ABSTRACT This study proposes a federated split learning framework for large language models (FedsLLM) integrated with rate‐splitting multiple access (RSMA), aimed at enhancing the efficiency and privacy of LLM training in wireless communication systems. By leveraging low‐rank adaptation (LoRA) to distribute computational loads and a fluid antenna system to dynamically optimize channel capacity, the framework effectively reduces training latency through joint optimization of learning accuracy and communication resources. Experimental results demonstrate that the proposed framework significantly outperforms traditional time‐division multiple access including time division multiple access, frequency division multiple access (FDMA), enhanced bandwidth FDMA, and fairness‐enhanced FDMA across multiple metrics: at a transmit power of 20 dBm, RSMA reduces task completion time by 8.3%; under 20 MHz bandwidth, it achieves a 25% performance improvement; and even with a data volume of 900 Kbits, it maintains a 12% advantage. The adopted alternating optimization algorithm converges rapidly, reaching 95% of the optimal value within only 5 iterations, substantially outperforming the fixed‐point method. Overall, FedsLLM‐RSMA effectively addresses privacy, computational and communication bottlenecks in distributed LLM training. Compared to TDMA, it reduces total training latency by 28% and improves communication efficiency by 35%, while achieving higher model accuracy and faster convergence. This work provides a viable pathway for efficient and scalable deployment of LLMs in 6G networks.

As the sixth-generation (6G) networks evolve towards a deep integration of communication and computation (ICC), they face challenges of inherent interference and resource competition between heterogeneous services. To address this issue, this paper investigates an uplink ICC system enhanced by a stacked intelligent metasurface (SIM), where SIM’s unique multi-layer structure transforms the wireless channel into a controllable, task-oriented medium. The system is designed to support the coexistence of over-the-air computation (AirComp) tasks, which require high-precision results, and traditional tasks that demand high-quality communication. To this end, we formulate a joint optimization framework aiming to minimize the total mean squared error (MSE) of all computation tasks while strictly guaranteeing the communication quality of service (QoS). To solve the highly non-convex problem of synergistically designing the system resources, we propose an efficient alternating optimization (AO) algorithm. Simulation results demonstrate that the proposed algorithm not only converges rapidly but also achieves up to a 95.2% reduction in total computation MSE compared to an ICC system without SIM, while also significantly outperforming other benchmark schemes, validating the great potential of SIM in proactively managing multi-service conflicts and enabling efficient ICC.

In this paper, we investigate an uplink multi-modal probabilistic semantic communication (PSCom) system based on probability graph in the satellite scenario. The system consists of both: semantic computation and traditional communication. Firstly, at the user end, the transmitted data is compressed based on the probabilistic graph. Then, the compressed data is transmitted to the satellite, which uses the same probabilistic graph to recover the received data. In the considered model, this paper addresses an optimization problem for multi-modal multi-user semantic communication across multiple time slots. An optimization problems formulated aiming to minimize the total energy consumption of the PSCom system, with satisfying the transmission time, transmission power, transmission bandwidth, local computation frequency, and transmission data requirements. To solve this problem, the Lyapunov drift-plus-penalty function based on online optimization is first used to transform the multi-slot problem into a stochastic single-slot problem, thereby converting the optimization problem into a trade-off between system energy consumption and queue length. Subsequently, an alternating algorithm is proposed to iteratively optimize, semantic compression rate, local computation frequency, transmission bandwidth, transmission power, and time allocation variables. Finally, simulation experiments demonstrates the effectiveness of the proposed algorithm.

To overcome the 180° coverage limitation of conventional reflective Reconfigurable Intelligent Surfaces (RIS) in challenging Non-Line-of-Sight (NLoS) environments like underground coal mines, this paper proposes the deployment of a Simultaneously Transmitting and Reflecting RIS (STAR-RIS). The STAR-RIS achieves full 360° signal coverage, effectively addressing the spatial constraints of complex tunnel topologies. Furthermore, we introduce a “Position-Flexible” approach, where the entire panel’s location is dynamically adjusted to maximize the average sum-rate across wideband OFDM subcarriers. By exploiting frequency diversity, the proposed system effectively combats the severe frequency-selective fading inherent in multipath-rich mine tunnels. This holistic movement strategy is specifically designed to enhance hardware reliability in harsh, dust-prone mining conditions by mitigating failure risks associated with complex element-wise mechanical actuation. We formulate a joint optimization problem involving broadband active beamforming, passive phase shifts, and the STAR-RIS coordinates. To solve this non-convex problem, an Alternating Optimization (AO) algorithm is developed. Specifically, the STAR-RIS location is optimized via Projected Gradient Ascent (PGA), while the beamforming and phase-shift coefficients are refined using Successive Convex Approximation (SCA) and Semidefinite Relaxation (SDR). Simulation results confirm that the proposed system significantly improves the sum rate, validating its effectiveness for robust underground wireless connectivity.

Physical layer security (PLS) provides information-theoretic confidentiality for next-generation wireless systems, yet its practical deployment is fundamentally constrained by the sensitivity and noise characteristics of classical radio receivers. Recent advances in quantum sensing, particularly Rydberg atom-based receivers (RAQRs), present a promising approach to address these limitations. This paper introduces a Rydberg atomic quantum multiple-input multiple-output (RAQ-MIMO) architecture for secure multi-user uplink communications, where an array of Rydberg vapor cells functions as a high-sensitivity, reconfigurable receiving front-end. We develop a comprehensive end-to-end signal and channel model that incorporates quantum sensor response, wireless fading, and eavesdropper behavior under passive interception. To maximize the achievable secrecy rate, we propose a joint optimization framework that adaptively tunes key system degrees of freedom, including the Rabi frequency of the local oscillator, phase configuration, digital combining matrix, and user transmit power. Extensive simulations validate that RAQ-MIMO substantially outperforms classical MIMO in terms of secrecy rate across varying transmission distances, power budgets, and user loads—particularly under low-signal to noise ratio (SNR) and extended-range regimes. Our work establishes a quantum-sensing-assisted security foundation for future high-assurance wireless networks, bridging atomic physics with scalable communication system design with scalable communication system design under narrowband operation.

Mobile edge computing–space-air-ground integrated network (MEC-SAGIN) is emerging as a crucial component of future wireless systems. Despite its potential, addressing network fluctuations while ensuring continuous low-latency computing services in highly dynamic environments remains a significant challenge. To address this issue, this paper proposes a fluid antenna (FA)-assisted MEC-SAGIN system, which enhances channel transmission conditions and reduces uplink task offloading latency by flexibly adjusting the antenna ports of edge computing users equipped with FAs. Specifically, we aim to minimize the maximum total computational delay (TCD) of edge computing tasks for ground users (GUs) and the satellite user (SU) by jointly optimizing the task offloading strategies, computational resource allocation, FA port positions, unmanned aerial vehicle (UAV) location, and the receive beamforming matrix. To solve this non-convex problem, we employ the block coordinate descent (BCD) technique to decompose the original problem into four subproblems. The subproblems are optimized using a combination of low-complexity iterative algorithms and the projected gradient descent (PGD) method to refine communication and computation configurations as well as FA port selection. Simulation results demonstrate that the FA-assisted scheme significantly improves the TCD performance of the MEC-SAGIN system. It maintains transmission stability and reliability in dynamic environments while outperforming conventional fixed-position antennas (FPAs) and random-port antenna schemes.

This paper investigates mobile edge computing (MEC)-aided multi-static integrated sensing and communication (ISAC) systems. In the considered system, each device offloads a task to the MEC server for computation. Meanwhile, a sensing receiver (SR) has to detect a target. To enhance the detection capabilities, the devices and base station (BS) collaboratively transmit radar signals, and the SR detects the target based on the received radar echoes. Specifically, the task execution period is divided into two phases. In the first phase, the devices offload part of their tasks while simultaneously transmitting radar signals for target sensing. In the second phase, the MEC server at the BS processes the offloaded tasks, and meanwhile devices along with the BS transmit radar signals for sensing. Thus, the system entities form a multi-static ISAC framework. Radar signal processing schemes are designed for both scenarios—with and without knowledge of the target response amplitudes. The corresponding cooperative detection probabilities under a constant false alarm probability constraint are derived. To ensure high energy efficiency, optimization problem is formulated to minimize energy consumption across all devices, subject to task computation and detection probability constraints. This is achieved by adjusting beamforming at the devices and BS, as well as task partitioning and phase duration allocation. For scenarios with knowledge of target response amplitudes, the problem is efficiently solved by iteratively optimizing beamforming subproblem, time and task division subproblem using weighted minimum mean square error method, successive convex approximation, and golden section search method. For scenarios without knowledge of target response amplitudes, the formulated bi-level optimization problem is first equivalently transformed into a single-level problem, which is then tackled using alternating optimization method, semidefinite programming, and Lagrangian dual method. Simulations validate the benefits of the proposed MEC-aided cooperative sensing scheme compared with numerous benchmarking schemes including traditional non-cooperative sensing scheme.

Multimodal learning emerges as a promising solution for high-precision localization, a cornerstone of 6G integrated sensing and communications (ISAC), by integrating measurements from different data sources. Yet its real-world deployment remains challenging because(i)the quality and relevance of different modalities fluctuate with frequency, noise, and antenna heterogeneity and(ii)spatial and fingerprint ambiguities under non-line-of-sight (NLOS) propagation obscure the mapping between channel measurements and positions. To overcome these challenges, we propose a spatial-context-aware dynamicfusion architecture built on the mixture-of-experts (SCADF-MoE) backbone. We first construct a million-scale comprehensive ray-tracing dataset measuring synchronized angle, distance, gain, and channel across diverse carrier frequencies, antenna geometries, and noise levels. A three-stage pre-processing pipeline then clusters neighboring points into short trajectories, enriching data samples with spatial context information. The resulting sequences are fed into SCADF-MoE: first, multimodal soft MoE blocks with learnable routing matrices dynamically fuse heterogeneous inputs according to their modality relevance in different environmental contexts; second, a modality-task MoE formulates position estimation as a multi-objective problem, simultaneously predicting coordinates of neighboring points to leverage their shared spatial correlations. Additionally, we introduce a regularization loss that enforces expert diversity and mitigates gradient conflicts during multi-task optimization. Simulations across three environments (dense-urban, suburban, canyon) and three heterogeneity dimensions (frequency, noise, antenna) demonstrate that SCADF-MoE achieves consistent sub-meter accuracy in all conditions, reducing overall MSE by 63%, and cuts unseen-NLOS error by 55% compared to state-of-the-art methods. To the best of our knowledge, this is the first work that leverages large-scale multimodal MoEs for high-precision ISAC localization.

In this paper, we propose an energy efficient wireless communication system based on fluid antenna relay (FAR) to solve the problem of non-line-of-sight (NLoS) links caused by blockages with considering the physical properties. Driven by the demand for the sixth generation (6G) communication, fluid antenna systems (FASs) have become a key technology due to their flexibility in dynamically adjusting antenna positions. Existing research on FAS primarily focuses on line-of-sight (LoS) communication scenarios, and neglects the situations where only NLoS links exist. To address the issues posted by NLoS communication, we design an FAR-assisted communication system combined with amplify-and-forward (AF) protocol. In order to alleviate the high energy consumption introduced by AF protocol while ensuring communication quality, we formulate an energy efficiency (EE) maximization problem. By optimizing the positions of the fluid antennas (FAs) on both sides of the FAR, we achieve controllable phase shifts of the signals transmitting through the blockage which causes the NLoS link. Besides, we establish a channel model that jointly considers the blockage-through matrix, large-scale fading, and small-scale fading. To maximize the EE of the system, we jointly optimize the FAR position, FA positions, power control, and beamforming design under given constraints, and propose an iterative algorithm to solve this formulated optimization problem. Simulation results show that the proposed algorithm outperforms the traditional schemes in terms of EE, achieving up to 23.39% and 39.94% higher EE than the conventional reconfigurable intelligent surface (RIS) scheme and traditional AF relay scheme, respectively.

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

In this paper, we study efficient codebook design for limited feedback in extremely large-scale multiple-input-multiple-output (XL-MIMO) frequency division duplexing (FDD) systems. It is worth noting that existing codebook designs for XL-MIMO, such as the polar-domain codebook, have not well taken into account user (location) distribution in practice, thereby incurring excessive feedback overhead. To address this issue, we propose in this paper a novel and efficient feedback codebook tailored to the user distribution. To this end, we first consider a typical scenario where users are uniformly distributed within a specific polar-region, based on which a sum-rate maximization problem is formulated to jointly optimize angle-range samples and bit allocation among angle/range feedback. This problem is challenging to solve due to the lack of a closed-form expression for the received power in terms of angle and range samples. By leveraging a Voronoi partitioning approach, we show that uniform angle sampling is optimal for received power maximization. For the more challenging range sampling design, we obtain a tight lower bound on the received power and show thatgeometricsampling, where the ratio between adjacent samples is constant, can maximize the lower bound and thus serves as a high-quality suboptimal solution. We then extend the proposed framework to accommodate more general non-uniform user distribution via an alternating sampling method. Furthermore, theoretical analysis reveals that as the array size increases, the optimal allocation of feedback bits increasingly favors range samples at the expense of angle samples. Finally, numerical results validate the superior rate performance and robustness of the proposed codebook design under various system setups, achieving significant gains over benchmark schemes, including the widely used polar-domain codebook.

Semantic communication emphasizes the transmission of meaning rather than raw symbols. It offers a promising solution to alleviate network congestion and improve transmission efficiency. In this paper, we propose a wireless image communication framework that employs probability graphs as shared semantic knowledge base among distributed users. High-level image semantics are represented via scene graphs, and a two-stage compression algorithm is devised to remove predictable components based on learned conditional and co-occurrence probabilities. At the transmitter, the algorithm filters redundant relations and entity pairs, while at the receiver, semantic recovery leverages the same probability graphs to reconstruct omitted information. For further research, we also put forward a multi-round semantic compression algorithm with its theoretical performance analysis. Simulation results demonstrate that our semantic-aware scheme achieves superior transmission throughput and satiable semantic alignment, validating the efficacy of leveraging high-level semantics for image communication.

In personalized Federated Learning (pFL), high data heterogeneity can cause significant gradient divergence across devices, adversely affecting the learning process. This divergence, especially when gradients from different users form an obtuse angle during aggregation, can negate progress, leading to severe weight and gradient update degradation. To address this issue, we introduce a new approach to pFL design, namely Federated Learning with Layer-wise Aggregation via Gradient Analysis (FedLAG), utilizing the concept of gradient conflict at the layer level. Specifically, when layer-wise gradients of different clients form acute angles, those gradients align in the same direction, enabling updates across different clients toward identifying client-invariant features. Conversely, when layer-wise gradient pairs make create obtuse angles, the layers tend to focus on client-specific tasks. In hindsights, FedLAG assigns layers for personalization based on the extent of layer-wise gradient conflicts. Specifically, layers with gradient conflicts are excluded from the global aggregation process. The theoretical evaluation demonstrates that when integrated into other pFL baselines, FedLAG enhances pFL performance by a certain margin. Therefore, our proposed method achieves superior convergence behavior compared with other baselines. Extensive experiments show that our FedLAG outperforms several state-of-the-art methods and can be easily incorporated with many existing methods to further enhance performance.

Federated learning (FL) is a promising distributed machine learning approach with enhanced data privacy protection. However, wireless communication remains a key bottleneck, directly affecting the efficiency and performance of FL. In this paper, we introduce a device-centric cell-free network to mitigate the negative effects of random fading and limited radio resources on FL. The convergence gap, representing the difference between the FL model’s performance and that of the optimal model, is analyzed to evaluate the impact of communication and computation factors, including inter-device interference, on FL performance. Then, access point (AP)-device association, transmission power, and computation frequency are jointly optimized to minimize the convergence gap. Lyapunov techniques are employed to decouple the long-term optimization into a series of online solvable problems. A deep reinforcement learning-based scheme is proposed to optimize the AP association and transmission power for devices, reducing the computational complexity from a prohibitive level to a real-time feasible quadratic level. Additionally, a closed-form solution for the optimal device computation frequency is derived. Simulation results show that the proposed scheme significantly outperforms the traditional cell-free FL and cellular FL schemes in both model training performance and energy efficiency.

In this paper, we focus on beamforming optimization for a multiuser and multi-target integrated sensing and communication (ISAC) system with non-ideal hardware at both the base station (BS) and the users. Specifically, by taking into account the impact of hardware impairments encountered in practice, we jointly optimize the transmit and receive beamforming at the ISAC BS to maximize the minimum radar output signal-to-interference-plus-noise ratio (SINR) for the multi-target sensing, subject to the constraints of multiuser communication requirements and transmit power limit. The formulated joint optimization is nonconovex and challenging to solve. To address this intricate optimization task, we start with a single-target scenario, for which we propose an optimal solution. In particular, we prove in theory that, even in the presence of general additive Gaussian distortions caused by transceiver hardware impairments, a matched filter (MF) radar receiver and a transmit beamforming determined through beampattern gain maximization criterion are optimal, which follow the same strategies as an ideal scenario with perfect hardware. Subsequently, for a general multi-target scenario, we derive a series of closed-form optimal radar receive beamforming. By substituting these solutions, we achieve an equivalent problem reformulation with respect to the transmit beamforming and propose an iterative algorithm to solve it. We also extend the optimization method to cases involving more realistic hardware impairments. Finally, we evaluate the effectiveness of the proposed algorithms and highlight their notable advantages compared to existing approaches through simulation results.

This paper investigates the delay minimization problem for multi-task federated learning (MTFL) systems at the network edge. We develop a novel MTFL framework, based on which the divergence bounds are derived for both task-related and task-unrelated scenarios, systematically quantifying the effects of user importance, user participation, and inter-task correlations on convergence behavior. Building upon these insights, a long-term joint optimization problem is formulated to minimize the overall training delay under the long-term divergence bounds and energy constraints. To address the coupling in multi-slot user scheduling, the optimization problem is decomposed into a single-slot joint resource allocation and task scheduling subproblem and a cross-slot user scheduling subproblem. The former is solved using block coordinate descent (BCD) combined with Johnson’s rule, while the latter is modeled as a constrained Markov decision process (CMDP) and addressed via a dueling double deep Q-network (D3QN) with cost shaping and prioritized experience replay. Numerical results verify the effectiveness of the proposed framework and convergence analysis, demonstrating its significant improvements over baseline schemes in terms of convergence and delay reduction.

In this paper, we incorporate physical knowledge into learning-based high-precision target sensing using the multi-view channel state information (CSI) between multiple base stations (BSs) and user equipment (UEs). Such kind of multi-view sensing problem can be naturally cast into a conditional generation framework. To this end, we design a bipartite neural network architecture, the first part of which uses an elaborately designed encoder to fuse the latent target features embedded in the multi-view CSI, and then the second uses them as conditioning inputs of a powerful generative model to guide the target’s reconstruction. Specifically, the encoder is designed to capture the physical correlation between the CSI and the target, and also be adaptive to the numbers and positions of BS-UE pairs. Therein the view-specific nature of CSI is assimilated by introducing a spatial positional embedding scheme, which exploits the structure of electromagnetic(EM)-wave propagation channels. Finally, a conditional diffusion model with a weighted loss is employed to generate the target’s point cloud from the fused features. Extensive numerical results demonstrate that the proposed generative multi-view (Gen-MV) sensing framework exhibits excellent flexibility and significant performance improvement on the reconstruction quality of target’s shape and EM properties.

Wireless federated learning (FL) facilitates collaborative training of artificial intelligence (AI) models to support ubiquitous intelligent applications at the wireless edge. However, the inherent constraints of limited wireless resources inevitably lead to unreliable communication, which poses a significant challenge to wireless FL. To overcome this challenge, we propose Sign-Prioritized FL (SP-FL), a novel framework that improves wireless FL by prioritizing the transmission of important gradient information through uneven resource allocation. Specifically, recognizing the importance of descent direction in model updating, we transmit gradient signs in individual packets and allow their reuse for gradient descent if the remaining gradient modulus cannot be correctly recovered. To further improve the reliability of transmission of important information, we formulate a hierarchical resource allocation problem based on the importance disparity at both the packet and device levels, optimizing bandwidth allocation across multiple devices and power allocation between sign and modulus packets. To make the problem tractable, the one-step convergence behavior of SP-FL, which characterizes data importance at both levels in an explicit form, is analyzed. We then propose an alternating optimization algorithm to solve this problem using the Newton-Raphson method and successive convex approximation (SCA). Simulation results confirm the superiority of SP-FL, especially in resource-constrained scenarios, demonstrating up to 9.96% higher testing accuracy on the CIFAR-10 dataset compared to existing methods.

This paper investigates the energy efficiency (EE) of the fluid antenna relay (FAR)-assisted wireless communication systems in non-line-of-sight (NLoS) scenarios. Unlike conventional fixed-position antenna systems, the FAR dynamically adjusts the spatial positions of fluid antennas (FAs), enabling efficient signal transmission through blockages. By integrating the amplify-and-forward (AF) protocol, the proposed FAR architecture amplifies and forwards signals while controlling phase shifts via FA reconfiguration. An optimization problem is formulated to maximize the system EE under given constraints. The problem is decomposed into three sub-problems including large-scale fading optimization, small-scale fading optimization, and joint power control and beamforming design optimization. These subproblems are solved iteratively with successive convex approximation (SCA) and Dinkelbach methods. Numerical simulation results demonstrate that the proposed algorithm significantly outperforms the existing STAR-RIS and AF relay schemes, improving EE of the system by up to 29.92% and 45.04%, respectively. The work in this paper bridges the research gap in FAS research with NLoS challenges and provides a framework for future FAR-enabled wireless communication systems.

In this paper, a novel resilient unmanned aerial vehicle (UAV) framework that enables UAVs to efficiently adjust their trajectories and antenna ports to serve disconnected users due to unexpected accidents is designed. In the proposed framework, a set of UAVs equipped with fluid antennas provide service for ground users. At the beginning, each UAV optimizes its three dimensional (3D) location and selects an antenna port to maximize the sum data rate of all users. During the service period, several UAVs may not be able to continue to serve ground users due to unexpected accidents. The remaining UAVs must adjust their trajectories and antenna ports to provide communication services for the users originally served by UAVs with accidents. This problem is formulated as an optimization problem that aims to maximize the total data rates of all users during the entire service period including the period that all UAVs can provide service, the period that some UAVs cannot provide service and the remaining UAVs must adjust their trajectories and antenna ports, and the period that the remaining UAVs find fixed locations to serve users. To solve this problem, an attention and gate recurrent unit (GRU) based reinforcement learning (AGRL) method is designed. In this method, the GRUs are utilized to capture previous UAV actions including trajectories and antenna port selections and states. The transformer is used to analyze the importance of previous UAV actions and states, thus further improving the total data rates of all users. To further improve the training speed of the designed AGRL method, we mathematically derive the optimally initial UAV locations. Simulation results show that the proposed AGRL method can improve the expected data rate of all users by up to 9.89% and 10.19% compared to the value function decomposition RL (VDRL) method and the proposed AGRL method without optimizing antenna port selection.

In this paper, a novel contrastive language–image pre-training (CLIP) model based on semantic The communication framework is designed. Compared to a standard neural network (e.g., convolutional neural network) based semantic encoders and decoders that require joint training over a common dataset, Our CLIP model-based method does not require any training procedures, thus enabling a transmitter to extract data meanings of the original data without neural network model training, and the receiver to train a neural network for follow-up task implementation without the communications with the transmitter. Next, we investigate the deployment of the CLIP model-based semantic framework over a noisy wireless network. Since the semantic information generated by the CLIP model is susceptible to wireless noise and the spectrum used for semantic information transmission are limited; it is necessary to optimize CLIP jointly model architecture and spectrum resource block (RB) allocation to maximize semantic communication performance while considering wireless noise, the delay and energy used for semantic communication. To achieve this goal, we use a proximal policy optimization (PPO) based reinforcement learning (RL) algorithm to learn how wireless noise affects the semantic communication performance, thus finding optimal CLIP model and RB for each user. Simulation results show that our proposed method improves the convergence rate by up to 40%, and the accumulated reward by 4x compared to soft actor-critic.

In this paper, we investigate a joint communication and computation resource allocation strategy for an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system employing fluid antenna (FA). Specifically, each user is equipped with an FA to offload the entire computation tasks to the MEC server deployed on the UAV. By dynamically selecting antenna ports, users can achieve latency-efficient edge computing services, especially advantageous in dynamic environments. To minimize the maximum execution delay of all the users, we jointly optimize the UAV location, FA port selection, and computation resource allocation, subject to computational capacity constraints. The original non-convex optimization problem is decomposed into three tractable subproblems within a block coordinate descent (BCD) algorithm. The optimal computing frequencies are derived in closed form, while the UAV location and FA port selection are optimized using low-complexity iterative algorithms based on successive convex approximation (SCA) and linear programming (LP) techniques. In addition to conventional benchmarks with fixed-position antennas (FPAs), we also introduce a reconfigurable intelligent surface (RIS)-assisted system as a comparative baseline. Simulation results demonstrate that the proposed FA-assisted scheme significantly outperforms both FPAs and RIS-assisted counterparts, with performance gains becoming more pronounced in multi-task and highly dynamic scenarios, establishing FA-assisted UAV-MEC as a promising solution for future deployments.

