Thymoma CT images exhibit significant scale variations and blurred boundaries, posing a challenge to automatic segmentation. The proposed model is a multi-scale dual attention and gated skip connection network (MSDACG-Net), which suppresses redundant features in skip connections through context gating (CoT Gate) and enhances context modeling capabilities at high resolution using a multi-scale dual attention aggregation (MSDA) module. The effectiveness of the method was validated through experimentation on a thymoma CT dataset that had been self-built. A comparison of the proposed model with a strong baseline reveals that MSDACG-Net improves Dice by 1.01%, reduces HD95 by 24%, and improves Recall by 0.59%, demonstrating superior overall accuracy, boundary approximation ability, and detection robustness. Moreover, cross-modal experiments on the publicly available polysegmentation dataset Kvasir-SEG demonstrate the proposed method’s capacity for effective generalisation.

Many current approaches to the medical image segmentation suffer from problems of limited perceptual range, insufficient modeling of remote contextual relation, and insufficient feature refinement. In this paper, we propose ADAU-MambaBot, a novel three-dimensional approach for the segmentation of coronary artery, which is integrating dynamically adjustable dilated convolution, symmetric attention mechanism, and a state-space architecture based on the idea of Mamba. The system contains self-adjusting dilation module, that automatically decides the receptive field sizes, and dual-direction attention, which can boost not only the channel-wise but also the spatial feature extraction. We also implement the graduated training to make the convergence more reliable. On the ImageCAS benchmark, we show that, with the given ADAU-MambaBot system, the Dice score is 83.91%, which outperforms the performance of U-MambaBot and UU-Mamba architectures. In the component analysis and the computational efficiency study, we find that although the proposed solution achieves improved segmentation accuracy and more robust training, the addition of the advanced components does cause more requirements of the computation.

This paper considers an integrated sensing and communication (ISAC) system, where two dual-functional base stations (BSs) serve their users and detect multiple targets. To improve detection accuracy while meeting communication quality of service, this paper proposes a two-phase cooperative target detection algorithm that relies on Capon-based adaptive beamforming and maximum likelihood estimation (MLE)-based hypothesis testing. Specifically, based on Capon’s detection results, the two BSs first scan targets with an omnidirectional beam and then track targets with a directional beam. Subsequently, multiple hypotheses regarding the locations of targets are established based on the detection results of the Capon method, and the MLE is employed for hypothesis testing to filter out ghost targets. Finally, simulation results show that the proposed algorithm achieves more precise angles-of-arrival estimation of multiple targets than conventional single-BS sensing, and enables high-precision localization by eliminating ghost targets.

Uncrewed Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) systems provide flexible and resilient computing capabilities for mobile users by leveraging UAVs as edge servers (ESs). However, in practical deployments, user devices typically exhibit spatially non-uniform distributions, which impose significant challenges for achieving optimal UAV placement. Conventional fixed or pre-determined deployment strategies cannot dynamically adapt to heterogeneous user distributions. To overcome these challenges, this article investigates an air–ground cooperative MEC architecture comprising multiple UAVs and terrestrial ESs. A multi-objective optimization problem incorporating UAV trajectory optimization is formulated to jointly minimize task offloading latency and total system energy consumption. As the problem is an NP-hard mixed-integer nonlinear programming model, a Joint Alternating Optimization framework for Task Offloading, Resource Allocation, and UAV Trajectory control (JAOTRU) is proposed. The JAOTRU framework adopts an iterative block coordinate descent structure to decouple the highly coupled optimization variables into two subproblems, which are efficiently solved via a differential evolution algorithm and a successive convex approximation technique, respectively. The simulation results show that the proposed JAOTRU method substantially surpasses several benchmark methods regarding total offloading latency and system energy efficiency, validating its effectiveness and robustness for MEC systems assisted by UAVs.

In this paper, we investigate the joint beamforming and power allocation problem in multi-antenna Cellular Vehicle-to-Everything (C-V2X) networks under uncertain channel state information (CSI). Our objective is to minimize the beamforming vector at the base station and the transmit powers of vehicle users while satisfying probabilistic quality-of-service (QoS) constraints. To address the uncertainty of CSI, we first develop an ellipsoid-based uncertainty set learning approach, which models the uncertain CSI as a symmetric ellipsoid. Building on this uncertainty set, we propose a robust counterpart transformation method to reformulate the joint beamforming and power allocation problem into a deterministic semi-definite problem without probabilistic constraints. Through our analysis, the ellipsoid-based uncertainty set exhibits significant conservatism when applied to uncertain CSI with asymmetric distributions. To mitigate this conservatism, we propose a support vector clustering (SVC)-based uncertainty set learning approach, which can tightly enclose the distribution of uncertain CSI. To further simplify the spatial structure of the SVC-based uncertainty set, we develop aK-Medoids-based equivalent set construction (KMSC) approach, significantly reducing the number of variables in the resulting robust equivalent problem. Finally, we conduct extensive simulations to evaluate the performance of our proposed robust approaches and compare them with non-robust methods.

