computationally efficient unsupervised deep learning algorithm. In addition, to further reduce the computational complexity, a computationally effective unsupervised deep learning algorithm is proposed to implement robust joint AP clustering and beamforming design with imperfect CSI in cell-free systems. Numerical results demonstrate that the proposed unsupervised deep learning algorithm achieves a higher worst-case sum rate under a smaller number of AP clustering with computational efficiency.

In this paper, we propose the decentralized likelihood ascent search (DLAS)-aided detection for the distributed large-scale multiple-input multiple-output (MIMO) systems to achieve more remarkable performance gains. With the help of DLAS, traditional distributed iterative methods are able to achieve better performance than the linear detection schemes such as ZF and MMSE. According to analysis, we derive the equivalent noise and the post-processing SNR for DLAS. More importantly, based on them, we demonstrate that the proposed DLAS-aided detection achieves the full received diversity. To further facilitate its implementation in practice, we design the decentralized effective ring (DER) architecture with significantly reduced bandwidth requirement and better parallel computation. Finally, simulation results demonstrate that the proposed DLAS-aided detection attains the same received diversity as ML detection while surpassing state-of-the-art decentralized schemes in terms of BER performance, with reduced complexity and bandwidth costs.

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.

Extremely large-scale multiple-input multiple-output (XL-MIMO) wideband systems may exhibit the severe delay spread, due to its spatial- and frequency-wideband (dual-wideband) effects. The typical orthogonal frequency division multiplexing (OFDM) technology have to insert a larger number of cyclic prefix (CP) to overcome the inter-symbol interference (ISI) induced by delay spread. The additional CP overhead will counteract the improvement of spectral efficiency by the large antenna array. To address the issue, this paper proposes a structured OFDM (SOFDM) modulation approach to reduce the CP overhead for wideband XL-MIMO systems with dual-wideband effects. As the ability to perform SOFDM is affected by the antenna architecture, we study the modulation technique considering different antenna structures, including fully-digital, phase shifter-based hybrid array, and dynamic metasurface antenna (DMA) architectures. Specifically, we first provide a mathematical model to represent a near-field channel with dual wideband effects. Based on the channel model, we develop the SOFDM modulation and then propose a joint spatial precoding and frequency domain equalization scheme to maximize the system spectral efficiency, where the solutions of precoding/combining and equalization matrices are derived for the three types of antenna array architectures. Numerical simulations indicate that the proposed scheme can effectively deal with the dual-wideband effects and significantly improve the spectral efficiency with low CP overhead.

The deployment of extremely large-scale MIMO (XL-MIMO) and short-wavelength signaling enhances communication capabilities and improves spectrum efficiency for future sixth-generation (6G) wireless communication. However, users may potentially be located in the near-field region due to the sharp increase in antenna array aperture. In the near-field region, the signal wave is spherical wave. Thus, the consideration of spatial angle and distance requires the development of novel channel estimation algorithms to reduce codebook overhead. This paper develops a novel scheme based on a low-size adaptive codebook to reconstruct the near-field channel. Initially, it is investigated that the angle spread for one channel path component is confined to a certain angular spatial region, which demonstrates the sparsity inherent in angular domain. Exploiting the angular sparsity inherent, we propose a novel adaptive joint sparse Bayesian learning (JSBL) estimation algorithm on all subcarriers to cater to reduce the codebook size. The proposed algorithm captures all spatial angular sparse information and then refines distance information so that the measurement codebook size only depends on the spatial angular resolution. Further, the proposed adaptive JSBL approach is extended to estimate the time-varying near-field channel. Moreover, Bayesian Cramér-Rao Bounds (BCRBs) are derived for quasi-static and temporal scenarios. Numerical simulations are presented to demonstrate that our approaches with low codebook overhead outperform other algorithms based on the angular-domain and polar-domain codebooks.

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. In this study, we address this challenge by reformulating the problem of angle-of-arrival (AoA) estimation into image reconstruction of spatial spectrum. To this end, we design a model-driven deep neural network (MoD-DNN), which can automatically calibrate the angular-dependent phase error. The proposed MoD-DNN approach employs an iterative optimization scheme between a convolutional neural network and a sparse conjugate gradient algorithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in enhancing spectrum calibration and AoA estimation.

With the advancement of network function virtualization, 5G RAN slice’s baseband processing functions, such as distributed unit and centralized unit, can be implemented via virtual machines in processing pools (PPs). When traffic demand decreases, we can sleep the low-utilized PPs and migrate the slice requests they serve to other PPs for energy savings. However, migrations of RAN slice can cause service interruptions, leading to degraded Quality of Service (QoS). Existing works have addressed the energy-efficient and QoS-guaranteed RAN slice migrations problems effectively. However, their scheduling schemes are based on fixed-time intervals, which inevitably leads to the over-provisioning issue. To ensure service quality, fixed-time interval scheduling requires resource allocation based on the maximum demand within a time interval, leading to resource wastage. To address this issue, we propose an Adaptive Threshold-Triggered Heuristic-assisted Deep Reinforcement Learning (ATT-HADRL) algorithm that schedules RAN slice migrations in response to tidal traffic demands. Simulation results confirm that the proposed ATT-HA-DRL algorithm not only reduces power consumption and minimizes resource wastage but also decreases the number of scheduling events and shortens the total migration time, thereby maintaining high service quality and outperforming fixed-interval scheduling approaches.

The deployment of extremely large-scale arrays and high-frequency signaling holds the potential to enhance communication capabilities and improve spectrum efficiency. However, channel estimation faces challenges due to the simultaneous consideration of spatial angles and distances, leading to storage constraints and energy spread. To cope with this issue, we analyze the sparsity inherent in beamspace domain representation and introduce an adaptive codebook scheme for extremely large-scale massive MIMO (XL-MIMO) channels. In this work, we transform multi-band channel estimation to sparse matrix recovery problem. Then, a novel adaptive joint sparse Bayesian learning algorithm is proposed to capture the angular-domain information and refine distance information iteratively without increasing codebook overhead for XL-MIMO channel estimation. Simulation results demonstrate our approach outperforms other algorithms based on the sampling angular-distance domain codebook with low codebook overhead.

The Langevin sampling method relies on an accurate score matching while the existing massive multiple-input multiple output (MIMO) Langevin detection involves an inevitable singular value decomposition (SVD) to calculate the posterior score. In this work, a massive MIMO sampling detection strategy that leverages the denoising diffusion model is proposed to narrow the gap between the given iterative detector and the maximum likelihood (ML) detection in an SVD-free manner. Specifically, the proposed score-based sampling detection strategy, denoted as approximate diffusion detection (ADD), is applicable to a wide range of iterative detection methods, and therefore entails a considerable potential in their performance improvement by multiple sampling attempts. On the other hand, the ADD scheme manages to bypass the channel SVD by introducing a reliable iterative detector to produce a sample from the approximate posterior, so that further Langevin sampling is tractable. Customized by the conjugated gradient descent algorithm as an instance, the proposed sampling scheme outperforms the existing score-based detector in terms of a better complexity-performance trade-off.

In future mobile networks, the demand for real-time, accurate localization of multiple signal sources is paramount, but the facilities are often resource-constrained and the deploying environments are complex. In this context, we present a lightweight deep neural network in this work, which is tailored for multi-source angle-of-arrival (AoA) estimation under low signal-to-noise-ratio (SNR) conditions. The network employs mobile inverted bottleneck convolution (MBConv), known for its enhanced feature extraction capabilities and resilience to noise. By leveraging a scale attention mechanism, we effectively integrate the outputs of each layer without the need for neural architecture search. Trained on multi-channel data under low SNR, the network formulates angle estimation as a multi-label classification task. Experimental results confirm that, the proposed network demonstrates superior accuracy in extreme noise conditions and with limited snapshots, outperforming existing methodologies in multi-source scenarios.

Evolving next-generation mobile networks is designed to provide ubiquitous coverage and networked sensing. With utility of multi-view sensing and multi-node joint transmission, cell-free is a promising technique to realize this prospect. This paper aims to tackle the problem of access point (AP) deployment in cell-free systems to balance the sensing accuracy and user rate. By merging the D-optimality with Euclidean criterion, a novel integrated metric is proposed to be the objective function for both max-sum and maxmin problems, which respectively guarantee the overall and lowest performance in multi-user communication and target tracking scenario. To solve the corresponding high dimensional non-convex multi-objective problem, the Soft actor-critic (SAC) is utilized to avoid risk of local optimal result. Numerical results demonstrate that proposed SAC-based APs deployment method achieves 20% of overall performance and 120% of lowest performance.

Dual-functional radar-communication (DFRC) is a promising direction in the future integrated sensing and communication system. The joint radar and communication (JRC) beamforming scheme is recently developed in DFRC systems. To address the JRC beamforming challenge, conventional approaches predominantly rely on convex optimization methods, which severely depend on precise channel estimation and entail a high computational complexity. Motivated by this, a deep learning-based optimization approach is investigated for tackling the JRC beamforming problem. To enhance the overall performance, we design a deep alternating neural network architecture. Simulation results verify that our proposed method guarantees the required sensing performance and outperforms numerical algorithms in terms of the average data rate of communication users.

In this paper, we propose a distributed solution to address the beam selection problem in millimeter-wave (mmWave) Cell-Free Massive multiple-input multiple-output (CF-mMIMO) systems. To realize the real-time optimization under dynamic environments with high performance and low complexity, we formulate the sum-rate maximization problem in mmWave CF-mMIMO as a Markov decision process and leverage deep reinforcement learning (DRL) to optimize the beam decision. Furthermore, we propose a distributed solution via multi-agent DRL (MA-DRL) framework to handle the extremely high action dimension and reduce the fronthaul requirements for communications between the access points (APs). Numerical simulations demonstrate the superiority of the proposed solution in the real-time sum-rate performance for mobile users as well as the fronthaul requirements reduction brought by the distributed architecture.

In millimeter wave (mmWave) systems with the typical two-stage hybrid precoding structure, it is necessary to determine the optimal transmit (TX)-and-receive (RX) beam pairs through beam training. However, existing beam training methods are time-consuming and costly, making them unsuitable for cell-free mmWave systems with a large number of TX-RX pairs. In this paper, we propose a fast and efficient beam training method that adopts a hierarchical codebook design and requires only a small number of wide-sweeping beams to achieve the optimal beam pairs based on deep learning and compressed sensing. Additionally, it simultaneously supports the user-centric cell-free access point (AP) clustering and the corresponding AP beam training process. The experimental results demonstrate that our approach can accurately predict the effective beams with high accuracy while significantly reducing the beam training time and overhead.

In the context of Ultra-Reliable Low Latency Communication (URLLC) scenarios, 5G incorporates numerous enhancements, with link adaptation (LA) being one of them. In the pursuit of reliability, a measurement-prediction-decision approach can be considered to enhance the accuracy of Modulation and Coding Scheme (MCS) decisions during LA, specifically by forecasting interference. In this paper, a two-stage uplink interference prediction algorithm is proposed. In the first stage, complex uplink interference values are decomposed to extract inherent patterns. In the second stage, leveraging the prior knowledge provided by the first stage, which enhances the algorithm's robustness and accuracy, inference is made. The experimental results demonstrate that the proposed interference prediction algorithm not only exhibits a significant improvement in accuracy but also contributes to a substantial enhancement in the performance of the communication system.

The cell-free network (CFN) is a promising technology capable of delivering high-reliability, high-data rate wireless communication services for Metaverse communication. This paper studies a joint optimization problem of user scheduling (US) and beamforming (BF) in CFN, where constraints of per access point (AP) power and the limited number of the scheduled users per AP are considered. In order to reduce the interaction overhead, this problem is investigated using local channel state information (CSI). Since this problem is a mixed-integer nonlinear programming (MINP) program with non-convexity and high complexity, we propose an alternating optimization framework to solve this problem. Specifically, we first adopt the weighted$l_{1}$-norm approximation to transform the discrete variables into the continuous variables. Then, we solve the rest of the problem by fractional programming, and solve the subproblems alternatively. The analysis of complexity and convergence analysis validate the efficiency and accuracy of the proposed algorithm. Numerical results show that the cross-layer design of the US&BF scheme is superior to the separate design of US&BF schemes. In addition, the proposed algorithm with local CSI achieves a comparable data rate to the algorithms with global CSI.

This paper concentrates on addressing the challenging problem of sensing channel estimation in cell-free integrated sensing and communication (ISAC) multiple-input multiple-output (MIMO) system. This challenge arises from the complex mixture of signals from both the direct sensing channel and target reflected sensing channel. To tackle this challenge, we introduce the digital twin (DT), as a powerful tool to exploit and characterize the inherent features of the target sensing channel by sampling data from the real world and interacting with it. To be specific, the DT model, designed as a generative adversarial network (GAN), is trained to be capable of generating the desired results from the coarse observations, where the distribution of the sensing channel in a particular cell-free ISAC system is implicitly learned via the adversarial process. With this basis, we propose a novel digital-twin-enabled channel estimation (DTE-CE) approach to enhance the performance of channel estimation, where the DTE-CE network is meticulously designed by utilizing the virtual channel matrix (VCM) model to facilitate the estimation process. Simulation results show the excellent performance of the proposed approach, as well as the effectiveness of our designed DTE-CE network, in terms of sensing channel estimation with different transmitting power and numbers of targets.

With the global promotion of fifth-generation (5G) communications, researches for sixth generation (6G) communications are being globally launched from various perspectives. Industrial Internet of Things (IIoT), which is the most representative application that reflects the ubiquitous connectivity characteristics of 6G, has attracted much attention. To explore for a feasible perspective in promoting ubiquitous connectivity and improving IIoT abilities, in this article, we seek solutions to incorporate two promising techniques, i.e., nonorthogonal multiple access (NOMA) and statistical signal transmission (SST). To accomplish such a motivation, we conceive the feasible transceiver architecture for the union of NOMA-SST technique, and elaborate workflow and detection procedures for both uplink and downlink modes. A dedicated resilient window strategy is designed thereafter, which largely strengthen weaker NOMA users’ SST performance without depressing stronger users. The proposed technique is finally testified through both simulation experiments and practical applications. Numerical results verify that NOMA-SST technique has satisfactory detection performance in common IIoT environments. It is also manifested that the proposed technique can refine various aspects, including recall rate and sensing accuracy of monitoring services, successful handling rate of emergency situations, etc., for practical IIoT applications. Such advantages are promising for practice.

Currently, there is a growing trend in deploying ubiquitous artificial intelligence (AI) applications at the network edge. As a promising framework that enables secure edge intelligence, federated learning (FL) has been paid attention, where the over-the-air computing technique has been adopted to enhance the communication efficiency. In this study, we focus on over-the-air FL over a large-scale network with numerous edge devices. Joint device selection and aggregate beamforming design is investigated under two different objectives, i.e., minimizing the aggregate error and maximizing the number of selected devices. Two combinatorial problems are formulated, which are demanding to solve especially in the large-scale network. To reduce the computational complexity, a random aggregate beamforming scheme is proposed, which employs random sampling instead of optimization to determine the aggregator beamforming vector. Notably, the implementation of the proposed scheme does not necessitate the full channel estimation. Asymptotic analysis reveals that the aggregate error asymptotically follows a Gaussian distribution, and the number of selected devices approximates a symmetrical distribution. The distribution parameters are explicitly expressed by the transmit power, the numbers of devices and selected devices. Simulation results confirm the theoretical analysis and demonstrate the effectiveness of the proposed random aggregate beamforming scheme.

Extremely large-scale multiple-input multiple-output (XL-MIMO) communication systems introduce a new communication paradigm called near-field communications, which identifies users’ location within the near-field (Fresnel’s region). In the near-field, beams can be steered in the angle and distance dimensions, resulting in an enormous codebook and a prolonged two-dimensional beam alignment (BA) process. To keep a low BA overhead while achieving low BA error, in this paper, we design a novel hierarchical codebook and a BA scheme for near-field XL-MIMO systems. Specifically, we first propose a novel spatial partition where the angle-offset effect is revealed and leveraged to improve the beam gain inside the coverage area. Based on the partition, we design distance-coarse and focusing beams. Distance-coarse beams are leveraged to construct the high level of the codebook for angle dimension alignment. In contrast, focusing beams construct the last level codebook for distance dimension alignment. Corresponding to the proposed codebook structure, our BA scheme is a tree search consisting of two stages: the angle aligning stage and the distance aligning stage. Next, we formulate the desired codebook design problem as difference convex optimization problems, where three beam design guidelines are considered to minimize the BA error rate raised by the near-field angle-offset effect. After that, the proposed optimization problem is solved by the constrained concave-convex procedure. Numerical simulations verify the angle offset effect and our designed near-field beam. Furthermore, we show that our BA scheme only utilizes one percent of overhead but achieves a lower BA error rate than exhaustive searching.

Intelligent reflecting surface (IRS) is a promising new paradigm for enhancing wireless information transmission (WIT) and wireless power transfer (WPT) cost-effectively in the future. In this paper, we study an IRS-aided full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid node (HN) operating in FD mode sends information signals to multiple devices in the downlink (DL), and meanwhile receives energy signals from a power station (PS) in the uplink (UL), both of which are assisted by an IRS. Our objective is to boost the weighted sum throughput by jointly optimizing the active transmit beamformer at the PS and HN, along with the passive reflection coefficients of the IRS. To deal with the formulated non-convex optimization problem with intricately coupled design variables, most of existing works employ the alternating optimization (AO) method, whose performance, however, is closely related to parameter initialization. In contrast, we develop two novel penalty-based algorithms for the single-device and multi-device cases, respectively. In particular, our proposed rank-one constraint reformulation method of matrix proves to be efficient, especially for the case where the objective function is a higher-order function of the IRS phase shifts. Numerical results demonstrate the superiority of our proposed design over benchmark schemes, and also unveil the necessity of the joint design of passive IRS beamforming and resource allocation for achieving better WPCN performance. Moreover, we draw useful insights into the fine-tuning of IRS deployment location in the studied WPCN.

In this paper, a deterministic sampling decoding strategy for multiple-input multiple output (MIMO) systems is studied, which performs probabilistic searching according to a probability threshold in the lattice Gaussian distribution. Motivated by model probabilistic twin (MPT), the randomness in obtaining the target decoding solution is overcome by the proposed probabilistic searching decoding (PSD) algorithm, which brings considerable decoding gains in both performance and complexity. Specifically, the decoding radius of PSD is derived while the decoding complexity in terms of the number of visited nodes during the searching is also upper bounded, leading to an explicit decoding trade-off. Meanwhile, we generalize PSD by the mechanism of candidate protection so that it enjoys a flexible performance between the suboptimal successive interference cancelation (SIC) decoding and the optimal maximum likelihood (ML) decoding by adjusting the initial search size$K$. Methods for further optimization and complexity reduction of the proposed PSD algorithm are also given. Finally, simulation results based on MIMO detection are presented to confirm the tractable and flexible decoding trade-off of the proposed PSD algorithm.

Beam selection for joint transmission in cell-free massive multi-input multi-output systems faces the problem of extremely high training overhead and computational complexity. The traffic-aware quality of service additionally complicates the beam selection problem. To address this issue, we propose a traffic-aware hierarchical beam selection scheme performed in a dual timescale. In the long-timescale, the central processing unit collects wide beam responses from base stations (BSs) to predict the power profile in the narrow beam space with a convolutional neural network, based on which the cascaded multiple-BS beam space is carefully pruned. In the short-timescale, we introduce a centralized reinforcement learning (RL) algorithm to maximize the satisfaction rate of delay w.r.t. beam selection within multiple consecutive time slots. Moreover, we put forward three scalable distributed algorithms including hierarchical distributed Lyapunov optimization, fully distributed RL, and centralized training with decentralized execution of RL to achieve better scalability and better tradeoff between the performance and the execution signal overhead. Numerical results demonstrate that the proposed schemes significantly reduce both model training cost and beam training overhead and are easier to meet the user-specific delay requirement, compared to existing methods.

In frequency division duplexing (FDD) cell-free massive MIMO, the acquisition of the channel state information (CSI) is very challenging because of the large overhead required for the training and feedback of the downlink channels of multiple cooperating base stations (BSs). In this paper, for systems with partial uplink-downlink channel reciprocity, and a general spatial domain channel model with variations in the average port power and correlation among port coefficients, we propose a joint-port-selection-based CSI acquisition and feedback scheme for the downlink transmission with zero-forcing precoding. The scheme uses an eigenvalue-decomposition-based transformation to reduce the feedback overhead by exploring the port correlation. We derive the sum-rate of the system for any port selection. Based on the sum-rate result, we propose a low-complexity greedy-search-based joint port selection (GS-JPS) algorithm. Moreover, to adapt to fast time-varying scenarios, a supervised deep learning-enhanced joint port selection (DL-JPS) algorithm is proposed. Simulations verify the effectiveness of our proposed schemes and their advantage over existing port-selection channel acquisition schemes.