This paper investigates the energy efficiency maximization problem in a resource-constrained visible light communication (VLC)-based probabilistic semantic communication (PSCom) system. In the considered model, light-emitting diode (LED) transmitters perform semantic compression, reducing data size at the cost of computation overhead. The compressed semantic information is transmitted to the users for semantic inference based on a shared knowledge base, which requires regular updates to maintain synchronization. Rate splitting multiple access (RSMA) is used to transmit both knowledge base and information data simultaneously. The goal is to maximize the energy efficiency of the system through optimizing transmit beamforming, direct current (DC) bias, rate allocation, and semantic compression ratio, considering both communication and computation costs. An alternating optimization algorithm, utilizing successive convex approximation and Dinkelbach method, is proposed to solve the problem. Simulation results validate the effectiveness of the proposed approach.

Fine-tuning large language models (LLM) in a distributed manner over edge devices with limited communication and computational resources presents substantial challenges in wireless networks. To tackle these issues, this paper proposes a novel Split Federated Learning framework tailored for LLM (SflLLM), which integrates split federated learning with parameter-efficient fine-tuning techniques. By employing model partitioning and low-rank adaptation (LoRA), SflLLM significantly reduces the computational load on edge devices. Moreover, the introduction of the federated server not only facilitates parallel training but also enhances privacy preservation. To accommodate the heterogeneous communication conditions and diverse computational capacities of edge devices—while accounting for the influence of LoRA rank selection on model convergence and training overhead—we formulate a joint optimization problem. This problem simultaneously optimizes subchannel allocation, power control, model split point selection, and LoRA rank configuration, with the objective of minimizing the overall training latency. An alternating optimization algorithm is developed to efficiently solve the proposed problem and accelerate the training process. Simulation results demonstrate that, compared to conventional methods, the proposed resource allocation scheme and adaptive LoRA rank selection strategy significantly reduce training latency.

This paper investigates integrated localization and communication in a multi-cell system, and proposes a coordinated beamforming algorithm to enhance target localization accuracy while preserving communication performance. Within this integrated sensing and communication (ISAC) system, the CramérRao lower bound (CRLB) is adopted to quantify the accuracy of target localization, with its closed-form expression derived for the first time. It is shown that the nuisance parameters can be disregarded without impacting the CRLB of time of arrival (TOA)based target localization. Capitalizing on the derived CRLB, we formulate a nonconvex coordinated beamforming problem to minimize the CRLB while satisfying signal-to-interference-plusnoise ratio (SINR) constraints in communication. To facilitate the development of solution, we reformulate the original problem into a more tractable form and solve it through semi-definite programming (SDP). Notably, we show that the proposed algorithm can always obtain rank-one global optimal solutions under mild conditions. Finally, numerical results demonstrate the superiority of the proposed algorithm over benchmark algorithms and reveal the performance trade-off between localization accuracy and communication SINR.

Unreliable communication is a critical bottleneck for the performance of federated learning (FL) in resourceconstrained wireless networks. To address this issue, we propose a priority-aware transmission strategy, where wireless resources are allocated preferentially based on the importance of data. Specifically, recognizing the crucial role of gradient direction in model updating, we transmit the sign and the modulus of local gradients separately, enabling the reuse of sign packets in the event of erroneous modulus transmission. Furthermore, we introduce a hierarchical resource allocation strategy in the proposed framework, prioritizing key gradients via bandwidth allocation across devices and the sign packet via power allocation at each device. Building upon the theoretical one-step convergence analysis, we formulate the resource allocation optimization problem in an explicit form, which facilitates an alternating optimization algorithm respectively applying the Newton method and technique of successive convex approximation (SCA). Numerical results show the superiority of the proposed scheme in both accuracy and convergence rate compared to existing baselines.

In this paper, we propose a Sustainable Sensing Federated Learning (S2FL) system where Internet-of-Things (IoT) devices and mobile users harvest energy wirelessly to perform data sensing, local Federated Learning (FL) training, and model update transmissions. We formulate a joint optimization problem that considers transmission power, CPU frequency, bandwidth allocation, and time allocation to maximize long-term learning efficiency. To solve this complex and dynamic problem, we develop an algorithm that integrates Deep Reinforcement Learning (DRL) with optimization techniques, leveraging the Deep Deterministic Policy Gradient (DDPG) algorithm for time allocation and a Lagrangian-Based Block Coordinate Descent (BCD) Method for per-slot resource optimization. Simulation results demonstrate that our proposed DDPG-based DRL-S2FL algorithm significantly outperforms benchmark schemes, achieving up to 15 % higher average reward compared to Deep Q-Networks (DQN) and 40 % greater performance than Random strategies. This work highlights the effectiveness of combining advanced optimization techniques with deep reinforcement learning to enhance federated learning performance in dynamic wireless environments.

In this paper, the problem of using active unmanned aerial vehicles (UAVs) and a base station (BS) to jointly localize a target UAV under global positioning system (GPS) spoofing attacks is studied. In the considered model, active UAVs transmit signals, which will be reflected by the target UAV and received by active UAVs. Based on the signal transmission time, active UAVs calculate the distance between the target UAV and active UAVs. Then, active UAVs transmit these distance measurement information and their GPS information to the BS for localizing the target UAV. During the localization process, the target UAV is equipped with a GPS jammer, which can interfere with GPS information of active UAVs. Since the localization accuracy depends on distance between the target UAV and active UAVs and GPS information accuracy of active UAVs, active UAVs must optimize their trajectories and determine whether to transmit measurement information to the BS for localizing the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error of the target UAV between the estimated and true positions of the target UAV by jointly optimizing the trajectories of active UAVs and determining distance information transmission scheme. To find the optimal solution, a historical observations and actions-based reinforcement learning (HOA-RL) method is proposed. Compared to traditional state-based reinforcement learning (RL) methods, the proposed method can capture the historical decision-making process of agents and optimally adjust trajectories of active UAVs and measurement information transmission scheme based on their observations. Simulation results show that the proposed method can achieve 44.4% and 70.4% gains in terms of reducing the positioning error of the target UAV compared to Qmix method and Qtran method, respectively.

In this paper, we propose a novel high-dimensional channel acquisition framework in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. It incorporates the user's approximate position to infer large-scale features such as multipath structure, performs real-time coarse estimation to capture rapidly varying features such as phase information, and finally fuses them to represent the complete channel. This framework effectively circumvents the impractical requirement for wavelength-level position accuracy in existing position-to-channel prediction methods, while incorporating spatial features that incur no additional pilot overhead to enhance channel representation. To implement this framework, we employ deep learning techniques and propose two specialized neural networks. First, we design a position-aided complex-domain multilayer perceptron mixer (CMixer) network that processes position coordinate and coarse estimate as two information flows, using convolutional interaction for cross-modal fusion. Furthermore, we propose an enhanced attention-based spatial channel deduction network (ASCDNet). This network replaces approximate position coordinates with sampled spatial neighborhood channels, a priori achieving modal alignment of input to improve fusion efficiency. Comprehensive experimental evaluations demonstrate the superior accuracy and strong robustness of the proposed methods. For instance, leveraging meter-level precision position coordinate, ASCDNet achieves 7.93~9.54 dB gains in normalized channel error and enables up to 64% pilot reduction compared to state-of-the-art estimation method.

Generalization from a specific scenario to the whole network is of particular importance in wireless artificial intelligence, which usually calls for scaling up in training data and brings unaffordable costs of both data collection and model training. Motivated by the fact that local similarity among wireless scenarios probably means certain similarity in wireless channel characteristics, collecting data from representative scenarios may be an efficient way to scale down the training dataset. In this paper, we propose a proactive approach for scenario diversity assessment so as to choose the most representative scenarios for training data collection. Specifically, this approach first utilizes publicly available coarse environmental maps to extract the geometric features, then combines these with electromagnetic signal propagation models to synthesize electromagnetic characteristic distributions associated with the scenario. By employing Wasserstein distance to quantify the similarity between electromagnetic characteristic distributions across different scenarios, scenario-granularity clustering is achieved for selecting representative scenarios to construct a global dataset with sufficient information for all scenarios. Experimental results demonstrate that the proposed approach can achieve a Pearson correlation coefficient above 0.8 w.r.t. cross-scenario generalization performance, and it further improves the final multi-scenario generalization, surpassing existing methods while requiring no pre-measurement data.

In integrated sensing and communication (ISAC) systems, effectively processing multi-view data from multiple transmitter-receiver pairs is critical for high-quality environment reconstruction. This paper proposes a generative learning method with multi-view channel feature aggregation for solving multi-user equipment (UE) multi-base station (BS) collaborative sensing problem. Specifically, based on the revealed similarities between different BS-UE transmission links, we design a flexible channel state information encoder shared among various transmitter-receiver views to learn scatterer features. Then, a concise aggregation mechanism is introduced for multi-view feature fusion. Subsequently, considering the intrinsic probabilistic properties of the scatterer point cloud, we employ a diffusion model to decode the aggregated feature for the final environment reconstruction. These designs enables the proposed scheme to structurally adapt to the dynamic changes in the positions and numbers of BSs and UEs, ultimately achieving high-quality multi-view collaborative sensing. Extensive numerical experiments validate the effectiveness and desirable properties of the proposed method.

The demand for accurate sensing in the complex environment, such as urban areas and indoor spaces, is critical for future wireless networks, where scattered signals become essential for sensing occluded targets under non-line-of-sight (NLOS) conditions. To reduce the demand for beam-sweeping and geometric assumptions, we propose a bistatic NLOS sensing technique that fully exploits the scattered signals. By modeling the scattering channel responses and leveraging sparsity-driven compressed sensing, our method achieves robust environmental reconstruction to estimate target positions, shapes, and orientations. The proposed algorithm is applicable for estimating the parameters of first-order and second-order scattering targets. Experimental results demonstrate its superiority in occluded target sensing and environmental mapping, thus offering an efficient solution for NLOS sensing in complex scenarios.

Superimposed pilot (SIP) schemes face significant challenges in effectively superimposing and separating pilot and data signals, especially in multiuser mobility scenarios with rapidly varying channels. To address these challenges, we propose a novel channel-aware learning framework for SIP schemes, termed CaSIP, that jointly optimizes pilot-data power (PDP) allocation and a receiver network for pilot-data interference (PDI) elimination, by leveraging channel path gain information, a form of large-scale channel state information (CSI). The proposed framework identifies user-specific, resource elementwise PDP factors and develops a deep neural network-based SIP receiver comprising explicit channel estimation and data detection components. To properly leverage path gain data, we devise an embedding generator that projects it into embeddings, which are then fused with intermediate feature maps of the channel estimation network. Simulation results demonstrate that CaSIP efficiently outperforms traditional pilot schemes and state-of-the-art SIP schemes in terms of sum throughput and channel estimation accuracy, particularly under high-mobility and low signal-to-noise ratio (SNR) conditions.

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

Millimeter-wave (mmWave) radar in beamforming mode offers high-resolution sensing with an enhanced signal-to-noise ratio (SNR). However, in sparse antenna arrays, the effectiveness of beamforming is limited by sidelobe and grating lobe interference, which degrades angular resolution and imaging performance. Traditional algorithms often discard sidelobe and grating lobe echoes as noise or interference, missing valuable information for improved resolution. In this paper, we propose a novel high-resolution imaging algorithm that integrates sidelobe and grating lobe effects into the data processing. By developing an enhanced frequency-modulated continuous wave (FMCW) radar beamforming signal model, we jointly process signals from all beam directions, including both the main lobe and non-main lobe reflections, using a least-squares (LS) approach. This method improves angular resolution, target discrimination, and noise suppression. Simulation results of range-angle map, chamfer distance, and success rate of distinguishing adjacent points demonstrate that our approach outperforms conventional beamforming (CBF) methods and canonical polyadic decomposition (CPD) methods, improving resolution and reducing sidelobe and grating lobe artifacts, offering a promising solution for high-resolution mmWave radar imaging in sparse array configurations.

Integrated sensing and communication (ISAC) systems with cell-free massive multiple-input-multiple-output (CFMM) frameworks can offer significant performance improvements for both sensing and communication functions. However, there is an obstacle to the system supporting an extremely large number of users in explosive terminal access scenarios. In this article, we propose a novel nonorthogonal multiple access (NOMA)-aided CFMM-ISAC architecture to enhance the connectivity and efficiency of the system. First, we consider a commonly used single-radar sensing scenario and jointly design user pairing and beamforming to maximize the minimum achievable communication rate while ensuring the sensing requirement. To solve the nonconvexity of the formulated optimization problem, the binary variables denoting the feasible pairing of users are first relaxed into continuous variables. An iterative optimization programming algorithm is then developed using the semidefinite relaxation (SDR), rank-one constraint omission (RCO), and successive convex approximation (SCA) approaches. To obtain feasible initial points of the iterative algorithm, we propose an initial point construction algorithm. Based on the obtained user pairing strategy, a corrective beamforming scheme is devised. We further extend our proposed algorithms to a generalized multiradar sensing scenario. Simulation results validate that the proposed joint user pairing and beamforming design achieves noticeable performance improvements compared to the existing methods.

Real-time and contactless monitoring of vital signs, such as respiration and heartbeat, alongside reliable communication, is essential for modern healthcare systems, especially in remote and privacy-sensitive environments. Traditional wireless communication and sensing networks fall short in meeting all the stringent demands of eHealth, including accurate sensing, high data efficiency, and privacy preservation. To overcome the challenges, we propose a novel integrated sensing, computing, and semantic communication (ISCSC) framework. In the proposed system, a service robot utilises radar to detect patient positions and monitor their vital signs, while sending updates to the medical devices. Instead of transmitting raw physiological information, the robot computes and communicates semantically extracted health features to medical devices. This semantic processing improves data throughput and preserves the clinical relevance of the messages, while enhancing data privacy by avoiding the transmission of sensitive data. Leveraging the estimated patient locations, the robot employs an interacting multiple model (IMM) filter to actively track patient motion, thereby enabling robust beam steering for continuous and reliable monitoring. We then propose a joint optimisation of the beamforming matrices and the semantic extraction ratio, subject to computing capability and power budget constraints, with the objective of maximising both the semantic secrecy rate and sensing accuracy. Simulation results validate that the ISCSC framework achieves superior sensing accuracy, improved semantic transmission efficiency, and enhanced privacy preservation compared to conventional joint sensing and communication methods.

With the development of communication technology, the Internet of Vehicles (IoV) is becoming increasingly important, enabling vehicle-to-everything communication for real-time information exchange and processing, thereby significantly enhancing traffic efficiency and safety. In this article, we consider a joint beamforming design problem in IoV, where the objective is to minimize transmission power, computation rate, and communication rate within the integrated sensing, communication, and computation (ISCC) framework. Moreover, reconfigurable intelligent surfaces (RISs) can provide additional spatial degrees of freedom to enhance the performance of ISCC systems in IoV within limited spectrum, energy resources, and complex interference management. To address the joint beamforming design problem, we present a cooperative beamforming algorithm called weight performance optimization (WPO), which explores three single-objective optimization problems in sensing, computation, and communication within the IoV context, using alternating optimization (AO) to simplify and solve these foundational elements of the WPO framework within limited resources and vehicle mobility, enhancing resource distribution while maintaining a balance between power efficiency and system performance. Numerical results demonstrate the efficiency and potential advantages of our proposed algorithms. Specifically, the results show that the sensing error of the WPO algorithm is reduced by up to 92.2% compared to existing popular algorithms, while the computation rate and communication rate are increased by more than 29.5% and 23.9%, respectively.

In this article, we investigate an accurate synchronization between a physical network and its digital network twin (DNT), which serves as a virtual representation of the physical network. The considered network includes a set of base stations (BSs) that must allocate its limited spectrum resources to serve a set of users while also transmitting its partially observed physical network information to a cloud server to generate the DNT. Since the DNT can predict the physical network status based on its historical status, the BSs may not need to send their physical network information at each time slot, allowing them to conserve spectrum resources to serve the users. However, if the DNT does not receive the physical network information of the BSs over a large time period, the DNT’s accuracy in representing the physical network may degrade. To this end, each BS must decide when to send the physical network information to the cloud server to update the DNT, while also determining the spectrum resource allocation policy for both DNT synchronization and serving the users. We formulate this resource allocation task as an optimization problem, aiming to maximize the total data rate of all users while minimizing the asynchronization between the physical network and the DNT. The formulated problem is challenging to solve by traditional optimization methods, as each BS can only observe a partial physical network, making it difficult to find an optimal spectrum allocation strategy for the entire network. To address this problem, we propose a method based on the gated recurrent units (GRUs) and the value decomposition network (VDN). The GRU component allows the DNT to predict future status using the historical data, effectively updating itself when the BSs do not transmit the physical network information. The VDN algorithm enables each BS to learn the relationship between its local observation and the team reward of all BSs, allowing it to collaborate with others in determining whether to transmit physical network information and optimizing spectrum allocation. Simulation results show that our GRU-based and VDN-based algorithm improves the weighted sum of data rates and the similarity between the status of the DNT and the physical network by up to 28.96%, compared to a baseline method combining GRU with the independent Q learning (IQL).

Over-the-air computation has emerged as a high-spectrum efficient and low-latency solution for model aggregation in federated learning (FL) by leveraging the superposition property of wireless channels. However, traditional over-the-air FL (AirFL) faces challenges such as signal misalignment, imperfect channel state information (CSI), and noisy fading channels. To enable more efficient and reliable AirFL in Internet of Things (IoT), we employ a multifunctional reconfigurable intelligent surface (MF-RIS) to alleviate mean square error (MSE) of AirFL model aggregation. By deriving the convergence analysis of AirFL in both convex and nonconvex settings, we unveil the impact of MSE on MF-RIS-aided AirFL under varying conditions. Based on the theoretical insights, we aim to minimize the MSE through a joint design of transceiver beamforming and MF-RIS coefficients, but it necessitates solving a mixed-integer nonlinear programming (MINLP) problem. To solve it efficiently, we propose an alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) approach and difference-of-convex (DC) programming. The efficacy of our approach is corroborated by numerical results, which underscore the performance gains achieved by the proposed algorithm. Additionally, the MF-RIS demonstrates remarkable proficiency in suppressing MSE and bolstering AirFL performance, even under conditions of imperfect CSI.

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

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

Over-the-air computation (AirComp) has emerged as an essential approach for enabling communication-efficient federated learning (FL) over wireless networks. Nonetheless, the inherent analog transmission mechanism in AirComp-based FL (AirFL) intensifies challenges posed by potential Byzantine attacks. In this paper, we propose a novel Byzantine-robust FL paradigm for over-the-air transmissions, referred to as federated learning with secure adaptive clustering (FedSAC). FedSAC aims to protect a portion of the devices from attacks through zero trust architecture (ZTA) based Byzantine identification and adaptive device clustering. By conducting a one-step convergence analysis, we theoretically characterize the convergence behavior with different device clustering mechanisms and uneven aggregation weighting factors for each device. Building upon our analytical results, we formulate a joint optimization problem for the clustering and weighting factors in each communication round. To facilitate the targeted optimization, we propose a dynamic Byzantine identification method using historical reputation based on ZTA. Furthermore, we introduce a sequential clustering method, transforming the joint optimization into a weighting optimization problem without sacrificing the optimality. To optimize the weighting, we capitalize on the penalty convex-concave procedure (P-CCP) to obtain a stationary solution. Numerical results substantiate the superiority of the proposed FedSAC over existing methods in terms of both test accuracy and convergence rate.

Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmission. However, most of the existing works focus on the DL-based transceiver design of the JSCC model, while ignoring the resource allocation problem in wireless systems. In this paper, we consider a downlink resource allocation problem, where a base station (BS) jointly optimizes the compression ratio (CR) and power allocation as well as resource block (RB) assignment of each user according to the latency and performance constraints to maximize the number of users that successfully receive their requested content with desired quality. To solve this problem, we first decompose it into two subproblems without loss of optimality. The first subproblem is to minimize the required transmission power for each user under given RB allocation. We derive the closed-form expression of the optimal transmit power by searching the maximum feasible compression ratio. The second one aims at maximizing the number of supported users through optimal user-RB pairing, which is solved by utilizing bisection search as well as interior-point algorithm. To reduce the computational complexity, we propose a heuristic greedy algorithm to obtain a simplified problem. Then the Bregman alternating direction method of multipliers (BADMM) based algorithm is adopted to decompose the simplified problem into several subproblems that can be computed in parallel. Simulation results validate the effectiveness of the proposed resource allocation methods in terms of the number of satisfied users with given resources. It is also shown that the BADMM-based algorithm can significantly reduce the computational complexity and retain high performance.

Service caching in mobile edge computing (MEC) networks involves pre-storing computation programs on MEC servers to efficiently handle users’ computational tasks. By pre-loading these programs, service caching can significantly reduce both computation and transmission delays, addressing the diverse computational requirements of users. However, optimal caching placement is essential due to limited caching capacity, which directly impacts the efficiency of computation offloading. Proper cache placement ensures that relevant programs are readily available, thereby maximizing offloading performance and minimizing delays. This paper investigates a multi-tier MEC network consisting of caching-enabled edge servers, a cloud server, and multiple users. Users offload computational tasks to proximate edge servers, where locally cached programs facilitate immediate processing, thereby mitigating delays. When programs are absent from the cache, tasks are offloaded to the cloud, leading to additional latency. We formulate an optimization problem to minimize the overall communication and computation delay by jointly optimizing caching placement, transmission power, bandwidth allocation, and computation capacity. To tackle the complexity of this mixed-integer nonlinear programming (MINLP) problem, we propose two novel algorithms. The first is a Dinkelbach and big-M-based algorithm that reformulates the problem into a mixed-integer second-order cone programming (MI-SOCP) problem, approximating a near-optimal solution. Recognizing the computational demands of MI-SOCP, we also develop a low-complexity algorithm based on successive convex approximation (SCA) and alternating methods, which efficiently yields a high-quality sub-optimal solution. Simulation results confirm the effectiveness of the proposed algorithms in reducing network delays and emphasize the critical role of caching in improving network performance.

Semantic communication is envisioned as a key technology for future wireless networks due to its high communication efficiency. However, research combining semantic communication and advanced multiple access techniques, such as rate splitting multiple access (RSMA), is still lacking. In this paper, the problem of joint communication and computation resource allocation for probabilistic semantic communication (PSCom) with RSMA is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data to multiple users with 1-layer RSMA. Due to limited communication resources, the BS is required to utilize semantic communication techniques to compress the original data. In this paper, we utilize knowledge graphs to represent semantic information and employ probabilistic graphs, which are shared between the BS and users, to further compress the knowledge graphs. The BS can use the probabilistic graph to compress the data to be transmitted, while the user can recover the compressed semantic information using the same shared probabilistic graph. The additional computation power required for semantic information compression inevitably results in a reduction in transmission power due to the limited total power budget. Considering the effect of semantic compression ratio, the semantic rate expression for RSMA is first obtained. Then, based on the obtained rate expression, an optimization problem is formulated with the aim of maximizing the sum of semantic rates of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is proposed, where the semantic compression ratio subproblem is addressed using a greedy algorithm, and the rate allocation and transmit beamforming design subproblem is solved using a successive convex approximation method. Numerical results validate the effectiveness of the proposed scheme.

With the explosive growth of maritime activities, it is expected to provide seamless communications with quality of service (QoS) guarantee over broad sea area. In the context, this paper proposes a space-air-ground-sea integrated maritime communication architecture combining satellite, unmanned aerial vehicle (UAV), terrestrial base station (TBS) and unmanned surface vessel (USV). Firstly, according to the distance away from the shore, the whole marine space is divided to coastal area, offshore area, middle-sea area and open-sea area, the maritime users in which are served by TBS, USV, UAV and satellite, respectively. Then, by exploiting the potential of integrated maritime communication system, a joint beamforming and trajectory optimization algorithm is designed to maximize the minimum transmission rate of maritime users. Finally, theoretical analysis and simulation results validate the effectiveness of the proposed algorithm.