Multi-unmanned aerial vehicle (UAV) assisted mobile edge computing enables aerial platforms to collaboratively provide computing services for delay-sensitive applications. In such systems, information freshness must be preserved while multiple UAVs simultaneously perform trajectory control, task offloading, and resource scheduling under practical energy, computation, and quality-of-service constraints. The Age of Information (AoI) metric inherently couples these decisions over long time horizons, making the design of effective coordination policies difficult for conventional optimization techniques and reinforcement learning methods. In this work, we develop a constraint-aware multi-agent decision transformer framework, referred to as Prompt-CMADT, to address AoI optimization in multi-UAV MEC networks. By casting multi-UAV coordination as a sequential decision modeling problem, the proposed framework captures long-term temporal dependencies among heterogeneous agents while explicitly accounting for system-level constraints. Moreover, a constraint-aware prompt mechanism is designed to steer policy generation toward feasible solutions, and an opponent action prediction module is introduced to alleviate inter-UAV resource contention. Numerical results demonstrate that Prompt-CMADT consistently reduces the average AoI and overall energy consumption, while improving resource utilization, when compared with representative baseline schemes.

The existing unmanned aerial vehicle (UAV) enabled integrated sensing and communications (ISAC) systems primarily focus on the sensing capabilities of the UAV itself, overlooking the fact that the existing ground access points (APs) can receive the reflected signals for passive sensing, which can enhance the overall sensing performance. To address this issue, this paper introduces a UAV empowered air-ground ISAC system, where a UAV serves multiple communication users and performs detection for a potential target simultaneously, with the help of several ground APs. Specifically, the UAV works in active mode by transmitting ISAC signals and extracting information from echoes reflected from the target. In contrast, the ground APs function as sensing receivers, receiving and processing the reflected sensing signals from the target. Considering the limited capacity of the wireless backhaul links, we propose a two-step joint detection method, which contains local detection and result fusion steps. By incorporating the knowledge about the distribution of the reflection coefficient, we propose a maximum a-posteriori ratio test (MAPRT) detector, which is a generalization of earlier approaches such as the generalized likelihood ratio test detectors. Subsequently, the asymptotic distribution of test statistics of the MAPRT detector is derived. Furthermore, to improve the target detection performance, we propose an optimization algorithm that jointly optimizes the placement and transmit beamformer of the UAV, aiming at minimizing the probability of fusion error while guaranteeing the quality of service requirements of the users. Finally, numerical results demonstrate the effectiveness of the proposed algorithm.

Suffering from serious rate degradation, how to improve transmission rate with low latency is a challenging issue in ultra-reliable and low-latency communications (URLLC), especially when there is no enough blocklength for data transmission. To handle this issue, we propose to integrate the reconfigurable intelligent surface (RIS) and superimposed pilot (SP) into massive multiple-input multiple-output (mMIMO) systems, where the SP ensures latency by simultaneously sending pilot and data while the RIS improves high transmission rate by reflecting the SP signals. Practically, we derive the finite blocklength ergodic achievable rate lower bound in closed form under imperfect channel estimation and pilot interference removal. Then, we maximize the weighted sum rate of all the users by jointly designing the power control of SP at each user and the phase shifts at the RIS. Due to the highly coupled variables, we first decompose the original problem into the phase shift design subproblem and the power control design subproblem, which are resolved by a genetic algorithm (GA) and an iterative algorithm based on geometric programming (GP). Then, a block coordinate descent algorithm is proposed. Correspondingly, the complexity and convergence of the proposed algorithms are analyzed. Finally, our numerical results demonstrate that the joint design scheme can bring effective rate improvement in stringent latency constraints.