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.

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.

The deployment of large-scale arrays in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems has enabled highly accurate localization by reaping the benefits of high angular resolution. However, the use of massive antennas in mmWave systems results in expensive hardware costs and computational burdens. This paper considers mobile user localization in quantized mmWave MIMO systems, where each base station (BS) antenna is equipped with low-resolution analog-to-digital converters. The proposed approach integrates the beamspace model with off-grid information to capture channel sparsity in the angular domain. The temporal correlation of angle-of-arrival (AoA) is characterized by a Markov process for moving users. To estimate channel gains and time-varying AoAs, while keeping the computational complexity low, we further develop generalized approximate message passing and AoA tracking methods. In dense multipath environments, determining line-of-sight (LoS) paths for precise localization poses a challenge. To address this issue, we propose a fast direct localization based on LoS identification that can also be applied when the LoS paths of some BSs are obstructed. In the final stage, the moving user locations are recovered via triangulation. Simulation results validate the effectiveness of the proposed algorithms and showcase the feasibility of implementing quantized mmWave systems for localization purposes.

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.

In this paper, the projected-gradient-descent (PGD) -based detector for massive MIMO system, which consists of two basic operations — projection and gradient descent (GD), is studied to achieve the performance improvement. Since the projection and GD step have different loss functions, necessary compromise has to be made to balance them during iterations. For this reason, the generalized PGD (GPGD) method is proposed with flexible choices of projection and GD. Different from performing projection and GD alternatively, we show that implementing projection after every multiple GD steps is a better solution. Meanwhile, the step-size of GD is also investigated for convergence efficiency. After that, by unfolding this proposed GPGD method with deep neural networks (DNN), the self-corrected auto-detector (SAD) is established to achieve better decoding performance, where enhancement by attention mechanism and extension by another iterative method are also given for performance improvement and efficiency.

Accurate channel estimation is an essential prerequisite for achieving significant performance gains in intelligent reflecting surface (IRS)-aided communication systems. Recent studies have shown that deep neural network-based channel estimation holds promise as a competitive alternative to conventional methods. However, existing neural network-based approaches typically involve manual design of network architectures through a trial-and-error process, demanding extensive domain knowledge and human resources. In this paper, we propose an automatic approach to construct a high-performance neural network architecture for channel estimation. Our method, called the channel estimation neural network architecture search (CENAS), utilizes a truncated back-propagation optimization search strategy to explore a neural network tailored for channel estimation. By carefully designing a search space tailored to channel estimation tasks, the automatically constructed network surpasses both conventional and deep learning-based channel estimation algorithms. The convergence of our framework’s network construction process is comprehensively analyzed, providing formal evidence of its convergence properties. Additionally, the proposed framework exhibits good generalization and applicability by allowing flexible adjustment of hyperparameters to generate networks with varying scales. Empirical results show the stability and the improved performance of CENAS framework, validating its effectiveness and desirability.

In this paper, we study low-complexity linear precoding for downlink massive multiple-input multiple-output (MIMO) systems, exploiting a statistical method. In sharp contrast to traditional linear precoding algorithms, our proposed efficient randomized iterative precoding algorithm (ERIPA) not only avoids costly matrix inversion but also considers the complexity reduction of matrix multiplication involved, thus enabling more efficient linear precoding. Additionally, ERIPA is demonstrated to have both exponentially fast and global convergence, making it adaptable to various practical scenarios of massive MIMO. We also investigate the convergence phenomenon of ERIPA in relation to the selection of the sampling distribution during random iterations. After that, the concept of conditional sampling is introduced to ERIPA such that significant system potential can be beneficially exploited in terms of both precoding performance and computational complexity. Finally, simulation results regarding the downlink massive MIMO are presented to confirm the superiorities of the proposed ERIPA.

In this paper, the cross-layer optimization problem of access point selection (APS) and beamforming (BF) in cell-free network (CFN) with local CSI is studied, where constraints of per AP power and the number of active APs are considered. Such a joint APS&BF optimization problem is modeled as a mixed-integer nonlinear programming (MINP) problem aiming at maximizing the sum rate of the whole system. Fractional programming (FP)-based and alternating optimization (AO)-based algorithms with weightedl1-norm approximation are proposed to solve this MINP problem. However, the latter performs better than the former, with higher complexity. A lightweight multi-head single-body graph neural network (MHSB-GNN) algorithm is proposed, where the nodes and structures are innovatively designed. The MHSB-GNN benefits from the different node updating modules for different user equipment (UE), which introduce extra prior information into the graph and mine specific information of different UEs. Moreover, the equivalence between GNN and FP-based algorithm is proved to provide interpretability and theoretical guarantees for MHSB-GNN. The analysis of convergence and complexity validates the accuracy and effectiveness of the FP and AO-based algorithms. Leveraging the existing APS and BF solver, it is shown that the three proposed algorithms guarantee comparable performance as the exhaustive search algorithm in performance and complexity.

Dual-functional radar-communication (DFRC) is a promising technology in future integrated sensing and communication systems. Since communication and sensing performance need to be taken into consideration for joint radar and communication (JRC) beamforming in the DFRC system, existing approaches mainly transform JRC beamforming problems into convex optimization problems and then solve them with classical convex solvers. These traditional solutions heavily rely on precise channel estimation and entail high computational complexity. In this paper, we investigate a deep learning-based optimization approach for JRC beamforming to enhance the spectral efficiency for communication users and guarantee the probability of detecting targets. To achieve better performance, we leverage the theoretical optimal structures of JRC beamforming and design an effective deep neural network architecture. To further reduce the computational burden in the training phase of neural network, we develope an improved orthogonal beamforming technique. Simulation results verify that our proposed algorithm guarantees the required sensing performance and outperforms numerical algorithms in terms of communication performance. The orthogonal beamforming technique achieves satisfactory performance with low computational complexity.

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.

Interleaved training has been studied for single-user and multi-user massive MIMO downlink with either fully-digital or hybrid beamforming. However, the impact of channel correlation on its average training overhead is rarely addressed. In this paper, we explore the channel correlation to improve the interleaved training for single-user massive MIMO downlink. For the beam-domain interleaved training, we propose a modified scheme by optimizing the beam training codebook. The basic antenna-domain interleaved training is also improved by dynamically adjusting the training order of the base station (BS) antennas during the training process based on the values of the already trained channels. Exact and simplified approximate expressions of the average training length are derived in closed-form for the basic and modified beam-domain schemes and the basic antenna-domain scheme in correlated channels. For the modified antenna-domain scheme, a deep neural network (DNN)-based approximation is provided for fast performance evaluation. Analytical results and simulations verify the accuracy of our derived training length expressions and explicitly reveal the impact of system parameters on the average training length. In addition, the modified beam/antenna-domain schemes are shown to have a shorter average training length compared to the basic schemes.

RAN slicing is a key technology in 5G communications for realizing differentiated service level agreement (SLA) guarantee. In this paper, we investigate the multi-slice multi-user RAN slicing. A dynamic bandwidth allocation scheme for RAN slicing is proposed based on hierarchical intelligence, where bandwidth pre-allocation and hyper-parameter tuning for MAC-layer schedulers are jointly optimized to maximize the system utility, i.e., the weighted sum of spectrum efficiency (SE) and SLA satisfaction ratio (SSR) of different slices. The problem is formulated as a twin-time scale Markov decision process (MDP), where the bandwidth pre-allocation and the scheduler parameter tuning are performed on a long-term scale (e.g., seconds) and a short-term scale (e.g., 100 ms), respectively, for which we propose a hierarchical twin-time scale Dueling deep Q learning network (TTS-DDQN) algorithm. And a new reward-clipping mechanism is proposed to get a better trade-off between stabilized training and higher system utility. In order to improve the algorithm robustness to time-varying traffic pattern, we further propose a traffic-aware module for more efficient sampling of the experience pool. Extensive simulations show that the traffic-aware TTS-DDQN outperforms existing typical DQN based algorithms, hard slicing and non slicing, etc.

Acquiring the wireless channel state information (CSI) is an essential task to reap the wireless system performance gain brought by intelligent reflecting surface (IRS). In this paper, we study an IRS-assisted wireless energy transfer (WET) network, where an energy receiver (ER) harvests the wireless energy transmitted from an energy transmitter (ET) with the help of an IRS. Different from the commonly adopted wireless CSI acquisition approaches such as pilot or codebook based methods, we propose a novel channel learning method that requires only one-bit feedback information from the ER. Specifically, each feedback bit indicates whether the increase or decrease of the harvested energy amount at the ER within the present interval as compared to the previous one. Based on the feedback information, the ET continually adjusts its transmit beamforming in subsequent channel learning intervals to help infer the cascaded ET-IRS-ER CSI. It is worth noting that an optimization technique named analytic center cutting plane method (ACCPM) is applied in the channel learning phase. Numerical results unveil that our proposed one-bit feedback based channel estimation method is able to effectively estimate the cascaded ET-IRS-ER channel, and greatly reduce the requirement on the hardware complexity of the ER simultaneously.

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.

By sharing the same hardware platform, spectral resource as well as transmit waveform, dual-functional radar-communication (DFRC) based integrated sensing and communication (ISAC) framework has been envisioned as a key technology for future wireless networks. Most DFRC beamforming works focus on block-level precoding, which fails to exploit constructive interference. To tackle this issue, we propose symbol-level joint radar sensing and communication beamforming algorithms in this paper. First, we formulate the problem of joint radar-communication beamforming based on symbol-level precoding (SLP) by incorporating constructive interference into SLP, so as to improve the energy efficiency. To address the formulated problem, we tailor a highly parallelizable iterative algorithm, which is shown to converge to stationary points. To achieve better performance, we further propose an efficient recursive optimization algorithm. In particular, the recursive algorithm monotonously improves the performance of interest as the recursive procedure proceeds.

In this paper, we consider an intelligent reflecting surface (IRS)-aided full-duplex (FD) wireless-powered communication network (WPCN), where a hybrid access point (HAP) operating in FD mode sends information signals to a device in the downlink (DL) and meanwhile receives energy signals from a power station (PS) in the uplink (UL) with the help of an IRS. Our objective is to maximize the achievable data rate from the HAP to the device by jointly optimizing the transmit covariance matrix at the PS, the transmit beamforming vector at the HAP, and the phase shift vector at the IRS. The optimal transmit beamformer at the HAP is derived in closed from, and the joint optimization of the transmit covariance matrix at the PS and the phase shift vector at the IRS results in an intractable non-convex problem. To tackle this challenge, we propose an efficient penalty-based algorithm consisting of two layers. In the inner layer, we iteratively increase the device's signal-to-interference-plus-noise ratio (SINR) by applying the Dinkelbach's transform. While in the outer layer, we gradually decrease the penalty parameter. Numerical results demonstrate the superiority of our proposed design over benchmark schemes, and also unveil the necessity of the joint design of passive IRS beamforming and active beamforming for achieving better WPCN performance.

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.

Accurate channel estimation is an indispensable prerequisite for intelligent reflecting surface (IRS) aided communication systems to achieve huge system performance gains. Current works show that deep neural network-based channel estimation is a promising solution to achieve competitive performance compared with the conventional methods. However, neural network-based approaches generally realize the channel estimation by manually designing network architectures in a trial-and-error manner which need complex neural network domain knowledge and tremendous computation resource. This paper automatically constructs a high-performance neural network architecture to obtain dedicated channel estimation schemes intelligently. Specifically, we propose a channel estimation neural network architecture search (CENAS) method based on gradient alternatively search strategy to search a channel estimation neural network. With the search space designed meticulously for the channel estimation task, the network searched by the proposed method outperforms the conventional and deep learning-based channel estimation algorithms.

In this paper, a cross-layer optimization problem of access point selection (APS) and beamforming (BF) in cell free network (CFN) has been studied, where constraints of per access point (AP) power and per user receiving data streams are considered. Such a cross-layer design of APS&BF problem is modeled as a mixed-integer nonlinear programming (MINP) program. Then, by adopting the weighted l1-norm approximation, the MINP problem is transformed into the sum logarithmic multiple-ratio form. To be specific, a novel and low-complexity mix-integer fractional programming (MIFP) algorithm is proposed to solve the transformed problem effectively. Convergence analysis validates that the proposed MIFP converges to a local optimal solution. Finally, numerical results show that cross-layer design of APS&BF scheme is superior to separate design of APS&BF schemes. In addition, the proposed MIFP has the approximate performance as partial exhaustive search 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.

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.

Joint access point (AP) clustering and beamforming design is an effective way to improve system performance and reduce signaling overhead for cell-free networks. However, conventional optimization methods usually solved the joint AP clustering and beamforming design by separately handling them, at the cost of high computing resources, especially when quality of service (QoS) constraint is also considered. To this end, this paper proposes a low-complexity unsupervised deep learning method to jointly optimize AP clustering and beamforming design, called as joint clustering and beamforming network (JcbNet). The JcbNet also designs a neural network to handle the QoS constraint to reduce the hyperparameters of loss function, and it introduces a learnable safety distance parameter in the loss function to reduce the violation rate of QoS constraint. In addition, the JcbNet is scalable since the dimensions of parameters and output beamforming vary with the dimension of input channel state information (CSI). The experimental results show that the JcbNet is low-complexity, and achieves a higher sum rate under a smaller number of AP clustering compared to traditional and deep learning algorithms such as weighted minimum mean square error (WMMSE), sparse WMMSE (S-WMMSE) and convolutional neural network (CNN).

User scheduling and beamforming design are two crucial yet coupled topics for multiuser wireless communication systems. They are usually addressed separately with conventional optimization methods. In this paper, cross-layer optimization problem is considered, namely, the user scheduling and beamforming are jointly discussed, subjecting to the requirement of per-user quality of service and the maximum allowable transmit power for multicell multiuser joint transmission networks. To achieve the goal, a mixed discrete-continuous variables combinational optimization problem is investigated with aiming at maximizing the sum rate of the communication system. To circumvent the original non-convex problem with dynamic solution space, we first transform it into a 0–1 integer and continuous variables optimization problem, and then obtain a tractable form with continuous variables by exploiting the characteristics of the 0–1 integer constraints. Finally, the scheduled users and the optimized beamforming vectors are simultaneously calculated by an alternating optimization algorithm. We also theoretically prove that the base stations allocate zero power to the unscheduled users. Furthermore, two heuristic optimization algorithms are proposed respectively based on brute-force search and greedy search. Numerical results validate the effectiveness of our proposed methods, and the optimization approach gets relatively balanced results compared with the other two approaches.

In this paper, we propose a robust max-min fairness transmission design for intelligent reflecting surface (IRS)-aided wireless network in the presence of user location uncertainty. In particular, the non-isotropic reflection property for the IRS element is considered. We investigate the joint design of the active transmit beamformer at the base station (BS) and the passive phase shift matrix along with the deployment orientation (facing/pointing direction) of the IRS for maximizing the worst-case minimum signal-to-interference-plus-noise ratio (SINR) received by the users. In order to show the potential gains obtained by adjusting the deployment orientation of the IRS, a single-input-single-output (SISO) system is studied where a closed-form signal-to-noise ratio (SNR) of the user is obtained. For the multi-user case, to solve the resulting non-convex problem, an inexact-alternating-optimization algorithm consisting of a double-loop iteration is proposed. Specifically, in the inner loop, an optimization problem with semi-infinite constraints needs to be solved to increase the worst-case min-SINR compared to the given SINR reference value. We first transform the semi-infinite constraints into linear matrix inequality (LMI) constraints with finite form by applying the Taylor expansion approximation method, the general S-procedure, and the general sign-definiteness lemma. Then an efficient alternating optimization (AO) algorithm based on the two-dimensional search method, negative square penalty (NSP) method, and successive convex approximation (SCA) technique is proposed. While in the outer loop, we update the given SINR reference value as the worst-case minimum SINR obtained after each inner loop iteration. The whole algorithm terminates when the updated SINR reference values converge. Simulation results demonstrate the effectiveness of the proposed algorithm, and also show the additional system performance gain brought by the optimization of the IRS deployment orientation compared to its counterpart with fixed IRS deployment orientation, especially for a smaller IRS element number and a more prominent non-isotropic reflection property of the IRS element.

In this paper, rapidly converging low-complexity iterative transmit precoding (TPC) techniques are proposed for the massive multiple-input multiple-output (MIMO) downlink. First of all, the proposed random block-based iterative TPC (RBI-TPC) algorithm performs its iterations by updating multiple rather than a single component at each instant, where the updating order of each block containing multiple components relies on the samples randomly sampled from a discrete distribution. Based on the analytically derived convergence rate, we demonstrate that improved convergence is achieved by the block-based update mechanism conceived since the correlation between multiple components can be beneficially exploited. Then, the random sampling that determines the updating order is studied. By applying conditional random sampling, the updating order is optimized based on the latest updates for attaining more rapid convergence. We also demonstrate that the associated updating order may become deterministic under specific conditions so that a fixed but optimized updating order can be used for facilitating the practical implementations, which paves the way for conceiving the ordered block-based iterative TPC (OBI-TPC) algorithm. Finally, the concept of successive over-relaxation (SOR) is adopted for further convergence improvement and simulations are presented to illustrate the performance improvements of the proposed RBI and OBI TPC algorithms compared to the existing low-complexity iterative TPC schemes.

In this paper, a new randomized iterative detection algorithm (NRIDA) is proposed for uplink large-scale MIMO systems, where the random iterations in it are designed to work for the detection model (denoted by$\mathbf {y}=\mathbf {Hx}+\mathbf {n}$) directly. Different from those traditional iterations designed for the linear system (denoted by$\mathbf {Ax}=\mathbf {b}$with$\mathbf {A}=\mathbf {H}^{H}\mathbf {H}$and$\mathbf {b}=\mathbf {H}^{H}\mathbf {y}$), we show that besides the complexity reduction about the matrix inversion, in the proposed NRIDA the computational complexity of matrix multiplication for the linear detection is also greatly reduced without any performance loss, thus leading to a much lower detection complexity. Meanwhile, according to convergence analysis, we demonstrate that the proposed NRIDA enjoys a globally exponential convergence performance, enabling it well suited to the various detection cases of interest. Besides, further complexity reduction and the choices of the sampling distribution in NRIDA are studied as well in full details. Moreover, in order to achieve a better detection trade-off between performance and complexity, we introduce the concept of the conditional sampling into NRIDA, which brings significant gains in both iteration convergence and efficiency. Finally, simulations with respect to the uplink large-scale MIMO detection are presented to illustrate the remarkable gains of the proposed NRIDA in both performance and complexity.

In this paper, we investigate joint user (UE) scheduling, base station (BS) clustering and beamforming design in a dense network. To reduce the computation burden, we propose a deep unfolding UE scheduling, BS clustering and beamforming (DU-USBCB) method based on the iterative weighted minimum mean square error (WMMSE) algorithm, where the UE scheduling and BS clustering are represented by the group sparsity of the transmit beamforming vectors. The proposed DU-USBCB neural network layer comprises receive coefficient, weight and transmit beamforming vector modules, the former two of which have closed-form expressions. For the third module, the group sparse transmit beamforming vectors are obtained by multiple steps of projected gradient descent and nonlinear sparsification, where the step-sizes are learned through unsupervised learning. We also propose a distributed UE selection (DUS) algorithm, which helps reduce the computation workload. Simulation results verify the effectiveness of the proposed DU-USBCB and DUS methods. The learned step-sizes are directly applied in the testing scenarios with different numbers of UEs, BSs and antennas as well as incomplete channel state information. Besides, our proposed methods can achieve comparable performance but with about 53% and 89% computation reduction compared to the RSRP-WMMSE and SWMMSE methods respectively.

Large antenna arrays and beamforming are necessary for the mmWave communication system, resulting in heavy time and energy consumption in the beam training stage. Therefore, dual-band operations are expected to be deployed in future communication systems, where low-frequency channels are used to meet basic communication needs, and millimeter wave (mmWave) channels are exploited when the high-rate transmission is required. Existing works utilize deep learning methods to extract low-frequency channel state information (CSI) to reduce the mmWave beam training overheads. However, an important limitation of deep learning approaches is that the model is usually trained in a given environment. When employed in an unseen environment, it usually requires a large amount of data to retrain. In this paper, a model-agnostic optimization algorithm based on meta-learning is proposed to provide a general mmWave beam prediction model. This model can be deployed to edge base stations and effectively adapted to the environment without the need for a heavy collection of data. Simulation results demonstrate that the proposed approach could reduce the model adaptation overheads. The meta-learning-based beam prediction model is robust and achieves high prediction accuracy and spectral efficiency in different signal-to-noise ratio (SNR) regimes.