The federated learning (FL) technique can provide a promising solution for the timely training of a deep learning model with the critical requirement of privacy protection. However, the existing FL frameworks still confront challenging issues including heterogeneous data sources, edge device heterogeneity, sensitive information leakage, nonconvex loss, and communication resource constraints which place obstacles in terms of practicality. In this article, first, a federated multitask learning (FedMTL) approach is introduced to reformulate the FL model as a multiobjective optimization problem which results in federated multigradient descent algorithm (FedMGDA) with a better model personalization against data heterogeneity and Byzantine attack. Second, a new semi-asynchronous model aggregation method is developed to asynchronously aggregate small partial clients for compensating impacts of the straggler and staleness. Third, a distributed differential privacy technique is applied to enhance the privacy protection of asynchronous FedMGDA with the convergence guarantee where the convergence analysis of differentially private asynchronous federated multiple gradient descent algorithm (DP-AsynFedMGDA) is studied for both the convex and the nonconvex loss functions. Empirical examples and comparative studies are presented to illustrate the effectiveness of the proposed DP-AsynFedMGDA.

Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: “Can we further exploit the advantages of transmission efficiency in wireless semantic communications while also alleviating its security disadvantages?”. Keeping this in mind, we propose SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption. Specifically, we first introduce semantic entropy, which indicates the expectation of various semantic scores regarding the transmission goal of the DLSC. Equipped with such semantic entropy, we can dynamically assign informative semantics to Orthogonal Frequency Division Multiplexing (OFDM) subcarriers with better channel conditions in a fine-grained manner. We also use the entropy to guide semantic key generation to safeguard communications over open wireless channels. By doing so, both transmission efficiency and channel security can be simultaneously improved. Extensive experiments over various benchmarks show the effectiveness of the proposed SemEntropy. We discuss the reason why our proposed method benefits secure transmission of DLSC, and also give some interesting findings, e.g., SemEntropy can keep the semantic accuracy remain 95% with 60% less transmission.

Recent research efforts on Semantic Communication (SemCom) have mostly considered accuracy as a main problem for optimizing goal-oriented communication systems. However, these approaches introduce a paradox: the accuracy of Artificial Intelligence (AI) tasks should naturally emerge through training rather than being dictated by network constraints. Acknowledging this dilemma, this work introduces an innovative approach that leverages the rate distortion theory to analyze distortions induced by communication and compression, thereby analyzing the learning process. Specifically, we examine the distribution shift between the original data and the distorted data, thus assessing its impact on the AI model's performance. Founding upon this analysis, we can preemptively estimate the empirical accuracy of AI tasks, making the goal-oriented SemCom problem feasible. To achieve this objective, we present the theoretical foundation of our approach, accompanied by simulations and experiments that demonstrate its effectiveness. The experimental results indicate that our proposed method enables accurate AI task performance while adhering to network constraints, establishing it as a valuable contribution to the field of signal processing. Furthermore, this work advances research in goal-oriented SemCom and highlights the significance of data-driven approaches in optimizing the performance of intelligent systems.

Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, playing a crucial role in enhancing the rate and spectral efficiency of wireless networks. As ELAA employs a multitude of antennas operating at higher frequencies, users are typically situated in the near-field region where the spherical wavefront propagates. Near-field beam training requires information on both angle and distance, which inevitably leads to a significant increase in the beam training overhead. To address this challenge, we propose a near-field beam training method based on deep learning. Specifically, we employ a convolutional neural network (CNN) to efficiently extract channel characteristics from historical data by strategically selecting padding and kernel sizes. The negative value of the user average achievable rate is utilized as the loss function to optimize the beamformer, maximizing the achievable rate in multi-user networks without relying on predefined beam codebooks. Once deployed, the model requires only pre-estimated channel state information (CSI) to compute the optimal beamforming vector. Simulation results demonstrate that the proposed scheme achieves more stable beamforming gains and substantially outperforms traditional beam training approaches. Furthermore, owing to the inherent traits of deep learning methodologies, this approach substantially diminishes the near-field beam training overhead.

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods.

Initial access (IA) is a procedure of establishing an initial connection between the base station (BS) and the users. In the fifth generation (5G) mobile communication system, the IA procedure includes beam management, which determines the beam pairs for random access (RA) and data transmission by beam sweeping. The existing beam sweeping method in the 3-rd generation partnership project (3GPP) standard mainly uses a predefined uniform beamforming codebook and sweeps the beams progressively, which is time-consuming and highly inflexible. In this paper, inspired by the fact that the highly non-uniform environment and user distribution mean part of the beam sweeping might be less beneficial, we propose a novel learning-based IA framework for the BS to optimize the beam sweeping patterns. Specifically, we resort to the deep reinforcement learning (DRL) approach to implicitly obtain the unknown environment and user distribution properties by continuously interacting with the environment, and then make decisions based on the rewards achieved by past actions. The simulation results show that our proposed scheme can save much time compared with the new radio (NR) and optimization methods under different datasets and conditions, which greatly improves the beam sweeping efficiency.

Initial random access is a crucial process in wireless communication networks, which sets up reliable connections between the base station (BS) and multiple active users. In this procedure, useful connection information can be naturally obtained to achieve user positioning, and the channel state information (CSI) of multiple users can be further exploited to realize environment sensing. On the other hand, environment sensing is highly related to user positioning as it requires user-specific CSI and benefits from multi-view observations from different user positions. Therefore, in this paper, we propose a joint initial random access, environment sensing, and user positioning framework. Specifically, oversampled cyclic prefixes (CPs) in orthogonal frequency division multiplexing (OFDM) systems, which contain rich environmental information, can be exploited to achieve enhanced channel estimation. Environment sensing and user positioning are further implemented based on the channel estimation results, and the scatter points are then clustered to reconstruct the environment objects. The simulation results show that the proposed framework can achieve a decimeter-level accuracy and a reconstruction ratio of about 89% for user positioning and environment sensing.

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

Task-oriented semantic communication enhances transmission efficiency by conveying semantic information rather than exact messages. Deep learning (DL)-based semantic communication can effectively cultivate the essential semantic knowledge for semantic extraction, transmission, and interpretation by leveraging massive labeled samples for downstream task training. In this paper, we propose a self-supervised learning-based semantic communication framework (SLSCom) to enhance task inference performance, particularly in scenarios with limited access to labeled samples. Specifically, we develop a task-relevant semantic encoder using unlabeled samples, which can be collected by devices in real-world edge networks. To facilitate task-relevant semantic extraction, we introduce self-supervision for learning contrastive features and formulate the information bottleneck (IB) problem to balance the tradeoff between the informativeness of the extracted features and task inference performance. Given the computational challenges of the IB problem, we devise a practical and effective solution by employing self-supervised classification and reconstruction pretext tasks. We further propose efficient joint training methods to enhance end-to-end inference accuracy over wireless channels, even with few labeled samples. We evaluate the proposed framework on image classification tasks over multipath wireless channels. Extensive simulation results demonstrate that SLSCom significantly outperforms conventional digital coding methods and existing DL-based approaches across varying labeled data set sizes and SNR conditions, even when the unlabeled samples are irrelevant to the downstream tasks.

Beyond 5G mobile networks are required to support the ultra-reliable data transmission with massive connectivity under high-mobility scenarios, where the delay-Doppler domain multiple access (DDMA) has been regarded as one of the potential candidates to avoid the severe performance degradation brought by double-dispersive channels. However, existing DDMA transceiver designs heavily rely on the shared codebooks or large guard space among user equipments (UEs), which restricts the flexibility, complexity, and spectral efficiency significantly. In this paper, we first propose the Zadoff-Chu training sequences-based frame structure and an element-wise iterative successive interference cancellation (SIC)-maximal ratio combining (MRC) detector for single-user transmission, which serves as the basis of flexible resource allocation in the delay-Doppler (DD) domain. The discussion is then extended to the MU downlink scenario, where rate splitting is adopted to effectively balance the noise and interference at the UE side to improve the bit error rate performance. For MU uplink cases, a non-orthogonal pilots-based iterative SIC-least square channel estimator is developed to promote the spectral efficiency while the iterative SIC-MRC detector is also provided. Simulation results demonstrate the excellent performance of the proposed DDMA scheme under typical DDMA patterns without guard space between UEs.

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

Communication objectives with high mobility bring severe Doppler shifts, causing the inter-carrier interference of orthogonal frequency division multiplexing (OFDM) system, which raises the requirements of novel modem schemes. However, existing modem schemes for high-mobility communications face challenges in adapting to diverse channel environments, involving complex channel estimation and etc. Fortunately, the potential of deep learning (DL) has been exploited in various communication applications. In order to design the consistent and robust modem scheme for different channel environments, we propose the DL-based architecture termed the closer twins (CTs) model, which borrows the idea from the Siamese structure in contrastive learning. In specific, two identical network backbones like twins can simultaneously process different channel inputs and make outputs consistent. We design a convlutional neural network called modem network (ModNet) as the backbone for the design of consistent and robust modem scheme. Moreover, to make traditional channel estimation and interpolation methods applicable to the designed modem scheme, a training-aided strategy called random-pilot (R-P) is proposed. In R-P strategy, we simulate the process of conventional channel estimation to modify the objective function of the modem scheme design. Furthermore, the performance of traditional channel estimation can be further improved by DL-based methods. We utilize the CTs model and design the backbone called estimation matrix network (EMNet) to optimize a linear channel estimation method, who outperforms the traditional methods with a similar complexity. Simulation results demonstrate that the proposed modem scheme outperforms OFDM, especially with high Doppler spread. The channel estimation strategy, supported by the R-P strategy and EMNet, achieves lower normalized mean square error compared with traditional methods, contributing to more reliable transmission.

In this paper, we introduce a novel uplink semanticaware wireless communication system, catering to multiple users by leveraging a shared probability graph between an access point (AP) and a base station (BS). Under imperfect channel state information (CSI), all users transmit data information to the AP through conventional bit transmission and the lower bound of signal estimation error is derived. An edge server equipped with the AP and a cloud server equipped with the BS execute information compression and recovery based on the shared probability graph, respectively. While semantic information compression incurs computational resource consumption, it significantly reduces communication resource usage. This paper addresses the challenge of minimizing system latency through jointly optimizing communication resource allocation, channel truncation threshold, and data compression ratio, considering limited wireless resources, signal estimation error, and the system’s energy budget. To solve the formulated non-convex time minimization problem, we decompose the optimization problem into four subproblems and solve each of them iteratively. In particular, the power allocation subproblem is transformed into a convex time allocation optimization problem. The bandwidth allocation subproblem is proven to be convex. The optimal channel truncation threshold is obtained through a bisection search. The data compression ratio optimization subproblem is a non-convex integer programming problem solved by the Gurobi optimizer. Numerical results show the effectiveness of the proposed algorithm, validating the necessity to consider imperfect CSI and semantic transmission and the superior performance of semantic communication compared to conventional bit transmission.

Space-air-ground integrated networks (SAGINs) are emerging as a pivotal element in the evolution of future wireless networks. Despite their potential, the joint design of communication and computation within SAGINs remains a formidable challenge. In this paper, the problem of energy efficiency in SAGIN-enabled probabilistic semantic communication (PSCom) system is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSCom technique to compress the transmitting data, while the GTs can automatically recover the missing information. The PSCom is underpinned by shared probabilistic graphs that serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Through analysis, the computation overhead function in PSCom is a piecewise function with respect to the semantic compression ratio. Therefore, it is important to make a balance between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, power, computation capacity, bandwidth, semantic compression ratio, and UAV location constraints. To solve this non-convex non-smooth problem, we propose an iterative algorithm where the closed-form solutions for computation capacity allocation and UAV altitude are obtained at each iteration. Numerical results show the effectiveness of the proposed algorithm.

System-level emulators have been used extensively for the design, debugging and evaluation of the system software. They work by providing a system-level virtual machine that can support a guest operating system (OS) running on a platform with the same or different native OS using the same or different instruction-set architecture. For such a system-level emulation, dynamic binary translation (DBT) is one of the core technologies. A recently proposed learning-based approach using automatically-learned translation rules has shown to improve DBT performance significantly with much higher quality translated code. However, it has only been used on user-level emulation, not system-level emulation. In applying this approach directly on QEMU for system-level emulation, we find it actually causes an unexpected performance degradation of 5% on average. By analyzing its main culprits in more detail, we find that the learning-based approach will by default use host registers to maintain the guest CPU states that include condition-code registers (or FLAG registers). In cases where QEMU needs to be involved (in which QEMU also needs to use the host registers), maintaining system states in the host registers for the guest, the host and QEMU during and between the context switches can cause undue overheads, if not handled carefully. Such cases include emulating system-level instructions, address translation and interrupts, which require the use of QEMU's helper functions. To achieve the intended performance improvement through better-quality code generated by the learning-based approach, we propose several optimization techniques that include reducing the overhead incurred in each context switch, the number of needed context switches, and better code scheduling to eliminate context switches. Our experimental results show that such optimizations can achieve an average of 1.36X speedup over QEMU 6.1 using SPEC CINT2006 and 1.15X on real-world applications in the system emulation mode.

In this paper, a novel clustered FL framework that enables distributed edge devices with non-IID data to independently form several clusters in a distributed manner and implement FL training within each cluster is proposed. In particular, our designed clustered FL algorithm must overcome two challenges associated with FL training. First, the server has limited FL training information (i.e., the parameter server can only obtain the FL model information of each device) and limited computational power for finding the differences among a large amount of devices. Second, each device does not have the data information of other devices for device clustering and can only use global FL model parameters received from the server and its data information to determine its cluster identity, which will increase the difficulty of device clustering. To overcome these two challenges, we propose a joint gradient and loss based distributed clustering method in which each device determines its cluster identity considering the gradient similarity and training loss. The proposed clustering method not only considers how a local FL model of one device contributes to each cluster but also the direction of gradient descent thus improving clustering speed. By delegating clustering decisions to edge devices, each device can fully leverage its private data information to determine its own cluster identity, thereby reducing clustering overhead and improving overall clustering performance. Simulation results demonstrate that our proposed clustered FL algorithm can reduce clustering iterations by up to 99% compared to the existing baseline.

Extremely Large-scale Array (ELAA) is considered a frontier technology for future communication systems, pivotal in improving wireless systems’ rate and spectral efficiency. However, as ELAA employs a multitude of antennas operating at higher frequencies, users are typically situated in the near-field region. This inevitably leads to a significant increase in the overhead of beam training, requiring two-dimensional beam searching in both the angle and the distance domain. To address this problem, we propose a learning-based codebook-free near-field beamforming method. We strategically select padding and kernel size of convolutional neural network to efficiently extract complex channel state information features. We optimize the beamformers to maximize achievable rates in a multi-user network without predefined beam codebooks. Our solution requires only pre-estimated channel state information for optimal beamforming vector derivation during deployment. Simulation results demonstrate stable beamforming gain compared to baseline schemes, and the deep learning approach substantially reduces near-field beam training overhead.

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

As three-dimensional acquisition technologies like LiDAR cameras advance, the need for efficient transmission of 3D point clouds is becoming increasingly important. In this paper, we present a novel semantic communication (SemCom) approach for efficient 3D point cloud transmission. Different from existing methods that rely on downsampling and feature extraction for compression, our approach utilizes a parallel structure to separately extract both global and local information from point clouds. This system is composed of five key components: local semantic encoder, global semantic encoder, channel encoder, channel decoder, and semantic decoder. Our numerical results indicate that this approach surpasses both the traditional Octree compression methodology and alternative deep learning-based strategies in terms of reconstruction quality. Moreover, our system is capable of achieving high-quality point cloud reconstruction under adverse channel conditions, specifically maintaining a reconstruction quality of over 37dB even with severe channel noise.

In this paper, the energy efficiency maximization problem in space-air-ground integrated network (SAGIN)-enabled probabilistic semantic communication (PSC) is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSC technique to compress the transmitted data, while the GTs can automatically recover the original data. In the considered PSC system, shared probability graphs serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Therefore, it is important to study the trade-off between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, semantic compression ratio, and UAV location constraints. To solve this non-convex problem, we propose an alternating algorithm. Numerical results show the effectiveness of the proposed algorithm.

In this paper, the problem of low-latency communication and computation resource allocation for digital twin (DT) over wireless networks is investigated. In the considered model, multiple physical devices in the physical network (PN) needs to frequently offload the computation task related data to the digital network twin (DNT), which is generated and controlled by the central server. Due to limited energy budget of the physical devices, both computation accuracy and wireless transmission power must be considered during the DT procedure. This joint communication and computation problem is formulated as an optimization problem whose goal is to minimize the overall transmission delay of the system under total PN energy and DNT model accuracy constraints. To solve this problem, an alternating algorithm with iteratively solving device scheduling, power control, and data offloading subproblems. For the device scheduling subproblem, the optimal solution is obtained in closed form through the dual method. Numerical results verify that the proposed algorithm can reduce the transmission delay of the system by up to 51.2% compared to the conventional schemes.

Cell-free massive multiple-input multiple-output (MIMO) is a promising technology to address user service inequity and frequent cell re-selection. However, most of the existing distributed detectors are based on the assumption that all users are served by each access point (AP) with simple linear detection methods, resulting in extremely high fronthaul overhead and performance degradation. In this paper, we propose a novel hybrid distributed detector for the user-centric cell-free massive MIMO system. Each AP pre-defines a load threshold to determine whether to use the low-complexity maximum ratio combining (MRC) algorithm or the high-performance expectation propagation (EP) algorithm to ensure detection accuracy while reducing the computational complexity. Moreover, we propose a sparsified EP (SEP) algorithm to further improve the detection performance, from the perspective of user interference suppression via channel sparsified transformation. Based on the properties of the distributed MRC-EP/MRCSEP detection scheme, an efficient hybrid fusion approach is proposed. Numerical results illustrate that the proposed hybrid distributed detectors achieve a good performance-complexity trade-off compared to the existing distributed detectors.

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

To achieve continuous massive data transmission with significantly reduced data payload, the users can adopt semantic communication techniques to compress the redundant information by transmitting semantic features instead. However, current works on semantic communication mainly focus on high compression ratio, neglecting the wireless channel effects including dynamic distortion and multi-user interference, which significantly limit the fidelity of semantic communication. To address this, this paper proposes a diffusion model (DM)-based channel enhancer (DMCE) for improving the performance of multi-user semantic communication, with the DM learning the particular data distribution of channel effects on the transmitted semantic features. In the considered system model, multiple users (such as road cameras) transmit semantic features of multi-source data to a receiver by applying the joint source-channel coding (JSCC) techniques, and the receiver fuses the semantic features from multiple users to complete specific tasks. Then, we propose DMCE to enhance the channel state information (CSI) estimation for improving the restoration of the received semantic features. Finally, the fusion results at the receiver are significantly enhanced, demonstrating a robust performance even under low signal-to-noise ratio (SNR) regimes, enabling the generation of effective object segmentation images. Extensive simulation results with a traffic scenario dataset show that the proposed scheme can improve the mean Intersection over Union (mIoU) by more than 25% at low SNR regimes, compared with the benchmark schemes.

To facilitate edge intelligence in Internet of Robotic Things (IoRT), over-the-air federated learning (AirFL) is a communication-efficient enabler by virtue of its high spectrum efficiency and low transmission latency. Supported by multi-functional reconfigurable intelligent surface (MF-RIS), the model aggregation process of AirFL can be facilitated thanks to full-space signal amplification. However, the learning performance of AirFL may be degraded by the uncertainty of wireless channels originated from inaccurate channel estimation and robot mobility. In this paper, we investigate the model aggregation problem of MF-RIS-aided AirFL in a dynamic IoRT system with imperfect channel state information (CSI). We aim to minimize the long-term mean square error (MSE) of AirFL model aggregation by jointly optimizing MF-RIS coefficients and transceiver beamforming. To enable online decision-making in the dynamic system, we propose a novel deep reinforcement learning (DRL)-based double-agent algorithm, where one agent first decides operating modes of MF-RIS elements, and then the other agent devises transceiver beamforming and other MF-RIS coefficients referring to the mode switching strategy. Numerical results unveil the effectiveness and robustness of the proposed DRL-based algorithm in suppressing long-term MSE under hostile CSI.

In this paper, we introduce a novel uplink semantic relay (SemRelay)-aided wireless communication system, catering to multiple users by leveraging a shared probability graph between the SemRelay and the base station (BS). In this system, users transmit text information to the SemRelay through conventional bit transmission, and the SemRelay compresses this information using a knowledge based characterized by probability graph before transmitting it to the BS through semantic communication. Then, the BS recovers the information based on the shared probability graph. While the semantic information compression incurs computational resource consumption, it significantly reduces communication resource usage. This paper addresses the challenge of minimizing overall system latency through jointly optimizing communication and computation re-source allocation, considering limited wireless resources and the system's energy budget. To address this problem, we introduce an efficient iterative algorithm, which employs block coordinate descent for communication resource allocation and exhaustive searching for determining the optimal data compression scheme. In particular, both power allocation subproblem and bandwidth allocation subproblem are proved to be convex. The complexity analysis of the proposed algorithm are also provided. Numerical results validate the effectiveness of the proposed algorithm and the superior performance of semantic communication compared to the conventional bit transmission.

Wireless communication systems operating in fading channels often demand pilots for channel estimation and data recovery, leading to substantial transmission overhead. In this paper, we propose a novel pilot-free semantic communication system designed for transmitting images over multi-user MIMO (MU-MIMO) fading channels. Specifically, our method involves extracting multi-scale semantic features from the source image at the transmitter, effectively embedding pilot-like information. At the receiver, we extract channel features from these semantic features at each scale, enabling the reconstruction of the source image without requiring explicit channel estimation and signal detection. To enhance the image reconstruction process, we introduce a novel module, called Resnet Transformer, which combines multi-head self-attention (MHSA) with Resnet block. Our experimental results demonstrate that this pilot-free system outperforms existing pilot-aided semantic communication methods in terms of perceptual quality and transmission efficiency.

By leveraging multi-view inputs to synthesize novel-view images, Neural Radiance Fields (NeRF) have emerged as a prominent technique in the realm of 3D object reconstruction. However, existing methods primarily focus on global scene reconstruction using large datasets, which necessitate substantial computational resources and impose high-quality requirements on input images. Nevertheless, in practical applications, users prioritize the 3D reconstruction results of on-demand specific object (OSO) based on their individual demands . Furthermore, the collected images transmitted through high-interference wireless environment (HIWE) leads to negatively impact the accuracy of NeRF reconstruction, thereby limiting its scalability. In this paper, we propose a novel on-demand Semantic Neural Radiance Fields (OSNeRF) scheme, which offers fast and robust 3D object reconstruction for diverse tasks. Within OSNeRF, semantic encoder is employed to extract core semantic features of OSOs from the collected scene images, semantic decoder is utilized to facilitate robust image recovery under HIWE conditions, lightweight renderer is employed for fast and efficient object reconstruction. Moreover, a semantic control unit (SCU) is introduced to guide above components, thereby enhancing the efficiency of reconstruction. Demonstrative experiments demonstrate that the proposed OSNeRF enables fast and robust object reconstruction in HIWE, surpassing the performance of state-of-the-art (SOTA) methods in terms of reconstruction quality.

In this paper, we propose an uplink multi-modal probabilistic semantic communication (PSCom) system that considers both communication and computation. In the considered PSCom model, the users and base station share the common knowledge, which is characterized by probability graph. Based on the shared probability graph, the original large-size semantic data is compressed into the small-size semantic information, which will introduce additional computation costs for semantic compression. Besides, semantic information recovered by the base station will also bring additional computation costs. Although this method incurs additional computation costs, it effectively reduces communication energy consumption. Based on the considered model, an optimization problem is formulated to minimize the total communication and computation energy consumption, considering latency, power budget, bandwidth, and semantic compression ratio constraints. To solve this mixed integer optimization problem, we first adopt a greedy algorithm for the semantic compression level selection of each model, thereby transforming the original problem into a continuous variable optimization problem. Then, derive the optimal solution of the communication power. Finally, the Lagrange multiplier method is used to determine the optimal bandwidth, which can result in a closed-form optimal solution. Simulation results validate the effectiveness of the proposed algorithm.

This paper investigates a mobile edge computing (MEC) aided multi-static integrated sensing and communication (ISAC) system. In the considered system, each device has a task to be offloaded to MEC server for computation. Meanwhile, a sensing receiver (SR) has to detect a point target. To enhance detection probability, devices and base station (BS) collaboratively transmit radar signals and then SR detects the target of interest based on received radar echoes. Specifically, the whole task execution period is divided into two phases. In the first phase, devices offload partial of their tasks and simultaneously transmit radar signals for target sensing. In the second phase, the MEC server at the BS computes offloaded tasks and meanwhile devices along with BS transmit radar signals for sensing. Therefore, the entities in the system constitute a multi-static ISAC framework. Radar signal processing for multi-static sensing system is designed and the corresponding cooperative detection probability is derived. To ensure high energy efficiency, an optimization problem is formulated to minimize energy consumption of all devices under task computation and detection probability constraints by adjusting devices’ and BS’s beamforming, task partitioning, and phase duration allocation. This problem is efficiently solved by iteratively optimizing beamforming subproblem, time and task division subproblem through weighted minimum mean square error, successive convex approximation and search methods. Simulations verify the effectiveness of the proposed MEC aided cooperative sensing algorithm compared with non-cooperative sensing scheme.