The existing unmanned aerial vehicle (UAV) enabled integrated sensing and communications (ISAC) systems primarily focus on the UAV’s own sensing capabilities, overlooking the potential of existing ground access points (APs) in performing passive sensing via the reflected signals—thus limiting the overall performance. To address this issue, this paper introduces a UAV empowered air-ground ISAC system, where a UAV cooperates with several ground APs in detecting a potential target, with the UAV and APs working in active and passive modes, respectively. Different from the conventional generalized likelihood ratio test detectors, we exploit the reflection coefficient’s distribution to design a maximum a-posteriori ratio test (MAPRT) detector and derive its asymptotic test statistic distribution. To break through the capacity limitations of the wireless backhaul links, we introduce a two-step joint detection method involving local detection and result fusion. Further, we propose an optimization algorithm to jointly optimize the UAV’s placement and transmit beamforming, aiming at enhancing the target detection performance. Numerical results demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose an unmanned aerial vehicle (UAV) enabled multi-stage integrated sensing and communications (ISAC) system, where a multi-antenna equipped UAV performs location sensing for a target whose location is initially unknown, while serves the communication users simultaneously, assisted by an existing receive access point. To improve the location sensing accuracy, we propose a multi-stage location sensing scheme, where the beamforming and placement of the UAV are dynamically adjusted in different stages. Specifically, in the first stage, without prior knowledge about the target’s location, the UAV fixes at the initial location and performs wide beam sensing to probe the target. In the following stages, given the coarse estimation result of the target’s location (obtained in the previous stage), the UAV adjusts its location and performs narrow beam sensing to locate the target. Besides, the quality of service requirements of the users are guaranteed in all stages. Based on the proposed sensing scheme, we formulate and solve two optimization problems to improve the sensing accuracy. Finally, numerical results demonstrate the effectiveness of the proposed algorithm.

Federated Learning (FL) is a promising paradigm for privacy-preserving collaborative intelligence in Internet of Vehicles (IoV) systems. In this work, vehicles are used to cooperatively train neural network models for the object recognition task. However, the inherent data heterogeneity across vehicles severely compromises the effectiveness of conventional FL frameworks that employ a unified global model. To address this challenge, we propose PFedOR - a confidence-aware personalized FL framework for vehicular object recognition. Specifically, we introduce a confidence quantification mechanism that uses public datasets on the server side to estimate class-specific confidence levels and data distribution patterns. Then, a personalized update strategy is employed, where batch normalization (BN) layers are regularized within a clustered structure through hierarchical clustering. At the same time, Non-BN layers are updated using similarity-based weighting across all vehicles. We conduct simulations on the nuImages dataset and experiment results demonstrate our approach’s superior performance against baseline methods, particularly under increasing data heterogeneity scenarios.

Cell-free multiple-input-multiple-output generally uses a star topology for superior communication but faces high costs due to long cables. An economical alternative, the stripe topology, is suitable for specific deployments but cannot meet user demands in densely populated areas due to limited fronthaul capacity. To address these limitations, we propose a hybrid network structure combining stripe and star topologies, ensuring system performance while reducing deployment costs. In such a network, joint precoding and fronthaul compression is considered to maximize system sum-rate and an alternating optimization (AO) algorithm is proposed. However, the AO algorithm involves an iterative process and complex matrix calculations, making it unsuitable for practical applications. To deal with this issue, we propose a low-complexity iterative gradient descent (IGD) algorithm with simple matrix operations. To further reduce online computational complexity, we propose a novel deep unfolding neural network (DUNN) scheme, which is interpretable and scalable, based on the IGD algorithm. Simulation results show that the hybrid topology significantly improves system capacity compared to the stripe-only topology. Additionally, the DUNN achieves a tradeoff between the achievable sum-rate performance and the corresponding computational complexity.

The cellular Internet of Things (IoT) enhanced by federated learning (FL) is a potential paradigm to leverage the vast amount of data generated by the IoT devices and offer various intelligent applications. Through its distributed learning manner, the privacy and delay problems of the learning process are well handled. Nevertheless, in the cellular IoT, FL requires multiple rounds of model parameters exchanging between the parameter server and multiple clients over unstable wireless links, which largely constrains the communication efficiency. Regarding this problem, we propose the training data cost ratio to evaluate the communication efficiency, and then by maximizing this metric, client scheduling, transmitting power, and bandwidth are jointly formulated. The formulated problem is decomposed via problem transformation and derivations, and then, the Lagrange method and greedy-based algorithms are developed to solve the subproblems efficiently. Simulation results verify the advantages of our algorithm in communication efficiency improvement. Moreover, it reveals that the proposed metric and joint optimization substantially obtain superior tradeoff between learning performance and resource consumption compared to the client number oriented optimization.