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.

Millimeter wave (mmWave) cell-free MIMO achieves an extremely high rate while its beam alignment (BA) suffers from excessive overhead due to a large number of transceivers. Recently, user location and probing measurements are utilized for BA based on machine learning (ML) models, e.g., deep neural network (DNN). However, most of these ML models are centralized with high communication and computational overhead and give no specific consideration to practical issues, e.g., limited training data and real-time model updates. In this paper, we study the probing beam-based BA for mmWave cell-free MIMO downlink with the help of broad learning (BL). For channels without and with uplink-downlink reciprocity, we propose the user-side and base station (BS)-side BL-aided incremental collaborative BA approaches. Via transforming the centralized BL into a distributed learning with data and feature splitting respectively, the user-side and BS-side schemes realize implicit sharing of multiple user data and multiple BS features. Simulations confirm that the user-side scheme is applicable to fast time-varying and/or non-stationary channels, while the BS-side scheme is suitable for systems with low-bandwidth fronthaul links and a central unit with limited computing power. The advantages of proposed schemes are also demonstrated compared to traditional and DNN-aided BA schemes.

Beam training and tracking (BTT) are key technologies for millimeter wave communications. However, since the effectiveness of BTT methods heavily depends on wireless environments, complexity and randomness of practical environments severely limit the application scope of many BTT algorithms and even invalidate them. To tackle this issue, from the perspective of stochastic process (SP), in this paper we propose to model beam directions as a SP and address the problem of BTT via process inference. The benefit of the SP design methodology is that environment priors and uncertainties can be naturally taken into account (e.g., to encode them into SP distribution) to improve prediction efficiencies (e.g., accuracy and robustness). We take the Gaussian process (GP) as an example to elaborate on the design methodology and propose novel learning methods to optimize the prediction models. In particular, beam training subset is optimized based on derived posterior distribution. The GP-based SP methodology enjoys two advantages. First, good performance can be achieved even for small data, which is very appealing in dynamic communication scenarios. Second, in contrast to most BTT algorithms that only predict a single beam, our algorithms output an optimizable beam subset, which enables a flexible tradeoff between training overhead and desired performance. Simulation results show the superiority of our approach.

Deep neural network-based learning methods have been considered promising techniques used in beam selection problems. However, existing research ignores the peculiar vulnerabilities of neural networks. The adversaries can use data poisoning to embed predefined triggers into a model during training time such that the neural network-based beam model may make an incorrect output decision of a test example when patched with the trigger. Data poisoning offers attackers the possibility to build backdoors. The goal of backdoors is often unethical, such as giving users a poor experience by manipulating infected models to output inappropriate beams. In this paper, first, we introduce a simple backdoor attack method by using data poisoning in a mmWave beam selection system. By numerical simulations, we verify that this poisoning attack is effective for neural networks with different structures. In addition, we explore the effect of poisoned data volume on the effect of backdoor attacks. The results show that the backdoor can be successfully implanted into the beam selection neural network. Besides, we fine-tune the trained model for a new wireless communication environment, and the results show that backdoors still exist even when the model is tuned with data from new scenarios. Then, we propose a machine unlearning solution to mitigate the backdoor of the trained beam selection model. The problem of eliminating backdoors is modeled as a minimax optimization problem. We propose a novel adversarial unlearning method along with label smoothing to solve the backdoor removal problem. We compared the proposed backdoor elimination method with the classical fine-tuning elimination method and the neural network pruning method through numerical simulations. The results show that the fine-tuning and the pruning methods cannot effectively remove the backdoor. The proposed machine unlearning method can make the trained model forget about the backdoor under the condition that the performance of the benign task (beam selection tasks when the trigger does not appear) is guaranteed to be slightly degraded. In summary, our work illustrates that data poisoning-based backdoor attacks may exist in wireless networks, and we propose a scheme to eliminate backdoors.

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 multiuser communication systems, user scheduling and beamforming (US-BF) design are two fundamental problems that are usually studied separately in the existing literature. In this work, we focus on the joint US-BF design with the goal of maximizing the set cardinality of scheduled users, which is computationally challenging due to the non-convex objective function and the coupled constraints with discrete-continuous variables. To tackle these difficulties, a successive convex approximation based US-BF (SCA-USBF) optimization algorithm is firstly proposed. Then, inspired by wireless intelligent communication, a graph neural network based joint US-BF (J-USBF) learning algorithm is developed by combining the joint US and power allocation network model with the BF analytical solution. The effectiveness of SCA-USBF and J-USBF is verified by various numerical results, the latter achieves close performance and higher computational efficiency. Furthermore, the proposed J-USBF also enjoys the generalizability in dynamic wireless network scenarios.

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 large scale reflector array of programmable metasurfaces is capable of increasing the power efficiency of backscatter communications via passive beamforming and thus has the potential to revolutionize the low-data-rate nature of backscatter communications. In this paper, we propose to design the power-efficient higher-order constellation and reflection pattern under the amplitude constraint brought by backscatter communications. For the constellation design, we adopt the amplitude and phase-shift keying (APSK) constellation and optimize the parameters of APSK such as ring number, ring radius, and inter-ring phase difference. Specifically, we derive closed-form solutions to the optimal ring radius and inter-ring phase difference for an arbitrary modulation order in the decomposed subproblems. For the reflection pattern design, we propose to optimize the passive beamforming vector by solving a multi-objective optimization problem that maximizes reflection power and guarantees beam homogenization within the interested angle range. To solve the problem, we propose a constant-modulus power iteration method, which is proven to be monotonically increasing, to maximize the objective function in each iteration. Numerical results show that the proposed APSK constellation design and reflection pattern design outperform the existing modulation and beam pattern designs in programmable metasurface enabled backscatter communications.

Deep learning (DL) is an emerging paradigm for accurate channel estimation for intelligent reflecting surface (IRS)-aided wireless communication systems. It has been proven to be a promising way to achieve better channel estimation performance for the IRS-aided wireless communication system than traditional methods (e.g., least-square algorithm). However, existing DL-based methods rely on ground truth (labels of the true channels) which is difficult to obtain in real networks. In this paper, we propose a self-supervised learning (SSL) method for the IRS channel estimation problem. No ground truth channel is needed in the training, while a simple and novel self-supervised denoising formula without a clean reference signal is presented. Particularly, in the training phase, the self-supervised signal and the input are the received signal vector and its noisy version, respectively. While in the inference phase the input is the estimated channel by using the least-square method and the output is the refined channel estimation. That is, our neural network-based channel estimation algorithm is not reciprocal for training and testing. We demonstrate that the proposed SSL solution has good convergence performance and generalization ability through numerical simulations. Interestingly, we find a “double descent” phenomenon in the learning curve during the test phase, i.e., when we gradually increase the number of training epochs, the performance first gets better, then becomes worse, and further gets better again. Besides, we propose to analyze SSL using the loss landscape and centered kernel alignment method. The results show that the self-supervised model has a similar loss landscape and representational similarity to the supervised model. We explored the effects of different signal-to-noise ratios (SNRs), different neural network sizes, and different training data volumes on our algorithm through numerical simulations. Extensive numerical simulation results show that our SSL algorithm is still competitive without ground truth. We also show that the developed scheme exhibits robustness to SNR ratio mismatch.

This paper studies the performance of multi-antenna proactive eavesdropping in 5G uplink systems with spatially correlated Rayleigh fadings, where the base station (BS) serves an illegal user and a multi-antenna legitimate monitor eavesdrops the suspicious link with the help of jamming attack. Based on a practical assumption of channel state information (CSI) in 5G uplink, i.e., imperfect instantaneous CSI of the suspicious link at the BS and that of eavesdropping link at the monitor, and only jamming channel statistics at the monitor, we first give a statistical jamming beamforming design. Then, semi-closed form expressions of eavesdropping non-outage probability and relative average eavesdropping rate are, respectively, derived for delay-sensitive and delay-tolerant scenarios. Via reasonable approximations and asymptotic analysis, we gain many insights on the effect of key system parameters, e.g., location-dependent channel path loss and angular spread, jamming energy and the number of antennas. Further, we provide the optimal energy allocation between pilot and data jamming and the optimal antenna allocation between jamming and eavesdropping under total energy constraint and total antenna number, respectively, which are both explicit functions of system parameters. Finally, simulation results validate our analytical results.

With the application of the fifth-generation wireless communication technologies, more smart terminals are being used and generating huge amounts of data, which has prompted extensive research on how to handle and utilize these wireless data. Researchers currently focus on the research on the upper-layer application data or studying the intelligent transmission methods concerning a specific problem based on a large amount of data generated by the Monte Carlo simulations. This article aims to understand the endogenous relationship of wireless data by constructing a knowledge graph according to the wireless communication protocols, and domain expert knowledge and further investigating the wireless endogenous intelligence. We firstly construct a knowledge graph of the endogenous factors of wireless core network data collected via a 5G/B5G testing network. Then, a novel model based on graph convolutional neural networks is designed to learn the representation of the graph, which is used to classify graph nodes and simulate the relation prediction. The proposed model realizes the automatic nodes classification and network anomaly cause tracing. It is also applied to the public datasets in an unsupervised manner. Finally, the results show that the classification accuracy of the proposed model is better than the existing unsupervised graph neural network models, such as VGAE and ARVGE.

The performance of wireless localization critically depends on the actual radio propagation environment. This paper proposes a novel framework towards environment-aware wireless localization, enabled by the emerging concept known as channel knowledge map (CKM). Specifically, we propose a line-of-sight (LoS) map-enabled environment-aware anchor selection scheme to minimize the Bayesian Cramer-Rao lower bound (BCRLB) of the positioning error. As the formulated problem is combinatorial, we propose an efficient greedy-based algorithm, which selects the best anchor node sequentially by ensuring that each newly selected anchor leads to the maximum reduction to the BCRLB. Simulation results show that the proposed environment-aware anchor selection can significantly outperform the benchmarking environment-ignorant schemes, including the min-distance based selection or simply activating all anchors.

In imperfect multiple-input multiple-output (MIMO) systems, model-based methods for performance prediction and optimization generally experience degradation in the dynamically changing environment with unknown interference and uncertain channel state information (CSI). To adapt to such challenging settings and better accomplish the network auto-tuning tasks, we propose a generic learnable model-driven framework. We further consider transmit regularized zero-forcing (RZF) precoding as a usage instance to illustrate the proposed framework. The overall process can be divided into three cascaded stages. First, we design a light neural network for refined prediction of sum rate based on coarse model-driven approximations. Then, the CSI uncertainty is estimated on the learned predictor in an iterative manner. In the last step the regularization term in the transmit RZF precoding is optimized. The effectiveness of the generic framework and the derivative method thereof is showcased via simulation results.

This paper considers the channel tracking and angle of arrival (AoA) acquisition for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, in which each antenna at the base station is equipped with low-resolution analog-to-digital converters (ADCs) to quantize the received signals. We utilize the beamspace Gauss-Markov model to capture the sparsity and temporal correlation of the time-varying mmWave channel, and an off-grid model is incorporated for an accurate AoA acquisition. Essentially, the beamspace channel tracking is a quantized sparse Bayesian learning problem, which is solved under the expectation maximization (EM) framework. We employ the variational inference to calculate the statistics in the expectation step. In this way, we propose a variational inference joint channel tracking and data detection (VIJ-CTDD) algorithm, in which the detected data symbols are reused to enhance the tracking without extra pilot overhead. Finally, extensive simulations validate the superiority of the proposed VIJ-CTDD algorithms over several existing works.

In this paper, we design a signal detector for OTFS modulation based on transform-domain maximal ratio combining (TD-MRC). The proposed scheme leverages the Doppler diversity and the circulant banded block diagonal structure of the effective channel matrix, which is computationally efficient compared to the traditional MRC detector due to its matrix-inversion-free nature. Another particularly appealing feature of TD-MRC is that its reliability performance improves as the maximal relative velocity increases. Numerous simulation results are presented to demonstrate the superior performance of the proposed method in comparison with the state-of-the-arts.

Orthogonal time frequency space (OTFS) has recently attracted widespread attention for it leverages frequency dispersion as a source of diversity and mathematically unifies the classical multiple-access schemes. However, as a multi-carrier modulation in nature, OTFS is susceptible to the problem of high peak-to-average power ratio (PAPR), especially when the number of symbols is large in order to obtain a high Doppler resolution at the receiver. In this work, we recast the problem of PAPR reduction as constrained optimization of the precoding matrix. To efficiently solve the underlying nonconvex maximum-norm minimization problem, we propose an iterative algorithm based on block coordinate descent. Simulation results show that the proposed method can significantly mitigate the PAPR without unduly compromising the reliability of data transmission.

Abstract This paper investigates a novel wireless information surveillance scheme assisted by reconfigurable intelligent surface (RIS) beamforming and artificial noise jamming cooperation, aiming at monitoring the information sent by an access point (AP) to a suspicious illegal user (SIU). It is assumed that the AP adopt the fixed maximum ratio transmission (MRT) precoding scheme, which is not affected by the information monitoring party. The goal of this paper is to maximize the effective information monitoring rate by jointly optimizing the RIS phase shifts, the receive beamforming vector of the legitimate receiver (LR), and the transmit beamforming vector along with jamming power of the jamming antenna (JA). The resultant optimization problem is non‐convex, and its optimization variables are highly coupled in the objective function and constraints. To tackle this difficulty, the optimization variables are optimized under the alternate optimization (AO) framework. Especially, the intractable RIS phase shifts are optimized by using Riemannian manifold optimization (RMO) algorithm under the penalty dual decomposition (PDD) framework and the semidefinite relaxation (SDR) technique, respectively. Numerical results verify the effectiveness of the proposed algorithms, and also demonstrate the superiority of the designed wireless information surveillance scheme over other benchmark schemes.

In Industrial Internet of Things (IIoT), reliability and latency are two important performance indicators. However, these two performance indicators are contradictory with each other, which are difficult to be enhanced simultaneously. In reality, many IIoT applications are in video or image format with critical requirements of both reliability and latency. In this article, we propose a scalable task offloading scheme for IIoT visual applications considering the unique scalable feature compared with general content. The proposed scheme demonstrates that partial content can be adaptively offloaded to a specific computing node to meet the reliability and latency requirements, meanwhile obtaining an excellent tradeoff between them. For optimization, a utility function is defined to characterize the tradeoff between reliability and latency. Then, a greedy algorithm is introduced to solve the problem of maximizing the utility function, where a near-optimal scheduling policy is adopted to achieve offloading association and properly offload data volume. Simulation results reveal that the proposed scalable task offloading scheme performs better than other benchmark schemes in balancing reliability and latency in IIoT visual applications, especially when the network traffic is moderate and channel conditions are undesirable.

Nowadays, wireless communication is rapidly reshaping entire industry sectors. In particular, mobile-edge computing (MEC) as an enabling technology for the Industrial Internet of Things (IIoT) brings a powerful computing/storage infrastructure closer to the mobile terminals and, thereby, significantly lowers the response latency. To reap the benefit of proactive caching at the network edge, precise knowledge on the popularity pattern among the end devices is essential. However: 1) the spatiotemporal variability of content popularity; 2) the data deficiency in privacy-preserving system; 3) the costly manual labels in supervised learning; as well as 4) the not independent and identically distributed (non-i.i.d.) user behaviors pose tough challenges to the acquisition and prediction of content popularities. In this article, we propose an unsupervised and privacy-preserving popularity prediction framework for MEC-enabled IIoT to achieve a high popularity prediction accuracy while addressing the challenges. Specifically, the concepts of local and global popularities are introduced and the time-varying popularity of each user is modeled as a model-free Markov chain. On this basis, we derive and validate the essential relationship between the local and global popularities and then propose an unsupervised recurrent federated learning (URFL) algorithm to predict the distributed popularity while achieving privacy preservation and unsupervised training. Moreover, a federated loss-weighted averaging (FedLWA) scheme for the parameter aggregation is further designed to alleviate the problem of non-i.i.d. user behaviors. Simulations indicate that the proposed framework can enhance the prediction accuracy in terms of a reduced root-mean-squared error by up to 60.5%–68.7% compared to other baseline methods, i.e., recommendation algorithms, centralized learning algorithms, and other distributed learning algorithms. Additionally, manual labeling and violation of users’ data privacy are both avoided.

The intelligent reflecting surface (IRS) technology is emerged as an enabling technology for beyond fifth-generation systems and Internet of Things networks in which the signal propagation is reconfigured to enhance wireless system performance. IRS consists of many passive elements and each reflecting the incident signal with a certain phase shift to collectively achieve the required beamforming. The IRS is to be a low profile and lightweight setting with a conformal geometry; hence, its position can be easily engineered to achieve certain performance enhancements. In the current literature, however, the flexibility in the IRS position is often overlooked since it is considered as a given fixture. We argue that optimizing the IRS position provides a new degree of freedom in the network design and enables extra performance gain. In this article, we analytically characterize the optimal IRS’s position to maximize the achievable system rate. We then obtain the optimal IRS positions for different IRS settings with fixed height and variable height and consider both cost-efficient equal phase shift IRS, and nonequal phase shift IRS that enables sophisticated beamforming. We further incorporate antenna directivity in our analysis and investigate its effect on the optimal IRS position in each case. Simulation results show that the provided optimal position yields higher performance than settings with random IRS locations. Our results provide significant practical insights on the network coverage design using the IRS.

Mobile edge computing (MEC) is a prominent computing paradigm which expands the application fields of wireless communication. Due to the limitation of the capacities of user equipments and MEC servers, edge caching (EC) optimization is crucial to the effective utilization of the caching resources in MEC-enabled wireless networks. However, the dynamics and complexities of content popularities over space and time as well as the privacy preservation of users pose significant challenges to EC optimization. In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks. Specifically, we consider the fact that content popularities are dynamic, complicated and unobservable, and formulate the maximization of cache hit rates on devices as distributed problems under the constraints of privacy preservation. In particular, we convert the distributed optimizations into distributed model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction. Subsequently, a P2D3PG algorithm is developed based on distributed reinforcement learning to solve the distributed problems. Simulation results demonstrate the superiority of the proposed approach in improving EC hit rate over the baseline methods while preserving user privacy.

In this paper, we consider the problem of joint beam selection and link activation across a set of communication pairs to effectively control the interference between communication pairs via inactivating part communication pairs in ultra-dense device-to-device (D2D) mmWave communication networks. The resulting optimization problem is formulated as an integer programming problem that is nonconvex and NP-hard. Consequently, the global optimal solution, even the local optimal solution, cannot be generally obtained. To overcome this challenge, this paper resorts to design a deep learning architecture based on graph neural network to finish the joint beam selection and link activation, with taking the network topology information into account. Meanwhile, we present an unsupervised Lagrangian dual learning framework to train the parameters of the GBLinks model. Numerical results show that the proposed GBLinks model can converge to a stable point with the number of iterations increases, in terms of the weighted sum rate. Furthermore, the GBLinks model can reach near-optimal solutions through comparing with the exhaustive scheme in small-scale ultra-dense D2D mmWave communication networks and outperforms GreedyNoSched and the SCA-based method. It also shows that the GBLinks model can generalize to varying network densities and network coverage regions of ultra-dense D2D mmWave communication networks.

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.

The fundamental intelligent reflecting surface (IRS) deployment problem is studied for IRS-aided downlink multi-user communication system, where IRSs are arranged to be deployed in a specific area for enhancing the desired signal and suppressing interference. Specifically, we aim to maximize the minimum achievable rate over all locations in a specific area by jointly optimizing the transmit beamforming at the access point (AP) as well as the placement and reflective beamforming at the IRS. The formulated problem is non-convex and thus difficult to be solved directly. To draw essential insights, we first consider the single-user case and the optimal solution is derived in closed-form. The result shows that the optimal locations are in the connecting line between the AP and user, and the IRSs can be optimally deployed along the connecting line. Besides, a hybrid offline and online design scheme is developed for the multi-user case, where an area discretization strategy and deep neural network (DNN)-based curve fitting technique are proposed for optimizing the IRS locations in the offline manner. Then, an online iterative algorithm is presented to solve the transmit and received beamformig vectors, respectively. Numerical results show that the performance gain is increased by optimizing the IRS locations.