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

In this paper, the problem of resource allocation for non-orthogonal multiple access (NOMA) enabled secure federated learning (FL) is investigated. In the considered model, a set of users participate in the FL training through transmitting their trained FL model parameters to the base stations (BSs) via NOMA techniques. To prevent data leakage, each user uses the differential privacy (DP) technique through adding Gaussian noise to its FL model parameters. The problem of minimizing overall privacy leakage of all FL participaring users is formulated as an optimization problem through jointly optimizing the connections between users and BSs, transmit power of the users, and the DP noise power. To solve the formulated non-convex optimization problem, a genetic algorithm is proposed to search for feasible solutions in which user connection matrix is taken as gene and the objective function value is taken as the fitness of solution. Simulation results show that the proposed genetic algorithm reduces privacy leakage by up to 73% compared to the conventional alternating optimization algorithm.

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

This paper investigates semantic energy efficiency in a non-orthogonal multiple access (NOMA) enabled integrated sensing and semantic communication (ISSC) system. The model involves the base station (BS) transmitting information to multiple users while performing target sensing using dedicated beamforming. In the considered model, the BS needs to transmit substantial text data to each user using text semantic communication techniques while sensing the targets with certain constraints. Our goal is to maximize semantic energy efficiency and meet semantic communication and sensing accuracy requirements. We formulate an optimization problem for the beamforming matrix and semantic parameter, employing the Dinkelbach's algorithm for simplification and proposing an iterative solution. Numerical results validate the efficacy of the NOMA-ISSC scheme.

In this paper, the problem of joint transmission and computation resource allocation for probabilistic semantic communication (PSC) network with rate splitting multiple access (RSMA) is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data, which is represented by substantial knowledge graphs, to multiple users. Due to limited communication resource, the BS needs to utilize semantic communication techniques to compress the large-sized data. In this paper, the semantic communication is enabled by shared probability graphs between the BS and users. The process of semantic compression requires computation power at the BS, which has an impact on limited power budget. Therefore, it is necessary to balance the power between transmission and computation. Based on the probability graph, the semantic rate related to semantic compression ratio is first theoretically formulated. Then, the problem is formulated as an optimization problem with the aim of maximizing the sum semantic rate of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is accordingly proposed to obtain a suboptimal solution. Numerical results validate the effectiveness of the proposed scheme.

Initial random access is a crucial process in wireless communication networks, which sets up reliable connections between multiple active users and the base station (BS). In this procedure, useful information, including beam pair, timing advance (TA), and channel state information (CSI), can be naturally obtained to achieve environment sensing and user positioning. Environment sensing is highly related to user positioning as it requires user-specific CSI, and more importantly, the multi-view observations from different user locations potentially benefit the fusion of the overall environment information. This makes joint environment sensing and user positioning of great significance. Moreover, initial random access provides observations from different users, avoiding the limited and insufficient observation of a single pair of transceivers, which helps to realize environment sensing in large scenarios. Therefore, in this paper, we propose a joint initial random access, environment sensing, and user positioning framework, exploiting direction, time-delay, reflection, and scattering information brought by beam pair, TA, and CSI. Furthermore, we illustrate the remarkable sensing and positioning performance of the proposed scheme in both light-of-sight (LoS) and non-light-of-sight (NLoS) scenarios.

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

This paper studies an end-to-end video semantic communication system for massive communication. In the considered system, the transmitter must continuously send the video to the receiver to facilitate character reconstruction in immersive applications, such as interactive video conference. However, transmitting the original video information with substantial amounts of data poses a challenge to the limited wireless resources. To address this issue, we reduce the amount of data transmitted by making the transmitter extract and send the semantic information from the video, which refines the major object and the correlation of time and space in the video. Specifically, we first develop a video semantic communication system based on major object extraction (MOE) and contextual video encoding (CVE) to achieve efficient video transmission. Then, we design the MOE and CVE modules with convolutional neural network based motion estimation, contextual extraction and entropy coding. Simulation results show that compared to the traditional coding schemes, the proposed method can reduce the amount of transmitted data by up to 25% while increasing the peak signal-to-noise ratio (PSNR) of the reconstructed video by up to 14%.

Semantic communication has emerged as a revolutionary paradigm within wireless edge networks, showcasing remarkable communication efficiency. In contrast to traditional bit-level communication systems, semantic communication systems exhibit superior effectiveness and precision, particularly in scenarios characterized by low signal-to-noise ratios (SNR). Nonetheless, the privacy of semantic communication poses a critical challenge that demands attention. Once the attacker intercepts the semantic information through continuous eaves-dropping, the private data would be leaked under adversarial environment. Moreover, in low SNR scenario, joint optimization of anti -eavesdropping and privacy reconstruction has not yet been studied, coupled with the intricate nature of designing a cross-layer semantic protection strategy. To address this concern, this paper presents a covert and reliable semantic communication (CRSC) framework via full-duplex receiver to counter continuous eavesdropper by concealing the entire transmission process. Furthermore, a newly-defined metric, namely covert semantic throughput (CST), is introduced to quantify the system's performance. Furthermore, we formulate the maximization of average CST during the semantic transmission period as a multi-constraint optimization problem. Subsequently, we propose a reinforcement learning (RL)-empowered adaptation algorithm to address the formulated problem. Through simulation results, the effectiveness and feasibility of proposed CRSC framework are demonstrated, with an observed maximum average CST improvement of up to 42% compared to conventional communication systems in the low SNR scenario.

In massive Multiple-Input Multiple-Output (MIMO) -Orthogonal Frequency Division Multiplexing (OFDM) systems, channel estimation incurs high pilot overhead due to the high channel dimension, prompting the exploration of various algorithms to mitigate this cost. Semi-blind estimation, which usually employs a traditional iterative algorithm based on Bayesian infer-ence, proves effective in enhancing estimation performance with a limited number of pilots. Meanwhile, Neural Network (NN)-based channel mapping and prediction methods have demonstrated potential in predicting the full channel matrix with the estimation of a proportion, reducing the pilot overhead. However, how to merge these two methods for less pilot overhead is yet to be investigated. This paper introduces a hybrid model and data driven method that combines semi-blind estimation with NN-based frequency domain channel mapping, leveraging data priors as the inference algorithm while harnessing the nonlinear mapping capability offered by NNs. The proposed architecture holds promise for extension to other applications where the synergistic combination of inference algorithms and NN s proves advantageous. Numerical results validate the effectiveness of the proposed algorithm.

Recently , over-the-air computation (OAC) has shown potential in realizing computation tasks over wireless transmission. Through proper transmit and receive beamforming design, multiple-input multiple-output (MIMO)-based OAC systems can even realize partial functions of neural networks (NNs). In this paper, we propose an OAC-NN with reconfigurable intelligent surface (RIS)-aided MIMO, in which the NN computation task can be realized through updating the RIS reflection matrix. In the proposed structure, the communication system can complete the overall simple NN-based tasks only through multiple rounds of transmissions without introducing any additional computing resources. Numerical results reflect the effectiveness of the proposed scheme and the tradeoff between communication costs and computing performance.

The utilization of millimeter-wave in vehicle-to-vehicle (V2V) communications can ensure high system capacity. However, in dense high-mobility environment, V2V communications will encounter severe resource collisions and require fre-quent beam training resulting in substantial signaling overhead. To address the above issues, we study the joint optimization problem of beam management and resource allocation in the millimeter-wave V2V communication system. Specifically, we propose a dual-layer deep reinforcement learning (DRL) archi-tecture that combines beam management and resource allocation into two interconnected tasks. Leveraging this dual-layer DRL architecture, we obtain a solution that involves interactive work between a communication module and an adaptive learning module. This approach is able to collect channel state information in real time and adapt to the ever-changing environment sufficiently. Simulation results show that the joint optimization scheme exhibits fast convergence, improves the effective achievable rate, and reduces the signaling overhead.

This paper investigates the secure resource allocation for a downlink integrated sensing and communication system with multiple legal users and potential eavesdroppers. In the considered model, the base station (BS) simultaneously transmits sensing and communication signals through beamforming design, where the sensing signals can be viewed as artificial noise to enhance the security of communication signals. To further enhance the security in the semantic layer, the semantic information is extracted from the original information before transmission. The user side can only successfully recover the received information with the help of the knowledge base shared with the BS, which is stored in advance. Our aim is to maximize the sum semantic secrecy rate of all users while maintaining the minimum quality of service for each user and guaranteeing overall sensing performance. To solve this sum semantic secrecy rate maximization problem, an iterative algorithm is proposed using the alternating optimization method. The simulation results demonstrate the superiority of the proposed algorithm in terms of secure semantic communication and reliable detection.

Millimeter-wave (mmWave) radar imaging is envisioned as a promising technology for wireless communication system. Through modeling radar imaging as a compressed sensing (CS) problem, iterative algorithms represented by sparsity adaptive matching pursuit (SAMP) can be effectively adopted for high-quality imaging. In this paper, we introduce adaptive stepsize mechanism to replace the fixed stepsize setting in the existing SAMP method, forming a novel stepsize-adaptive SAMP (SA-SAMP) algorithm for mmWave radar imaging. The adaptive stepsize selection enhances the precision of sparsity estimation, thereby resulting in an augmented imaging speed while realizing high-quality results. Experiments on real radar dataset demon-strate the performance gains of the proposed algorithm in terms of peak signal-to-noise ratio, target-to-clutter ratio, and structure similarity index.

Abstract In this work, we consider a federated learning model in a wireless system with multiple base stations and inter‐cell interference. We apply a differentially private scheme to transmit information from users to their corresponding base station during the learning phase. We show the convergence behavior of the learning process by deriving an upper bound on its optimality gap. Furthermore, we define an optimization problem to reduce this upper bound and the total privacy leakage. To find the locally optimal solutions of this problem, we first propose an algorithm that schedules the resource blocks and users. We then extend this scheme to reduce the total privacy leakage by optimizing the differential privacy artificial noise. We apply the solutions of these two procedures as parameters of a federated learning system where each user is equipped with a classifier and communication cells have mostly fewer resource blocks than numbers of users. The simulation results show that our proposed scheduler improves the average accuracy of the predictions compared with a random scheduler. In particular, the results show an improvement of over 6%. Furthermore, its extended version with noise optimizer significantly reduces the amount of privacy leakage.

Mobile edge computing (MEC) in the next generation networks will provide computation services at the network edge to enrich the capabilities of mobile devices and lengthen their battery lives. However, the performance of MEC cannot be guaranteed, when large size local tasks are uploaded to the server simultaneously causing network congestion. As a new paradigm that focuses on transmitting the meaning of messages, semantic communications reveals the significant potential to reduce the network traffic. In this paper, we propose a semantic-aware joint communication and computation resource allocation framework for MEC systems. In the considered system, random tasks arrive at each terminal device (TD), which needs to be computed locally or offloaded to the MEC server. To further release the transmission burden, each TD sends the small-size extracted semantic information of tasks to the server instead of the original large-size raw data. An optimization problem of joint semantic-aware division factor, communication and computation resource management is formulated. The problem aims to minimize the energy consumption of the whole system, while satisfying long-term delay and processing rate constraints. To solve this problem, an online low-complexity algorithm is proposed. In particular, Lyapunov optimization is utilized to decompose the original coupled long-term problem into a series of decoupled deterministic problems without requiring the realizations of future task arrivals and channel gains. Then, the block coordinate descent method and successive convex approximation algorithm are adopted to solve the current time slot deterministic problem by observing the current system states. Moreover, the closed-form optimal solution of each optimization variable is provided. Simulation results show that the proposed algorithm yields up to 41.8% energy reduction compared to its counterpart without semantic-aware allocation.

In mission-critical Industrial Internet of Things (IIoT), multiple sensors make independent observations at different locations and then transmit them to the base station (BS) to obtain a global system state vector. Uploading observation information to the BS by active sensors is a multiple access process. As the task is to complete state estimation instead of maximizing the physical-layer capacity, conventional multiple access schemes cannot be applied directly to mission-critical IIoT applications. Therefore, next generation multiple access (NGMA) techniques are urgently needed to realize the key performance indicators for the design of IIoT networks. Note that, in mission-critical IIoT systems, each sensor can only obtain the observation of a subset of state variables, and the BS only cares about the state information embedded in that observation not the identities of the sensors. This indicates that the whole process of data transmission and state estimation can be totally unsourced, thus resulting in a highly efficient IIoT system implementation. Based on this crucial finding, in this article, we propose an unsourced multiple access (UMA)-based mission-critical IIoT system. Moreover, a decoupled UMA (D-UMA) scheme is proposed to improve transmission efficiency and state estimation performance. We analyse the fundamental aspects of how our design affects and guarantees the controllability, observability, and stability of an IIoT control system. Simulation results verify the remarkable performance of the proposed scheme compared with the conventional orthogonal multiple access (OMA) and nonorthogonal multiple access (NOMA) schemes.

The unmanned aerial vehicle (UAV) swarms have shown great potential to serve next-generation communication networks with their extraordinary flexibility, affordability, and the ability to collaboratively and autonomously provide Line-of-Sight (LoS) services. However, autonomous collaboration under wireless dynamics is challenging. Distributed learning (DL) provides a chance for the UAV swarms to operate intelligently under sophisticated dynamics, such that they can be applied to wireless communication service scenarios, as well as applications including multidirectional remote surveillance, and target tracking. In this survey, we first introduce several popular DL frameworks that are capable of managing a UAV swarm, these include federated learning (FL), multiagent reinforcement learning (MARL), distributed inference (DI), and split learning (SL). We also present a comprehensive overview of how these DL frameworks manage UAV swarms in regard to trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite–drone integration. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such as reconfigurable intelligent surfaces (RISs), virtual reality (VR), and semantic communications (SemComs), and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL-enabled UAV swarms. In summary, this survey provides a concise survey of various DL applications for UAV swarms in extensive scenarios.

This article introduces an approach to harness digital twin (DT) technology in the realm of integrated sensing and communications (ISACs) in sixth-generation (6G) Internet of Everything (IoE) applications. We consider moving targets in a vehicular network and use DT to track and predict the motion of the vehicles. After predicting the location of the vehicle at the next time slot, the DT designs the assignment and beamforming for each vehicle. The real-time sensing information is then utilized to update and refine the DT, enabling further processing and decision making. In the DT, an extended Kalman filter (EKF) is used for the precise motion prediction. This model incorporates a dynamic Kalman gain, which is updated at each time slot based on the received echo signals. The state representation encompasses both the vehicle motion information and the error matrix, with the posterior Cramér-Rao bound (PCRB) employed to assess sensing accuracy. We consider a network with two roadside units (RSUs), and the vehicles need to be allocated to one of them. To optimize the overall transmission rate while maintaining acceptable sensing accuracy, an optimization problem is formulated. Since, it is generally hard to solve the original problem, the Lagrange multipliers and fractional programming are employed to simplify this optimization problem. To solve the simplified problem, this article introduces both the greedy and heuristic algorithms by optimizing both the vehicle assignments and predictive beamforming. The optimized results are then transferred back to the real space for ISAC applications. Recognizing the computational complexity of the greedy and heuristic algorithms, a bidirectional long short-term memory (LSTM)-based recurrent neural network (RNN) is proposed for efficient beamforming design within the DT. Simulation results demonstrate the effectiveness of the DT-based ISAC network. Notably, the LSTM-based RNN method achieves similar transmission rates as the heuristic algorithm but with significantly reduced computational complexity.

Unmanned aerial vehicles (UAVs) equipped with high definition (HD) cameras, intelligent sensors, computing, and communication modules can be deployed to execute crowdsensing tasks by leveraging federated learning (FL), e.g., air quality perception and ground target detection. FL can reduce transmission stress and protect data privacy when training models, which is suitable for resource constrained Internet of UAVs. Nevertheless, the incentive issues about information asymmetry and bounded rationality impede the applications of FL-enabled Internet of UAVs. The existing FL incentive approaches focus on the risk-free condition, where task publishers are capable of making decisions with complete rationality by utilizing expected utility theory. In fact, task publishers under risk conditions are often bounded rational, whose risk-awareness makes the utility models more sophisticated. To overcome the above problems, we present a prospect theory (PT)-based incentive mechanism for FL-enabled Internet of UAVs. We first leverage PT to model the task publisher’s risk-awareness behavior and construct the subjective utility model. Thereafter, we utilize the framing effect of PT to design the optimal contract to maximize the subjective utility. Simulation results demonstrate that, compared with the baseline method, the proposed incentive mechanism has better performance.

The Industrial Internet of Things (IIoT) is a typical future application for the mobile networks. Unmanned aerial vehicle (UAV) has attracted a great deal of interest in relay assisted wireless communication systems due to its benefits of high mobility, rapid deployment and high probability of line-of-sight (LoS) transmissions. This paper proposes the three-dimensional (3-D) geometry-based non-stationary channel models for wireless communication underlaying UAV-based relay assisted industrial internet of things (IIoT) networks in the sub-terahertz (sub-THz) band. In order to accurately describe the propagation characteristics of the UAV-based wireless channels in the sub-THz band, the propagation gain and atmospheric absorption gain in free space, LoS path, single UAV-based relay and double UAVbased relay propagation paths are considered in the proposed channel models. The channel impulse response (CIR) expressions of different propagation paths are derived respectively. The statistical properties of the channel models including path loss, channel capacity, temporal auto-correlation function (T-ACF), and Doppler power spectral density (DPSD) at 140 GHz band are investigated and analysed, with a performance comparison at 60 GHz band.

The advent of Industry 4.0 has positioned the industrial Internet of Things (IIoT) as a cornerstone of future industry. In this article, the problem of spectral-efficient communication and computation resource allocation for distributed reconfigurable intelligent surfaces (RISs) assisted probabilistic semantic communication (PSC) in IIoT is investigated. In the considered model, multiple RISs are deployed to serve multiple users, while PSC adopts compute-then-transmit protocol to reduce the size of the transmission data. To support the high-rate transmission, the semantic compression ratio, transmit power allocation, and distributed RISs deployment must be jointly considered. This joint communication and computation problem is formulated as an optimization problem whose goal is to maximize the sum semantic-aware transmission rate of the system under the total transmit power, phase shift, RIS-user association, and semantic compression ratio constraints. To solve this problem, a many-to-many matching scheme is proposed to solve the RIS-user association subproblem, the semantic compression ratio subproblem is addressed following the greedy policy, while the phase shift of RIS can be optimized using the tensor-based beamforming. Numerical results verify the superiority of the proposed algorithm.

In this paper, we investigate delay-optimized edge caching in the integrated satellite-terrestrial network with diverse content popularity distribution and user access modes. Based on the cooperation among the base stations, the satellite and the gateway, we propose a three-layer caching architecture to provide content service for both base station access users and satellite access users. Considering diverse content preferences for users in different areas, we formulate the content placement problem with the objective to minimize the average content retrieving delay of the network. By introducing the concept of the delay reduction gain and the caching benefit, we first derive the optimal caching strategy for base stations in different areas separately. Then, we propose two algorithms to calculate the cooperative caching strategy of the network, in which reduced search space is applied based on theoretical analysis. While the dynamic programming algorithm can achieve the optimal solution of the content placement problem, the submodular optimization based algorithm can provide guaranteed performance with relatively low complexity. Simulation results show that the proposed caching strategies can effectively improve the network delay performance.

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

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

The formation control of multi-agent systems has increasingly drawn attention for fulfilling numerous emerging applications and services. To achieve high-accuracy formation, the location awareness of all agents becomes an essential requirement. In this paper, we address the problem of network localization and formation control in a cooperative system with asynchronous agents. In particular, we formulate the joint localization and synchronization of agents as a statistical inference problem. The underlying probabilistic model is represented by a factor graph from which a message-passing algorithm is designed that computes approximations of the marginals of unknown variables, i.e. agents’ locations and clock offsets. Due to the Euclidean-norm operator involved in their computation no parametric closed-form expressions of the messages exist. As a compromise, implemented message-passing methods therefore resort to approximations of these messages. Conventional methods rely either on a first-order Taylor expansion of the norm operation or on non-parametric representations, e.g. by means particle filters (PFs), to compute such approximations. However, the former approach suffers from poor performance while the latter one experiences high complexity. The proposed message-passing algorithm in this paper is parametric. Specifically, it passes Gaussian messages that can be essentially obtained by suitably augmenting the factor graph and applying on it a hybrid method for combining belief propagation and variational message passing. Subsequently, the agents can exploit the estimated locations for determining the control policy. Two types of control policy are designed based on the optimization of a generalized cost function. We show that the proposed scheme enjoys a reduced complexity for multi-agent localization while achieving the desired formation with excellent accuracy.

Multiple access (MA) is a crucial part of any wireless system and refers to techniques that make use of the resource dimensions (e.g., time, frequency, power, antenna, code, and message) to serve multiple users/devices/machines/ services, ideally in the most efficient way. Given the increasing need of multifunctional wireless networks for integrated communications, sensing, localization, and computing, coupled with the surge of machine learning (ML)/artificial intelligence (AI) in wireless networks, MA techniques are expected to experience a paradigm shift in 6G and beyond. In this article, we provide a tutorial, survey, and outlook on past, emerging, and future MA techniques and pay particular attention to how wireless network intelligence and multifunctionality will lead to a rethinking of those techniques. This article starts with an overview of orthogonal, physical-layer multicasting, space domain, power domain (PD), rate-splitting, code-domain MAs, MAs in other domains, and random access (RA), and highlights the importance of conducting research in universal MA (UMA) to shrink instead of grow the knowledge tree of MA schemes by providing a unified understanding of MA schemes across all resource dimensions. It then jumps into rethinking MA schemes in the era of wireless network intelligence, covering AI for MA such as AI-empowered resource allocation, optimization, channel estimation, and receiver designs, for different MA schemes, and MA for AI such as federated learning (FL)/edge intelligence and over-the-air computation (AirComp). We then discuss MA for network multifunctionality and the interplay between MA and integrated sensing, localization, and communications, covering MA for joint sensing and communications, multimodal sensing-aided communications, multimodal sensing and digital twin-assisted communications, and communication-aided sensing/localization systems. We finish with studying MA for emerging intelligent applications such as semantic communications (SeComs), virtual reality (VR), and smart radio and reconfigurable intelligent surfaces (RISs), before presenting a roadmap toward 6G standardization. Throughout the text, we also point out numerous directions that are promising for future research.

In this paper, the problem of joint user scheduling and computing resource allocation in asynchronous mobile edge computing (MEC) networks is studied. In such networks, edge devices will offload their computational tasks to an MEC server, using the energy they harvest from this server. To get their tasks processed on time using the harvested energy, edge devices will strategically schedule their task offloading, and compete for the computational resource at the MEC server. Then, the MEC server will execute these tasks asynchronously based on the arrival of the tasks. This joint user scheduling, time and computation resource allocation problem is posed as an optimization framework whose goal is to find the optimal scheduling and allocation strategy that minimizes the energy consumption of these mobile computing tasks. To solve this mixed-integer non-linear programming problem, the general benders decomposition method is adopted which decomposes the original problem into a primal problem and a master problem. Specifically, the primal problem is related to computation resource and time slot allocation, of which the optimal closed-form solution is obtained. The master problem regarding discrete user scheduling variables is constructed by adding optimality cuts or feasibility cuts according to whether the primal problem is feasible, which is a standard mixed-integer linear programming problem and can be efficiently solved. By iteratively solving the primal problem and master problem, the optimal scheduling and resource allocation scheme is obtained. Simulation results demonstrate that the proposed asynchronous computing framework reduces 87.17% energy consumption compared with conventional synchronous computing counterpart.

In this paper, we propose an adaptive semantic resource allocation paradigm with semantic-bit quantization (SBQ) compatible with existing wireless communications, where the inaccurate environment perception introduced by the additional mapping relationship between semantic metrics and transmission metrics is solved. Specifically, SBQ is a hybrid uniform-non-uniform quantization method, which aims to facilitate the coding between semantics and bits. In order to investigate the performance of semantic communication networks, the quality of service for semantic communication (SC-QoS), including the semantic quantization efficiency (SQE) and transmission latency, is proposed for the first time. A problem of maximizing the overall effective SC-QoS is formulated by jointly optimizing the transmit beamforming of the base station, the bits for semantic representation, the subchannel assignment, and the bandwidth resource allocation. To address the non-convex formulated problem, an intelligent resource allocation scheme is proposed based on a hybrid deep reinforcement learning (DRL) algorithm, where the intelligent agent can perceive both semantic tasks and dynamic wireless environments. Simulation results demonstrate that our design can effectively combat semantic noise and achieve superior performance in wireless communications compared to several benchmark schemes. Furthermore, compared to mapping-guided paradigm based resource allocation schemes, our proposed adaptive scheme can achieve up to 13% performance improvement in terms of SC-QoS.