This paper explores the potential of employing rate-splitting multiple access to enhance the achievable rate and energy efficiency (EE) of an uplink cell-free massive multiple-input multiple-output (MIMO) system, where the access points (APs) are configured with low-resolution analog-to-digital converters (ADCs) to minimize the hardware expense and power consumption. Taking the large-scale fading decoding, ADC quantization, and imperfect successive interference cancellation into consideration, a rigorous closed-form rate expression is derived within Ricean fading environments. This analytical framework facilitates an in-depth analysis of the rate performance with respect to various system parameters. To quantify the benefits of low-resolution ADCs, a power consumption model is subsequently incorporated into the analysis, facilitating an evaluation of the system’s EE. Furthermore, the optimization of power control coefficients and receiver weights is tackled through the formulation of weighted sum-rate (WSR) and EE maximization problems. Two efficient alternative algorithms are then proposed to determine their optimal solutions. The theoretical propositions and the efficacy of the proposed WSR and EE optimization algorithms are substantiated through comprehensive simulations.

In this paper, we study the transmit and receive beamforming vectors in a downlink integrated sensing and communication (ISAC) system, where a base station (BS) performs the downlink communication with user equipments (UEs) and active sensing tasks simultaneously. While reflected signals are utilized for passive sensing at the receive access points (RAPs). We adopt different fusion strategies based on the backhaul capacity between the RAPs and BS. Specifically, in the scenarios with unlimited backhaul capacity, the sensing signals received by the BS and RAPs are forwarded to the central controller (CC) for signal fusion. In contrast, in the scenarios with limited backhaul capacity, the BS and each RAP make independent decisions and transmit their binary inference results to the CC for result fusion. Furthermore, we explore two cases of the signal-to-interference-plus-noise ratio (SINR) with and without the sensing interference cancellation (Case-1 SINR and Case-2 SINR). By optimizing the beamforming vectors according to different fusion strategies, we aim to maximize sensing performance while ensuring the minimum SINR requirement for the UEs subject to the power budget at the BS. Finally, numerical results demonstrate that the proposed beamforming optimization schemes can reach the upper bound of performance under both fusion strategies. It is also shown that adding the sensing signals generally improve the sensing performance in Case-1 SINR.

Clustered vehicular federated learning (CVFL) facilitates data sharing and collaborative decision-making among vehicles, thus refining traffic behavior and demonstrating the immense potential for transforming intelligent transportation systems into a reality. However, non-independent and identically distributed data and diverse model requirements among vehicular clients hinder the feasibility of a one-size-fits-all model. Besides, “selfish” vehicular clients may be unwilling to participate in learning tasks because of the huge resource consumption of the training process. To address these challenges, in this paper, we first group local models using the adaptiveK-means-based model grouping method and then aggregate the models within each group to generate CVFL models for subsequent multi-model training. Secondly, we propose a dynamic matching-based clustering method based on the local data quality and similarity to achieve efficient vehicular client clustering. Subsequently, a meticulously crafted hierarchical incentive mechanism, grounded in a three-stage Stackelberg game, is introduced to incentivize both cluster heads and members in a layered fashion, with the initiation stemming from the CVFL server. To determine the optimal strategies for the three-stage game, an iterative algorithm is proposed, and near-optimal analytical solutions are obtained with reduced complexity. The simulation results demonstrate that our CVFL system, augmented with the hierarchical incentive mechanism, can effectively motivate multiple clusters to train multiple models in parallel, thus improving overall efficiency.

In addition to enhancing wireless communication coverage quality, reconfigurable intelligent surface (RIS) technique can also assist in positioning. In this work, we consider RIS-assisted superimposed pilot and data transmission without the assumption availability of prior channel state information and position information of mobile user equipments (UEs). To tackle this challenge, we design a frame structure of transmission protocol composed of several location coherence intervals, each with pure-pilot and data-pilot transmission durations. The former is used to estimate UE locations, while the latter is time-slotted, duration of which does not exceed the channel coherence time, where the data and pilot signals are transmitted simultaneously. We conduct the Fisher Information matrix (FIM) analysis and derive Cram´er-Rao bound (CRB) for the position estimation error. The inverse fast Fourier transform (IFFT) is adopted to obtain the estimation results of UE positions, which are then exploited for channel estimation. Furthermore, we derive the closed-form lower bound of the ergodic achievable rate of superimposed pilot (SP) transmission, which is used to optimize the phase profile of the RIS to maximize the achievable sum rate using the genetic algorithm. Finally, numerical results validate the accuracy of the UE position estimation using the IFFT algorithm and the superiority of the proposed SP scheme by comparison with the regular pilot scheme.