Mobile-Edge Computing (MEC) is an emerging paradigm in the Internet of Vehicles (IoV) to meet the ever-increasing computation demands of smart applications. To provide satisfactory computation performance, it is of significant importance to conduct computation offloading in IoV. In this paper, we investigate a multi-vehicle IoV system assisted by MECs with limited computation resources, where vehicles with complex applications can offload their subtasks to MEC servers. Applications are modeled as interdependent subtasks with general random task graphs, different from existing works with independent ones. To maximize the average logarithmic data processing rate (LDPR), the computation offloading problem is formulated as a time-average optimization with long-term constraints, which results from variable vehicle number, various applications and time-varying communication channels. To reduce the cooperation overhead, we propose a multi-agent Proximal Policy Optimization algorithm (Ly-MAPPO) which requires local observation only to solve the subproblems achieved by Lyapunov optimization technique in real time. In addition, to improve the performance of the Ly-MAPPO algorithm, Graph Convolutional Neural Network (GCN) is introduced to extract inter-dependencies between subtasks. Extensive simulations show that the GCN embedded Ly-MAPPO outperforms other baseline algorithms, e.g., greedy algorithm and gene algorithm, etc., for different traffic loads and computation resources in MEC servers.

Hybrid heterogeneous cellular networks (HCNets) with sub-6G and millimeter-wave (mmWave) base stations (BSs) achieves good performance-and-cost tradeoffs. Via noticing the negligible retrieving latency and the potential joint transmission (JT) gain of sub-6G BSs, we propose a novel cache-enabled user association scheme for hybrid HCNets with limited local storage, in which partial sub-6G local storage caches contents with relatively low popularity for higher overall content diversity. To sufficiently exploit the large bandwidth of mmWave BSs, they are endowed with higher association priority. Then, the success probability and meta distribution of achievable rate are studied for the proposed scheme, respectively. We further provide the optimization of caching parameters and the condition for superior performance over the MPC-based scheme. Analytical and numerical results show that the network performance can be improved for larger sub-6G BS cooperation size and the array size at mmWave BSs, while the mmWave BS density has two adverse effect for low-to-medium and high rate threshold. For the proposed scheme, the content diversity in combination with JT gain benefits the success probability. And its performance advantage increases for larger local storage and/or higher association probability with mmWave BSs.

The problem of beam alignment and tracking in high mobility scenarios such as high-speed railway(HSR) becomes extremely challenging, since large overhead cost and significant time delay are introduced for fast time-varying channel estimation. To tackle this challenge, we propose a learning-aided beam prediction scheme for HSR networks, which predicts the beam directions and the channel amplitudes within a period of future time with fine time granularity, using a group of observations. Concretely, we transform the problem of high-dimensional beam prediction into a two-stage task, i.e., a low-dimensional parameter estimation and a cascaded hybrid beamforming operation. In the first stage, the location and speed of a certain terminal are estimated by maximum likelihood criterion, and a data-driven data fusion module is designed to improve the final estimation accuracy and robustness. Then, the probable future beam directions and channel amplitudes are predicted, based on the HSR scenario priors including deterministic trajectory, motion model, and channel model. Furthermore, we incorporate a learnable non-linear mapping module into the overall beam prediction to allow non-linear tracks. Both of the proposed learnable modules are model-based and have a good interpretability. Compared to the existing beam management scheme, the proposed beam prediction has (near) zero overhead cost and time delay. Simulation results verify the effectiveness of the proposed scheme.

In frequency division duplex (FDD) massive multi-input multi-output (MIMO), the two-stage beamforming (TSB) using channel covariance matrices significantly reduces the downlink training length (DTL) and channel feedback. Nevertheless, most of the TSB methods focus on the one-ring channel. In this paper, we consider the multiple scatterer clusters (MSC) channel in massive MIMO systems and propose a TSB method based on cluster group. To reduce the channel state information (CSI) feedback, for each cluster we use a cluster-group-based eigen-prebeamformer to sparsify the effective channel matrix. The DTL is also reduced by a graph-based downlink training design. We further develop a multi-user beamformer to mitigate the inter-user interference. To further reduce the DTL, two other methods are also proposed based on the vertex and edge deletion. Simulation results confirm the efficiency of the proposed schemes in improving the effective spectral efficiency.

Symbol detection (SD) plays an important role in a digital communication system. However, most SD algorithms require channel state information (CSI), which is often difficult to estimate accurately. As a consequence, it is challenging for these SD algorithms to approach the performance of the maximum likelihood detection (MLD) algorithm. To address this issue, we employ both semi-supervised learning and ensemble learning to design a flexible parallelizable approach in this paper. First, we prove theoretically that the proposed algorithms can arbitrarily approach the performance of the MLD algorithm with perfect CSI. Second, to enable parallel implementation and also enhance design flexibility, we further propose a parallelizable approach for multi-output systems. Finally, comprehensive simulation results are provided to demonstrate the effectiveness and superiority of the designed algorithms. In particular, the proposed algorithms approach the performance of the MLD algorithm with perfect CSI, and outperform it when the CSI is imperfect. Interestingly, a detector constructed with received signals from only two receiving antennas (less than the size of the whole receiving antenna array) can also provide good detection performance.

Federated learning (FL) is an emerging paradigm for distributed machine learning that uses the data and the computational power of user devices while maintaining user privacy (e.g., position and motion track). It has been proved a promising way to help the learning-based millimeter wave (mmWave) system achieve efficient link configuration. However, FL systems have an inherent vulnerability to backdoor attacks during training, and this has not received attention in current FL-based beam selection research. The goal of a backdoor attacker is to implant a backdoor in the model such that at test time, the model will mispredict a certain family of inputs, and corrupt the performance of the trained model on specific sub-tasks. We study backdoor attacks in an FL-based beam selection system based on a deep neural network that utilizes user location information. Specifically, we propose a backdoor attack scheme that can be configured in the real world. The attacker’s trigger is an obstacle placed in certain locations. When the model encounters an input with these obstacles, the backdoor will be triggered, and the model will output the beam specified by the attacker. Through experiments, we show that the proposed attack can achieve a high attack success rate in a system without a defense mechanism. Moreover, we show that the traditional norm-clipping defense method cannot effectively defend against our attack. Furthermore, we propose a new backdoor attack defense method and verify the effectiveness of this scheme through experiments. In addition, we propose a backdoor detection method: the federated noise titration method, which can diagnose whether the model has a backdoor. Overall, our work explored backdoor attacks, defenses, and detection of the FL-based mmWave beam selection system.

Non-orthogonal multiple access (NOMA) exhibits superiority in spectrum efficiency which is particularly essential in the Internet of Things (IoT) system involving massive number of device connections. This paper addresses the achievable rate improvement for the downlink NOMA system, in the context of imperfect successive interference cancellation (SIC), by means of the improper Gaussian signaling (IGS) technique. We investigate a basic scenario where the strong user transmits the conventional proper data, while the weak user adopts an improper signaling scheme. The users’ data rates are first formulated in terms of the impropriety degree of the IGS, under residual interference introduced by the imperfect SIC. In this way, analytical expressions for the best improper transmission can be characterized by jointly optimizing the user’s power and the impropriety degree, where their sufficient and necessary conditions are provided. When the strong user transmits with its maximum power, the IGS scheme always increases the achievable rate of the strong user while the weak user may also benefit. When the weak user transmits with its maximum power, such a scheme enables us optimize the achievable rate of the strong user under various levels of channel-to-noise ratios (CNR) and imperfect SIC. Finally, when both the users are imposed by quality of service (QoS) constraints, a Q-learning based solution is proposed to maximize their sum rate. Simulations on the downlink NOMA system support the analysis.

In wireless networks, the optimization problems generally have complex constraints and are usually solved via utilizing the traditional optimization methods that have high computational complexity and need to be executed repeatedly with the change of network environments. In this paper, to overcome these shortcomings, an unsupervised deep unrolling framework based on projection gradient descent (PGD), i.e., unrolled PGD network (UPGDNet), is designed to solve a family of constrained optimization problems. The set of constraints is divided into two categories according to the coupling relations among optimization variables and the convexity of constraints. One category of constraints includes convex constraints with decoupling among optimization variables, and the other category of constraints includes non-convex or convex constraints with coupling among optimization variables. Then, the first category of constraints is directly projected onto the feasible region, while the second category of constraints is projected onto the feasible region using a neural network. Finally, an unrolled sum rate maximization network (USRMNet) is designed based on UPGDNet to solve the weighted SR maximization problem for the multiuser ultra-reliable low latency communication system. Numerical results show that USRMNet has a comparable performance with low computational complexity and an acceptable generalization ability in terms of the user distribution.

In this paper, the random iterative method is introduced to massive multiple-input multiple-output (MIMO) systems for the efficient downlink linear precoding. By adopting the random sampling into the traditional iterative methods, the matrix inversion within the linear precoding schemes can be approximated statistically, which not only achieves a faster exponential convergence with low complexity but also experiences a global convergence without suffering from the various convergence requirements. Specifically, based on the random iterative method, the randomized iterative precoding algorithm (RIPA) is firstly proposed and we show its approximation error decays exponentially and globally along with the number of iterations. Then, with respect to the derived convergence rate, the concept of conditional sampling is introduced, so that further optimization and enhancement are carried out to improve both the convergence and the efficiency of the randomized iterations. After that, based on the equivalent iteration transformation, the modified randomized iterative precoding algorithm (MRIPA) is presented, which achieves a better precoding performance with low-complexity for various scenarios of massive MIMO. Finally, simulation results based on downlink precoding in massive MIMO systems are given to show the system gains of RIPA and MRIPA in terms of performance and complexity.

Beam alignment and tracking for millimeter-wave communication networks in highly mobile scenarios, such as high-speed railway, suffer from large overhead cost and time delay loss. To solve this problem, we propose a learning-aided beam management scheme, which divides the high-dimensional beam prediction procedure into two stages, i.e., parameter estimation and hybrid beamforming. The locations and velocities of the mobile terminals are estimated using the maximum likelihood criterion, and a data fusion module is employed to further improve the estimation accuracy and robustness. Then, the next probable beam directions and the corresponding hybrid precoders are derived based on the estimated parameter set. Numerical simulations show that, the proposed method yields significantly lower overhead cost and time delay compared to the existing beam management scheme.

In this paper, we investigate the problem of edge caching (EC) optimization in a multi-user privacy-preserving mobile edge computing (MEC) system. The time-varying content popularity is considered and the primary objective is to maximize the EC hit rate on each caching entity in the distributed network. To this end, we introduce the concept of local and global popularities and cast the time-varying local popularities as model-free Markov chains. Next, an unsupervised recurrent federated learning (URFL) algorithm is proposed to predict the popularities while achieving privacy-preserving goal. The underlying distributed optimization problem is then reformulated as a distributed Markov decision process and solved by the privacy-preserving distributed deep deterministic policy gradient algorithm incorporating the URFL algorithm. Simulation results demonstrate the superiority of the proposed scheme in terms of prediction error and hit rate over the baseline methods.

Orthogonal time frequency space (OTFS) as an emerging modulation technique in the 5G and beyond era exploits full time-frequency diversity and is robust against doubly-selective channels in high mobility scenarios. In this work, we consider an OTFS modulation based automotive joint radar-communication system and focus on the design of low-complexity parameter estimation algorithm for radar targets. It is well known that target parameter estimation in OTFS radar is computationally much more expensive than the orthogonal frequency division multiplex based platform, which hampers low-cost and real-time implementation. In this context, an efficient Bayesian learning scheme is proposed for OTFS automotive radars, which leverages the structural sparsity of radar channel in the delay-Doppler domain. We also reduce the dimension of the measurement matrix by incorporating the prior knowledge on the motion parameter limit of the true targets. Numerical simulation results are presented to demonstrate the superior performance of the proposed method in comparison with the state-of-the-art.

Image-level weakly supervised semantic segmentation is a challenging task. As classification networks tend to capture notable object features and are insensitive to over-activation, class activation map (CAM) is too sparse and rough to guide segmentation network training. Inspired by the fact that erasing distinguishing features force networks to collect new ones from non-discriminative object regions, we using relationships between CAMs to propose a novel weakly supervised method. In this work, we apply these features, learned from erased images, as segmentation super-vision, driving network to study robust representation. In specifically, object regions obtained by CAM techniques are erased on images firstly. To provide other regions with seg-mentation supervision, Erased CAM Supervision Net (ECS-Net) generates pixel-level labels by predicting segmentation results of those processed images. We also design the rule of suppressing noise to select reliable labels. Our experiments on PASCAL VOC 2012 dataset show that without data annotations except for ground truth image-level labels, our ECS-Net achieves 67.6% mIoU on test set and 66.6% mIoU on val set, outperforming previous state-of-the-art methods.

This paper focuses on the outage performance of a downlink integrated satellite-terrestrial relay network (ISTRN), which includes a satellite (S) transmitting messages to a user (D) with the help of K relays ({Rk, k =1,⋯,K}) using a threshold-based decode-and-forward (DF) transmission protocol. In addition, we consider the case of relay selection based on out-dated channel state information (CSI) to better reflect the actual scenario. The shadowed-Rician (SR) fading and Nakagami-m fading are considered for the channel model of the S-Rklinks and Rk-D links, respectively. In order to reveal the impact of the parameters on the performance of the considered network, the outage probability is adopted as a performance criterion and the corresponding exact closed-form expressions are derived. Further, in order to investigate the influence of system parameters more intuitively, asymptotic outage probabilities are considered for the two cases: 1) high average SNR for S-Rklink and Rk-D link; 2) high average SNR for Rk-D link. The asymptotic analysis results exactly reveal the influence of the relevant system parameters on the decoding gain and the diversity gain. Finally, simulation and numerical results verify the correctness of the analysis.

Ultra-Dense Network (UDN) has become a key technology in 5G communication systems. By deploying low power micro base stations (BSs) densely and flexibly to reduce the distance between access nodes and user equipments (UEs), the spectrum efficiency and energy efficiency of the network can be improved effectively. But at the same time, it also poses new challenges for power control and user association. In this paper, the joint optimization problem of user association and power control of the downlink in a UDN scenario is considered. To make full use of channel information, we build a graph model with UEs as nodes and leverage the Spectral Clustering algorithm for user association. Then we build a graph model with BSs as nodes for the UDN scenario and train an unsupervised graph neural network to achieve power allocation. The analysis of the simulation results verifies the convergence of the proposed scheme which is effective in achieving user association and power control in UDN.

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.

Federated learning (FL) as a promising edge-learning framework can effectively address the latency and privacy issues by featuring distributed learning at the devices and model aggregation in the central server. In order to enable efficient wireless data aggregation, over-the-air computation (AirComp) has recently been proposed and attracted immediate attention. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To combat this effect, the concept of dynamic learning rate (DLR) is proposed in this work. We begin our discussion by considering multiple-input-single-output (MISO) scenario, since the underlying optimization problem is convex and has closed-form solution. We then extend our studies to more general multiple-input-multiple-output (MIMO) case and an iterative method is derived. Extensive simulation results demonstrate the effectiveness of the proposed scheme in reducing the aggregate distortion and guaranteeing the testing accuracy using the MNIST and CIFAR10 datasets. In addition, we present the asymptotic analysis and give a near-optimal receive beamforming design solution in closed form, which is verified by numerical simulations.

In this paper, we propose a downlink multiple-input single-output (MISO) transmission scheme, which is assisted by an intelligent reflecting surface (IRS) consisting of a large number of passive reflecting elements. In the literature, it has been proved that nonorthogonal multiple access (NOMA) can achieve the same performance as computationally complex dirty paper coding, where the quasi-degradation condition is satisfied, conditioned on the users' channels fall in the quasi-degradation region. However, in a conventional communication scenario, it is difficult to guarantee the quasi-degradation, because the channels are determined by the propagation environments and cannot be reconfigured. To overcome this difficulty, we focus on an IRS-assisted MISO NOMA system, where the wireless channels can be effectively tuned. We optimize the beamforming vectors and the IRS phase shift matrix for minimizing transmission power. Furthermore, we propose an improved quasi-degradation condition by using IRS, which can ensure that NOMA achieves the capacity region with high possibility. For a comparison, we study zero-forcing beamforming (ZFBF) as well, where the beamforming vectors and the IRS phase shift matrix are also jointly optimized. Comparing NOMA with ZFBF, it is shown that, with the same IRS phase shift matrix and the improved quasi-degradation condition, NOMA always outperforms ZFBF. At the same time, we identify the condition under which ZFBF outperforms NOMA, which motivates the proposed hybrid NOMA transmission. Simulation results show that the proposed IRS-assisted MISO system outperforms the MISO case without IRS, and the hybrid NOMA transmission scheme always achieves better performance than orthogonal multiple access.

Frequency-difference electrical impedance tomography (fdEIT) was originally developed to mitigate the systematic artifacts induced by modeling errors when a baseline dataset is unavailable. Instead of fine anatomical imaging, only coarse anomaly detection has been addressed in current fdEIT research mainly due to its low spatial resolution. On the other hand, there has been not enough study on fdEIT reconstruction algorithm as well. In this article, we propose an efficient and high-spatial-resolution algorithm for simultaneously reconstructing multiple fdEIT frames corresponding to inject currents with multiple frequencies. The electrical impedance tomography reconstruction problem is considered within a hierarchical Bayesian framework, where both intratask spatial clustering and intertask dependency are automatically learned and exploited in an unsupervised manner. The computation is accelerated by adopting a modified marginal likelihood maximization approach. Real-data experiments are conducted to verify the recovery performance of the proposed algorithm.

Driven by the explosive growth of Internet of Things (IoT) devices with stringent requirements on latency and reliability, ultra-reliability and low latency communication (uRLLC) has become one of the three key communication scenarios for the 5th generation (5G) and 6G communication systems. In this paper, we focus on the beamforming design problem for the downlink multiuser uRLLC system. Since the strict demand on the reliability and latency, in general, short packet transmission is a favorable way for uRLLC systems, which indicates the classical Shannon’s capacity formula is no longer applicable. With the finite blocklength transmission, the achievable rate is greatly influenced by the reliability and finite blocklength. Using the developed achievable rate formula for finite blocklength transmission, we respectively formulate the problems of interest as the weighted sum rate maximization, energy efficiency maximization, and user fairness optimization by considering the maximum allowable transmission power and minimum rate requirement. These problems considered are non-convex and are hard to obtain the global optimal solution, even for the local optimal solution. To overcome these difficulties, some important insights have been discovered by analyzing the function of achievable rate. For example, an analytical solution of the minimum rate requirement is provided with respective to the signal-to-interference-plus-noise ratio. Based on the discovered results, we provide algorithms to optimize the beamforming vectors and power allocation, which are guaranteed to converge to a local optimum solution to the formulated problems with low computational complexity. Our simulation results reveal that our proposed beamforming algorithms outperform the zero-forcing beamforming algorithm with equal power or water filling allocation widely used in the existing literatures.

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.

Instance segmentation is one of the fundamental vision tasks. Recently, fully convolutional instance segmentation methods have drawn much attention as they are often simpler and more efficient than two-stage approaches like Mask R-CNN. To date, almost all such approaches fall behind the two-stage Mask R-CNN method in mask precision when models have similar computation complexity, leaving great room for improvement. In this work, we achieve improved mask prediction by effectively combining instance-level information with semantic information with lower-level fine-granularity. Our main contribution is a blender module which draws inspiration from both top-down and bottom-up instance segmentation approaches. The proposed BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer, thus being fast in inference. BlendMask can be easily incorporate with the state-of-the-art one-stage detection frameworks and outperforms Mask R-CNN under the same training schedule while being faster. A light-weight version of BlendMask achieves 36.0 mAP at 27 FPS evaluated on a single 1080Ti. Because of its simplicity and efficacy, we hope that our BlendMask could serve as a simple yet strong baseline for a wide range of instance-wise prediction tasks.

Deep learning based image denoising methods have been extensively investigated. In this paper, attention mechanism enhanced kernel prediction networks (AME-KPNs) are proposed for burst image denoising, in which, nearly cost-free attention modules are adopted to first refine the feature maps and to further make a full use of the inter-frame and intra-frame redundancies within the whole image burst. The proposed AME-KPNs output per-pixel spatially-adaptive kernels, residual maps and corresponding weight maps, in which, the predicted kernels roughly restore clean pixels at their corresponding locations via an adaptive convolution operation, and subsequently, residuals are weighted and summed to compensate the limited receptive field of predicted kernels. Simulations and real-world experiments are conducted to illustrate the robustness of the proposed AME-KPNs in burst image denoising.

Adverse impacts of in-phase and quadrature-phase (I/Q) imbalance in both the transmitter (Tx) and receiver (Rx) are quantified for the generalized frequency division multiplexing (GFDM) based transmission over frequency selective fading channels. To this end, we first equip the standard signal-to-interference-plus-noise (SINR) performance evaluation with the ability to consider second-order noncircular (improper) signals, and thus precisely evaluate performance deterioration caused by I/Q distortions over the in-phase (I) and quadrature-phase (Q) channels of a transmission system. Next, we propose a novel means to evaluate the individual SINR contributions from both the channels of GFDM, and hence, provide more meaningful insights into the underlying wireless transmission in the presence of complex non-circularity. This is accompanied by an account of complete augmented second-order statistics of I/Q imbalanced GFDM waveforms which caters for various sources of complex improperness. Simulations in the GFDM system setting support our analysis.