In recent years, the uploading of massive personal images has increased the security risks, mainly including privacy breaches and copyright infringement. Adversarial examples provide a novel solution for protecting image privacy, as they can evade the detection by deep neural network (DNN)-based recognizers. However, the perturbations in the adversarial examples typically meaningless and therefore cannot be extracted as traceable information to support copyright protection. In this paper, we designed a dual protection scheme for image privacy and copyright via traceable adversarial examples. Specifically, a traceable adversarial model is proposed, which can be used to embed the invisible copyright information into images for copyright protection while fooling DNN-based recognizers for privacy protection. Inspired by the training method of generative adversarial networks (GANs), a new dynamic adversarial training strategy is designed, which allows our model for achieving stable multi-objective learning. Experimental results show that our scheme is exceptionally robust in the face of a variety of noise conditions and image processing methods, while exhibiting good model migration and defense robustness.

In this paper, we investigate a novel physical layer secure (PLS) potential technique which is summarized into a category of index modulation (IM) scheme, namely the spatial field digital modulation (SFDM). In an SFDM communication system, bit streams are modulated in the spatial distribution of electromagnetic (EM) field. Orbital angular momentum (OAM) mode groups can be utilized for beamforming and thus realize an OAM based SFDM (OAM-SFDM) system. This kind of IM systems that utilize the distribution of EM field to transmit information can radiate different signals to different directions and possess inherent anti-eavesdropping capability. Since an eavesdropper lacks prior knowledge, it is difficult to construct appropriate and effective estimators for signal demodulation. However, such system is vulnerable when potential eavesdroppers adopt clustering algorithm, a type of non-realtime demodulation. We propose two PLS strategies with different computational complexity, the power hopping (PH) method and the symbol variation (SV) method, to confront single-antenna eavesdroppers and multi-antenna eavesdroppers, respectively. Both approaches are key-free, and do not require any prior channel state information (CSI) about eavesdroppers. The mentioned methods could constantly disrupt the statistical properties of eavesdroppers’ channels and even their received signals. Theoretical analysis and numerical simulations have been conducted to validate the feasibility of these two schemes. Furthermore, a prototype of OAM-SFDM based PLS communication system is built in a realistic scenario. The experimental results demonstrate that both PH method and SV method can effectively resist clustering algorithm in their corresponding application scenarios.

The next-generation wireless network is envisioned to support real-time broadband communication (RTBC) to provision services for immersive applications. Such applications (e.g., virtual reality, VR) usually need to simultaneously transmit multi-modal (e.g., visual, audio and haptic) data streams that have different traffic characteristics and transmission requirements, within multiple transmission time intervals (TTIs). In this paper, we formulate an optimization problem of multi-user multiple-input multiple-output (MIMO) precoding within multiple TTIs for multi-modal data transmission. As it is hard to find an optimal solution, we first resort to a novel soft actor-critic (SAC)-based learning approach. Specifically, a lightweight reinforcement learning architecture is employed to learn the adaptive priority weight of each user within multiple TTIs by taking into account its remaining multi-modal data amount and dynamic interaction state. The learned priority weights are then input to an iterative weighted minimum mean-square error (WMMSE) algorithm to adjust the precoder matrix and user transmission rates. With a scalable state design, the proposed algorithm can be tailored to different numbers of potential or active users. We also provide another practical solution to the formulated multi-TTI precoding problem, which transforms the problem into a single-TTI optimization problem by adding the quality-of-service (QoS) constraints into the traditional WMMSE problem and then solves it using the alternating direction method of multipliers (ADMM). Simulation results demonstrate the robustness and efficiency of the proposed algorithms, which show that the SAC-based precoding algorithm can achieve a 50.0% increment in system capacity compared to traditional WMMSE and a significant reduction in time complexity compared to the QoS-constrained WMMSE algorithm.

Symbiotic radio (SR) offers potential benefits for large-scale Internet of Things networks through its spectrum and resource-sharing capabilities between primary and secondary systems. However, in high-mobility scenarios, rapidly changing channels present significant challenges for reliable and low-latency SR communications. In response to this challenge, this paper proposes employing orthogonal time-frequency space (OTFS) modulation for SR systems in hostile environments. By processing signals in the delay-Doppler (DD) domain, the fast-varying channels can be efficiently characterized using a few quasi-static DD domain parameters. To enable joint primary and secondary transmissions in SR, we propose modulating primary information using phase-shift keying modulation, while the secondary information is modulated into the frequencies of the periodic rectangular wave. However, the time-varying secondary signals are intertwined with unknown DD-domain channels, making it difficult to differentiate between secondary information and the original channel state information (CSI). To overcome this challenge, we introduce coherent detection methods for secondary information using both amplitude-based and sparsity-based techniques, leveraging the spectral characteristics of signal combinations with different frequencies. Moreover, recognizing that the periodic rectangular wave of the secondary transmission would reshape the Doppler profile of the equivalent channel in a highly structured manner, we propose an off-grid structured-sparse Bayesian learning-based CSI estimator. With the obtained equivalent CSI, we propose a low-complexity symbol-wise detection algorithm for detecting primary information, leveraging the pilot guards and interference cancellation technique. Finally, numerical results validate the effectiveness and superiority of the proposed estimator and detector.

We propose a novel deep learning-based algorithm for channel prediction and energy efficiency (EE) optimisation in an intelligent reflecting surface (IRS) aided Terahertz communication system. Specifically, a multi-antenna base station with an IRS with massive reflecting elements is designed to serve multiple moving users. A deep learning-based prediction-optimisation scheme is presented where we first propose a transformer encoder with channel index embedding (TE-CIE) deep learning model for time-varying channel prediction. With the help of channel prediction, an EE optimisation algorithm is then designed to maximise the EE in advance based on the predicted channel state information (CSI). Finally, we combine both methods to construct a deep learning-based prediction-optimisation scheme. Specifically, the future CSI is predicted by TE-CIE and the IRS phase-shift and precoding matrices are optimised in advance. Simulation results demonstrate that our proposed channel prediction method achieves close-to-optimal performance in terms of low mean absolute error and a much faster speed than the two baseline models. We demonstrate that the proposed EE optimisation algorithm outperforms the baseline algorithms in terms of much better EE under diverse parameter settings. Finally, the proposed prediction-optimisation scheme achieves at least twice the EE improvement compared to the baseline methods in the literature.

Unmanned aerial vehicle (UAV) swarms have become a promising solution to enhance modern wireless communication in complicated environments. However, due to the existence of real-world malicious attacks, the performance of prediction and optimisation methods used for UAV swarms are easily degraded. In this paper, we propose a novel deep learning-based user mobility prediction, user assignment and drone position optimisation scheme for a UAV swarm-enabled wireless communication system in the presence of malicious Global Navigation Satellite System (GNSS) spoofing attackers. Specifically, a robust deep learning-based user mobility prediction model, namely denoising autoencoder recurrent transformer (DART), is designed. Additionally, two efficient user assignment and drone position optimisation methods are proposed. The proposed deep learning model forecasts user locations, on which we construct and solve assignment and position optimisation problems. Simulation results show that the proposed deep learning-based prediction-optimisation scheme can provide up to 30% higher overall sum rate compared with the adversarially trained long short-term memory (LSTM) baseline and almost double the overall sum rate compared with the vanilla LSTM baseline.

In this paper, we propose an innovative learning-based channel prediction scheme so as to achieve higher prediction accuracy and reduce the requirements of huge amounts and strict sequential format of channel data. Inspired by the idea of the neural ordinary differential equation (Neural ODE), we first prove that the channel prediction problem can be modeled as an ODE problem with a known initial value by analyzing the physical process of electromagnetic wave propagation within a mobile environment. Then, we design a novel physics-inspired spatial channel gradient network (SCGnet), which represents the derivative process of channel varying as a special neural network and can obtain the gradients at any relative displacement needed for the ODE solving. With the SCGnet, the static channel at any location served by the base station is accurately inferred through consecutive propagation and integration. Finally, we design an efficient recurrent positioning algorithm based on some prior knowledge of user mobility to obtain the velocity vector and propose an approximate Doppler compensation method to make up the instantaneous angular-delay domain channel. Only discrete historical channel data is needed for the training, whereas only a few fresh channel measurements are needed for the prediction, which ensures the scheme’s practicability. Comprehensive evaluations show that the proposed scheme is most efficient in representing, learning, and predicting mobile wireless channels.

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

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

In this paper, the problem of maximizing the sum of data rates of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 28.6% gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS.

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

Initial access (IA) is a procedure of establishing an initial connection between the base station (BS) and the user. In the fifth generation (5G) millimeter wave (mmWave) communication system, the IA procedure includes beam management, which determines the beam pair for random access and data transmission by beam sweeping. The existing beam sweeping method in the 3-rd generation partnership project (3GPP) standard mainly uses a predefined uniform beamforming codebook and sweeps the beams progressively, which is time-consuming and highly inflexible. Note that the non-uniform and quasi-stationary environment and user cluster distribution information can be exploited for the BS to optimize the beam sweeping patterns. Therefore, this paper proposes a novel reinforcement learning (RL) framework for IA to efficiently acquire beam sweeping patterns. Specifically, a dimension-reduced beamforming codebook is designed to solve the problem of large search space and a comprehensive RL environment is constructed for the BS to capture the properties of environment layout and user distributions. Simulation results verify the remarkable performance of our proposed schemes in terms of beam sweeping efficiency.

Precoding design for the downlink of multiuser multiple-input multiple-output (MU-MIMO) systems is a fundamental problem. In this paper, we aim to maximize the weighted sum rate (WSR) while considering both quality-of-service (QoS) constraints of each user and per-antenna power constraints (PAPCs) in the downlink MU-MIMO system. To solve the problem, we reformulate the original problem to an equivalent problem by using the well-known weighted minimal mean square error (WMMSE) framework, which can be tackled by iteratively solving three subproblems. Since the precoding matrices are coupled among the QoS constraints and PAPCs, we adopt alternating direction method of multipliers (ADMM) to obtain a distributed solution. Simulation results validate the effectiveness of the proposed algorithm.

The 5.5th Generation is envisioned to support the Real-time Broadband Communication (RTBC) scenarios, which needs to satisfy the enhanced Mobile Broadband(eMBB) and Ultra-Reliable Low-Latency Communication (uRLLC) services requirements simultaneously. As an essential technique to improve the capacity of systems, pre coding in RTBC faces the challenge of finding the optimal solution over long-term transmission with multimodal streams for the system. To meet these requirements, we propose a soft actor-critic (SAC) based multiple transmission time interval (TTl) intelligent precoding algorithm that optimizes the multi-user precoding scheme by learning the priority weight of each user in the iterative weighted minimum mean-square error (MMSE) algorithm. Considering the remaining multimodal data and dynamic activation state of real-time interaction users, we design a novel and lightweight reinforcement learning architecture scalable to different numbers of potential or active users. Simulation results demonstrate the robustness and superiority of our precoding algorithm, which achieves 50% performance improvement in system capacity compared to the weighted MMSE algorithm.

Future communication networks will integrate the sensing functions, requiring the system to utilize limited radio resources to simultaneously achieve high-throughput communication and high-precision sensing. Compressive sensing technology is of great potential for such applications. In this paper, we design an efficient multiple-input multiple-output (MIMO) - orthogonal frequency division multiplexing (OFDM) integrated sensing and communication (ISAC) system based on compressive sensing. Specifically, we obtain the delay-Doppler and angle of departure (AoD) / angle of arrival (AoA) measurements by exploiting the sparse and random time-frequency resource allocation patterns and array structures. Our approach leverages the sparsity of environmental information and employs the Kronecker method to construct a compressive measurement matrix with Vandermonde structure. Our method provides high-resolution performance comparable to Nyquist sampling, while significantly reducing the usage of time-frequency resource units and the number of antennas, without introducing excessive additional hardware links for sensing and computation. Numerical simulations demonstrate the feasibility of the method, and our results indicate that compressive sensing recovery algorithms outperform traditional method with a lower probability of recovery errors and better robustness.

In this paper, we investigate the semantic-aware efficient sampling policy for remote state estimation in a digital twin (DT) empowered smart factory with multiple wireless sensing devices and an edge server. In this setting, wireless sensing devices must continuously sample the factory states and transmit semantic-aware sensing data to the server. Using the received sensing data, the server builds a realtime DT mapping remotely that analyzes and predicts the events in the factory. Since the DT requires continuous data transmission, maintaining the DT inevitably consumes significant amounts of limited wireless resources. To address this issue, we reduce the required amount of data transmission by making wireless devices only send the semantic-aware sensing data that indicates the occurrence of events, otherwise stay idle. In particular, we first invoke the age of incorrect information (AoII) to measure the semantic of the sensing data, which represents the freshness of the concerned events. Next, we formulate an optimization problem that minimizes the long-term AoII of remote state estimation through the devices deciding whether to sample the factory states at each time slot. To solve this problem, we first transform the original problem into a state-wise constrained Markov decision programming (CMDP) and then propose a soft actor-critic (SAC) based algorithm to learn a sampling policy to take sample actions within the sampling rate constraint, while considering packet error. Simulation results show that, the proposed algorithm can reduce the number of samples by up to 44% compared to the error-based sampling scheme, with the same estimation accuracy.

The integration of sensing and communication (ISAC) is an essential function of future wireless systems. Due to its large available bandwidth, millimeter-wave (mmWave) ISAC systems are able to achieve high sensing accuracy. In this paper, we consider the multiple base-station (BS) collaborative sensing problem in a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) mmWave communication system. Our aim is to sense a remote target shape with the collected signals which consist of both the reflection and scattering signals. We first characterize the mmWave's scattering and reflection effects based on the Lambertian scattering model. Then we apply the periodogram technique to obtain rough scattering point detection, and further incorporate the subspace method to achieve more precise scattering and reflection point detection. Based on these, a reconstruction algorithm based on Hough Transform and principal component analysis (PCA) is designed for a single convex polygon target scenario. To improve the accuracy and completeness of the reconstruction results, we propose a method to further fuse the scattering and reflection points. Extensive simulation results validate the effectiveness of the proposed algorithms.

In this paper, the use of channel state information (CSI) for indoor positioning is investigated. In the considered model, a base station (BS) equipped with several antennas sends pilot signals to a user that transmits the received pilot signals back to the BS. The BS will use the received CSI data to estimate the position of the user. To this end, we formulate this positioning problem as an optimization problem aiming to minimize the mean square error between the estimated position and the actual position of the user. To solve this problem, we design a complex-valued neural network (CVNN) based positioning algorithm. Compared to real-valued neural networks (RVNNs) that need to convert complex-valued CSI data into real-valued data, the proposed method uses original CSI data to train the CVNN model for user positioning. Since the output of our proposed algorithm is complex-valued and it consists of the real and imaginary parts, we can use it to implement two learning tasks. Based on this property, two use cases of the proposed algorithm are proposed: 1) the algorithm directly outputs the estimated position of the user. Here, the real and imaginary parts of an output neuron represent the 2D coordinates of the user, 2) the algorithm outputs two CSI features (i.e., line-of-sight/non-line-of-sight transmission link classification and time of arrival (TOA) prediction) which can be used in traditional positioning algorithms. Simulation results demonstrate that our designed CVNN based algorithm can reduce the mean positioning error between the estimated position and the actual position by up to 11.1%, compared to a RVNN based method which has to transform CSI data into real-valued data.

Millimeter-wave (mmWave) imaging over wireless networks is one of the potential technologies in the design of integrated sensing and communication (ISAC) systems. To achieve complete and accurate sensing of the large-scale complex environment, multiple views from different user equipments (UEs) and base stations (BSs) in a wireless network should be fully and cooperatively exploited. In this paper, based on the uplink channels of the wireless cellular network, we propose a multi-view mmWave imaging architecture. In the proposed architecture, a single BS centrally or multiple BSs jointly process the transmitted data of UEs. Taking into account the complex physical propagation characteristics of mmWave in the environment, especially the occlusion effect, we exploit the multi-view sensing of the environment from various UEs and BSs. To solve the multi-view sensing problem for the considered model, we propose a generalized-approximate-message-passing-based multi-view sparse vector reconstruction (GAMP-MVSVR) algorithm to obtain the imaging results. In the proposed algorithm, a multi-layer factor graph is proposed to describe the data receiving and sending relationship, as well as the occlusion effect of mmWave propagation. The sum-product algorithm (SPA) is used to iteratively solve the imaging result. Specifically in each iteration, the occlusion relationship between the target object in the environment is recalculated according to the proposed occlusion detection rule, and in turn, used to estimate the scattering coefficients of the target objects. Simulation results verify the effectiveness of the proposed algorithm.

Benefited from the advances of deep learning (DL) techniques, deep joint source-channel coding (JSCC) has shown its great potential to improve the performance of wireless transmission. However, most of the existing works focus on the DL-based transceiver design of the JSCC model, while ignoring the resource allocation problem in wireless systems. In this paper, we consider a downlink resource allocation problem, where a base station (BS) jointly optimizes the compression ratio (CR) and power allocation as well as resource block (RB) assignment of each user according to the latency and performance constraints to maximize the number of users that successfully receive their requested content with desired quality. To solve this problem, we first decompose it into two subproblems without loss of optimality. The first subproblem is to minimize the required transmission power for each user under given RB allocation. We derive the closed-form expression of the optimal transmit power by searching the maximum feasible compression ratio. The second one aims at maximizing the number of supported users through optimal user-RB pairing, which we solve by utilizing bisection search as well as Karmarkar's algorithm. Simulation results validate the effectiveness of the proposed resource allocation method in terms of the number of satisfied users with given resources.

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

To enable ultra-high throughputs while addressing the potential blockage problem, maintaining an adaptive access point (AP) planning is critical to mmWave networking. By investigating the hidden interaction between the environment map and the placement of mmWave APs, we develop an adaptive AP planning (E-app) approach that can accurately sense the environment dynamics, reconstruct the obstacle map, and then predict the placements of mmWave APs adaptively. Specifically, our solution leverages mmWave radio itself to sniff the unacceptable performance degradation through sensing only a small fraction of observation points that are identified by a sparsity-aware analytical model, thereby accurately triggering a prediction module for AP positioning when necessary. Extensive evaluations show a very high prediction accuracy for our solution, which can provide around 25% improvement on user throughput performance in mmWave WLANs. This intelligent AP-planning framework well handles the environment dynamics that affect the average-case network performance, which is of utmost interest for network deployers because of its usage convenience and adaptivity.

With the rapid development of deep learning, the parameters of modern neural network models, especially in the field of Natural Language Processing (NLP) are extremely huge. When the parameters of the model are larger even than the storage memory of a single device, it is necessary to split the original big learning model into different parts with each part assigned to one device, thus realizing joint model training over different devices (i.e., distributed training). In this paper, we aim to introduce the advanced coding scheme into the distributed parallel framework, which leads to the perfect combination of coding and the underlying calculation of neural networks. The proposed scheme is not only able to avoid the impact of poor computing power or low bandwidth and even dropped devices (stragglers) on system performance but also reduce the communication load between different devices, thereby greatly improving the performance of distributed parallel systems.

Viewing channel state information (CSI) as a fingerprint to infer user position is a promising technology. Recently, with the help of deep learning (DL) methods, the performance of CSI-based positioning has been further improved. However, the performance of DL methods is highly dependent on the number of training samples. Thus, achieving high performance with limited training samples has become an important topic. Analyzing the prior properties of the task and introducing the prior into the neural network is an effective means. In this paper, by analyzing the physical correlation between CSI and position, we point out that the CSI of each subcarrier is a valid fingerprint of user position in a massive multiple-input multiple-output (MIMO) system. With this property, we propose a positioning scheme based on multi-carrier cumulative learning neural network (MCCNet). Instead of directly learning the mapping from the entire MIMO-orthogonal frequency division multiplexing (OFDM) CSI to position, MCCNet first learns to map from the CSI of each subcarrier to position and then accumulates the extracted features from each subcarrier for the final position inference. This a priori design makes the feature extraction from the entire CSI downsize to the CSI of each subcarrier, reducing the learning burden. Simulation experiments on line-of-sight and non-line-of-sight scenarios both show that compared to the existing methods, MCCNet can reduce the averaged position error by at least 53% with the same training samples or achieve the same performance with only a quarter of training samples.

In this paper, we consider the problem of integrated sensing and communication (ISAC) system design over wireless networks. Specifically, the mobile station (MS) in the ISAC system sends uplink communication signal beams to the base station (BS), and the BS accomplishes the environment sensing by processing the propagation gain from received beams. Since the uniform discretization of the environment scenario has inaccurate descriptions of the BS/MS position and object occlusion relationship, we propose a non-uniform pixel discretization method. According to the location of the transceiver and the direction of beams, we discretize the environment into layered non-uniform pixels, which reflect the occlusion relationship between objects in the environment. Based on the sparse features of environmental scatterers, we propose an environment sensing algorithm based on compressed sensing and approximate message passing. The proposed algorithm achieves accurate environment sensing by iteratively removing occlusion interference. At the same time, with the continuous sweeping of the transmitting and receiving beams, the environment sensing results gradually improve. Finally, simulation results demonstrate the effectiveness of the proposed ISAC system design and algorithm.

Integrated sensing and communication (ISAC) systems aim to obtain the environment information using the wireless communication signals. However, most existing methods for ISAC systems require additional communication or hardware overheads, which pose a significant challenge for the resource-constrained wireless communication system. To address this issue, we propose a novel sidelobe-enhanced beam sweeping scheme, which leverages the extra information provided by the sidelobe compared to the traditional beam-based sensing algorithm. The proposed scheme takes into account the complete beam pattern including sidelobes, exploiting the different characteristics in each direction to simultaneously utilize multiple spatial angles. By effectively exploiting the sidelobes rather than treating them as interference, the proposed scheme can achieve comprehensive environmental information acquisition with high efficiency. Simulation results demonstrate that the proposed algorithm yields remarkable performance improvement.

Frequency modulated continuous wave (FMCW) radar can precisely detect moving objects utilizing the Doppler information. However, only exploiting the Doppler information in one frame can usually lead to object false detection when static background has large radar cross section or the moving objective occludes some static background. In this paper, we investigate the moving objective detection problem with FMCW radar through utilizing the Doppler information in multiple frames to increase objective detection accuracy. To solve this problem, an online tensor robust principal component analysis (RPCA) algorithm is proposed with low hardware and computation complexity. The proposed algorithm can maintain the intrinsic tensor data structure. Experimental results show that the proposed algorithm can accurately detect the static background and moving object even for the case of occlusion or static object with large RCS.

A large number of new applications are emerging in the future sixth-generation (6G) communication systems. Federated learning (FL) enables massive user equipments (UEs), such as mobile phones and Internet of Things (IoT) devices, to cooperatively learn a shared model for prediction in various applications, while keeping the training data local. However, in practical scenarios, there are still some problems in deploying FL systems, including serving a large number of active UEs, longtime delay, and the risk of UEs’ privacy leakage. To tackle these issues, we introduce unsourced random access (URA) into the FL systems. URA can support massive connectivity and its unsourced property can protect the UEs’ identity privacy. Moreover, considering the trade-off between communication and computation performance and the various importance of different UEs’ local models in training epochs, two importance metrics are designed. The UEs can decide their own active probability according to the metrics among the communication rounds, which avoids the additional cost of being scheduled by the base station (BS) and maximums the use of the limited communication resources to ensure UEs with higher priority can upload trained models, thus improving the training efficiency. Simulation results verify the remarkable communication and computation performance of the proposed schemes.

In this paper, we present a probability graph-based semantic information compression system for scenarios where the base station (BS) and the user share common background knowledge. We employ probability graphs to represent the shared knowledge between the communicating parties. During the transmission of specific text data, the BS first extracts semantic information from the text, which is represented by a knowledge graph. Subsequently, the BS omits certain relational information based on the shared probability graph to reduce the data size. Upon receiving the compressed semantic data, the user can automatically restore missing information using the shared probability graph and predefined rules. This approach brings additional computational resource consumption while effectively reducing communication resource consumption. Considering the limitations of wireless resources, we address the problem of joint communication and computation resource allocation design, aiming at minimizing the total communication and computation energy consumption of the network while adhering to latency, transmit power, and semantic constraints. Simulation results demonstrate the effectiveness of the proposed system.

Polar code is envisioned as a promising candidate for ultra-reliable low-latency communications (URLLC) in fifth-generation (5G) communication and beyond. To decode polar code, a successive cancellation list (SCL) decoder with a large list size can provide near maximum likelihood (ML) decoding performance. However, a large list size will lead to unacceptable spatial complexity, making it impractical. When the list size is small, although the complexity is low, its performance still needs to be improved. The main reason is that the sequence features implied in log-likelihood ratio (LLR) sequences are lost during calculating path metrics used for path selection. Because of the excellent sequence feature extraction ability of the long short-term memory (LSTM) network, we propose an LSTM-based path selection mechanism to replace the path metric-based path selection mechanism in SCL. In our proposed scheme, the LSTM network selects the surviving path according to the LLR sequences corresponding to the current paths. Simulation results show the effectiveness of the proposed LSTM-based path selection mechanism.