In this paper, we propose an unmanned aerial vehicle (UAV) enabled multi-stage integrated sensing and communications (ISAC) system, where a multi-antenna equipped UAV performs location sensing for a target whose location is initially unknown, while serves the communication users simultaneously, with the aid of an existing receive access point (RAP). By fusing the measurement results of the UAV and RAP, the location of the target is estimated. To improve the location sensing accuracy, we propose a multi-stage location sensing scheme. Specifically, in the first stage, in the absence of prior knowledge about the target’s location, the UAV fixes at the initial location and adjusts the beamformer to perform wide beam sensing to probe the target. In the following stages, with the previous coarse estimation result of the target’s location, the UAV performs narrow beam sensing by jointly adjusting the placement and also transmit beamformer. Besides, the quality of service requirements of the users are guaranteed in all stages. Accordingly, optimization problems are formulated for the first and following stages, respectively. By involving the semidefinite relaxation technique and then solving a quadratic semidefinite programming problem, the solution in the first stage is obtained. In the following stages, we jointly apply the alternating optimization, successive convex approximation, trust region, and also Dinkelbach’s methods to address the intricate coupling between the UAV placement and beamformer. Finally, numerical results demonstrate the effectiveness of the proposed algorithms.

This paper investigates secure transmission in the uplink of a cell-free massive multiple-input multiple-output (MIMO) system employing three distinct multiple access strategies: rate-splitting multiple access (RSMA), non-orthogonal multiple access (NOMA), and space-division multiple access (SDMA). RSMA, functioning as a unifying paradigm, merges the merits of both NOMA and SDMA, and holds substantial promise for enhancing system secrecy. We derive closed-form expressions for secrecy spectral efficiency (SE) under Rician fading channels and imperfect channel knowledge assumptions. The secrecy SE is subsequently evaluated across a range of system configurations, encompassing varying access point (AP) and user numbers, AP and eavesdropper antenna dimensions, line-of-sight probabilities, successive interference cancellation conditions, and multiple access protocols. Harnessing these expressions, we establish an optimization framework for the users’ power control coefficients and APs’ receiving weights to maximize the sum secrecy SE while ensuring quality-of-service secrecy requirements for users. Additionally, an alternative optimization algorithm is proposed to ascertain a high-quality solution. Comprehensive simulations substantiate our theoretical propositions and evaluate the efficacy of the proposed sum secrecy SE maximization algorithm.

Autonomous aerial vehicles (AAVs) with cache are considered as an efficient technique to enhance serving capabilities of traditional wireless networks in terms of network coverage and capacity. However, with the introduction of AAVs, new challenges such as trajectory design and AAV-user association occur. In this paper, we consider a caching AAV-assisted wireless network and formulate a user fairness problem by jointly optimizing AAV-user association, trajectory design, and bandwidth allocation of the AAVs, which is mixed-integer and non-convex. In order to find solutions, we decompose the original problem into three subproblems and propose an iterative algorithm based on block alternating descent and successive convex approximation methods. In addition, computational complexity is analyzed. Finally, simulation results validate the efficiency of the proposed algorithm, compared to benchmark algorithms.

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). Considering the limited capacity of backhaul links, the signals received at the RAPs cannot be sent to the central controller (CC) directly. Instead, a novel metric of result aggregation for IAPS is proposed. Specifically, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at minimizing the probability of error at the CC under communication quality of service constraints, an algorithm of power optimization is proposed. Finally, numerical results validate the positive effect of dedicated sensing symbols and the potential of the proposed IAPS scheme.

Traditional cell-free massive multiple-input multiple-output (CF-mMIMO) systems face challenges of resource scarcity, cognitive limitations, and coverage blind spots, which primarily stem from the extensive deployment of long cables connecting each access point to the central processing unit in the system. To maximize the minimum achievable user rate and enhance the performance of a downlink CF-mMIMO system, we propose an innovative scheme that jointly integrates user association, unmanned aerial vehicle (UAV) placement, and transmission power allocation, with UAV-satellite assisted. The scheme also considers stringent constraints, including maximum power capacities, cross-layer interference limitations, and essential coverage demands. Confronting the complexity of the initial non-convex optimization challenge, we dissect it into more tractable sub-problems that encompass user association, UAV placement, and power allocation. Our approach employs an iterative algorithm, systematically resolving these sub-problems in sequence. Simulation results conclusively demonstrate the efficacy of the proposed scheme in optimizing system resource allocation and achieving comprehensive coverage.