Mobile edge computing (MEC) has received broad attention as an effective network architecture and a key enabler of the wireless virtual reality (VR) video service which is expected to take a huge share of communication traffic. In this work, we investigate the scenario of multi-tiles-based wireless VR video service with the aid of MEC network, where the primary objective is to minimize the system energy consumption and the latency as well as to arrive at a tradeoff between these two metrics. To this end, we first cast the time-varying view popularity as a model-free Markov chain and use a long short-term memory autoencoder network to predict its dynamics. Then, a mixed strategy, which jointly considers the dynamic caching replacement and the deterministic offloading, is designed to fully utilize the caching and computing resource in the system. The underlying multiobjective optimization problem is reformulated as a partially observable Markov decision process and solved by using a deep deterministic policy gradient algorithm. The effectiveness of the proposed scheme is confirmed by numerical simulations.

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.

In recent years, network densification and edge caching become effective approaches to reduce the burden on the fronthaul links and the content delivery latency for wireless communication systems. However, maximizing system spectral efficiency cannot directly provide any insight on their energy requirements/efficiency for cache-enabled millimeter-wave (mmWave) radio access networks (RANs). In this paper, we study the design of energy-efficient transceiver, consisting of analog and digital precoder/combiner, for the delivery phase of the downlink of cache-enabled mmWave RANs. Due to the non-convexity of the delivery rate and objective, the coupling between the digital and analog precoders/combiners, and the constant module constraint on the elements of analog precoders/combiners, the problem of interest is non-convex and hard to obtain the global optimal solution, even the local optimal solution. To this end, we first overcome these challenges one-by-one and then transform the original problem into tractable one. Finally, an algorithmic framework that converges to the Karush-Kuhn-Tucker solution with provable is developed to achieve the design of energy-efficient transceiver. Numerical results are provided to evaluate the performance of the proposed algorithm, where fully digital precoding is used as benchmark.

Proposed by Maddah-Ali and Niesen, a coded caching scheme has been verified to alleviate the load of networks efficiently. Recently, a new technique called placement delivery array (PDA) was proposed to characterize the coded caching scheme. In this paper, we consider a caching system in the scope of ultra dense networks (UDNs). Each base station (BS) has a finite cache and stores some contents. We propose an efficient coded content caching scheme called double coded caching to make the transmission robust to in-and-out wireless network quality. Then the dynamic caching and multicast scheduling are considered to jointly minimize the average delay and power of the content-centric wireless networks. This stochastic optimization problem can be formulated as a Markov decision process (MDP) with unknown transition probabilities and large state space. We propose a deep reinforcement learning approach to deal with the decision problem. Our algorithm uses a variational auto-encoder (VAE) neural network to approximate the state sufficiently, and uses a weighted double Q-learning scheme to reduce variance and overestimation of the Q function. Numerical results demonstrate that the proposed double coded caching scheme increases the probability of the successful transmission, and the caching and scheduling policy can effectively reduce the delay and the power consumption.

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.

In this paper, we propose a joint spatial division and multiplexing (JSDM) beamforming based on a neighbor scheme for frequency division duplex (FDD) massive multi-input multi-output (MIMO) systems. The neighbor-based JSDM (N-JSDM) can fully utilize signal space, leading to higher spectral efficiency over the conventional JSDMs. The reason is that for the neighbor scheme, neighbors and non-neighbors are classified adaptively by the angles of departure (AoD), and the prebeamformer is designed to mitigate the non-neighbors' interference by the statistical channel state information. The effective channel matrix after the prebeamformer then becomes a band matrix, from which the downlink training length (DTL) and the channel feedback length are much smaller than the number of antennas. Moreover, an optimal prebeamformer which is proved to be able to achieve the same system capacity as the full CSI system is proposed, followed by a suboptimal prebeamformer with constrained DTL, and a DFT-based prebeamformer. On the other hand, the neighbors' interference is mitigated using the banded channel state information. Simulation results validate the good performance of the proposed N-JSDM.

The use of the millimeter wave (mmwave) spectrum for next generation mobile communication systems has gained significant attention recently. Large antenna arrays along with beamforming techniques are required to combat the large path-loss at mmwave frequencies. However, the existing beam designs often cause a large peak to average power ratio, and thus require power-inefficient power amplifiers (PAs). In this paper, we propose power-efficient beam design methods that facilitate the use of power-efficient PAs. Specifically, we design digital and hybrid analog-digital mmwave beams that possess a per-antenna constant envelope (PACE) and thus are highly power-efficient. Meanwhile, we also minimize the ripples in the mainlobe and sidelobe of the beams and consider both infinite and finite resolution phase shifters. To this end, we first propose an efficient feasible point search method to provide a feasible solution for the considered difficult beam design problem. Then, a novel hybrid analog-digital mapping algorithm is developed to map a designed digital beam to a hybrid analog-digital beam. To achieve better performance, we propose an improved hybrid analog-digital beam design method employing further optimization based on the feasible point. The proposed method is applicable to both infinite-resolution and finite-resolution phase shifters. Comprehensive simulation results are provided to demonstrate the effectiveness and superiority of the proposed beam designs.

Non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) have been recognized as promising technologies for the beyond fifth generation networks to achieve significant capacity improvement and delay reduction. In this paper, the technologies of hybrid NOMA and MEC are integrated. In the hybrid NOMA MEC system, multiple users are classified into different groups and each group is allocated a dedicated time slot. In each group, a user first offloads its task by sharing a time slot with another user, and then solely offloads during a time interval. To reduce the delay and save the energy consumption, we consider jointly optimizing the power and time allocation in each group as well as the user grouping. As the main contribution, the optimal power and time allocation is characterized in closed form. In addition, by incorporating the matching algorithm with the optimal power and time allocation, we propose a low complexity method to efficiently optimize user grouping. Simulation results demonstrate that the proposed resource allocation method in the hybrid NOMA MEC systems not only yields better performance than the conventional OMA scheme but also achieves quite close performance as global optimal solution.

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.

Coded caching is a promising approache to support low latency transmission over broadcast wireless networks. The process of selecting the nodes that forward coded messages can be considered as a set cover problem. However, existing research efforts don't focuse on solving the set cover problem. This paper investigates the problem of the cover problem for coded caching in wireless networks. First, we propose a novel coded caching method using deep neural networks. Then, we establish a mathematical model for cover problem of the coded caching system. Then, we propose a deep learning approach to solve it. Different from previous works, our proposed deep neural architecture uses sequence-to- sequence model to learn the solutions. Finally, numerical results are given to demonstrate the proposed coded caching method have lower load than traditional coded caching method, and that proposed method for solving the cover problem can effectively implement coded caching with lower computational complexity.

In this paper, we consider a nonorthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system where the power and time are jointly optimized to reduce the energy consumption and delay. In order to achieve a tradeoff between energy consumption and delay, we introduce weighting factors, and the optimization problem is formulated to minimize the weighted sum of energy consumption and delay. In the literature, only two offloading strategies, i. e., orthogonal multiple access (OMA) and pure NOMA, are mainly considered. In this paper, we investigate a third strategy, hybrid NOMA, which contains the strategies of OMA and pure NOMA. As the main contribution, we analytically characterize the optimal resource allocation, i. e., the joint power and time allocation, for two-scheduled-user case. Simulation results show that the proposed resource allocation method in hybrid NOMA systems yields lower energy consumption and delay than the conventional OMA scheme.

The hardware implementation of large-scale multi-antenna systems requires power-efficient power amplifiers (PAs). However, the existing beamforming designs often cause a large peak-to-average power ratio and have to rely on power-inefficient PAs. In this paper, we propose a unified power-efficient beamforming design framework, which incorporates per-antenna constant envelope constraints to improve the power efficiency. We further propose an efficient algorithm named "sequential outer approximation procedure" (SOAP) to search a feasible point. Power-efficient design for beam pattern synthesis is developed based on SOAP.

This paper investigates the optimal design of multiple panel arrays (MPAs) for line-of-sight (LoS) multiple-input multiple-output (MIMO) communication systems. We use the spherical wave channel model and give a geometric model to model the LoS channel, which allows the receive antenna arrays to have azimuth rotation, elevation angle rotation, up-down offset and left-right offset distance. Based on the geometric model, we derive the optimal antenna design conditions for achieving the maximum channel capacity and spatial multiplexing gain according to the effective degrees of freedom. The results show that the proposed antenna design can achieve high channel space freedom when the receive antennas have angle rotation and offset, and is suitable for the case where the receive antennas have a large left-right offset distance.

Non-orthogonal multiple access (NOMA) relies on power domain multiplexing via successive interference cancellation (SIC). Current NOMA system designs are based on information rate and assume perfect SIC. However, error propagation in SIC is inevitable in practice. This paper considers imperfect SIC and investigates the NOMA system design from the perspective of minimum error probability. Specifically, we consider the uncoded and the coded NOMA systems and derive the error probabilities of the users. Then, we aim to minimize the average error probability of the users by searching for appropriate power allocation. Numerical results are provided to evaluate the minimum error performance of NOMA along with useful insights.

This study investigates a novel unmanned aerial vehicle (UAV)‐based wireless sensor network, where the UAV acts as a flying base station to serve multiple wireless sensor nodes (SNs). The authors goal is to maximise the system energy efficiency of the UAV while satisfying the fairness among SNs by jointly optimising the UAV trajectory and UAV time allocation. The formulated problem is shown to be a non‐convex fractional optimisation problem, which is hard to tackle. To this end, they decompose the original problem into two sub‐problems, and the block coordinate descent method and successive convex optimisation technique are employed to solve these two sub‐problems iteratively. Specifically, in the first sub‐problem, the optimal UAV time allocation is obtained by maximising the minimum achievable rate of SNs with given UAV trajectory constraints. In the second sub‐problem, the UAV trajectory is achieved by minimising the energy consumption of the UAV with the given UAV time allocation. Subsequently, an iterative algorithm is proposed to optimise the time allocation and UAV trajectory alternately. Furthermore, the convergence and complexity of their proposed algorithm are provided. Numerical results show that the proposed scheme outperforms the existing benchmark strategies in terms of energy efficiency.

Recently, edge caching and multicasting arise as two promising technologies to support high-data-rate and low-latency delivery in wireless communication networks. In this paper, we design three transmission schemes aiming to minimize the delivery latency for cache-enabled multigroup multicasting networks. In particular, full caching bulk transmission scheme is first designed as a performance benchmark for the ideal situation where the caching capability of each enhanced remote radio head (eRRH) is sufficient large to cache all files. For the practical situation where the caching capability of each eRRH is limited, we further design two transmission schemes, namely partial caching bulk transmission (PCBT) and partial caching pipelined transmission (PCPT) schemes. In the PCBT scheme, eRRHs first fetch the uncached requested files from the baseband unit (BBU) and then all requested files are simultaneously transmitted to the users. In the PCPT scheme, eRRHs first transmit the cached requested files while fetching the uncached requested files from the BBU. Then, the remaining cached requested files and fetched uncached requested files are simultaneously transmitted to the users. The design goal of the three transmission schemes is to minimize the delivery latency, subject to some practical constraints. Efficient algorithms are developed for the low-latency cloud-edge coordinated transmission strategies. Numerical results are provided to evaluate the performance of the proposed transmission schemes and show that the PCPT scheme outperforms the PCBT scheme in terms of the delivery latency criterion.

This letter investigates the robust beamforming design for multigroup multicast in a backhaul-limited cloud radio access network. Users requesting the same content form a multicast group, served by remote radio heads (RRHs) cooperatively. Each RRH acquires the requested contents from baseband unit via backhaul links. We first formulate the robust beamforming design as maximizing the sum of the minimum rate of users in each multicast group under the transmission power and backhaul constraints. Due to the introduction of the channel estimation error and inter-user interference, the considered problem becomes more complex and difficult to address directly. To overcome these difficulties, convex approximation methods are adopted to transform the original problem into convex one. Then, an effective optimization algorithm is developed to address the resulting problem. Numerical results demonstrate the effectiveness of the proposed robust beamforming design of multigroup multicast transmission.

In this paper, we investigate the downlink transmission for cache-enabled fog radio access networks aiming at maximizing the delivery rate under the constraints of fronthaul capacity, maximum transmit power, and size of files. To reduce the delivery latency and the burden on fronthaul links and make full use of the local cache and baseband signal processing capabilities of enhanced remote radio heads (eRRHs), a two-level transmission scheme including cache-level and network-level transmission is proposed. In cache-level transmission, only requested files cached at the local cache are transmitted to the corresponding users. The duration of cache-level transmission is the delay caused by the transfer between the baseband unit (BBU) and eRRHs as well as the signal processing at the BBU. The remaining requested files are jointly transmitted to the corresponding users at network-level transmission. For cache-level transmission, a centralized optimization algorithm is first presented and then a decentralized optimization algorithm is provided to avoid the exchange of signaling among eRRHs. Meanwhile, another centralized optimization algorithm is presented to tackle the optimization problem for network-level transmission. All presented algorithms are proved to converge to the Karush-Kuhn-Tucker solutions of the problems. Numerical results are provided to validate the effectiveness of the proposed transmission scheme as well as evaluating the system performance.

The millimeter-wave (mmWave) ultra-dense networks are more suitable for wireless power transfer, since the short-distance transmissions experience less pathloss and the base station (BS) packed with large-scale antenna arrays can achieve significant array gains. However, the secrecy performance of the simultaneous wireless information and power transfer (SWIPT) mmWave ultra-dense networks has not been investigated so far. In this paper, we consider the secure communications in downlink SWIPT mmWave ultra-dense networks, where the energy-constrained users extract energy and information from the mmWave signals in the presence of multiple eavesdroppers. First, the analytical expressions of the energy-information coverage probability are derived for both power splitting and time switching policies using stochastic geometry. Then, we derive the closed-form expressions of the secrecy probability in the presence of multiple independent or colluding eavesdroppers. Finally, the effective secrecy throughput (EST), which can measure the network energy coverage, secure, and reliable transmission performance in a unified manner, is derived. Theoretical analysis and simulation results reveal that the EST first increases and then decreases with the increasing of the transmit power, power/time splitting ratio, codeword transmission rate, and confidential information rate. Furthermore, reducing the beamwidth of the signal at BSs can decrease the information leakage and improve the EST.

In this paper, we consider the reconstruction of three-dimensional (3-D) conductivity distribution using electrical impedance tomography (EIT) technique. A high-resolution and efficient algorithm is developed to solve the EIT inverse problem. The presented algorithm is extended upon a recently proposed novel EIT reconstruction approach based on structure-aware sparse Bayesian learning (SA-SBL). The correlation between proximal layers in the 3-D geometry are incorporated into the structure prior to improve the reconstruction accuracy. In addition, an efficient approach based on approximate message passing is developed to accelerate the large-scale 3-D learning process. To validate the algorithm, numerical experiments using real recorded data are conducted. The visual and quantitative-metric comparisons show that the proposed method outperforms the existing methods in terms of reconstruction accuracy and computational complexity in all test cases. The SA-SBL-based reconstruction approach can preserve the 3-D structure of medical volume, reduce the systematic artifacts, and improve the computational efficiency.

Generalized frequency division multiplexing (GFDM) has become one of the most important waveform candidates for beyond 5G (B5G) communications. However, physical distortions, such as in-phase and quadrature (I/Q) imbalance caused by the imperfections of radio frequency (RF) components within direct-conversion transceivers (DCTs), may cause severe performance degradation in GFDM-based wireless systems. To this end, we first conduct a rigorous sum rate analysis to quantify the impact of I/Q imbalance in both the transmitter and the receiver on the GFDM wireless transmission. An efficient I/Q imbalance compensation scheme is next proposed based on pilots; this is achieved through a nonlinear least squares analysis of the joint channel and I/Q imbalance estimation, and a simple symbol detection procedure. For rigor, the Cramer-Rao lower bounds for both the I/Q imbalance parameters and the channel coefficients are also derived. The simulation results illustrate that the mean square error performance of the proposed estimator closely approaches the corresponding CRLB over static frequency selective channels, thus significantly reducing the sensitivity of GFDM DCTs to physical I/Q impairments.

In this paper, we study the design of a hybrid precoder, consisting of an analog and a digital precoder, for the delivery phase of downlink cache-enabled millimeter-wave (mm-wave) radio access networks (CeMm-RANs). In CeMm-RANs, enhanced remote radio heads (eRRHs), which are equipped with local cache and baseband signal processing capabilities in addition to the basic functionalities of conventional RRHs, are connected to the baseband processing unit via fronthaul links. Two different fronthaul information transfer strategies are considered, namely, hard fronthaul information transfer, where hard information of uncached requested files is transmitted via the fronthaul links to a subset of eRRHs, and soft fronthaul information transfer, where the fronthaul links are used to transmit quantized baseband signals of uncached requested files. The hybrid precoder is optimized for maximization of the minimum user rate under a fronthaul capacity constraint, an eRRH transmit power constraint, and a constant-modulus constraint on the analog precoder. The resulting optimization problem is non-convex, and hence, the global optimal solution is difficult to obtain. Therefore, convex approximation methods are employed to tackle the non-convexity of the achievable user rate, the fronthaul capacity constraint, and the constant modulus constraint on the analog precoder. Then, an effective algorithm with provable convergence is developed to solve the approximated optimization problem. The simulation results are provided to evaluate the performance of the proposed algorithms, where fully digital precoding is used as the benchmark. The results reveal that except for the case of a large fronthaul link capacity, soft fronthaul information transfer is preferable for CeMm-RANs. Furthermore, surprisingly, hybrid precoding outperforms fully digital precoding with soft fronthaul information transfer for medium-to-large file sizes and fronthaul capacity limited mm-wave cloud RANs.

This paper investigates the problem of proactive caching for multi-view 3D videos in the fifth generation (5G) networks. We establish a mathematical model for this problem, and point out that it is difficult to solve the problem with traditional dynamic programming, then we propose a deep reinforcement learning approach to solve it. First, we model the proactive caching system for multi-view 3D videos as a Markov decision process jointing views selection and local memory allocation. Then, we present an actor-critic, model-free algorithm based on the deep deterministic policy gradient to find effective proactive caching policy. Since the action space is affected by the system state, we embed dynamic k-Nearest Neighbor algorithm into actor-critic algorithm to implement the deep reinforcement learning algorithm working in an action space of variable size. Finally, the numerical results are given to demonstrate that the proposed solution can effectively maintain high-quality user experience for high-mobility 5G users moving among small cells. We also investigate the impact of configuration of critical parameters on the performance of the algorithm.

Millimeter wave (mmWave) communication emerges as an enabling technology for Gbps transmission. A further performance enhancement can be achieved by incorporating mmWave communication into fog radio access networks (F-RANs), which alleviate the large path loss of mmWave signals by shortening the distance between transmitters and users and reduce the latency by caching at enhanced remote radio heads (eRRHs). The full benefit of mmWave F-RANs is leveraged on a joint design of signal processing at the centralized baseband unit (BBU) and distributed eRRHs. In this paper, we propose an energy efficient hybrid precoding design for the downlink mmWave F- RANs with wireless fronthaul links, where digital and hybrid precoders are exploited at the BBU and eRRHs, respectively. We develop an effective method to solve the resulting difficult precoding optimization problem and provide numerical results to demonstrate the effectiveness of the proposed mmWave F-RAN design.

Millimeter wave (mmwave) communication operating in the band of 30-300 GHz is promising to provide Gbps data rates owing to its abundant spectrum resource, and has attracted increasing attention. Cooperative transmission, by converting undesired interferences into useful signals, is able to further improve performance of mmwave systems. In this paper, we propose a novel cooperative transmission scheme for mmwave communication networks, where each mobile user is cooperatively served by multiple access points (APs) that use hybrid precoders. Our goal is to maximize the system energy efficiency, which leverages on a joint design of the hybrid precoders of all APs. The formulated problem is a difficult nonlinear fractional programming subject to unit modulus constraints. We propose an efficient algorithm by incorporating penalty decomposition and block coordinate descent methods. Numerical results are provided to confirm the effectiveness of the proposed algorithm and reveal some important insights.

In this paper, we study the beam-based training design for the single-user (SU) hybrid massive antenna system based on outage probability performance. First, an interleaved training design is proposed where the feedback is concatenated with the training procedure to monitor the training status and to have the training length adaptive to the channel realization. Then, the average training length and outage probability are derived for the proposed interleaved training and SU transmission. Analytical results and simulations show that the proposed interleaved scheme achieves the same outage performance as the traditional full-training scheme but with significant saving in the training overhead.