This article investigates the resource allocation of a reconfigurable intelligent surface (RIS)-aided joint communication and sensing (JCAS) system in a coal mine scenario. In the JCAS system, an RIS is implemented at the corner of the zigzag tunnels to improve the complicated wireless environment, where ground obstacles frequently block direct links. In addition, a wireless backhaul base station with a limited energy budget is deployed in the depth of the mine to sense the target area and provide Internet of Things (IoT) services and communication services for users. Furthermore, a data center is placed on the ground to analyze the obtained data and route the communication data. Under this deployment, a joint optimization problem of RIS phase-shift matrix, RIS element switches, and area sensing time is proposed. We aim to maximize the successful sensed bits under total completion time, and maximum transmit power constraints. In order to solve this problem, an iterative algorithm is proposed. The successive convex approximation (SCA)-based algorithm is used for the RIS phase-shift matrix optimization subproblem. For the sensing time optimization subproblem, the quadratic approximation method is proposed to optimize the number of area perceptions. The coordinate descent method is utilized to optimize the RIS element switches. Simulation results show that the energy efficiency is improved by up to 38%, and 7% increases the specific data size compared with the benchmark solutions.

Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastructure centered on the Internet of Drones has set off a new revolution in automatic image recognition. This emerging technology relies on sharing ground-truth-labeled data between unmanned aerial vehicle (UAV) swarms to train a high-quality automatic image recognition model. However, such an approach will bring data privacy and data availability challenges. To address these issues, we first present a semisupervised federated learning (SSFL) framework for privacy-preserving UAV image recognition. Specifically, we propose a model parameter mixing strategy to improve the naive combination of federated learning and semisupervised learning methods under two realistic scenarios (labels-at-client and labels-at-server), which is referred to as federated mixing (FedMix). Furthermore, there are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules in different environments, i.e., statistical heterogeneity. To alleviate the statistical heterogeneity problem, we propose an aggregation rule based on the frequency of the client’s participation in training, namely, the FedFreq aggregation rule, which can adjust the weight of the corresponding local model according to its frequency. Numerical results demonstrate that the performance of our proposed method is significantly better than those of the current baseline and is robust to different non-independent and identically distributed(IID) levels of client data.

In Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV), accessing multiple users and providing high-precision positioning are both vital. This paper aims to design an efficient deep learning approach to extend current Channel State Information (CSI)-based positioning to Frequency Division Multiple Access (FDMA) mode. In FDMA mode, different users are allocated with different subcarriers, making the user CSI have diverse frequency domain characteristics. The diverse frequency domain characteristics bring huge interference to the neural network for stable position inference, and efficient designs are required to handle this challenge. This paper proposes a novel approach named multi-frequency fusion learning for CSI-based positioning. By first using a shareable method to extract position-related features from CSI on each subcarrier independently and then fusing the obtained features, the designed neural network obtains excellent frequency domain flexibility to cope with the diverse frequency address challenge in FDMA mode. Meanwhile, we provide the feasibility analysis of this learning approach in massive Multiple-Input Multiple-Output (MIMO) systems to ensure its stable application. Based on the architecture of multi-frequency fusion learning, we propose two specific positioning schemes with differentiated designs. One is a Multi-Frequency Ensemble Network (MFENet), which extracts and fuses frequency-independent features to ensure the network is utterly unharmed by the complicated frequency domain characteristics. The other is a Multi-Frequency Cumulative Network (MFCNet), which uses sufficient feature accumulation to achieve high precision positioning. The key performance indices and applications on vehicles are comprehensively compared with popular deep-learning methods. Experiment results show the effectiveness and superiority of the proposed schemes.

It is our pleasure to share with you this Special Issue, which brings together a diverse set of articles dealing with various aspects of semantic and goal-oriented communications, providing a snapshot of research activities in this highly active research area. Wireless communications and networking research has traditionally focused on improving the capacity and throughput of the underlying wireless network. However, recent explosion in data-driven machine learning applications and their reliance on huge datasets collected by edge devices have raised legitimate concerns that the increasing data traffic might soon overwhelm the capacity of current networks despite ongoing efforts to increase their capacity and efficiency. Also, most of the edge intelligence applications impose stringent delay constraints, which cannot be met by naive forwarding of data samples for processing at the receiver end. This made it obvious to researchers in both academia and industry that it is essential to analyze the “value” or “relevance” of collected data, and filter and prioritize the delivery of data based on its value/relevance as well as the wireless channel and network conditions. In this context, data value will be closely connected to the underlying signals and processes that generate the data, e.g., text, image, video, or sensor data, and what the receiver intends to do with the received data. This subjectivity of data value makes semantic and goal-oriented communication a rather elusive research topic, which has led to both an increasingly rich and active area of investigation, but also a controversial one, mainly due to the lack of clear and widely agreed-upon definitions of some of the core concepts and formulations. Despite these disagreements, there is almost unanimous consensus on the importance and potential impact of this line of investigation for the design of future communication systems and networks.

Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, thereby providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications.

In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks with rate splitting is investigated. In the considered model, a base station (BS) first extracts semantic information from its large-scale data, and then transmits the small-sized semantic information to each user which recovers the original data based on its local common knowledge. At the BS side, the probability graph is used to extract multi-level semantic information. In the downlink transmission, a rate splitting scheme is adopted, while the private small-sized semantic information is transmitted through private message and the common knowledge is transmitted through common message. Due to limited wireless resource, both computation energy and transmission energy are considered. This joint computation and communication problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under computation, latency, and transmit power constraints. To solve this problem, an alternating algorithm is proposed where the closed-form solutions for semantic information extraction ratio and computation frequency are obtained at each step. Numerical results verify the effectiveness of the proposed algorithm.

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. As over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication, thus by substituting the wireless transmission in the traditional split ML framework with OAC, the communication load can be eased. In this paper, we propose to deploy split ML in a wireless multiple-input multiple-output (MIMO) communication network utilizing the intricate interplay between MIMO-based OAC and NN. The basic procedure of the OAC split ML system is first provided, and we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over a MIMO channel carrying out multi-stream analog communication. The precoding and combining matrices which are regarded as trainable parameters, and the MIMO channel matrix, which are regarded as unknown (implicit) parameters, jointly serve as a fully connected layer of the NN. Most interestingly, the channel estimation procedure can be eliminated by exploiting the MIMO channel reciprocity of the forward and backward propagation, thus greatly saving the system costs and/or further improving its overall efficiency. The generalization of the proposed scheme to the conventional NNs is also introduced, i.e., the widely used convolutional NNs. We demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations.

Densely deployed light emitting diodes (LEDs) typically lead to high spatial correlation in multiple-input multiple-output (MIMO) visible light communication (VLC) systems. Although precoding can effectively alleviate the spatial correlation issue, most existing precoding algorithms require a dedicated baseband chain for each LED, leading to high energy consumption and hardware complexity when a large number of LEDs are used. In this paper, a successive interference cancellation (SIC)-based precoding scheme with sub-connected architecture (SIC-SA) is proposed. In the considered model, each baseband chain is connected to an LED sub-array containing multiple LEDs to reduce the complexity. Since, in this case, SIC-based precoding can only determine the signal of each baseband chain for an LED sub-array, while its target is to mitigate the spatial correlation between individual LEDs, the electrical/optical power of each LED must also be jointly optimized to accomplish the target. This joint SIC-based precoding, power allocation, and direct current offset design problem is formulated as an achievable sum rate maximization problem under dimming control and electrical power constraints. A two-step iterative algorithm is proposed to solve this problem. In the first step, the SIC-based precoding is designed to alleviate the multi-user interference. In the second step, the power allocation of LEDs is optimized by matrix decomposition and convex optimization, and a closed-form solution of DC offset is derived. Furthermore, considering the dynamic scenarios, a SIC-based precoding scheme with dynamic sub-connected architecture (SIC-DSA) is proposed, in which a switch network is used to adaptively adjust the LED sub-array structure based on the channel state information. Simulation results show that the proposed SIC-SA and SIC-DSA respectively achieve 0.1340 bps/Hz/W and 0.1305 bps/Hz/W energy efficiency gains over the zero-forcing precoding scheme with SA, when the signal-to-noise ratio is 30 dB.

With the continuous trend of data explosion, delivering packets from data servers to end users causes increased stress on both the fronthaul and backhaul traffic of mobile networks. To mitigate this problem, caching popular content closer to the end-users has emerged as an effective method for reducing network congestion and improving user experience. To find the optimal locations for content caching, many conventional approaches construct various Mixed Integer Linear Programming (MILP) models. However, such methods may fail to support online decision making due to the inherent curse of dimensionality. In this paper, a novel framework for proactive caching is proposed. This framework merges model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. For parallel training and simple design purposes, the proposed MILP model is first decomposed into a number of sub-problems and, then, Convolutional Neural Networks (CNNs) are trained to predict content caching locations of these sub-problems. Furthermore, since the MILP model decomposition neglects the network resources (such as caching space and link bandwidth) competition among sub-problems, the CNNs' outputs have the risk to be infeasible solutions. Therefore, two algorithms are provided: the first uses predictions from CNNs as an extra constraint to reduce the number of decision variables; the second employs CNNs' outputs to accelerate local search. Numerical results show that the proposed scheme can reduce 71.6% computation time, whose computation time reaches around 28.9 ms, with only 0.8% additional performance cost compared to the MILP solution, which provides high quality decision making in pseudo real-time.

In this paper, the problem of the deployment and resource management for visible light communication (VLC)-enabled, reconfigurable intelligent surfaces (RISs)-assisted unmanned aerial vehicle (UAV) networks is investigated. In the considered model, UAVs provide terrestrial users with wireless services and illumination simultaneously. Moreover, RISs are utilized to further improve the channel quality between UAVs and users. This joint placement and resource management problem is constructed aiming at acquiring the optimal UAV deployment, RISs phase shift, user and RIS association that satisfies the users’ needs with minimum consumption of the UAVs’ energy. An iterative algorithm that alternately optimizes continuous and binary variables is proposed to solve this mixed-integer programming problem. Specifically, RISs phase shift optimization is solved by phases alignment method and semidefinite program algorithm. Next, the successive convex approximation algorithm is proposed to settle the UAV deployment problem. The user and RIS association variables are relaxed to the continuous ones before adopting the dual method to find the optimal solution. Moreover, a greedy algorithm is proposed as an alternative to RIS association optimization with low complexity. Simulation results show that the proposed two schemes harvest the superior performance of 34.85% and 32.11% energy consumption reduction over the case without RIS, respectively.

In this paper, we aim to design an effective learning-based channel state information (CSI) feedback scheme for the multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems from a physics-inspired perspective. We first argue that the CSI matrix of a MIMO-OFDM system is physically closer to a two-dimensional (2-D) sequence rather than an image due to its apparent unsmoothness, non-scalability, and translational variance within both the spatial and frequency domains. On this basis, we introduce a 2-D long short-term memory (LSTM) neural network to represent the CSI and propose a 2-D sequence-to-sequence (Seq2Seq) model for CSI compression and reconstruction. Specifically, one two-layer 2-D LSTM is used for CSI feature extraction, and the other is used for CSI representation and reconstruction. The proposed scheme can not only fully utilize the unique 2-D characteristics of CSI but also preserve the index information and unsmooth features of the CSI matrix compared with current convolutional neural network (CNN) based schemes. We show that the computational complexity of the proposed scheme is linear in the number of transmit antennas and subcarriers. Its key performances, like reconstruction accuracy, convergence speed, generalization ability after short-term training, and robustness to lossy feedback, are comprehensively compared with existing popular convolutional networks. Experimental results show that our scheme can bring up to nearly 7 dB gain in reconstruction accuracy under the same overhead and reduce feedback overhead by up to 75% under the same accuracy compared with the conventional CNN-based approaches.

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

Federated learning (FL) in a mobile edge network faces challenges from both communication and learning per-spectives. The typically non-i.i.d. data can lead to slow convergence and low accuracy. To ease these challenges, frequent communications between user equipments (UEs) and the cen-tral macro base station (MBS) are necessary, aggravating the communication burden. In this paper, a novel hierarchical FL framework is proposed to alleviate the biased convergence of the global model, achieving better communication and computation efficiency. Specifically, the UEs are adaptively clustered and allocated to specific small base stations (SBSs) according to channel conditions, geographic locations, and data distributions. The SBSs are further aggregated to the MBS, forming a hier-archical FL framework. The joint user clustering and wireless resource allocation optimization problem is formulated. To solve this problem, a cross entropy (CE) based method with low computational complexity is proposed. Simulation results validate that the proposed hierarchical FL system can save more than 87 percent training time under the EMNIST Letters dataset, achieving fast convergence and significantly improving the system efficiency.

This paper investigates an interesting wireless sensing problem which aims to focus on a specific target from the complicated background by exploiting the signals of a wireless communication system. In the considered context, multiple users send pilot signals to the base station (BS), which consistently collects and processes the received signals and gradually figures out the target within the focused area. This is by no means easy since all the background scatterers within the environment may produce severe interference to the received signals, which incurs possible divergence and large sensing error, especially when only limited wireless resource is available for sensing. To solve this issue, an iterative focusing algorithm is proposed, in which a rough sensing of the overall environment is performed to obtain an initial blurred imaging result. Then based on the initial environment sensing result, the proposed algorithm iteratively and gradually removes the background scatterers to obtain more and more accurate sensing results of the target object with limited system resource overhead. Simulation results verify the convergence and effectiveness of the proposed algorithm.

High spatial correlation is one of the main factors limiting the communication performance of multiple-input multiple-output (MIMO) visible light communication (VLC), which can be alleviate by precoding. However, most existing precoding algorithms require a dedicated baseband chain for each light emitting diode (LED), which may result in high energy consumption and hardware complexity, especially when LEDs are densely deployed. To solve this issue, a successive interference cancellation-based precoding scheme with sub-connected architecture (SIC-SA) is proposed, where each baseband chain is connected to an LED sub-array. In this case, since the SIC-based precoding can only determine the signal of each baseband chain for an LED sub-array, the electrical/optical power of each LED must be jointly optimized to alleviate the spatial correlation among individual LEDs. This joint SIC-based precoding, power allocation and direct current offset design problem is formulated as an achievable sum rate maximization problem under dimming control and electrical power constraints. To solve this problem, the original problem is separated into two subproblems. In the first subproblem, the SIC-based precoding is calculated by successive null space computation. With the obtained SIC-based precoding, the second subproblem optimizes the power allocation and direct-current offset. Finally, these two subproblems are iteratively solved to obtain a convergent solution. Simulation results demonstrate that the proposed SIC-SA achieves 0.1306 bps/Hz/W and 0.1340 bps/Hz/W improvements in terms of energy efficiency compared to minimum mean square error precoding with fully-connected architecture (FA) and zero-forcing precoding with FA, respectively, when the signal-to-noise ratio is 30 dB.

In this paper, the problem of maximizing the sum rate of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 13.3 % gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS.

In this paper, we aim at an efficient multi-beam combining design with only requiring receive power measurements for a millimeter-wave (mmWave) multi-input multi-output (MIMO) communication system. A spectrum efficiency maximization problem is formulated with both beam selection and power constraints. To solve this problem, a reinforcement learning (RL)-based multi-beam combining algorithm is proposed. In particular, a self-attention deep deterministic policy gradient (DDPG) scheme is used to adaptively learn the serving beam sets and the corresponding combining weights without any channel state information (CSI). Moreover, the transformer is integrated into the DDPG to precisely capture the signal directions and relevant strengths. Experimental results show the effectiveness of the proposed learning structure in terms of system achievable rate, convergence, and network robustness.

Obtaining accurate channel state information (CSI) is crucial and challenging for multiple-input multiple-output (MIMO) wireless communication systems. The conventional channel estimation method cannot guarantee the accuracy of mobile CSI while requiring high signaling overhead. Through exploring the intrinsic correlation among a set of historical CSI instances randomly obtained in a certain communication environment, channel prediction can significantly increase CSI accuracy and save signaling overhead. In this paper, we propose a novel channel prediction method based on ordinary differential equation (ODE)-recurrent neural network (RNN) for accurate and flexible mobile MIMO channel prediction. Different from existing works using sequential network structures for exploring the numerical correlation between observed data, our proposed method tries to represent the implicit physics process of path responses changing by a specially designed continuous learning network with ODE structure. Due to the targeted design of the learning network, our proposed method fits the mathematics feature of CSI data better and enjoy higher network interpretability. Experimental results show that the proposed learning approach outperforms existing methods, especially for long time interval of the CSI sequence and large channel measurement error.

In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks is investigated. In the considered model, each user first extracts the semantic information from its large-scale data, and then transmits the small-sized semantic information to the base station (BS) which recovers the original data. Due to the limited energy budget of wireless users, both local computational energy and transmission energy must be considered. This joint computation and communication problem is formulated as an optimization problem whose goal is to minimize the total energy consumption of the network under a latency constraint. To solve this problem, an iterative algorithm is proposed where the optimal solution for joint bandwidth allocation, power control, and computation frequency optimization problem can be obtained. Numerical results show the effectiveness of the proposed algorithm.

In this paper, the problem of power allocation is investigated for a multi-user downlink visible light communication (VLC) system with non-orthogonal multiple access (NOMA). In this considered system, not only input-independent Gaussian noise but also input-dependent shot noise are considered due to the properties of realistic VLC channels. This problem is posed as a joint problem of alternating current power and direct current (DC) power under the optical and electrical domain constraints in VLC as well as specific power constraints for NOMA decoding, whose goal is to maximize the minimum signal to interference plus noise ratio (SINR) among all users. Although this problem is non-convex, a geometric programming (GP) based algorithm is proposed to convert the original problem into a convex one, thus the optimal solution is obtained. Furthermore, special cases where lower DC offset is preferred are investigated. Simulation results verify that the DC offset has significant impacts on the performance of NOMA VLC systems with shot noise, and our proposed scheme achieves better SINR performance over the conventional schemes.

The multi-task federated learning (FL) problem in the wireless communication system is investigated in this paper. The base station (BS) and wireless users cooperatively perform a two-task FL algorithm in the established model. Users use their local datasets to train two local models of two different tasks. The trained local model of only one task is transmitted to the BS at each time and the BS aggregates the obtained models to calculate a global model, which will be sent back to all users. Since the resources for wireless transmission, such as transmit power and number of subcarriers are limited, the BS have to allocate resources reasonably to minimize the time consumption of the FL procedure while meeting the required learning performance. On the other hand, users are dynamically arranged to participate in different tasks in each iteration. This resource allocation and users arrangement problem is formulated as an optimization problem which aims to minimize time consumption of the two-task FL procedure. To address this nonconvex problem, we first decompose it into two convex sub-problems. Then we propose an iterative algorithm to solve this problem via iteratively obtaining the optimal solution of the joint power control and communication round optimization subproblem, and user arrangement subproblem. Simulation results of this multi-task FL system show that the proposed algorithm can reduce 7.02% and 9.67% completion time compared to the uniform and random user selection schemes respectively.

This paper investigates the problem of secrecy energy efficiency for an unmanned aerial vehicle (UAV) assisted joint sensing and communication system. In the considered system, there exists one UAV, one legal user, and one eavesdropper. The UAV needs to complete multiple tasks in multiple time cycles. In each time cycle, the UAV first flies to sense one task and then transmits the sensing results to the legal user. To maximize the secrecy energy efficiency of the system, a joint sensing and transmission time and UAV location optimization problem is formulated. To solve this non-convex fractional programming problem, the original problem is first divided into two subproblems. Each sub-problem can be easily transformed to a convex one by using the successive convex approximation (SCA) method and the Dinkelbach’s approach. Then, an iterative algorithm based on the alternating method is proposed. Simulation results reveal that our proposed algorithm is superior to the conventional algorithms in terms of secrecy energy efficiency.

Abstract In this paper, a reconfigurable intelligent surface (RIS) assisted downlink non‐orthogonal multiple access (NOMA) network is considered, where a base station communicates with a pair of users with the assistance of a RIS. The performance of RIS‐assisted downlink NOMA networks is investigated by exploiting the coherent phase shifting design. In particular, the central limit theorem based Gaussian approximation is introduced to model the sum of independent and identically distributed random variables and derive the approximate expressions of the outage probability for two users. Furthermore, the upper bounds for the outage probability are obtained based on the property of the Bessel function. Additionally, both the asymptotic outage probabilities and the asymptotic upper bounds at high signal‐to‐noise ratio are derived and the diversity order achieved by the network is obtained. Simulation results are provided to validate the theoretical findings and demonstrate that RIS‐NOMA can achieve superior outage performance compared to RIS‐assisted orthogonal multiple access and conventional full‐duplex decode‐and‐forward relaying schemes. Moreover, it can be observed that the outage performance of RIS‐NOMA can be tremendously enhanced with increasing the number of reflecting elements.

This article integrates nonorthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS plays an important role in adjusting the decoding order of hybrid users for efficient interference mitigation and omnidirectional coverage extension. To capture the impact of nonideal wireless channels on AirFL, a closed-form expression for the optimality gap (also known as the convergence upper bound) between the actual loss and the optimal loss is derived. This analysis reveals that the learning performance is significantly affected by the active and passive beamforming schemes, as well as wireless noise. Furthermore, when the learning rate diminishes as the training proceeds, the optimality gap is explicitly shown to converge with a linear rate. To accelerate convergence while satisfying quality-of-service requirements, a mixed-integer nonlinear programming (MINLP) problem is formulated by jointly designing the transmit power at users and the configuration mode of STAR-RIS. Next, a trust-region-based successive convex approximation method and a penalty-based semidefinite relaxation approach are proposed to handle the decoupled nonconvex subproblems iteratively. An alternating optimization algorithm is then developed to find a suboptimal solution for the original MINLP problem. Extensive simulation results show that: 1) the proposed framework can efficiently support NOMA and AirFL users via concurrent uplink communications; 2) our algorithms achieve a faster convergence rate on independent and identically distributed (IID) and non-IID settings compared to the existing baselines; and 3) both the spectrum efficiency and learning performance are significantly improved with the aid of the well-tuned STAR-RIS.

The fundamental communication paradigms in the next-generation mobile networks are shifting from connected things to connected intelligence. The potential result is that current communication-centric wireless systems are greatly stressed when supporting computation-centric intelligent services with distributed big data. This is one reason that makes federated learning come into being, it allows collaborative training over many edge devices while avoiding the transmission of raw data. To tackle the problem of model aggregation in federated learning systems, this article resorts to multiple reconfigurable intelligent surfaces (RISs) to achieve efficient and reliable learning-oriented wireless connectivity. The seamless integration of communication and computation is actualized by over-the-air computation (AirComp), which can be deemed as one of the uplink nonorthogonal multiple access (NOMA) techniques without individual information decoding. Since all local parameters are uploaded via noisy concurrent transmissions, the unfavorable propagation error inevitably deteriorates the accuracy of the aggregated global model. The goals of this work are to 1) alleviate the signal distortion of AirComp over shared wireless channels and 2) speed up the convergence rate of federated learning. More specifically, both the mean-square error (MSE) and the device set in the model uploading process are optimized by jointly designing transceivers, tuning reflection coefficients, and selecting clients. Compared to baselines, extensive simulation results show that 1) the proposed algorithms can aggregate model more accurately and accelerate convergence and 2) the training loss and inference accuracy of federated learning can be improved significantly with the aid of multiple RISs.

In existing computing systems, such as edge computing and cloud computing, several emerging applications and practical scenarios are mostly unavailable or only partially implemented. To overcome the limitations that restrict such applications, the development of a comprehensive computing paradigm has garnered attention in both academia and industry. However, a gap exists in the literature, owing to the scarce research, and a comprehensive computing paradigm is yet to be systematically designed and reviewed. This study introduces a novel concept, calledaerial computing, via the amalgamation of aerial radio access networks and edge computing, which attempts to bridge the gap. Specifically, first, we propose a novel comprehensive computing architecture that is composed of low-altitude computing (LAC), high-altitude computing (HAC), and satellite computing platforms, along with conventional computing systems. We determine that aerial computing offers several desirable attributes: global computing service, better mobility, higher scalability and availability, and simultaneity. Second, we comprehensively discuss key technologies that facilitate aerial computing, including energy refilling, edge computing, network softwarization, frequency spectrum, multiaccess techniques, artificial intelligence, and big data. In addition, we discuss vertical domain applications (e.g., smart cities, smart vehicles, smart factories, and smart grids) supported by aerial computing. Finally, we highlight several challenges that need to be addressed and their possible solutions.