Federated Learning (FL) is a new way of training models used in Internet of Things (IoT) systems. It is a method that maintains the privacy of client devices while improving model accuracy and reliability. However, there is a problem in FL applications due to the lack of incentives. Clients have different motivations and produce different quality datasets, which leads to a divergence in the quality of local models uploaded to the central server. To address this issue, we propose a new incentive model based on the Stackelberg game. The mechanism we suggest distributes rewards based on the quality of the models uploaded to the server by each client, rather than the amount of data trained. We transform the model into two optimization problems, and we propose a linear complexity algorithm to solve them. This algorithm can achieve the optimal solution and greatly reduce computational complexity, as shown in our experimental results.

Incentive mechanism is an effective approach to encourage user participation in the Federated Learning (FL) process and improve training efficiency. However, current research often focuses on scenarios with complete information or weak information asymmetry between the server and users, and few studies consider incentive mechanism design in strong asymmetric information scenarios. Meanwhile, most works assume that users' resource contributions to model performance are independent of each other, which is not consistent with practical situations. To tackle these challenges, we design an incentive contract tailored for scenarios with strong information asymmetry. Our contract leverages the probability distribution of user types to ensure its appropriateness. Furthermore, taking into account the correlation of the users' resource contributions, we propose an iteration algorithm to determine the set of optimal contract items that satisfy the constraints of individual rationality (IR) and incentive compatibility (IC). Our simulation results show that our contract can effectively motivate multiple users to take part in the training process, enabling the server to achieve utility close to those in weak asymmetric information scenarios while maintaining robustness.

Federated learning (FL) framework enables user devices to collaboratively train a global model based on their local data sets without privacy leak. However, the training performance of FL is degraded when the data distributions of different devices are incongruent. Fueled by this issue, we consider a clustered FL (CFL) method where the devices are divided into several clusters according to their data distributions and are trained simultaneously. Convergence analysis is conducted, which shows that the clustered model performance depends on cosine similarity, device number per cluster, and device participation probability. Besides, to quantify the training performance, the utility of clustered model training is defined based on the analysis results. Then, aiming at optimizing the system utility, a joint problem of resource allocation and device clustering is formulated, which is solved by decoupling it into two subproblems. First, given the results of device clustering, a low-complexity iterative algorithm based on the convex optimization theory is proposed to make the bandwidth allocation and the transmit power control. Then, according to the individual stability, a coalition formation algorithm is proposed for the device clustering. Finally, the real-data experiments on the classification tasks (e.g., MNIST, CIFAR-10, and CIFAR-100) validate the results of convergence analysis and advantages of the proposed algorithm in terms of the test accuracy.

This letter establishes a novel transmission framework that amalgamates downlink (DL) training with rate-splitting multiple access, thereby being expected to enhance the spectral efficiency (SE) of a cell-free massive multiple-input multiple-output enabled Internet of Things (IoT) system. Considering a correlated Ricean fading environment coupled with imperfect channel knowledge, we derive a closed-form expression for the achievable SE and evaluate the DL SE under a variety of system configurations. Our comprehensive simulations corroborate the theoretical findings and yield critical insights pertinent to the system’s architectural design.

The Industrial Internet of Things (IIoT) is emerging as a promising technology that can accelerate the application of industrial intelligence to smart factories. Because of the sensitive nature of user data, federated learning (FL) which performs distributed machine learning while preserving data privacy, is leveraged to meet the accuracy and privacy requirements of IIoT end devices/clients. However, the unreliable communications in IIoT may result in possible single-point failures in the typical single-server FL framework, thereby negatively affecting the training efficiency. In this paper, we study on the client scheduling problem in a multi-server FL framework for the communication reliability and training efficiency improvement. Specifically, we focus on a semi-decentralized FL (SD-FL) framework, where edge servers and clients collaborate to train a shared global model through unreliable intra-cluster model aggregation and inter-cluster model consensus because of the model transmission error in client-server and server-server communication. Then, a client-server association optimization problem is formulated, with the objective of minimizing the global training loss. Resorting to the convergence analysis of SD-FL, the original problem is simplified and transformed into an integer nonlinear programming problem to guide us to design a high-efficiency client scheduling scheme. Finally, experimental results show that the proposed scheme significantly outperforms the baselines in terms of the test accuracy and training loss.