Zero-forcing (ZF) precoding plays an important role for massive MIMO downlink due to its near optimal performance in high signal-to- noise (SNR) region. However, the high computation cost of the involved matrix inversion hinders its application in practical large-scale systems. In this paper, we adopt the first order Neumann series (NS) expansion for a low-complexity approximation of matrix inversion. Compared to existing NS based schemes, we introduce a relaxation parameter jointly with one user's channel interference to others into the precondition matrix and propose the identity-plus- column NS (ICNS) method. By further exploiting the multi-user diversity gain via choosing the user with largest interference to others, the ordered ICNS method is also proposed. Moreover, the closed-form sum-rate approximation of the ICNS method is derived. Simulations verify our analytical results and the advantage of the proposed schemes over other existing low-complexity ZF precodings for massive MIMO systems with correlated channels and not-so-small loading factor.

The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR.

In this paper, we employ the unmanned aerial vehicle (UAV) as a flying base station (BS) to offload the data computing tasks from mobile terminal (MT) for saving mobile energy consumption. Our goal is to minimize consumption of the computational tasks at MT by jointly designing the resource partitioning scheme and bit allocation strategy. Specifically, the portion of total bits for local computation at MT is optimized, and the other portion of bits is computed by jointly optimizing the number of bits transmitted in the uplink, the number of bits computed locally at UAV and the number of bits transmitted in the downlink. The formulated problem has been shown in a convex form, which has optimal solutions. Instead of solving original problem using standard convex optimization techniques, we propose a resource partitioning scheme and bit allocation strategy based on dual decomposition, which has been shown in a low computational complexity. Furthermore, the numerical results are provided to demonstrate the superiority of our proposed scheme over the compared benchmarks.

Traveling by bicycle has become a rising trend recently for its convenience and flexibility, which calls for considerate bicycle trip planning schemes. While research for traditional trip planning has focused on quantized quality of point-of-interest (POI) or correlations among POIs, problems appear for distinct influential factors in bicycle trips and being unable to plan in a foreseeable stage with satisfying various demands of cyclists. In this paper, to alleviate the deficiencies of conventional approaches that merely concentrating on temporary interests and fully depending on greedy algorithm, the active Q-learning algorithm derived from reinforcement learning (RL) is adopted for Q-value iteration for planning overall optimal bicycle trips. To further meet personal improvised demands such as containing some specific places in the trip, Tailored Trip is provided and a dynamic and flexible place inserting algorithm is proposed to automatically tweak the trip and keep the planning optimum status. Experiments have been conducted to intuitively evaluate the performance of our schemes on two real-world datasets. The planning results clearly illustrate that the optimal node choosing policy is continuously reinforced in our schemes and the result for Tailored Trip highlights the guarantee of overall superiority after trip tweaking.

Millimeter wave (mmwave) communication has attracted increasing attention owing to its abundant spectrum resource. The short wavelength at mmwave frequencies facilitates placing a large number of antennas in a small space, and the mmwave channels are likely to be sparse in the directions. These two new features promise enhanced security by directional precoding. To explore this potential, we investigate the design of directional hybrid digital and analog precoding for the multiuser mmwave communication system with multiple eavesdroppers. Particularly, we consider two cost-efficient sub-connected hybrid architectures, i.e., multi-subarray architecture and switched-phased-array architecture, and optimize the hybrid precoding under per-antenna constant envelope (CE) constraints. The goal of our design is to guarantee the receive quality of the legitimate users while minimizing the power leaked to the eavesdroppers, so as to realize a directional transmission for a general mmwave channel. The resulting problems are very challenging due to the nonlinear CE constraints and binary integer constraint from antenna selection. To address them, we leverage exact penalty function methods to find efficient solutions to the CE hybrid directional precoding. Our analysis shows that the proposed algorithm is able to converge to a stationary point under some mild conditions. Simulation results are finally provided to confirm the effectiveness of the proposed schemes and their superiority over the existing schemes under both single-path and multi-path mmwave channels.

This paper studies the robust secure beamforming (BF) issue of fifth generation (5G) cellular system operating at millimeter wave frequency and coexisting with a satellite network. By employing an uniform planar array at the base station (BS) and assuming known imperfect angle-of-arrival-based channel state informations of multiple eavesdroppers (Eves), a constrained optimization problem is first formulated to maximize the worst-case achievable secrecy rate of the cellular user under the constraints of the transmit power of BS and the interference threshold of satellite earth station. Then, we propose two robust BF methods to solve the complex optimization problem for both coordinated and uncoordinated Eves. For the case of coordinated Eves, we propose a heuristic BF scheme, which transfers the worst-case problem into a min-max one such that the BF weight vectors can be obtained analytically. For uncoordinated Eves, we convert the non-convex problem into a convex one, and further propose an iterative penalty function-based algorithm to obtain the optimal BF weight vectors. Finally, simulation results are provided to confirm the effectiveness and superiority of the proposed robust BF schemes.

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.

Zero-forcing (ZF) precoding plays an important role for massive MIMO downlink due to its near optimal performance. However, the high computation cost of the involved matrix inversion hinders its application. In this paper, we adopt the first order Neumann series (NS) for a low-complexity approximation. By introducing a relaxation parameter jointly with the channel non-orthogonality between one selected user and others into the precondition matrix, we propose the identity-plus-column NS (ICNS) method. By further choosing the user with the least channel orthogonality with others, the ordered ICNS method is also proposed. Moreover, the sum-rate approximations of the proposed ICNS method and the competitive existing identity matrix based NS (INS) method are derived in closed-form, based on which the performance loss of ICNS due to inversion approximation compared with ideal ZF and its performance gain over INS are explicitly analyzed for three typical massive MIMO scenarios. Finally, simulations verify our analytical results and also show that the proposed two designs achieve better performance-complexity tradeoff than ideal ZF and existing low-complexity ZF precodings for practical large antenna number, correlated channels, and not-so-small loading factor.

This paper investigates secure cooperative transmissions in a dual-hop MIMO relay system using a combined transmit antenna selection (TAS) and maximal-ratio combining (MRC) scheme over the Nakagami-m fading channels, where an adaptive decode-and-forward relaying protocol and an multi-antenna eavesdropper are considered. Due to the feedback delay, channel state information (CSI) for TAS might be outdated at both the source and the relay. To evaluate the secrecy performance of the TAS/MRC scheme and the impacts of outdated CSI, the closed-form expressions for the metrics of exact ergodic secrecy rate and exact secrecy outage probability are derived under both perfect and outdated CSI conditions in a channel feedback error model. In order to explicitly reveal the behaviors of the secrecy performance in high signal-to-noise ratio regime, asymptotic expressions for both the metrics are further derived. As validated by simulation results, analytically numerical results demonstrate that outdated the CSI always results in a loss in the secrecy performance, but the loss can be recovered by increasing the number of antennas at legitimate receivers. Furthermore, the outdated CSI yields a reduced secrecy diversity order, whereas only the perfect CSI leads to the full secrecy diversity order.

The abundance of spectrum in the millimeter-wave (mm-wave) bands makes it an attractive alternative for future wireless communication systems. Such systems are expected to provide data transmission rates in the order of multi-gigabits per second in order to satisfy the ever-increasing demand for high rate data communication. Unfortunately, mm-wave radio is subject to severe path loss, which limits its usability for long-range outdoor communication. In this paper, we propose a multi-hop mm-wave wireless network for outdoor communication, where multiple full-duplex buffered relays are used to extend the communication range, while providing end-to-end performance guarantees to the traffic traversing the network. We provide a cumulative service process characterization for the mm-wave propagation channel with self-interference in terms of the moment generating function of its channel capacity. Then, we then use this characterization to compute probabilistic upper bounds on the overall network performance, i.e., total backlog and end-to-end delay. Furthermore, we study the effect of self-interference on the network performance and propose an optimal power allocation scheme to mitigate its impact in order to enhance network performance. Finally, we investigate the relation between relay density and network performance under a sum power constraint. We show that increasing relay density may have adverse effects on network performance, unless the self-interference can be kept sufficiently small.

For millimeter wave (mm-wave) communication systems, a lens antenna array with single-carrier transmission and path delay compensation is a promising technique for realizing cost-effective large multiple-input multiple-output communications with limited number of radio frequency chains. In this paper, we study the multi-user mm-wave downlink lens antenna array system for the general frequency-selective channels. By leveraging the angle-dependent energy focusing property of the lens antenna array and the angular sparsity of mm-wave channels, we investigate the low-complexity single-carrier transmission scheme with path delay pre-compensation applied at the base station (BS). The resulting signal-to-interference-plus-noise ratio (SINR) is derived by taking into account both the residual inter-symbol interference and inter-user interference. Based on the derived SINR expression, we propose an effective joint antenna selection and beamforming scheme by utilizing the group sparse optimization to accommodate for the limited number of RF chains at the BS. Thus, the proposed scheme can obtain the approximate performance with the fully digital case and has a better performance than the conventional orthogonal frequency-division multiplexing mode for the frequency-selectivity channels. Numerical results are provided to verify the effectiveness of the proposed schemes.

Large-scale antenna arrays which provide high beamforming gain are commonly used to combat the serious path-loss in millimeter wave (mmwave) systems. Traditionally, the beamforming is completely implemented at the baseband or the digital domain, which, however, causes high hardware cost and power consumption. The hybrid precoding method which can effectively avoids these problems is, therefore, more attractive. However, the design of hybrid precoder is challenging due to the constant modules of phase shifters. To solve this problem, we propose a novel hybrid precoding approach from the perspective of geometric construction in this paper. The new method can significantly reduce the number of RF chains meanwhile still achieve an almost optimal performance in terms of sum rate. The proposed algorithm is compared with the popular orthogonal matching pursuit algorithm (OMP) via numerical simulations, and shows that our proposal can increase the system spectral efficiency with reduced computational complexity.

This paper studies beam training design for a codebook- based beamforming millimeter wave (mmwave) system where multiple antenna arrays are employed and each array is capable of beamforming independently. To reduce the training overhead and the complexity of subsequent beam direction search, we propose a cooperative multi- subarray beam training method. Specifically, from the perspective of excluding noneffective beam direction combinations and thus reducing search space, method and criterion of beam superposition are proposed to construct a wide beam from multiple narrow beams corresponding to multiple subarrays. Then, a cooperative multisubarray beam training scheme is proposed based on the proposed criterion. Finally, simulation results show that the proposed scheme achieves a spectral efficiency close to that of the optimal exhaustive search scheme, while has greatly reduced training overhead and computational complexity.

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.

This paper exploits the potential of large antenna arrays to develop a secure millimeter wave (mmwave) transmission scheme. To reduce the peak-to-average power ratio (PAPR), the idea of constant envelope precoding (CEP) is introduced to improve the power efficiency of power amplifiers. In the CEP scheme, only phase variation of each antenna is used to form the desired signal at the target receiver, while the sum power of noise-free signals received by all eavesdroppers is minimized for secure transmission. A nonconvex optimization problem is formulated with equality and unit modulus constraints. To tackle the nonconvex constraints, the augmented Lagrangian penalty method is employed to address the challenging problem. An efficient iterative algorithm is further proposed to tackle the problem of precoder design. Simulation results confirm the effectiveness and superiority of the proposed CEP secure transmission scheme.

In this study, the authors address the downlink resource (subchannels and power) allocation problem for device‐to‐device communication with wireless power transfer technique in a cellular network to improve the energy efficiency (EE). The considered problem is formulated as maximising the weighted EE and is solved by leveraging a game‐theoretic learning approach. Specifically, they first prove that an exact potential game applies to the resource allocation problem and there exists the best Nash equilibrium (NE) which is the optimal solution of the optimisation problem. Then, aiming to this optimisation problem with imperfect information, a robust and distributed learning algorithm is proposed and is proved that it can converge to the best NE. Finally, numerical results verify the effectiveness of the proposed scheme.

In cloud radio access network, a baseband unit (BBU) performs the baseband processing for a cluster of low‐power low‐cost remote radio heads (RRHs) that are connected to the BBU through low‐latency fronthaul links. In this study, the authors study the optimisation of two energy‐efficient compression and precoding strategies which take transmit power constraint, fronthaul capacity constraint and user specific rate constraint into account. To overcome the non‐convexity nature of the original problem, they first transform the objective of the original problem into a parameterised subtractive form and obtain an approximate convex problem via the successive convex approximation. Then, an effective optimisation algorithm with provable convergence is designed to solve the effective problem. Numerical results reveal that the proposed scheme outperforms the conventional maximum sum rate and minimum total power consumption schemes in terms of the energy‐efficiency criterion. In particular, compression after precoding strategy outperforms compression before precoding strategy when both of their RRHs perform the same user scheduling, while the opposite conclusion can be drawn otherwise.

In massive multiple‐input multiple‐output (MIMO) high‐speed train (HST) wireless communication systems (WCS), due to the large‐dimension and rapidly time‐varying property, channel tracking rather than conventional channel estimation is more feasible. Due to the non‐linear channel variation, the channel is first decomposed into sub‐channels with different angle of departure. By exploiting the property of near line of sight (LoS) and location information, Kalman filtering is used to track the channel by predicting and modifying the channel gain of the LoS sub‐channel. To further improve the tracking performance, a location‐based sequentially optimal beam pattern design is proposed. Since placing pilot beams for many receive antennas with different locations in time or frequency division manner costs much system resource, the authors further propose a low‐complexity grouping algorithm based on location. Finally, two transmission designs based on the proposed channel tracking scheme and the location only, respectively, are introduced. Simulations validate the efficiency of the authors' proposed tracking scheme and the corresponding transmission design, which shows the importance of location information and channel tracking in massive MIMO HST‐WCS.

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.

Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to the rates of multiple gigabit per second per user. Though mmWave can be readily used in stationary scenarios, such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed. This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond). We first summarize the recent channel measurement campaigns and modeling results. Then, we discuss in detail recent progresses in multiple input multiple output transceiver design for mmWave communications. After that, we provide an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity. Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed.

For the potential of providing rates of multiple Giga-bps in a single channel, millimeter wave (mmWave) communications have recently attracted substantial research interest. While mmWave technology is already being used in stationary scenarios such as indoor hotspots or backhaul, it is challenging to use mmWave frequencies in mobile networks, where transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, many significant technical challenges must be tackled. The main objective of this IEEE JSAC Special Issue on “Millimeter wave communications for future mobile networks” is to collect the most recent technical advances in mmWave for future mobile networks. The response from the community to the call has been overwhelming. We received 96 submissions with a call period short than 4 months. Many of the submissions are from the most well known research groups in the field. After a strict review process, we decided to accept 38 papers, which will be published in two issues. The papers were selected based on the technical relevance and merits. Unfortunately, due to space limitations, a number of interesting papers were not selected, despite the merits that they had. We sincerely hope those papers can find other publishing venues.

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.

This letter studies the cooperative precoding design for a coexisting wireless energy transfer (WET) and cognitive radio (CR) system, where the WET system share the same spectrum with the CR system. Different from the traditional wireless networks, interference here is regarded as a useful rather than harmful resource. Specifically, we address the transmit covariance design to minimize the total transmit power at the energy transmitter and the secondary transmitter while satisfying secrecy rate, energy harvesting, and interference temperature constraints. We propose an iterative algorithm to tackle the formulated nonconvex optimization problem, and prove that it could converge to a Karush-Kuhn-Tucker point of the original problem. Simulation results are finally provided to illustrate the effectiveness of our proposed algorithm.

Owing to abundant spectrum resources, millimeter wave (mmwave) communication promises to provide Gbps data rates, which, however, may be restricted by large path-loss. Thus, antenna arrays are commonly used along with beam alignment (BA) as an important step to achieve the array gain. Efficient BA relies on the beam training codebook design. In this paper, we propose a new hierarchical codebook to achieve uniform BA performance with low overhead. To better elaborate on the design principle, a single-path channel model is considered first to frame the proposal. The codebook design is formulated as an optimization problem, where the ripple in the main/side lobes is constrained such that each training beam is close to the ideal one with a flat magnitude response and a narrow transition band. Then, we propose an efficient algorithm to find such a beam training codebook. Furthermore, we derive closed-form expressions of the BA misalignment probability or error rate of the proposed beam training codebook. Our results reveal that using the proposed codebook, the error rate of tree-search-based BA exponentially decreases with the SNR for a given channel, and linearly decreases in the log-log coordinate axis for a fading channel. We further propose a power allocation scheme used in different training stages to further improve the BA performance. Finally, the proposed framework is extended to the more complex case of multi-path channels. Numerical results confirm the effectiveness of the proposed training codebook and power allocation scheme as well as the accuracy of the performance analysis.

Statistical beamforming is an important technique for multi-user massive MIMO downlink, since it depends on the downlink channel covariance only. In this paper, we first derive an explicit analytical sum-rate expression for generic channel covariance-based beamforming scheme. Then, a low-complexity joint statistical beamforming and user scheduling algorithm via greedy search is proposed, where the beamforming is based on the signal-to-leakage-and-noise-ratio (SLNR) for closed-form design and tractable analysis, while the user scheduling is based on the derived sum-rate expression. Further, with the help of large-scale asymptotic simplifications and the introduction of the interference user number parameter, a simple analytical sum-rate expression of the joint algorithm is derived for channels with flat power beam spectrum. The expression explicitly exhibits the sum-rate behavior with respect to different network parameters and captures the effect of sum-rate-based user scheduling. Finally, simulation results are provided to verify our analytical results and to show the advantage of the proposed joint design compared with existing schemes.

To meet the ever-increasing demands on higher throughput and better network delay performance, 60 GHZ networking is proposed as a promising solution for the next generation of wireless communications. To successfully deploy such networks, its important to understand their performance first. However, due to the unique fading characteristic of the 60 GHz channel, the characterization of the corresponding service process, offered by the channel, using the conventional methodologies may not be tractable. In this work, we provide an alternative approach to derive a closed-form expression that characterizes the cumulative service process of the 60 GHz channel in terms of the moment generating function (MGF) of its instantaneous channel capacity. We then use this expression to derive probabilistic upper bounds on the backlog and delay that are experienced by a flow traversing this network, using results from the MGF-based network calculus. The computed bounds are validated using simulation. We provide numerical results for different networking scenarios and for different traffic and channel parameters and we show that the 60 GHz wireless network is capable of satisfying stringent quality-of-Service (QoS) requirements, in terms of network delay and reliability. With this analysis approach at hand, a larger scale 60 GHz network design and optimization is possible.

When massive MIMO technology is combined with dense heterogeneous networks (HetNets), the user association and power allocation problems are fundamentally different although the energy- efficiency benefits can be intensified. This paper aims to investigate the energy efficient joint user association and power allocation problem in downlink massive MIMO empowered dense HetNets under proportional fairness criterion. The joint optimization problem is a non-convex mixed-integer nonlinear program (MINLP) which is NP-hard, and hence it is difficult to efficiently obtain exact solution. In order to obtain the highquality suboptimal solution, the joint optimization problem is first decomposed into two subproblems with alternating iterative method. Then a two-layer iterative suboptimal algorithm is proposed to solve the joint optimization problem with guaranteed convergence. The involved association subproblem adopts dual decomposition to achieve the optimal association index, whilst the power allocation subproblem allocates the transmit power of each BS with Newton's method. Numerical results verify the effectiveness of our proposed algorithm and show that our proposed algorithm outperforms conventional association schemes in the enhancement of energy efficiency performance. Furthermore, it can be seen that the energy efficiency performance is enhanced by increasing the number of antennas at macro base station (MBS).

The selection of nonzero taps for sparse linear equalizer under the criterion of minimum mean square error (MMSE) is investigated. In some applications such as underwater acoustic communications, the computational resource in terms of the number of nonzero channel taps is given in existing channel equalizers. In this context, the joint determination of positions and weights of nonzero taps of sparse equalizer is considered and then formulated as a subset selection problem. A fast algorithm that uses two levels of loops is proposed to iteratively update each entry of the subset. The computational complexity of the proposed algorithm is analyzed. To make the work comprehensive, the sparse equalizer design where the number of nonzero taps is not given is also investigated. Simulation results show that around 60% equalizer taps can be saved with no more than 1dB of performance loss. Moreover, compared to the OMP algorithm, the proposed algorithm can save 33% equalizer taps achieving the same bit error rate (BER) of 0.001.