The core requirement of massive Machine-Type Communication (mMTC) is to support reliable and fast access for an enormous number of machine-type devices (MTDs). In many practical applications, the base station (BS) only concerns the list of received messages instead of the source information, introducing the emerging concept of unsourced random access (URA). Although some massive multiple-input multiple-output (MIMO) URA schemes have been proposed recently, the unique propagation properties of millimeter-wave (mmWave) massive MIMO systems are not fully exploited in conventional URA schemes. In grant-free random access, the BS cannot perform receive beamforming independently as the identities of active users are unknown to the BS. Therefore, only the intrinsic beam division property can be exploited to improve the decoding performance. In this paper, a URA scheme based on beam-space tree decoding is proposed for mmWave massive MIMO system. Specifically, two beam-space tree decoders are designed based on hard decision and soft decision, respectively, to utilize the beam division property. They both leverage the beam division property to assist in discriminating the sub-blocks transmitted from different users. Besides, the first decoder can reduce the searching space, enjoying a low complexity. The second decoder exploits the advantage of list decoding to recover the miss-detected packets. Simulation results verify the superiority of the proposed URA schemes compared to the conventional URA schemes in terms of error probability.

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

Reconfigurable intelligent surface (RIS) can be employed in a cell-free system to create favorable propagation conditions from base stations (BSs) to users via configurable elements. However, prior works on RIS-aided cell-free system designs mainly rely on the instantaneous channel state information (CSI), which may incur substantial overhead due to extremely high dimensions of estimated channels. To mitigate this issue, a low-complexity algorithm via the two-timescale transmission protocol is proposed in this paper, where the joint beamforming at BSs and RISs is facilitated via alternating optimization framework to maximize the average weighted sum-rate. Specifically, the passive beamformers at RISs are optimized through the statistical CSI, and the transmit beamformers at BSs are based on the instantaneous CSI of effective channels. In this manner, a closed-form expression for the achievable weighted sum-rate is derived, which enables the evaluation of the impact of key parameters on system performance. To gain more insights, a special case without line-of-sight (LoS) components is further investigated, where a power gain on the order of$\mathcal {O}(M)$is achieved, with$M$being the BS antennas number. Numerical results validate the tightness of our derived analytical expression and show the fast convergence of the proposed algorithm. Findings illustrate that the performance of the proposed algorithm with two-timescale CSI is comparable to that with instantaneous CSI in low or moderate SNR regime. The impact of key system parameters such as the number of RIS elements, CSI settings and Rician factor is also evaluated. Moreover, the remarkable advantages from the adoption of the cell-free paradigm and the deployment of RISs are demonstrated intuitively.

Flying platforms, such as unmanned aerial vehicles (UAVs) are a promising solution for future small cell networks. UAVs can be used as aerial base stations (BSs) to enhance coverage, capacity and reliability of wireless networks. Also, with recent advances of self interference cancellation (SIC) techniques in full-duplex (FD) systems, practical implementation of FD BSs is feasible. In this paper, we investigate the problem of resource allocation for multi-small cell networks with FD-UAVs as aerial BSs with imperfect SIC. We consider three different scenarios: a) maximizing the DL sum-rate; b) maximizing the UL sum-rate; and finally c) maximizing the sum of UL and DL sum-rates. The aforementioned problems result in non-convex optimization problems, therefore, successive convex approximation algorithms are developed by leveraging D.C. (Difference of Convex functions) programming to find sub-optimal solutions. Simulation results illustrated validity and effectiveness of the proposed radio resource management algorithms in comparison with ground BSs, in both FD mode and its half-duplex (HD) counterpart. The results also indicate those situations where using aerial BS is advantageous over ground BS and reveal how FD transmission enhances the network performance in comparison with HD one.

In this paper, we propose a joint transceiver beamforming design method for hybrid full-duplex (FD) and half-duplex (HD) ad-hoc networks to cancel co-channel interference, thereby to improve system spectral efficiency. To characterize network performances, we derive a general expression of transmission capacity upper bound (TC-UB) plus its two compact versions by using a stochastic geometry model. Due to the proposed beamforming design and hybrid-duplex consideration, the exact TC and conventional methods to obtain TC-UBs are not applicable. This motivates us to exploit the UB of the largest eigenvalue of desired signals, Alzer’s inequality for the incomplete gamma function, and dominating interference region to formulate one general TC-UB and two of its compact versions. The numerical results show that the proposed beamforming method outperforms the existing beamforming strategies in terms of exact TC, especially when the number of transmit antennas is larger than the number of receiver antennas per node pair. In addition, the derived general TC-UB can provide relatively close TC performance as the exact ones, and its two compact versions can at least give order-wise TC performance. Moreover, we find the break-even points, where FD outperforms HD with different system configurations.

In this paper, the problem of maximizing the wireless users’ sum-rate for uplink rate splitting multiple access (RSMA) communications is studied. In the considered model, the message intended for a single user is split into two sub-messages with separate transmit power and the base station (BS) uses a successive decoding technique to decode the received messages. To maximize each user’s transmission rate, the users must adjust their transmit power and the BS must determine the decoding order of the messages transmitted from the users to the BS. This problem is formulated as a sum-rate maximization problem with proportional rate constraints by adjusting the users’ transmit power and the BS’s decoding order. However, since the decoding order variable in the optimization problem is discrete, the original maximization problem with transmit power and decoding order variables can be transformed into a problem with only the rate splitting variable. Then, the optimal rate splitting of each user is determined. Given the optimal rate splitting of each user and a decoding order, the optimal transmit power of each user is calculated. Next, the optimal decoding order is determined by an exhaustive search method. To further reduce the complexity of the optimization algorithm used for sum-rate maximization in RSMA, a user pairing based algorithm is introduced, which enables two users to use RSMA in each pair and also enables the users in different pairs to be allocated with orthogonal frequency. For comparisons, the optimal sum-rate maximizing solutions with proportional rate constraints are obtained for non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA). Simulation results show that RSMA can achieve up to 10.0, 22.2, and 81.2 percent gains in terms of sum-rate compared to NOMA, FDMA, and TDMA.

With the aim of integrating over-the-air federated learning (AirFL) and non-orthogonal multiple access (NOMA) into an on-demand universal framework, this paper proposes a reconfigurable intelligent surface (RIS)-aided hybrid network by leveraging the RIS to flexibly adjust the decoding order of heterogeneous data. A new metric of computation rate is defined to measure the performance of AirFL users. Upon this, the objective of this work is to maximize the achievable hybrid rate by jointly optimizing the transmit power, controlling the receive scalar, and designing the reflection coefficients. Since the concurrent transmissions of all computation and communication signals are aided by the discrete phase-shifting elements at the RIS, the formulated problem (P0) is a challenging mixed-integer programming problem. To tackle this intractable issue, we decompose the original problem (P0) into a non-convex problem (P1) and a combinatorial problem (P2), which are characterized by the continuous and discrete variables, respectively. For the transceiver design problem (P1), the power allocation subproblem is first solved by difference-of-convex programming, and then the receive control subproblem is addressed by successive convex approximation, where the closed-form expressions of simplified cases are derived to obtain deep insights. For the reflection design problem (P2), a relaxation-then-quantization method is adopted to find a suboptimal solution for striking a trade-off between complexity and performance. Afterwards, an alternating optimization algorithm is developed to solve the non-linear non-convex problem (P0) iteratively. Finally, simulation results reveal that i) the proposed RIS-aided hybrid network can support on-demand communication and computation efficiently, ii) the system performance can be improved by properly selecting the location of the RIS, and iii) the designed algorithms are also applicable to conventional networks with only AirFL or NOMA users.

In this paper, the problem of enhancing the quality of virtual reality (VR) services is studied for an indoor terahertz (THz)/visible light communication (VLC) wireless network. In the studied model, small base stations (SBSs) transmit high-quality VR images to VR users over THz bands and light-emitting diodes (LEDs) provide accurate indoor positioning services for them using VLC. Here, VR users move in real time and their movement patterns change over time according to their applications, where both THz and VLC links can be blocked by the bodies of VR users. To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users. Based on the user positions, each SBS must generate corresponding VR images and establish THz links without body blockage to transmit the VR content. The problem is formulated as an optimization problem whose goal is to maximize the average number of successfully served VR users by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm that adopts a meta-learning approach is proposed. The proposed meta policy gradient (MPG) algorithm enables the trained policy to quickly adapt to new user movement patterns. In order to solve the problem of maximizing the average number of successfully served users for VR scenarios with large numbers of users, a low-complexity dual method based MPG algorithm (D-MPG) with a low complexity is proposed. Simulation results demonstrate that, compared to a baseline trust region policy optimization algorithm (TRPO), the proposed MPG and D-MPG algorithms yield up to 26.8% and 21.9% improvement in the average number of successfully served users as well as 81.2% and 87.5% gains in the convergence speed, respectively.

This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficients matrix of the RISs. This problem is posed as a joint optimization problem of transmit beamforming and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, two iterative algorithms are proposed for the single-user case and multi-user case. For the single-user case, the phase optimization problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the optimal RIS on-off status is obtained by using the dual method. For the multi-user case, a low-complexity greedy searching method is proposed to solve the RIS on-off optimization problem. Simulation results show that the proposed scheme achieves up to 33% and 68% gains in terms of the energy efficiency in both single-user and multi-user cases compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.

This paper proposes a new, compatible, unified framework which integrates non-orthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) via concurrent communication. In particular, a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is leveraged to adjust the signal processing order for efficient interference mitigation and omni-directional coverage extension. With the aim of investigating the impact of non-ideal wireless communication on AirFL, we provide a closed-form expression for the optimality gap over a given number of communication rounds. This result reveals that the learning performance is significantly affected by the resource allocation scheme and channel noise. To minimize the derived optimality gap, a mixed-integer non-linear programming (MINLP) problem is formulated by jointly designing the transmit power at users and configuration mode at the STAR-RIS. Through developing an alternating optimization algorithm, a suboptimal solution for the original MINLP problem is obtained. Simulation results show that the learning performance in terms of training loss and test accuracy can be effectively improved with the aid of the STAR-RIS.

In this paper, the problem of audio based semantic communication is investigated over wireless networks. In the considered model, wireless edge devices must transmit large-sized audio data to a server using semantic communication techniques. The techniques enable the transmission of audio semantic information which captures the contextual features of audio signals. To extract the semantic information from audio signals, a wave to vector (wav2vec) architecture based autoencoder that consists of convolutional neural networks (CNNs) is proposed. The proposed autoencoder enables high-accuracy audio transmission with small amounts of data. To further improve the accuracy of semantic information extraction, federated learning (FL) is implemented over multiple devices and a server. Simulation results show that the proposed algorithm can converge effectively and can reduce the mean square error (MSE) between the recovered audio signals and the source audio signals by nearly 100 times, compared to a traditional coding scheme.

This paper investigates the optimization of physical layer security in multiple-input multiple-output (MIMO) enabled visible light communication (VLC) networks. In the considered model, one transmitter equipped with light-emitting diodes (LEDs) intends to send confidential messages to legitimate users while one eavesdropper attempts to eavesdrop on the communication between the transmitter and legitimate users. This security problem is formulated as an optimization problem whose goal is to minimize the sum mean-square-error (MSE) of all legitimate users while meeting the MSE requirement of the eavesdropper thus ensuring the security. To solve this problem, the original optimization problem is first transformed to a convex problem using successive convex approximation. An iterative algorithm with low complexity is proposed to solve this optimization problem. Simulation results show that the proposed algorithm can reduce the sum MSE of legitimate users by up to 40% compared to a conventional zero forcing scheme.

Federated learning (FL) is widely used in privacy sensitive applications for isolated data islands, with the aim of achieving distributed model training, privacy enhancement and model sharing. Electromyographic (EMG) signals are a type of data collected from wearable sensors of subjects which are distributed on multiple devices, highly personalized and play an important role in several applications including prosthetic hand control, sign languages, grasp recognition, etc. This paper utilizes the FL method to detect single and combined finger movements based on EMG signals. The existing research on FL for wearable healthcare faces challenges of variable probability distributions of data, the need for prerequisite knowledge of server model and computational burdens in parameter transmission. To address these problems, this paper proposes a communication efficient FL framework in which each device only needs to transmit the weight matrices of local models to the server for model aggregation. To further reduce the FL transmission delay, a joint learning and resource allocation problem is formulated via optimizing transmit power of each device, time allocation, and user selection. To solve the delay minimization problem, the objective function is first converted to a tractable expression and then the difference of two convex functions programming is adopted. Simulation results using real EMG signals show that the proposed FL framework with personalized training process successfully detects single and combined finger movements for distributed users. Two public EMG datasets with 10 and 15 different finger movements are employed. Over 98% overall test accuracy is achieved in both datasets which surpasses the conventional learning framework by 1.6% and 0.5% on average. Different scenarios with regard to access points and users are investigated and the convexity of the proposed model is discussed.

This paper investigates the deployment of federated learning (FL) over an over-the-air computation (AirComp) and intelligent reflecting surface (IRS) based wireless network. In the considered system, devices transmit locally trained machine learning (ML) models to the base station (BS) which aggregates the received ML models and generates a shared global ML model. The devices can directly transmit ML models to the BS or using IRS. Meanwhile, AirComp is used to aggregate ML models that are transmitted from the devices to the BS. To minimize the energy consumption of devices, an energy minimization problem is formulated, which jointly optimizes the device selection, phase shift matrix, decoding vector, and power control. To seek the solution, the original optimization problem is divided into four sub-problems. Then the fractional program, greedy algorithm, matrix derivation, and weighted minimum mean square error methods are used to compute the phase shift matrix, device selection vector, decoding vector, and transmit power, respectively. Simulation results show that the proposed algorithm can reduce 11.2% energy consumption of devices compared to an FL algorithm that is implemented at a network without any IRSs.

In this paper, the implementation of a distributed primal-dual algorithm over realistic wireless networks is investigated. In the considered model, the users and one base station (BS) cooperatively perform a distributed primal-dual algorithm for controlling and optimizing wireless networks. In particular, each user must locally update the primal and dual variables and send the updated primal variables to the BS. The BS aggregates the received primal variables and broadcasts the aggregated variables to all users. Since all of the primal and dual variables as well as aggregated variables are transmitted over wireless links, the imperfect wireless links will affect the solution achieved by the distributed primal-dual algorithm. Therefore, it is necessary to study how wireless factors such as transmission errors affect the implementation of the distributed primal-dual algorithm and how to optimize wireless network performance to improve the solution achieved by the distributed primal-dual algorithm. To address these challenges, the convergence rate of the primal-dual algorithm is first derived in a closed form while considering the impact of wireless factors such as data transmission errors. Based on the derived convergence rate, the optimal transmit power and resource block allocation schemes are designed to minimize the gap between the target solution and the solution achieved by the distributed primal-dual algorithm. Simulation results show that the proposed distributed primal-dual algorithm can reduce the gap between the target and obtained solution by up to 52% compared to the distributed primal-dual algorithm without considering imperfect wireless transmission.

In this paper, the problem of optimal passive beamforming design is studied for a reconfigurable intelligent surface (RIS) assisted full-duplex (FD) communication system. In the studied model, two devices communicate with each other using one RIS under the FD mode. Each of the device will receive not only the message from the other device but also the self-interference. The main problem of this work is to minimize the sum transmit power by jointly optimizing the reflection coefficients matrix and the transmit power of devices. To solve this problem, a dual method is proposed, where the dual problem is formulated as a semidefinite programming problem. After solving the dual problem, the phase beamforming of the RIS is obtained in the closed form. Simulation results show that the proposed scheme can reduce up to 66% sum transmit power compared to a conventional RIS assisted half-duplex mode.

In this paper, the problem of enhancing the quality of virtual reality (VR) services is studied for an indoor terahertz (THz)/visible light communication (VLC) wireless network. In the studied model, small base stations (SBSs) transmit high-quality VR images to users over THz bands and light-emitting diodes (LEDs) provide accurate indoor positioning services for VR users using VLC. Here, VR users move in real time and their movement patterns change over time according to their application. Both THz and VLC links can be blocked by the bodies of VR users. To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users. Based on the user positions, each SBS must generate corresponding VR images and build THz links without body blockage to transmit the VR content. The problem is formulated as an optimization problem whose goal is to maximize the sum successful transmission probability of all VR users by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm using meta-learning framework is proposed. The proposed algorithm can effectively solve the formulated problem and enable the trained policy to quickly adapt to new user movement patterns. Simulation results demonstrate that, compared to a baseline trust region policy optimization algorithm (TRPO), the proposed meta-learning solution yields a 78% improvement in the convergence speed and about 16.4% improvement in the sum successful transmission probabilities of all VR users.

In this paper, the implementation of a distributed primal-dual learning algorithm over realistic wireless networks is investigated. In the considered model, the users and one base station (BS) cooperatively perform a distributed primal-dual learning algorithm for controlling and optimizing wireless networks. In particular, each user must locally update the primal and dual variables and send the updated primal variables to the BS. The BS aggregates the received primal variables and broadcasts the aggregated variables to all users. Since all of the primal and dual variables as well as aggregated variables are transmitted over wireless links, the imperfect wireless links will affect the solution achieved by the distributed primal-dual algorithm. Therefore, it is necessary to study how wireless factors such as transmission errors affect the implementation of the distributed primal-dual algorithm and how to optimize wireless network performance to improve the solution achieved by the distributed primal-dual algorithm. To address these challenges, the convergence rate of the primal-dual algorithm is provided in a closed form while considering the impact of wireless factors such as data transmission errors. Simulation results show that the proposed distributed primal-dual algorithm can reduce the gap between the target and obtained solution compared to the distributed primal-dual learning algorithm without considering imperfect wireless transmission.

Reconfigurable Intelligent Surfaces (RISs) have been recently considered as an energy-efficient solution for future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Channel estimation and signal recovery in RIS-based systems are among the most critical technical challenges, due to the large number of unknown variables referring to the RIS unit elements and the transmitted signals. In this paper, we focus on the downlink of a RIS-assisted multi-user Multiple Input Single Output (MISO) communication system and present a joint channel estimation and signal recovery scheme based on the PARAllel FACtor (PARAFAC) decomposition. This decomposition unfolds the cascaded channel model and facilitates signal recovery using the Bilinear Generalized Approximate Message Passing (BiG-AMP) algorithm. The proposed method includes an alternating least squares algorithm to iteratively estimate the equivalent matrix, which consists of the transmitted signals and the channels between the base station and RIS, as well as the channels between the RIS and the multiple users. Our selective simulation results show that the proposed scheme outperforms a benchmark scheme that uses genie-aided information knowledge. We also provide insights on the impact of different RIS parameter settings on the proposed scheme.

Non-orthogonal multiple access (NOMA)-based device-to-device (D2D) network has attracted widespread attention since it can address the problem of spectrum shortage in the next-generation communication networks. However, the robust resource allocation problem in this network has not been well investigated. In this paper, we aim for maximizing the energy efficiency (EE) of a NOMA-based D2D network with simultaneous wireless information and power transfer technique under imperfect channel state information. The considered problem is modeled as a non-convex optimization problem that considers the maximum tolerable outage probability of each D2D user (DU), the successive interference cancellation decoding order, and the maximum transmit power of base station and DUs, where the transmit power, power splitting factor, and resource block assignment factor are jointly optimized. Since the formulated mixed-integer fractional programming problem with outage probability constraints is non-convex and difficult to solve, we firstly transform it into a non-probabilistic problem through a relaxation approach, and then, transform it into a convex one by using the variable-substitution approach and Dinkelbach's method. Finally, an EE-based iterative algorithm is proposed to solve this intractable problem. Simulation results show that the proposed algorithm has a fast convergence and low outage probability.

Abstract Demands for stable and efficient transmission are increasing due to the ongoing expansion of global wireless data traffic. One of the most promising ways to moderate burden of traffic load and decrease the queuing delay is to design cross‐layer protocols. This paper considers a utility maximization problem for multipath ad hoc wireless networks via joint cross‐layer congestion control, routing and scheduling design. In the formulated utility maximization problem, rate, scheduling and queuing delay constraints are involved under the condition of fixed channels and time varying channels. To solve this utility maximization problem, two scheduling methods are proposed, that is, perfect scheduling and distributed scheduling. In perfect scheduling, all nodes in the network contribute to the scheduling process, while distributed method only takes into account adjacent nodes. For both methods, the global convergence is proved. A comprehensive simulation evaluation is performed which shows that this proposed algorithms outperform existing cross layer protocols in terms of increasing source rate and reducing congestion price.

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

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, the sum-rate maximization problem is studied for wireless networks that use downlink rate splitting multiple access (RSMA). In the considered model, the base station (BS) divides the messages that can be transmitted to its users into a “private” part and a “common” part. Here, the common message is a message that multiple users want to receive and the private message is a message that is dedicated to only a specific user. The RSMA mechanism enables a BS to adjust the split of common and private messages so as to control the interference by decoding and treating interference as noise and, thus optimizing the data rate of users. To maximize the users' sum-rate, the network can determine the rate allocation for the common message to meet the rate demand, and adjust the transmit power for the private message to reduce the interference. This problem is formulated as an optimization problem whose goal is to maximize the sum-rate of all users. To solve this nonconvex maximization problem with a single-antenna BS, the optimal power used for transmitting the private message to the users is first obtained in closed form for a given rate allocation and common message power. Based on the optimal private message transmit power, the optimal rate allocation is then derived under a fixed common message transmit power. Subsequently, an iterative algorithm is proposed to obtain a suboptimal solution of common message transmit power. To solve this nonconvex maximization problem with a multiple-antenna BS, a successive convex approximation method is utilized. Simulation results show that the RSMA can achieve up to 15.6% and 21.5% gains in terms of data rate compared to non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA), respectively.

This article investigates the problem of resource allocation for multiuser communication networks with a reconfigurable intelligent surface (RIS)-assisted wireless transmitter. In this network, the sum transmit power of the network is minimized by controlling the phase beamforming of the RIS and transmit power of the base station. This problem is posed as a joint optimization problem of transmit power and RIS control, whose goal is to minimize the sum transmit power under signal-to-interference-plus-noise ratio (SINR) constraints of the users. To solve this problem, a dual method is proposed, where the dual problem is obtained as a semidefinite programming problem. After solving the dual problem, the phase beamforming of the RIS is obtained in the closed form, while the optimal transmit power is obtained by using the standard interference function. Simulation results show that the proposed scheme can reduce up to 94% and 27% sum transmit power compared to the maximum ratio transmission (MRT) beamforming and zero-forcing (ZF) beamforming techniques, respectively.

In this article, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that generates a global FL model and sends the model back to the users. Since all training parameters are transmitted over wireless links, the quality of training is affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS needs to select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To seek the solution, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can improve the identification accuracy by up to 1.4%, 3.5% and 4.1%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation, 2) a standard FL algorithm with random user selection and resource allocation, and 3) a wireless optimization algorithm that minimizes the sum packet error rates of all users while being agnostic to the FL parameters.

In this paper, the problem of energy efficient transmission and computation resource allocation for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model to a base station (BS) which aggregates the local FL model and broadcasts it back to all of the users. Since FL involves an exchange of a learning model between users and the BS, both computation and communication latencies are determined by the learning accuracy level. Meanwhile, due to the limited energy budget of the wireless users, both local computation energy and transmission energy must be considered during the FL process. This joint learning and communication problem is formulated as an optimization problem whose goal is to minimize the total energy consumption of the system under a latency constraint. To solve this problem, an iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived. Since the iterative algorithm requires an initial feasible solution, we construct the completion time minimization problem and a bisection-based algorithm is proposed to obtain the optimal solution, which is a feasible solution to the original energy minimization problem. Numerical results show that the proposed algorithms can reduce up to 59.5% energy consumption compared to the conventional FL method.

This paper investigates the problem of resource allocation for a wireless communication network with distributed reconfigurable intelligent surfaces (RISs). In this network, multiple RISs are spatially distributed to serve wireless users and the energy efficiency of the network is maximized by dynamically controlling the on-off status of each RIS as well as optimizing the reflection coefficient matrix of the RISs. This problem is posed as a joint optimization problem of transmit power and RIS control, whose goal is to maximize the energy efficiency under minimum rate constraints of the users. To solve this problem, an alternating algorithm is proposed by solving two sub-problems iteratively. The phase optimization sub-problem is solved by using a successive convex approximation method, which admits a closed-form solution at each step. Moreover, the RIS on-off optimization sub-problem is solved by using the dual method. Simulation results show that the proposed scheme achieves up to 27% and 68% gains in terms of the energy efficiency compared to the conventional RIS scheme and amplify-and-forward relay scheme, respectively.