Most existing works on dual-function radar-communication (DFRC) systems mainly focus on active sensing, but ignore passive sensing. To leverage multi-static sensing capability, we explore integrated active and passive sensing (IAPS) in DFRC systems to remedy sensing performance. The multi-antenna base station (BS) is responsible for communication and active sensing by transmitting signals to user equipments while detecting a target according to echo signals. In contrast, passive sensing is performed at the receive access points (RAPs). We consider both the cases where the capacity of the backhaul links between the RAPs and BS is unlimited or limited and adopt different fusion strategies. Specifically, when the backhaul capacity is unlimited, the BS and RAPs transfer sensing signals they have received to the central controller (CC) for signal fusion. The CC processes the signals and leverages the generalized likelihood ratio test detector to determine the present of a target. However, when the backhaul capacity is limited, each RAP, as well as the BS, makes decisions independently and sends its binary inference results to the CC for result fusion via voting aggregation. Then, aiming at maximize the target detection probability under communication quality of service constraints, two power optimization algorithms are proposed. Finally, numerical simulations demonstrate that the sensing performance in case of unlimited backhaul capacity is much better than that in case of limited backhaul capacity. Moreover, it implied that the proposed IAPS scheme outperforms only-passive and only-active sensing schemes, especially in unlimited capacity case.

Cell-free massive multiple-input-multiple-out (CF-mMIMO) systems provide limited coverage because of expensive wired fronthaul between access points (APs) and central processing unit (CPU). To address this challenge, we propose a novel framework where an unmanned aerial vehicle (UAV), acting as an aerial AP, works coherently with the ground APs to expand the coverage of conventional CF-mMIMO system. To fully utilize the spectrum resource, the wireless fronthaul between the CPU and UAV shares the total bandwidth with the radio access networks. Considering limited power supply of the UAV and for the goal of green communications, we formulate a weighted sum power minimization problem to jointly optimize downlink beamforming and fronthaul compression, as well as UAV placement. The formulated problem is a mixed timescale problem, thus we propose a two-timescale optimization framework in which the UAV placement is optimized in each long timescale based on statistical channel state information (CSI), then the downlink beamforming and fronthaul compression are optimized in each short timescale based on instantaneous CSI. Specifically, uplink-downlink duality and semidefinite relaxation (SDR) based alternating optimization techniques are introduced to find solutions to the short timescale issue, while successive convex approximation and SDR methods are invoked to find solutions to the long timescale issue. Finally, simulation results corroborate the performance of the proposed algorithm.

Rate-splitting multiple access (RSMA) has emerged as a potent paradigm shift in wireless communications, demonstrating resilience to channel state information (CSI) inaccuracies and significant rate enhancements. This work investigates RSMA’s application within the context of ultra-reliable and low-latency communication (URLLC) for the forthcoming Internet-of-Everything networks. Specifically, we integrate RSMA with a cell-free massive multiple-input multiple-output (MIMO) architecture to support URLLC demands. Considering the imperfect CSI, attributable to pilot contamination and thermal noise, we derive rigorous lower-bound expressions for the downlink achievable rates. These expressions are applicable to short-packet communication scenarios and RSMA strategy over spatially correlated Rician fading channels. Utilizing these analytical expressions, we perform an exhaustive rate performance evaluation, varying system parameters such as the numbers of pilots, access points (APs), devices, and antennas per AP, alongside different multiple access techniques. Furthermore, we address the power control coefficient design for both common and private streams, framing it as an optimization problem aimed at maximizing the weighted sum-rate and enhancing URLLC service quality. To tackle this non-convex challenge, we introduce a geometric programming-based path-following algorithm, which iteratively converges to the solution. The theoretical underpinnings and the efficacy of the proposed power optimization algorithm are corroborated through extensive simulation results.

Ultra-reliable and low-latency communication (URLLC) is recognized as the most challenging use case for the next generation of wireless networks. Existing research on URLLC is based on the regular pilot (RP) scheme, which is tough to ensure a high transmission rate with stringent latency and reliability requirements due to the impact of finite blocklength, especially in massive connectivity scenarios. In this paper, we propose to use generalized superimposed pilot (GSP) scheme for URLLC transmission in massive multi-input multi-output (mMIMO) systems. Distinguishing from the conventional superimposed pilot (SP) scheme, the GSP scheme eliminates mutual interference between the pilot and data, where the data length is optimized, and the data symbols are precoded to spread over the whole transmission block. With the GSP scheme, we first formulate a weighted sum rate maximization problem by jointly optimizing the data length, pilot power, and data power and then derive closed-form results, including suboptimal data length and achievable rate lower bounds with maximum-ratio combining (MRC) and zero-forcing (ZF) detectors, respectively. Based on the closed-form results, we provide the corresponding iterative algorithms for the MRC and ZF cases where the problems are transformed into geometry program format by using log-function and successive convex approximation methods. Finally, the performance of the RP, SP, and GSP schemes are compared through simulation results, which reflect the superiority and robustness of the GSP scheme in URLLC scenarios.