To combat bursty errors caused by wireless channels, interleaving is usually employed to randomize these errors with an aim to make error correction codes more effective. Subsequently, the design of efficient interleaving strategies becomes an attractive topic. Our main contributions of this paper consist: i) put forward equivalent distance sum from the physical scenarios as the metric of an interleaving sequence, which can give an overall but meticulous characterization of interleaving; ii) combine unequal grouping strategy with group interleaving to exploit the potential of interleaving which mostly outperforms classical block interleaving; iii) develop a practical algorithm to generate the near-optimal interleaving sequence with arbitrary length based on underlying insights. Numerical results under IEEE 802.11aj (45 GHz) millimeter-wave system in single carrier mode validate the effectiveness of the algorithm.

This paper investigates the joint power control and subchannel allocation problem for device-to-device (D2D) and small cell uplink communications in a cellular network to improve the cellular throughput. For this considered throughput maximization problem, we propose to solve it leveraging a game-theoretic learning approach. However, there is an intractable issue for obtaining the optimal power allocation profile in the continuous space. To this end, we first deduce the optimal power expressions under any given subchannel allocations. Based on the optimal power profile, we then formulate the subchannel allocation problem into a game framework. Next, aiming to this optimization problem, a cloud-assisted learning algorithm with conditioned strategies is proposed to converge to an equilibrium point which maximizes the optimization objective. Finally, numerical results verify the effectiveness of the proposed scheme.

To fully exploit the spatial multiplexing gains and array gains of massive multiple-input-multiple-output (MIMO), the channel state information must be obtained accurately at the transmitter side (CSIT). However, conventional channel estimation solutions are not suitable for Frequency-Division Duplexing (FDD) multi-user massive MIMO systems, due to overwhelming pilot and feedback overhead. In this paper, We find that part of the user channels tend to exhibit an approximate beam-blocked sparsity. To exploit this property, we propose a novel blocked compressive channel estimation scheme based on user grouping to reduce the pilot and feedback overhead. More specifically, we adopt user grouping by making the users in one group have similar channel covariance, which makes the channels in one group exhibit beam block sparsity. Then users feed the compressed measurements back to BS and the BS performs the CSIT recovery. Using the beam block sparsity, an optimal block orthogonal matching pursuit algorithm (OBOMP) is developed which effectively recovers the channel parameters. Numerous simulation results demonstrate our proposed scheme outperforms conventional solutions.

Delayed channel state information (CSI) degrades the system performance and predictor can mitigate the effects of outdate CSI. In massive multiple input multiple output (MIMO) systems with large dimensional channel vectors, low‐complexity prediction can reduce operation time and process latency. This study adopts a low‐complexity channel predictor based on polynomial fitting for the massive MIMO system. Compared with the conventional Wiener predictor, it does not need statistical channel estimation and avoids matrix inversion. The authors derive the approximate signal‐to‐interference‐plus‐noise ratio (SINR) with predicted channel information and the approximate gaps of the average rate per user between using perfect CSI, the predicted CSI provided by Wiener predictor and polynomial fitting, respectively, in the uplink massive MIMO system. The authors also analyse the normalised mean square error of prediction. The performance is investigated in a more practical and general angle of departure spectrum model with a concentration direction and a spreading factor. Simulations validate that the SINR approximations are tight, and show that the polynomial fitting with a proper prediction order can achieve a satisfying performance, when the concentration direction and the spreading factor are small.

This paper studies joint beamforming and power allocation for multicell multiuser multi‐antenna systems with the objective of maximising the minimum signal‐to‐interference‐plus‐noise ratio (max–min SINR). The authors first consider developing an iterative algorithm to achieve the optimal performance by extending the uplink–downlink duality for finite‐scale wireless communication systems. The solution is then generalised to achieve the asymptotically optimal multicell beamforming with the aim to reduce the overhead of signalling exchange between coordinated base stations based on large dimension random matrix theory. Based on that, an efficient multicell beamforming algorithm is proposed to asymptotically achieve the max–min SINR. To further solve the complexity issue of large dimensional matrix inversion involved in the calculation of beamforming vectors, they propose a low‐complexity beamforming calculator based on truncated polynomial expansion approach. Numerical results validate the effectiveness of the authors’ proposed algorithms and show that they can achieve the optimal or asymptotically optimal performance in a massive multi‐input multi‐output system with low complexity and small backhaul overhead.

In this study, the authors propose an energy‐efficient user association scheme to maximise the overall energy efficiency for downlink heterogeneous cellular networks, and formulate it as a non‐linear and mixed‐integer optimisation problem. Such a problem includes user association problem and power control problem. Since the formulated problem is in a fractional and mixed‐integer form, it is challenging for designers to achieve the optimal solutions of this problem. To this end, they design an effective three‐layer iterative algorithm. In the first layer, the energy efficiency parameter is found via bisection method. In the second layer, association index and transmit power are optimised alternately. In the third layer, the user association problem is solved via dual decomposition method and the transmit power is updated by employing a power update function. In addition, they further give some convergence analyses for some parts (user association algorithm and power control algorithm) of the proposed algorithm, and also give some complexity analyses for the whole algorithm. Numerical results show that, compared with non‐energy‐efficient association, the energy‐efficient association has significant superiorities on load balancing level, system throughput and energy efficiency.

A hierarchy precoding approach is proposed in this study for multi‐cell multiuser systems with any number of base stations and that of users, which is suitable for any number of data streams. The key feature of this approach is aligning the inter‐user interferences within the same cell to the room spanned by the inter‐cell interferences, by which both the inter‐cell and inter‐user interferences are cancelled simultaneously. Then, the inter‐stream interference for each user can be easily tackled. It is found that the interference alignment‐based hierarchy precoder achieves to the full freedom of degree. With interference‐free transmissions achieved by the proposed precoder, the transmit power is optimised in an analytical expression by maximising the sum rate and minimising the sum weighted mean square error. Extensive simulations demonstrate the effectiveness of the proposed method.

Wireless networks face the challenge of increasing energy consumption while satisfying the unprecedented demand for higher data rates. Energy-efficient transmission has been regarded as a key technology for the next-generation wireless system. Meanwhile, to reduce the cost, in practice, a base station usually has less radio chains than the antennas, which makes antenna selection an appealing transmission strategy. This letter addresses the problem of joint optimization of energy-efficient beamforming and antenna selection for downlink multiuser systems. The nonconvexity arising from both the nonlinear fractional programming and the ℓ0-(quasi)norm presents the main difficulty in solving the joint optimization problem. Nevertheless, we develop an effective algorithm to address this problem. Numerical results are given to validate the effectiveness and the performance of the developed algorithm.

A novel low-complexity multiuser adaptive modulation (MAM) scheme is proposed for uplink massive multiple input and multiple output systems with zero-forcing detection, which requires only slow-varying large-scale shadowing information for the users. Closed-form expressions are derived for the average spectral efficiency (ASE) and bit error outage (BEO). In addition, for MAM with joint power control, optimal power adaptation strategy and switching thresholds are obtained. Compared with the conventional fast adaptive modulation, which requires fast-varying small-scale fading information, the proposed MAM scheme achieves similar ASE performance with slight increase in BEO.

This paper studies the secure beamforming design for simultaneous wireless information and power transfer in a multiple-input single-output broadcast channel with confidential messages and external eavesdroppers, where each receiver adopts the power splitting (PS) scheme to decode information and harvest energy concurrently, and it is also seen as a potential eavesdropper for messages not intended for it. Our objective is to minimize the total transmit power while guaranteeing the individual secrecy rate and energy harvesting constraints at each receiver by jointly optimizing transmit beamforming vectors, artificial noise covariance, and receive PS ratios. Both scenarios of perfect and imperfect channel state information (CSI) at the transmitter are considered. For the perfect CSI case, we propose a two-stage optimization approach to solve the original non-convex problem global optimality, and also provide a low-complexity suboptimal solution based on the particle swarm optimization algorithm. Furthermore, we also extend the above result to the colluding eavesdroppers scenario. For the imperfect CSI case, we propose a worst-case-based robust formulation, where the CSI errors are norm-bounded. With the aid of S-Procedure, we derive the equivalent forms for constraints and then transform the non-convex robust design into a convex optimization problem. Simulation results are finally presented to demonstrate the performance of our proposed schemes.

This paper investigates the ergodic achievable secrecy rate (EASR) of multiple-antenna amplify-and-forward relay systems, where one eavesdropper can wiretap the relay. To reveal the capability of the multiple-antenna relay in improving the secrecy performance, we derive new tight closed-form expressions of the EASR for three secure transmission schemes: artificial noise aided precoding (ANP), destination based jamming (DBJ) and eigen-beamforming (EB). We also derive the lower bounds of the EASR for ANP and DBJ with a large antenna array at the relay, and investigate their corresponding asymptotic performance in the high SNR and low SNR regimes to show valuable intrinsic insights as well. Based on the asymptotic analysis, we optimally allocate the power to the information signal and the artificial noise. Both the analysis and simulation results indicate that, in the moderate-to-high SNR regime, ANP achieves considerable performance gain over DBJ and EB, while in the low SNR regime, EB outperforms the other two schemes with equal power allocation. As SNR grows large, the EASR of EB approaches a constant independent of the first hop channel. Moreover, in the high SNR regime, it is optimal to allocate around half of total power to artificial noise for ANP and most of the power to artificial noise for DBJ.

In this paper, we propose a secure transmission scheme for simultaneous wireless information and power transfer (SWIPT) in a multiple-input single-output (MISO) broadcast channel with confidential messages, where each receiver utilizes the power splitting approach to decode information and harvest energy simultaneously, and it also acts as a potential eavesdropper for the independent message sent to the others. By jointly optimizing the transmit beamforming vectors, covariance of artificial noise, and receive power splitting (PS) ratios for all receivers, we aim to maximize the total harvested energy while guaranteeing the secrecy rate constraint at each receiver and the total transmit power constraint at the transmitter, which is a non-convex optimization and hard to solve. In this paper, we propose a two-stage optimization approach based iterative algorithm to tackle such a challenging problem. Moreover, we prove that the proposed algorithm can achieve convergence, and also analyze its computational complexity. Finally, simulation results are provided to demonstrate the performance of our proposed algorithm.

This paper investigates the ergodic secrecy capacity (ESC) of multiple-antenna amplify-and-forward relay systems, where one eavesdropper can wiretap the relay. To reveal the capability of the multiple- antenna relay in improving the secrecy performance, we derive new tight closed-form lower bounds of the ESC for two secure transmission schemes: artificial noise aided precoding (ANP) and eigen-beamforming (EB). We also derive the lower bound of the ESC for ANP with a large antenna array at the relay, and investigate their corresponding asymptotic performance in the high and low SNR regimes. Based on the asymptotic analysis, we optimally allocate the power to the information signal and the artificial noise. Both the analysis and simulation results indicate that, in the moderate-to-high SNR regime, ANP achieves considerable performance gain over EB, while in the low SNR regime, EB outperforms ANP with equal power allocation. As SNR grows large, the ESC of EB approaches a constant only related to the number of relay antennas. Moreover, in the high SNR regime, it is optimal to allocate around half of total power to artificial noise for ANP.

The sparse channel estimation which sufficiently exploits the inherent sparsity of wireless channels, is capable of improving the channel estimation performance with less pilot overhead. To reduce the pilot overhead in massive MIMO systems, sparse channel estimation exploring the joint channel sparsity is first proposed, where the channel estimation is modeled as a joint sparse recovery problem. Then the block coherence of MIMO channels is analyzed for the proposed model, which shows that as the number of antennas at the base station grows, the probability of joint recovery of the positions of nonzero channel entries will increase. Furthermore, an improved algorithm named block optimized orthogonal matching pursuit (BOOMP) is also proposed to obtain an accurate channel estimate for the model. Simulation results verify our analysis and show that the proposed scheme exploring joint channel sparsity substantially outperforms the existing methods using individual sparse channel estimation.

This paper investigates the antenna selection scheme choosing the antenna with the largest channel gain to the the cellular user for device-to-device (D2D) communication underlaying cellular networks. We derive an exact closed-form expression of the ergodic achievable rate and examine its asymptotic behavior in the high signal-to-noise ratio (SNR) regime. It is demonstrated that the high SNR approximation can be much improved by a higher transmit power ratio between the base station (BS) and the D2D transmitter in the small cell setting where all users are closely located. However, in the macro cell setting in which only the D2D terminals are fairly close, the influence of the transmit power ratio becomes insignificant. In addition, we present upper and lower bounds of the ergodic achievable rate. Based on these results, we illustrate that the D2D communication cannot help in the cellular network for elevating the rate when the transmit SNR at the BS grows high. If the BS SNR is lower, then the D2D communication can effectively increase the ergodic achievable rate. Numerical results are provided to justify the correctness of the expressions and the relevant performance analysis.

This paper considers a downlink massive MIMO frequency division duplexing (FDD) system. Due to the large number of antennas, the required length of training sequence for downlink training significantly increases in FDD mode, which leads to prohibitive overhead in real system. Thus, in this work we investigate how the length of training sequence affects the system performance. For this purpose, we derive an analytical expression of the ergodic achievable rate from a worst case viewpoint with the the training sequence length as a parameter in it. It is revealed from the analytical results that i.) the length of training sequence divided by the number of base station antennas approaches to zero yet the achievable rate can increase to infinity as long as the antenna number is sufficient large; ii.) there is a ceiling effect on the achievable rate if the antenna number grows large with any fixed training length. Furthermore, we propose a guideline for the selection of the training length. Numerical results validate the derivations and analysis.

To make full utilize the limited resources in heterogeneous cellular networks (HCNs), a proper offloading scheme is widely advocated. However, such scheme often lead to a bad result that the offloaded users achieves lower signal-to-interference-plus-noise-ratios (SINRs) than these users in macro-cells. To partially alleviate the SINR degradation, we consider an interference avoidance technique, i.e., a resource (frequency) partitioning strategy that turns off some fraction of such resource in a macrocell and saves it for low-power base stations (BSs). Naturally, an optimal offloading scheme should be closely coupled with the resource partitioning, and in turn an optimal partition decides the offloading performance. In this paper, we maximize a sum-utility with joint offloading and interference avoidance for HCNs. Considering that the formulated problem is in a nonlinear mixed-integer form and difficult to tackle, we introduce a dual decomposition method to develop an effective distributed algorithm. We reveal that load balancing, by itself, is insufficient, and additional interference avoidance is required for improving the system performance. Meanwhile, we show that the proposed scheme can provide a load balancing gain and an interference avoidance gain.

In this paper, a massive MIMO downlink frequency division duplexing (FDD) system over correlated Rayleigh fading channel is considered. It is well known that the length of training sequence not only affects the accuracy of channel estimation but also accounts for the rate loss resulting from training overhead. However, as the number of the base station antennas becomes large, the required length of training sequence cannot increase unlimitedly. Thus, in this work we derive the analytical expression of achievable rate and investigate the impacts of the training sequence length on system asymptotic performance. It is discovered from the analytical results in two-fold that (1) the length of training sequence normalized by the antenna number approaches to zero yet the system capacity is guaranteed to positive infinity as long as the antenna number is large enough; (2) the transmission capability saturates to a certain level if the antenna number grows to very large with any given training length. Simulation results verify the theoretical derivations and demonstrate the performance limit.

This paper investigates the resource allocation issue between downlink training stage and data transmission stage for the frequency division duplexing (FDD) massive multiple-input multiple-output system from the viewpoint of energy efficiency (EE). For a given total energy budget during a coherence period, how to jointly select the training duration, training power and data power is of great significance for the system EE. Thus, an optimization problem of energy-efficient resource allocation is put forward. Since the analytical expression of the involved average spectral efficiency (SE) is intractable, a closed-form approximation of the SE is deduced using deterministic equivalent. Based on the simplified expression, the original non-convex fractional optimization problem is transformed into an equivalent problem in subtractive form by the means of fraction programming, which includes an achievable solution. Then, an iterative algorithm is proposed. Numerical results validates the benefits of the proposed resource allocation scheme.

In this paper, we design a secure transmission scheme for multiple-input single-output (MISO) broadcast channel with simultaneous wireless information and power transfer (SWIPT), where a multi-antenna transmitter simultaneously transmit independent confidential messages to multiple potentially malicious receivers, in the presence of external eavesdroppers. Our objective is to minimize the total transmit power while guaranteeing the security communication and energy harvesting constraints by jointly optimizing the transmit beamforming vectors, covariance of artificial noise, and power splitting ratios, which is non-convex optimization and hard to tackle. We first solve this non-convex problem by using the technique of semi-definite relaxation (SDR), and then prove that the relaxation is tight and thus achieves the globally optimal solution of the original problem. Simulation results are finally presented to demonstrate the performance of our proposed scheme.

This paper studies joint power allocation and beam-forming for energy efficient communication in coordinated multi-cell multi-user downlink systems. The considered energy efficiency maximization problem which takes both dynamic and static power consumption into account is non-convex and hard to tackle. To address it, the optimization problem is first transformed into a parametric subtractive form using the classical fractional programming method. Then, by introducing the concept of the interference temperature used in cognitive radio networks, the parameterized subtractive form optimization problem is further decomposed into a master problem and a set of subproblems. Based on that, we exploit the convex approximation to develop a decentralized multi-cell multi-user algorithm, which is shown to converge and only needs limited information exchange between the coordinated BSs. Numerical results show that the proposed decentralized energy efficiency algorithm outperforms conventional power allocation algorithms and exhibits a performance close to the optimal centralized solution.

In this paper, we investigate the achievable ergodic sum-rate for multiuser massive multiple-input multiple-output systems, where different array configurations such as uniform linear arrays (ULAs), uniform planar arrays (UPAs) and uniform circular arrays (UCAs), are deployed at the base station (BS). The investigation is carried out based on a three dimensional spatial propagation channel model by taking into account both the azimuth and elevation angular domains. We first investigate the value of the inner product of two channel vectors, for which closed-form expressions are derived with different array configurations in line-of-sight channel. After that, the effects of the BS antenna number, the angles of departure of users, and the inter-antenna spacing on the inner product, further, on the achievable ergodic rate, are investigated. Finally, theoretical results are verified via numerical simulations, which is shown that deploying ULAs outperforms the other array configurations (UPAs and UCAs) in terms of improving the achievable ergodic rate and the larger inter-antenna spacing can contributes substantially to the achievable sum-rate.

This paper studies coordinated beamforming design for massive MIMO heterogeneous network where the macro BS (MBS) is equipped with a large antenna array. To suppress the severe cross-layer interference with limited inter-BS communications, especially to avoid the exchange of instantaneous massive MIMO channels, we propose a two-stage coordinated beamforming scheme to realize space division interference mitigation through exchange of hybrid long-term and short-term channel information. In the first stage, we propose a space partitioning codebook for the macro cell with each codebook (beam) covers a certain area. Based on that we remove from the codebook the beams that cause severe interference to the lower-power cells on a statistical or long-term basis. The left beams are then used as the outer precoder at the MBS, which equivalently serves as a dimension reduction matrix and is able to eliminate the interference to the lower-power cells; While in the second stage, the MBS designs its inner precoder based on the reduced-dimensional instantaneous effective channel to improve the performance of its own users. Furthermore, we extend our approach to the case of three dimensional (3D) beamforming, by designing additional beams in the azimuth angel direction. Numerical results show that our proposal is particularly effective when the number of antennas at the MBS is sufficiently large.

This study addresses the pilot scheduling problem in multiuser multi‐cell massive multiple‐input–multiple‐output (MIMO) systems, aiming at mitigating the inter‐cell interference (pilot contamination), which constitutes a bottleneck of the system performance. First, the authors investigate the pilot reuse and scheduling problem in cellular systems with unlimited number of base station antennas, only considering the large fading coefficients. Three low‐complexity pilot scheduling schemes are then proposed by maximising the system achievable sum rate, including the greedy algorithm, the tabu search (TS) algorithm and the greedy TS algorithm. Second, they investigate the pilot reuse problem among the inter‐cell user terminals (UTs) under spatially correlated channels for massive MIMO transmission, trying to distinguish UTs sharing the same time‐frequency resources from the spatial domain. A closed‐form expression of the non‐asymptotic downlink achievable rate is derived by exploiting the second‐order channel statistical information. On the basis of the degree of the UTs’ covariance matrices overlap with each other, they further propose a spatial orthogonality‐based greedy pilot scheduling algorithm. The proposed approaches can provide much better performance in the presence of pilot contamination. Theoretical analysis and numerical results both verify the effectiveness of the proposed algorithms.

In this study, the authors propose a user association scheme with quality of service support for load balancing in heterogeneous cellular networks (HCNs), which jointly considers user's achievable rate and load level of each BS instead of only utilising the former. To reveal how HCNs should self‐organise, the authors formulate it as a network‐wide weighted utility maximisation problem. Note that the formulated problem is a non‐linear mixed‐integer one, and its optimal solutions may be very difficult to be found when it is large‐scale. To solve the proposed problem, the authors design a low‐complexity distributed algorithm via dual decomposition. Numerical results show that, compared with the range expansion association (REA) and best power association (BPA), the strategy has a higher load balancing level (LBL) and a lower call blocking probability (CBP). Meanwhile, the proposed algorithm occupies a very fast convergence rate when its parameters are set properly.