In this paper, the sum-rate maximization problem is studied for wireless networks that use downlink rate splitting multiple access (RSMA). In the considered model, each base station (BS) divides the messages that must be transmitted to its users into a “private” part and a “common” part. Here, the common message is a message that all users want to receive and the private message is a message that is dedicated to only a specific user. The RSMA mechanism enables a BS to adjust the split of common and private messages so as to control the interference by decoding and treating interference as noise and, thus optimizing the data rate of users. To maximize the users' sum-rate, the network can determine the rate allocation for the common message to meet the rate demand, and adjust the transmit power for the private message to reduce the interference. This problem is formulated as an optimization problem whose goal is to maximize the sum-rate of all users. To solve this nonconvex maximization problem, the optimal power used for transmitting the private message to the users is first obtained in closed form for a given rate allocation and common message power. Based on the optimal private message transmission power, the optimal rate allocation is then derived under a fixed common message transmission power. Subsequently, a one-dimensional search algorithm is proposed to obtain the optimal solution of common message transmission power. Simulation results show that the RSMA can achieve up to 19.6% and 23.5% gains in terms of data rate compared to non-orthogonal multiple access (NOMA) and orthogonal frequency-division multiple access (OFDMA), respectively.

In this paper, the problem of joint subcarrier and power allocation is studied for multi-cell orthogonal frequency-division multiplexing (OFDM) systems. This joint subcarrier and power resource allocation problem is formulated as an optimization problem whose goal is to maximize the system spectral efficiency. To solve the proposed problem, the original optimization problem is first decomposed into two subproblems: subcarrier allocation and power allocation. By solving these two subproblems, an initial subcarrier and power allocation scheme is accordingly obtained. An multi-agent reinforcement learning (MARL) algorithm is proposed to further increase the spectral efficiency. In particular, using the proposed MARL algorithm, each BS can adapt its allocation scheme according to the wireless environmental states. Numerical results show that the proposed MARL method can achieve up to 53.6% gain in terms of spectral efficiency compared to the conventional scheme. The proposed MARL scheme also converges more rapidly than the conventional single-agent Q-learning approach.

In this paper, we employ Cooperative Rate-Splitting (CRS) technique to enhance the Secrecy Sum Rate (SSR) for the Multiple Input Single Output (MISO) Broadcast Channel (BC), consisting of two legitimate users and one eavesdropper, with perfect Channel State Information (CSI) available at all nodes. For CRS based on the three-node relay channel, the transmitter splits and encodes the messages of legitimate users into common and private streams based on Rate-Splitting (RS). With the goal of maximizing SSR, the proposed CRS strategy opportunistically asks the relaying legitimate user to forward its decoded common message. During the transmission, the eavesdropper keeps wiretapping silently. To ensure secure transmission, the common message is used for the dual purpose, serving both as a desired message and Artificial Noise (AN) without consuming extra transmit power comparing to the conventional AN design. Taking into account the total power constraint and the Physical Layer (PHY) security, the precoders and timeslot allocation are jointly optimized by solving the nonconvex SSR maximization problem based on Sequential Convex Approximation (SCA) algorithm. Numerical results show that the proposed CRS secure transmission scheme outperforms existing Multi-User Linear Precoding (MU-LP) and Cooperative Non-Orthogonal Multiple Access (C-NOMA) strategies. Therefore, CRS is a promising strategy to enhance the PHY security in Multi-antenna BC systems.

Reconfigurable intelligent surface (RIS) is a promising solution to improve spectrum efficiency and promote cost- effectively wireless communication in the future. In this paper, RIS is deployed between a single-antenna base station (BS) and multiple single-antenna users to assist downlink non-orthogonal multiple access (NOMA) transmission. Considering the fairness among users, our goal is jointly optimizing the power allocation, decoding order, and the phase shifts to maximize the minimum user rate under total power constraint. To solve this minimum rate maximization problem, the optimal power allocation and the optimal fair rate are first revealed with a given phase shift vector. Then, the phase shift vector is optimized via maximizing the worst channel gain, which can determine the lower bound of the fair rate. The phase shift vector optimization problem is relaxed to a convex semidefinite program (SDP) and an efficient algorithm is proposed to obtain a rank-one solution. Simulation results show that our proposed algorithm can enhance the fair rate compared to the conventional scheme.

Reconfigurable intelligent surface (RIS) is an emerging technology to achieve energy-efficient wireless communication. This technology has the potential of turning the wireless environment, which is highly probabilistic in nature, into a programmable and partially deterministic space. This paper focuses on the joint user clustering, passive beamforming and power allocation for the downlink RIS-assisted nonorthogonal-multiple-access (NOMA) system, with the target of maximizing energy efficiency. This is an optimization problem which is solved by optimizing three sub-problems iteratively. In particular, user clustering sub-problem is solved with a matching algorithm, power allocation sub-problem is solved with the difference of two convex functions (DC) programming, and passive beamforming sub-problem is solved with univariate search technique. Simulation results demonstrate that the downlink RIS-assisted NOMA system can improve the energy efficiency by 7.8%-23.1%, compared with NOMA system without RIS and traditional orthogonal-multiple-access (OMA) system without RIS.

This paper investigates the energy efficiency maximization problem in a downlink reconfigurable intelligent surface (RIS) assisted visible light communication (VLC) system. The energy efficiency maximization problem is formulated via jointly optimizing time allocation, power control, phase shit matrix under the unique power constraints in VLC. To solve this non-convex energy efficiency maximization problem, the original problem is first simplified to an equivalent problem with a smaller number of variables. Then, an alternating algorithm with low complexity is accordingly proposed to obtain a suboptimal solution via iteratively solving the joint time allocation and power control subproblem, and the phase shift matrix adjustment subproblem. The simulation results show that the proposed algorithm can achieve up to 0.127dB gain in terms of energy efficiency compared to the conventional interior point method.

In this paper, the optimization of the system energy efficiency (EE) is studied for a reconfigurable intelligent surface (RIS) assisted full-deplex (FD) communication system. In the studied model, two devices communicate with each other using one RIS under the FD mode. Each of the devices will receive not only the message from the other device but also the self-interference. The main problem of this work is to maximize EE by jointly optimizing the reflection coefficients matrix and the transmit power of devices. To solve this problem, a nonlinear fractional programming based algorithm is used to transform the fractional optimization problem into a subtractive problem. Then the transmit power and the RIS phase shifts matrix are optimized by an iterative method. Simulation results show that the proposed scheme can achieve up to 200% gain in terms of EE compared to a conventional RIS assisted half-duplex mode.

For crowded and hotspot area, unmanned aerial vehicles (UAVs) are usually deployed to increase the coverage rate. In the considered model, there are three types of services for UAV assisted communication: control message, non-realtime communication, and real-time communication, which can cover most of the actual demands of users in a UAV assisted communication system. A bandwidth allocation problem is considered to minimize the total energy consumption of this system while satisfying the requirements. Two techniques are introduced to enhance the performance of the system. The first method is to categorize the ground users into multiple user groups and offer each group a unique RF channel with different bandwidth. The second method is to deploy more than one UAVs in the system. Bandwidth optimization in each scheme is proved to be a convex problem. Simulation results show the superiority of the proposed schemes in terms of energy consumption.

Unmanned aerial vehicle (UAV)-assisted communication is a promising technology in future wireless communication networks. UAVs can not only help offload data traffic from ground base stations (GBSs) but also improve the Quality of Service (QoS) of cell-edge users (CEUs). In this article, we consider the enhancement of cell-edge communications through a mobile relay, i.e., UAV, in multicell networks. During each transmission period, GBSs first send data to the UAV, and then the UAV forwards its received data to CEUs according to a certain association strategy. In order to maximize the sum rate of all CEUs, we jointly optimize the UAV mobility management, including trajectory, velocity, and acceleration, and association strategy of CEUs to the UAV, subject to minimum rate requirements of CEUs, mobility constraints of the UAV, and causal buffer constraints in practice. To address the mixed-integer nonconvex problem, we transform it into two convex subproblems by applying tight bounds and relaxations. An iterative algorithm is proposed to solve the two subproblems in an alternating manner. Numerical results show that the proposed algorithm achieves higher rates of CEUs as compared with the existing benchmark schemes.

With the rapid development of cloud computing, the user-centric networks with the baseband unit pool have attracted a great deal of attentions in academic and industrial fields. However, limited fronthaul capacity and computation resource have become the bottlenecks inevitably. Thus, this paper investigates the power-efficient transmission in user-centric networks by considering both fronthaul capacity and computation resource constraints, where multiple access points (APs) and user equipments (UEs) are distributed. Specifically, a joint optimization of the beamforming vectors, AP-UE association strategy and transmission time is proposed to minimize the total power consumption (TPC). The formulated mixed integer non-linear problem (MINLP) is NP-hard. To address this problem, the MINLP is first transformed into a convex one via the successive convex approximation and semidefinite relaxation methods. Then, an iterative but effective algorithm is designed by using the property of the solution and applying the Lagrangian dual method. Simulation results show that the proposed algorithm converges rapidly and outperforms benchmark algorithms in terms of TPC.

Device-to-Device (D2D) communications incorporated with content caching have been regarded as a promising way to offload the cellular traffic data. In this paper, the caching strategy is investigated to maximize the D2D offloading gain with the comprehensive consideration of user collaborative characteristics as well as the physical transmission conditions. Specifically, for a given content, the number of interested users in different groups is different, and users always ask the most trustworthy user in proximity for D2D transmissions. An analytical expression of the D2D success probability is first derived, which represents the probability that the received signal to interference ratio is no less than a given threshold. As the formulated problem is nonconvex, the optimal caching strategy for the special unbiased case is derived in a closed form, and a numerical searching algorithm is proposed to obtain the globally optimal solution for the general case. To reduce the computational complexity, an iterative algorithm based on the asymptotic approximation of the D2D success probability is proposed to obtain the solution that satisfies the Karush-Kuhn-Tucker conditions. The simulation results verify the effectiveness of the analytical results and show that the proposed algorithm outperforms the existing schemes in terms of offloading gain.

In this paper, the problem of dynamical deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities for optimizing the energy efficiency of UAV-enabled networks is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Since ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem which jointly optimizes UAV deployment, user association, and power efficiency while meeting the illumination and communication requirements of users. To solve this problem, an algorithm that combines the machine learning framework of gated recurrent units (GRUs) with convolutional neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. Given the prediction of illumination distribution, the original nonconvex optimization problem can be divided into two sub-problems and is then solved using a low-complexity, iterative algorithm. Then, the proposed algorithm enables UAVs to determine the their deployment and user association to minimize the total transmit power. Simulation results using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 68.9% reduction in total transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution and user association.

In this paper, the problem of maximizing sum-rate for uplink rate splitting multiple access (RSMA) communications is studied. In the considered model, each user transmits two messages to the base station (BS) with separate transmit power and the BS will use a successive decoding technique to decode the received messages. To maximize each user's transmission rate, the users must adjust their transmit power and the BS must determine the decoding order of the messages transmitted from the users to the BS. This problem is formulated as a sum-rate maximization problem with proportional rate constraints by adjusting the users' transmit power and the BS's decoding order. However, since the decoding order variable in the optimization problem is discrete, the original minimization problem with transmit power and decoding order variables can be transformed into a problem with only the rate splitting variable. Then, the optimal rate splitting of each user is determined. Given the optimal rate splitting of each user and a decoding order, the optimal transmit power of each user is determined. Next, the optimal decoding order is determined by an exhaustive search method. To further reduce the complexity of the optimization algorithm used for sum-rate maximization in RSMA, a user pairing based algorithm is introduced, which enables two users to use RSMA in each pair and also enables the users in different pairs to be allocated with orthogonal frequency. Simulation results show that RSMA can achieve up to 10.0%, 22.2%, and 83.7% gains in terms of rate compared to non-orthogonal multiple access (NOMA), frequency division multiple access (FDMA), and time division multiple access (TDMA).

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users perform an FL algorithm that trains their local FL models using their own data and send the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL learning algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to 1) an optimal user selection algorithm with random resource allocation and 2) a random user selection and resource allocation algorithm.

In this paper, the problem of optimizing the deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs. Therefore, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users. To solve this problem, an algorithm based on the machine learning framework of gated recurrent units (GRUs) is proposed. Using GRUs, the UAVs can model the longterm historical illumination distribution and predict the future illumination distribution. In order to reduce the complexity of the prediction algorithm while accurately predicting the illumination distribution, a Gaussian mixture model (GMM) is used to fit the illumination distribution of the target area at each time slot. Based on the predicted illumination distribution, the optimization problem is proved to be a convex optimization problem that can be solved by using duality. Simulations using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 22.1% reduction in transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution. The results also show that UAVs must hover at areas having strong illumination, thus providing useful guidelines on the deployment of VLCenabled UAVs.

This paper considers a full-duplex (FD) mobile-edge computation (MEC) system with non-orthogonal multiple access (NOMA) and energy harvesting (EH), where one group of users simultaneously offload task data to the base station (BS) via NOMA and the BS simultaneously receive data and broadcast energy to other group of users with FD. We aim at minimizing the total energy consumption of the system via power control, time scheduling and offloading data allocation. To solve this nonconvex problem, we first transform it into an equivalent problem with less variables. The equivalent problem is shown to be convex in each vector with the other two vectors fixed, which allows us to design an iterative algorithm with low complexity. Simulation results show that the proposed algorithm achieves better performance than the conventional methods.

This paper investigates the dynamic access point (AP) clustering and precoding problem in the downlink of user-centric virtual cell networks. The goal is to maximize the weighted sum spectral efficiency (SE) while satisfying the power constraints and AP clustering constraints in adjacent time slots (TSs). By adopting the random walk mobility to model the mobile user equipments' movement behaviors, we consider dynamic and time-varying channel conditions. Therefore, the weighted sum SE maximization programming takes the form of discrete-time sequence of mixed-integer non-convex optimization problems. In this paper, we propose to solve this sequential problem in two stages. In the first stage, a dynamic AP clustering approach based on discrete particle swarm optimization is developed. This approach takes the advantage of the channel correlation by exploiting the relationship between AP clustering solutions in adjacent TSs to improve the SE performance and reduce complexity. In the second stage, given the AP clustering solution obtained in the first stage, a distributed precoding algorithm is devised via applying the weighted minimum mean square error method. By combining these two stages, we propose a novel dynamic AP clustering and precoding algorithm (DAPC-Pre). The effectiveness of the proposed DAPC-Pre algorithm is verified by the simulation results. In particular, the proposed algorithm converges fast and significantly outperforms benchmark algorithms in terms of sum SE under different dynamic environments.

This paper investigates an uplink non-orthogonal multiple access (NOMA)-based mobile-edge computing (MEC) network. Our objective is to minimize a linear combination of the completion time of all users’ tasks and the total energy consumption of all users including transmission energy and local computation energy subject to computation latency, uploading data rate, time sharing and edge cloud capacity constraints. This work can significantly improve the energy efficiency and end-to-end delay of the applications in future wireless networks. For the general minimization problem, it is first transformed into an equivalent form. Then, an iterative algorithm is accordingly proposed, where closed-form solution is obtained in each step. For the special case with only minimizing the completion time, we propose a bisection-based algorithm to obtain the optimal solution. Also for the special case with infinite cloud capacity, we show that the original minimization problem can be transformed into an equivalent convex one. Numerical results show the superiority of the proposed algorithms compared with conventional algorithms in terms of completion time and energy consumption.

In this paper, we consider the sum power minimization problem via jointly optimizing user association, power control, computation capacity allocation, and location planning in a mobile edge computing (MEC) network with multiple unmanned aerial vehicles (UAVs). To solve the nonconvex problem, we propose a low-complexity algorithm with solving three subproblems iteratively. For the user association subproblem, the compressive sensing-based algorithm is accordingly proposed. For the computation capacity allocation subproblem, the optimal solution is obtained in closed form. For the location planning subproblem, the optimal solution is effectively obtained via one-dimensional search method. To obtain a feasible solution for this iterative algorithm, a fuzzy c-means clustering-based algorithm is proposed. The numerical results show that the proposed algorithm achieves better performance than the conventional approaches.

Network coding is widely adopted in ad hoc networks to enhance the network throughput while few attention has been paid on applying it in device- to-device (D2D) networks. In this paper, we consider a relay-assisted D2D network with inter- session network coding, where multiple D2D users coexist in an underlaying manner. We aim to maximize the sum throughput via joint power and resource group allocation, which is formulated as a mixed integer programming problem. To solve this problem, an iterative algorithm with low complexity is accordingly proposed. Simulation results verify the effectiveness and superiority of the proposed algorithm compared with existing algorithms.

This paper studies energy efficient resource allocation for a machine-to-machine enabled cellular network with nonlinear energy harvesting, especially focusing on two different multiple access strategies, namely nonorthogonal multiple access (NOMA) and time division multiple access (TDMA). Our goal is to minimize the total energy consumption of the network via joint power control and time allocation while taking into account circuit power consumption. For both NOMA and TDMA strategies, we show that it is optimal for each machine type communication device (MTCD) to transmit with the minimum throughput, and the energy consumption of each MTCD is a convex function with respect to the allocated transmission time. Based on the derived optimal conditions for the transmission power of MTCDs, we transform the original optimization problem for NOMA to an equivalent problem which can be solved suboptimally via an iterative power control and time allocation algorithm. Through an appropriate variable transformation, we also transform the original optimization problem for TDMA to an equivalent tractable problem, which can be iteratively solved. Numerical results verify the theoretical findings and demonstrate that NOMA consumes less total energy than TDMA at low circuit power regime of MTCDs, while at high circuit power regime of MTCDs TDMA achieves better network energy efficiency than NOMA.

This letter investigates joint power control and user clustering for downlink nonorthogonal multiple access systems. Our aim is to minimize the total power consumption by taking into account not only the conventional transmission power but also the decoding power of the users. To solve this optimization problem, it is firstly transformed into an equivalent problem with tractable constraints. Then, an efficient algorithm is proposed to tackle the equivalent problem by using the techniques of reweighted ℓ1-norm minimization and majorization-minimization. Numerical results validate the superiority of the proposed algorithm over the conventional algorithms including the popular matching-based algorithm.

In this paper, we aim to minimize the average file transmission delay via bandwidth allocation and cache placement in two-tier heterogeneous networks with limited storage capacity, which consists of cache capacity and buffer capacity. For average delay minimization problem with fixed bandwidth allocation, although this problem is nonconvex, the optimal solution is obtained in closed form by comparing all locally optimal solutions calculated from solving the Karush-Kuhn-Tucker conditions. To jointly optimize bandwidth allocation and cache placement, the optimal bandwidth allocation is first derived and then substituted into the original problem. The structure of the optimal caching strategy is presented, which shows that it is optimal to cache the files with high popularity instead of the files with big size. Based on this optimal structure, we propose an iterative algorithm with low complexity to obtain a suboptimal solution, where the closed-from expression is obtained in each step. Numerical results show the superiority of our solution compared with the conventional cache strategy without considering cache and buffer tradeoff in terms of delay.

In this paper, we consider the sum α-fair utility maximization problem for joint downlink (DL) and uplink (UL) transmissions of a wireless powered communication network via time and power allocation. In the DL, the users with energy harvesting receiver architecture decode information and harvest energy based on simultaneous wireless information and power transfer. While in the UL, the users utilize the harvested energy for information transmission, and harvest energy when other users transmit UL information. We show that the general sum α-fair utility maximization problem can be transformed into an equivalent convex one. Trade-offs between sum rate and user fairness can be balanced via adjusting the value of α. In particular, for zero fairness, i.e., α = 0, the optimal allocated time for both DL and UL is proportional to the overall available transmission power. Trade-offs between sum rate and user fairness are presented through simulations.

In this paper, we investigate the problems of sum power minimization and sum rate maximization for multi-cell networks with non-orthogonal multiple access. Considering the sum power minimization, we obtain closed-form solutions to the optimal power allocation strategy and then successfully transform the original problem to a linear one with a much smaller size, which can be optimally solved by using the standard interference function. To solve the nonconvex sum rate maximization problem, we first prove that the power allocation problem for a single cell is a convex problem. By analyzing the Karush-Kuhn-Tucker conditions, the optimal power allocation for users in a single cell is derived in closed form. Based on the optimal solution in each cell, a distributed algorithm is accordingly proposed to acquire efficient solutions. Numerical results verify our theoretical findings showing the superiority of our solutions compared with the orthogonal frequency division multiple access and broadcast channel.

In this paper, we consider the network utility maximization problem with various user priorities via jointly optimizing user association, load distribution, and power control in a load-coupled heterogeneous network. In order to tackle the nonconvexity of the problem, we first analyze the problem by obtaining the optimal resource allocation strategy in closed form and characterizing the optimal base station load distribution pattern. Both observations are shown essential in simplifying the original problem and making it possible to transform the nonconvex load distribution and power control problem into convex reformulation via exponential variable transformation. An iterative algorithm with low complexity is accordingly presented to obtain a suboptimal solution to the joint optimization problem. Simulation results show that the proposed algorithm achieves better performance than conventional approaches.

We consider the problem of minimizing the total transmission power for a OFDM network where mutual interference exists among cells, with the power and load constraints for each base station (BS) and the rate demand constraint for every user. To solve the power minimization problem, we develop a distributed resource allocation and power control algorithm with low complexity. The complexity of the proposed algorithm is also analyzed. Numerical results show that the proposed algorithm is superior to the conventional schemes in terms of power consumption.

In this paper, we investigate the sum energy minimization problem in an uplink machine-to- machine (M2M) system with energy harvesting. To solve this nonconvex sum energy minimization problem, we first transform it into an equivalent convex problem and provide the optimal condition of the original problem. Then, we propose a low- complexity iterative scheme, which yields the optimal solution. The complexity of the proposed scheme is also analyzed. Numerical results show that the proposed scheme can achieve good performance.

Full‐duplex relay‐assisted device‐to‐device (D2D) communication underlaying fifth generation cellular networks allow devices to exchange information directly and extend coverage via relay strategy. In this study, the authors consider such a scenario, where the D2D communications assisted by fixed‐location full‐duplex relays in interference existing circumstance. Different from previous works, they assume that there are two types of users, cellular users and D2D users. They investigate power control problem and coverage probability performance in the previously assumed situation. Therefore, an effective power control scheme is of great importance to suppress interference between D2D and cellular communications, which can improve the total system throughput and spectral efficiency. To describe it, they formulate a power control optimisation problem for cellular communication and propose a simple on–off power control algorithm for D2D communication. They also obtain an analytic expression for the coverage probability of the cellular link using stochastic geometry according to the proposed algorithm. Simulation results follow to show the rates of both cellular and D2D links in the various numbers of D2D transceivers.

This paper studies pilot reuse and data transmit power control in a D2D underlay massive MIMO system over fading channels. In order to reduce the length of pilots, we propose to reuse a set of orthogonal pilots among DUEs, and the graph coloring based pilot allocation (GCPA) algorithm is utilized to allocate pilots to DUEs. Linear minimum mean square error (LMMSE) filters are used for signal detection. We then derive the lower bound of D2D links' average signal-to-interference-plus-noise ratio (SINR), and formulate a power control problem to minimize D2D links' data transmit power under the target SINR constraints. An iterative method converging to the unique optimal solution is proposed. Simulation results show that the analytical lower bound of the average SINR closely matches the simulated average SINR. What's more, pilot resources can be saved greatly by pilot reuse, and the effect of pilot contamination to the system can be almost neglected by allocating proper number of pilots to DUEs and applying GCPA algorithm.

In this paper, we consider the problem of maximizing the sum energy efficiency (EE) for LTE networks where the interferences occur between cells. Cell load, transmit rate and transmit power, where cell load demonstrates the average resource usage in a cell, are considered in the signal-to- interference-and-noise-ratio (SINR) model. Exploiting the properties of sum EE, we prove that operating at full load is optimal and provide a distributed power control algorithm. With all other powers fixed, we transform the original nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form. In high SINR situation, the transformed problem in subtractive form is proved a convex problem. Numerical results demonstrate the remarkable improvements in terms of EE.

The problem is to obtain the optimal number of base stations (BSs) and their locations on minimized power consumption with requirements of coverage rate and cell load. In this paper, we propose a mathematical model of evaluation index for cell planning and identify the optimization problem. The system model characterizes the total power consumption of planning area, taking into account the radio power, baseband power and cooling system loss. Two other problems, base station number search algorithm and cell sites planning with fixed cell number, are provided to solve the original problem. Simulation result proves that the algorithm enables to find a suboptimal solution, which relatively decreases the total power consumption under the planning constraints.

This paper exploits the noncentral communication architecture for increasing system throughput with popular video files cached in user equipments, and transmitted through D2D links under control of base station. A cell is divided into equilateral hexagon clusters among which frequency resources are reused. In order to reduce inter-cluster interference, we propose to limit the transmit power of each user and adopt relay-assisted D2D communication when direct D2D link can not be established in a cluster. Simulation results show that the proposed scheme can achieve higher spectral efficiency without much degradation in system throughput when compared with the scenario where different frequency resources are used by different clusters.