In heterogeneous cellular networks (HCNs), the user association is a challenging topic since some different base stations coexist. Moreover, because of the asymmetric uplink and downlink in HCNs, a joint uplink and the downlink association algorithm should be designed to improve the system performance. For the practical implementation, the authors need to ensure that the algorithm is highly effective. Thus, they try to design an association strategy that jointly maximises downlink sum rate and minimises uplink sum power, and formulate it as a sum‐utility maximisation problem. To solve this problem, they design a centralised association algorithm via a gradient descent method, and develop a distributed association algorithm via dual decomposition. Simulation results show that, compared with the signal strength‐based association, range expansion association and the method proposed by Ye, their scheme has some significant advantages in the mass.

This paper investigates the problem of joint base station selection and resource allocation in an orthogonal frequency division multiple access (OFDMA) heterogeneous cellular network. The original throughput maximization problem is NP-hard and we propose solving it by using game theoretic stochastic learning approaches. To this end, we first transform the original problem into a tractable form, which has a weighted utility function. Then we prove that an exact potential game applies and it exists the best Nash equilibria which is a near optimal solution of the original problem when an efficient solution method of the weights is employed. To obtain the optimal solution, we redesign the utility function by leveraging a state space to formulate the original problem into an ordinal state based potential game, which is proved that it exists a recurrent state equilibrium point that maximizes system throughput. Furthermore, we propose two different variants of Max-logit learning algorithm based on these two games respectively: one is a simultaneous learning algorithm with less information exchange, which achieves the best Nash equilibria point of the exact potential game and the other is an efficient learning algorithm for the ordinal state based potential game, which can converge to the global optimization solution. Finally, numerical results are given to validate that theoretical findings.

The integration of cooperative transmission into satellite networks is regarded as an effective strategy to increase the energy efficiency as well as the coverage of satellite communications. This paper investigates the performance of an amplify-and-forward (AF) hybrid satellite-terrestrial relay network (HSTRN), where the links of the two hops undergo Shadowed-Rician and Rayleigh fading distributions, respectively. By assuming that a single antenna relay is used to assist the signal transmission between the multi-antenna satellite and multi-antenna mobile terminal, and multiple interferers corrupt both the relay and destination, we first obtain the equivalent end-to-end signal-to-interference-plus-noise ratio (SINR) of the system. Then, an approximate yet very accurate closed-form expression for the ergodic capacity of the HSTRN is derived. The analytical lower bound expressions are also obtained to efficiently evaluate the outage probability (OP) and average symbol error rate (ASER) of the system. Furthermore, the asymptotic OP and ASER expressions are developed at high signal-to-noise ratio (SNR) to reveal the achievable diversity order and array gain of the considered HSTRN. Finally, simulation results are provided to validate of the analytical results, and show the impact of various parameters on the system performance.

In this paper, we investigate joint beamforming and power allocation in multicell multiple-input single-output (MISO) downlink networks. Our goal is to maximize the utility function defined as the ratio between the system weighted sum rate and the total power consumption subject to the users’ quality of service requirements and per-base-station (BS) power constraints. The considered problem is nonconvex and its objective is in a fractional form. To circumvent this problem, we first resort to an virtual uplink formulations of the the primal problem by introducing an auxiliary variable and applying the uplink-downlink duality theory. By exploiting the analytic structure of the optimal beamformers in the dual uplink problem, an efficient algorithm is then developed to solve the considered problem. Furthermore, to reduce further the exchange overhead between coordinated BSs in a large-scale antenna system, an effective coordinated power allocation solution only based on statistical channel state information is reached by deriving the asymptotic optimization problem, which is used to obtain the power allocation in a long-term timescale. Numerical results validate the effectiveness of our proposed schemes and show that both the spectral efficiency and the energy efficiency can be simultaneously improved over traditional downlink coordinated schemes, especially in the middle-high transmit power region.

In this paper, we investigate the uplink rate of multicell massive multiple-input multiple-output (MIMO) systems. We assume the channel is estimated through uplink training with MMSE estimation. Unlike previous studies, the channel between users and the base station (BS) in the same cell is modeled to be Ricean fading, in which the fast fading matrix is assumed to have a deterministic component as well as a Rayleigh-distributed random component, and the Ricean K-factor of each user is supposed to be different. The effect of pilot contamination is analyzed and we derive a closed-form approximation for the achievable uplink rate that holds for any finite number of BS antennas (M). Based on it, we find that increasing the proportion of line-of-sight (LOS) component can improve the uplink performance. In particular, with both very large M and Ricean K-factor, the uplink rate grows infinite, which means that the pilot contamination can be eliminated completely. However, the increase of users' transmit power will make the uplink rate approach a constant value even with unlimited M. In addition, we show that with no reduction in the rate performance, each user's power can be most scaled down to 1/√M with Rayleigh fading channel and to 1/M with non-zero Ricean K-factor.

This paper addresses robust beamforming (BF) design for the uplink transmission of wireless networks, where the base station (BS) equipped with a very large number of antennas communicates with multiple users on the same frequency band simultaneously. Based on the assumption that the wireless channels undergo correlated Rayleigh fading, we first formulate an optimization problem to maximize the output signal-to-interference-plus-noise ratio (SINR) of the intended users. Then, by using the fact that channel uncertainty is norm-bounded and imperfect channel state information (CSI) is available at the BS, we transform the optimization problem to a support vector machine (SVM) regression one, and obtain the robust solution for the BF weight vectors by means of quadratic programming (QP) technique or iterative reweighted least squares (IRWLS) procedure. The computational cost of the proposed robust BF scheme depends on the number of channel vector samples rather than that of the antennas, thus it is suitable for the wireless systems with large-scale antennas. Finally, the efficiency and superiority of the proposed new scheme are confirmed through computer simulation.

In this paper, a robust transmission scheme for simultaneous wireless information and power transfer (SWIPT) in the presence of an eavesdropper is proposed. With imperfect channel state information (CSI) at the transmitter, the optimal transmit covariance matrix is obtained based on the maximization of the worst case harvested energy for the energy receiver (ER) while guaranteeing the achievable secrecy rate constraint for the information receiver (IR). To solve such a challenging nonconvex problem, in this paper, a two-stage optimization approach is proposed. In the first stage, the original problem is transformed into a robust design problem, which can be further converted into a convex semidefinite program (SDP) problem by using S-procedure as a tool. In the second stage, the optimal transmit covariance matrix is obtained via the aid of one-dimensional search algorithm. Finally, simulation results are provided to illustrate the robustness and effectiveness of the proposed method.

Spectral efficiency (SE) and energy efficiency (EE) are both essential in future wireless communications. To improve the system performance on these two metrics, in this paper we consider the weighted sum rate maximization (WSRMax) problem subject to per-cell EE constraints and per-BS transmit power constraints in multicell multiuser downlink systems. This problem is difficult in its original form due to the introduction of new EE constraints. We first reveal that the original problem can be transformed into an equivalent parameterized polynomial form by introducing some auxiliary variables. By exploiting the concavity property with respect to each variable in the equivalent problem, an efficient block coordinate ascent algorithm is then proposed with guaranteed convergence property. Numerical results show that compared with the conventional WSRMax algorithm, in addition to fulfilling the EE requirement of each cell, our algorithm achieves a better system EE performance at the cost of a slight sum rate performance loss at a certain region and offers a new insight on the SE-EE tradeoff in wireless communication systems.

This paper investigates the performance of a two-way decode-and-forward (DF) multi-antenna relay network. A joint antenna selection scheme for all nodes is first proposed based on the maximizing the worse received signal to noise ratio (SNR) of two end users. Then, we derive the probability density function (PDF) and cumulative distribution function (CDF) of the received SNRs of both users. We also achieve the closed-form expressions of average bit error rate (BER) and outage probability of the relay system. Furthermore, we reveal the asymptotic behavior of our system when transmitting SNR or the number of antennas is large. Our analysis shows that the proposed DF antenna selection scheme achieves full diversity. The numerical results finally verify the accuracy of our analysis.

In this paper, we propose a load-aware and QoS- aware user association strategy that jointly considers the load of each BS and user's achievable rate instead of only utilizing the latter, and formulate it as a network-wide weighted utility maximization problem to reveal how a heterogeneous cellular network should self-organize. This is a nonlinear mixed-integer optimization problem, and its optimum solutions are very difficult to be obtained when it is large scale one. To solve the proposed problem, we relax association indicator variables and adopt a gradient descent method to find optimum solutions. Then, each user is associated with some BS with a maximum association indicator taken from solutions of the relaxed optimization problem. Experimental results show that, compared with the best power association and range expansion association, our strategy has a lower call blocking probability and a higher load balancing level.

This study considers the joint transmission precoding design for downlink multicell multiuser multiple‐input single‐output systems where imperfect channel variances are available at the base stations. The authors aim to tackle the robust signal‐to‐interference‐plus‐noise ratio (SINR) balancing problem to maximise the minimum worst‐case user rate. To solve the non‐convex problem, a duality relationship between the downlink max–min worst‐case SINR optimisation problem and the virtual uplink min–max worst‐case SINR optimisation problem is first revealed. Based on this, a new algorithm is developed to solve the virtual problem by using jointly the sub‐gradient method and the geometric programming methods, whose achieved solution is finally converted to the downlink. Their analysis shows that the proposed algorithm is guaranteed to converge and has lower computational complexity than conventional approaches. Moreover, computer simulations validate the effectiveness of the proposed method and show that the proposed algorithm has fast convergence and achieves a performance close to that of the brute search method.

Energy-efficient communications has attracted much interest in the research of 5G cellular systems. In this paper, we study energy-efficient coordinated beamforming design for heterogeneous multicell multiuser downlink systems. The considered problem is formulated as maximizing the weighted sum per-cell energy efficiencies (WSPEEMax) subject to predefined per-user target rate demands, maximum leakage interference power constraints, and per-BS transmit power constraints. This formulation is more general than the conventional EE optimization problem and provides a unified way to consider the EE of heterogeneous networks. However, it is hard to tackle due to the weighted sum-of-ratios form of the objective function and the non-convex nature of per-user target rate constraints. To address it, we propose to first transform the original problem into a polynomial form optimization by introducing some auxiliary variables and then further reveal their equivalence in finding the solution. Then, an efficient block coordinate ascent optimization algorithm is developed to solve the equivalent problem by exploiting the concave nature of the considered problem with respect to each optimization variable. To further improve the network EE, we also develop an energy-efficient transmission method for each small-cell network. Finally, extensive numerical results are provided to verify the effectiveness of the proposed schemes and show that both the EE and spectral efficiency (SE) of heterogeneous network can be significantly improved by energy-efficient coordinated multiple-input multiple-output (MIMO) transmission.

This paper investigates the coordinated beam-forming design for multi-cell MISO downlink beamforming system, aiming at maximizing the sum rate. In the proposed scheme, convex approximation approach is used to first recast the primal non-convex problem into an approximate problem of minimizing the sum of weighted inverse SINR. Then, an alternating optimization method is developed to address the approximate problem based on uplink-downlink duality. We show that our solution is globally optimal in the case of two-BS cooperation with a total power constraint, and is also effective in a general case. When extending to per-BS power constraints, an alternating optimization algorithm with provable convergence to stationary point is proposed following a similar procedure. Our simulation results show that the proposed scheme has a fast convergence and achieves a sum rate performance very close to the optimal performance obtained by exhaustive search.

This paper proposes a new codebook structure called Kronecker-product based codebook (KPC), where each codeword is the Kronecker product of two oversampled DFT codewords in both the horizontal and vertical domains. The KPC is especially suitable for the three-dimensional (3D) multiuser multi-input multi-output (MU-MIMO) systems. Besides, channel state information feedback based on the best companion cluster scheme is investigated. Since all codewords have been grouped into several clusters, each user feeds back its best precoding matrix index, best interference cluster index and channel quality information, then the BS pairs and schedules users according to the received feedback. Different codewords clustering methods affect the performance of the limited feedback schemes. We proposes two kinds of codewords clustering methods based on 3D beam patterns, including both the symmetric and asymmetric one. Simulation shows that with properly clustered codewords, our proposed 3D MU-MIMO feedback scheme has a significant throughput gain against 2D MU-MIMO feedback scheme.

In this paper we analyze the performance of a small cell network where the locations of the base stations are generated according to Poisson distribution and linear multiuser precoding is employed. The performance metrics of both the overall outage probability (OOP) and the symbol error probability (SEP) are investigated for this small cell network. Tight closed-form expressions for the OOP and the average SEP are derived, and an asymptotic approximation to the OOP and the average SEP are also obtained, respectively. Their accuracy are validated via our numerical results. In addition, we propose a new access mechanism to maximize the energy efficiency and further evaluate its performance analytically. Both theoretical and numerical results show that the proposed scheme could effectively improve the efficiency of the small cell heterogeneous network.

This paper considers robust beamforming design for coordinated multiple point joint transmission systems with imperfect channel state information at the base stations (BSs). A robust lower bound duality relation between the downlink max-min worst-case SINR optimization problem and the virtual uplink min-max worst-case SINR optimization problem is first revealed. Based on this, an iterative optimization method is then proposed using the subgradient theory to solve the virtual uplink problem, by which the solution to the downlink optimization problem is easily obtained. It is proved that the convergence of the proposed algorithm can be guaranteed with monotonic boundary sequence theorem and the subgradient theory. Numerical simulation verifies the effectiveness of the proposed method.

We investigate pilot contamination problem for very large MIMO multi-cell TDD system. The asymptotic sum rate of two typical precoding schemes, i.e., the single-cell zero forcing (ZF) precoding and the multi-cell minimum mean square error (MMSE) based coordinated precoding are first derived. Results show that these two schemes have the same asymptotic sum rate expression as the number of the base station antenna going to infinity, revealing that coordinated precoding based on local channel state information (CSI) only provides marginal gain in the presence of pilot contamination. Based on our derivations, a pilot scheduling scheme is further proposed to mitigate pilot contamination, which could provide much better performance. Numerical results finally verify our derivations and the proposed scheme.

In this paper we study energy efficient joint power allocation and beamforming for coordinated multicell multiuser downlink systems. The considered optimization problem is in a non-convex fractional form and hard to tackle. We propose to first transform the original problem into an equivalent optimization problem in a parametric subtractive form, by which we reach its solution through a two-layer optimization scheme. The outer layer only involves one-dimension search for the energy efficiency parameter which can be addressed using the bi-section search, the key issue lies in the inner layer where a non-fractional sub-problem needs to tackle. By exploiting the relationship between the user rate and the mean square error, we then develop an iterative algorithm to solve it. The convergence of this algorithm is proved and the solution is further derived in closed-form. Our analysis also shows that the proposed algorithm can be implemented in parallel with reasonable complexity. Numerical results illustrate that our algorithm has a fast convergence and achieves near-optimal energy efficiency. It is also observed that at the low transmit power region, our solution almost achieves the optimal sum rate and the optimal energy efficiency simultaneously; while at the middle-high transmit power region, a certain sum rate loss is suffered in order to guarantee the energy efficiency.

In this paper, a new analytical expression of the max-min SINR duality between the multi-cell downlink and the virtual uplink subject to per-BS power constraints is firstly given. Based on that, a hierarchical iterative scheme is proposed to solve the virtual uplink optimization problem. The uplink solution is then converted to achieve the solution to the multi-cell downlink beamforming problem. Simulation results show that, in contrast to existing multi-cell beamforming schemes, the proposed scheme achieves better performance in terms of both the worst-user rate and the rate per energy.

In this paper, we address the problem of the coordinated beamforming design for multi-cell multiple input single output (MISO) downlink system subject to per-BS power constraints. The objective is taken as the maximization of the minimum signal-to-interference plus noise ratio (SINR), while a complete analysis of the duality between the multi-cell downlink and the virtual uplink optimization problems is provided. A hierarchical iterative scheme is proposed to solve the virtual uplink optimization problem, whose solution is then converted to derive the one of the multi-cell downlink beamforming problem. The proposed algorithm is proved to converge to a stable point. Additional, the complexity of the proposed algorithm is analyzed. Simulation results show that, in contrast to existing multi-cell beamforming schemes, the proposed algorithm achieves better performance in terms of both rate per energy (RPE) and the worst-user rate.

This paper addresses coordinated downlink beamforming optimization in multicell time division duplex (TDD) systems where a small number of parameters are exchanged between cells but with no data sharing. With the goal to reach the point on the Pareto boundary with max-min rate fairness, we first develop a two-step centralized optimization algorithm to design the joint beamforming vectors. This algorithm can achieve a further sum-rate improvement over the max-min optimal performance, and is shown to guarantee max-min Pareto optimality for scenarios with two base stations (BSs) each serving a single user. To realize a distributed solution with limited intercell communication, we then propose an iterative algorithm by exploiting an approximate uplink-downlink duality, in which only a small number of positive scalars are shared between cells in each iteration. Simulation results show that the proposed distributed solution achieves a fairness rate performance close to the centralized algorithm while it has a better sum-rate performance, and demonstrates a better tradeoff between sum-rate and fairness than the Nash Bargaining solution especially at high signal-to-noise ratio.

We address the problem of combining limited feedback information with long-term channel statistical information in the design of downlink SDMA schemes. A novel combining method is developed to improve the quality of channel knowledge at the base station. More specifically, a set of novel feedback parameters is proposed and a related method is developed to estimate a representation of the multiuser channel vectors at the base station. This method utilizes the hybrid information by combining instantaneous channel feedback and long-term channel statistics, and is based on a channel phase codebook designed using the generalized Lloyd algorithm. The estimated channel knowledge at the base station can be used for joint design of multiuser precoding and opportunistic scheduling. The advantage of the proposed scheme over existing CSI quantization based SDMA schemes is further confirmed by computer simulations.

The analysis shows that the conventional codebook construction of Grassmannian subspace packing can not control the performance loss caused by a linear receiver, while a proper unitary perturbation to the codebook is capable of compensating for this performance loss. This paper therefore proposes a novel hierarchical codebook to exploit the gain of unitary perturbation. The hierarchical codebook consists of a Grassmannian subcodebook and a proposed perturbation subcodebook. To implement precoding, this paper also presents a successive codeword selection scheme, thus the receiver would successively selects two preferred codewords from Grassmannian and perturbation subcodebooks. With the feedback binary indices of these selections, the transmitter uses the product of two preferred codewords as the precoder. The theoretical analysis of the proposed precoding technique shows that the usage of the perturbation subcodebook can improve to a certain degree the system performance in terms of throughput as well as BER, with a small additional feedback overhead, and the proposed codebook would reduce the computational and storage requirements in contrast to the conventional codebook. It is also shown via computer simulations that the proposed technique gives a better BER performance than the single Grassmannian codebook based precoding technique does, even if the feedback overhead remains the same.

An iterative linear transceiver design scheme under sum mean squared error minimization criterion is proposed. By modifying the structure of transceiver, the complex computation of Lagrangian multiplier within traditional MMSE transceiver design can be effectively obviated, and thus the whole system complexity can be greatly reduced. Because the Lagrangian multiplier has analytical solution, transmit preceding matrix also has closed-form solution, and can be solved easily with fixed- point iterations. The receiver filter is worked out independently with MMSE criterion at each terminal, and the downlink signaling of each receive filter from base station is not necessary- Simulations demonstrate that the proposed scheme is effective.

In this paper, the conventional Grassmannian codebook for precoding is first analyzed, showing that the performance loss caused by linear receivers was not taken into account. To tackle the performance loss issue, a novel hierarchical codebook consisting of a Grassmannian subcodebook and a perturbation subcodebook is then proposed for precoding systems with linear receivers. A two-step codeword selection scheme that uses the product of two codewords selected from the subcodebooks as the precoder is also presented. Our analysis shows that the perturbation subcodebook is able to compensate for the performance loss from linear receivers. Compared with the Grassmannian codebook, the superiority of the proposed codebook in terms of search complexity as well as throughput/ BER is further confirmed by computer simulations.

This paper proposes a robust precoding for the space time block codes (STBC) and spatial multiplexing (SM) hybrid system. The overall precoding is disassembled into independent precoder optimization of different STBC groups by decomposing the overall channel matrix into several independent virtual channel matrices, this method will considerably reduce the amount of the required feedback information. Also, the precoder optimization is based on a new derived SER bound and considers the uncertainty of the channel feedback. Simulation results demonstrate the performance of the proposed technique.