Clustered Vehicular Federated Learning (CVFL) can be used to improve traffic safety, increase traffic efficiency, and reduce vehicle carbon emissions. Therefore, it is extremely promising in intelligent transportation systems. However, in practice, it is difficult to accurately cluster vehicular clients with mobility according to data distribution. In addition, vehicular clients may be reluctant to contribute their computation and communication resources to perform learning tasks if the CVFL server does not give them proper incentives. In this paper, we would like to address the above issues. Specifically, considering the mobility of vehicular clients, we first propose a clustering method to cluster vehicular clients into several clusters based on the cosine similarity between the model gradient of local vehicular clients and the K-means method. Then, we design a set of optimal contracts specifically for the clusters, aiming to motivate them to select the optimal number of intra-cluster iterations for model training and give the closed-form solution to the contracts under the constraints of individual rationality, incentive compatibility, and task accuracy. The proposed contract theory based incentive mechanism not only effectively motivates every cluster, but also overcomes the information asymmetry problem to maximize the utility of the CVFL server. Finally, simulation results validate the effectiveness of the proposed clustering method and the designed contract.

Reconfigurable intelligent surface (RIS) is a promising paradigm for implementing intelligent reconfigurable wireless propagation environments in the 6G era. However, most of the existing studies focus on utilizing RIS deployed on buildings to provide services to users or constructing a RIS-assisted system framework for static users, which greatly limited application in real-time changing vehicular communication environments. As a result, combining unmanned aerial vehicles (UAVs) with RIS (RIS-UAV) plays a crucial role in various wireless networks due to their high mobility. To maximize the communication rate between base station (BS) and mobile vehicle, we propose a position prediction strategy for vehicles that facilitates real-time adjustment of UAV trajectories and RIS phase shifts, enhancing communication in dynamic environments. Deep reinforcement learning (DRL) algorithm is utilized to solve the above question, which achieves a good effect on convergence in continuous action space. Simulation results demonstrate that compared with benchmark schemes, the algorithm we suggested has significant performance gains, that is to maximize the communication rate under system constraints and guarantee the reliability of the communication.

This paper investigates the achievable rate and spectral efficiency (SE) of an uplink cell-free massive multiple-input multiple-output Internet-of-Things (mMIMO-IoT) network over Ricean fading channels, where both access points and user equipments (UEs) are equipped with multiple antennas. We derive tight closed-form expressions for the lower-bound achievable rate and SE under maximum ratio combining and imperfect channel state information (CSI). Moreover, we propose a target-signal-to-interference-plus-noise-ratio-tracking opportunistic power control (TOPC) algorithm with gradual soft UE removal to mitigate the effects of unsupported UEs. The proposed TOPC algorithm is fully distributed, as each UE updates its transmit power based on local CSI. Numerical results show that adding more antennas at the UEs can enhance the achievable rate, but may degrade the achievable SE due to the increased pilot overhead. Moreover, the Ricean fading channels offer much higher achievable rate and SE than the Rayleigh fading channels, and our TOPC algorithm exhibits satisfactory performance in various aspects.

In the realm of industrial Internet of Things, the imperative for ultra-reliable and low-latency communication (URLLC) is underscored by the demand for up to 99.999% reliability and sub-microsecond latency. In this paper, we delve into a downlink cell-free massive multiple-input multiple-output (MIMO) system designed to facilitate URLLC, operating over spatially correlated Rician fading channels with inherent phase shifts. Utilizing short-packet transmission and accounting for imperfect channel state information, we derive stringent closed-form expressions for the lower-bound achievable rates, considering both phase-aware and phase-unaware minimum mean squared error estimations. Employing these expressions, we execute an in-depth performance analysis across diverse system configurations, including the availability of phase shifts and the counts of access points (APs), connected devices, antennas per AP, and pilot sequences. Additionally, we propose a path-following power control algorithm that employs geometric programming to enhance the downlink sum-rate. This algorithm is meticulously designed to meet the stringent latency and reliability requirements of URLLC for all connected devices. The theoretical underpinnings and the efficacy of the proposed power control algorithm are substantiated through extensive simulations.

In this paper, we investigate the secure transmission in the downlink of a cell-free massive multiple-input multiple-output (mMIMO) system that relies on rate-splitting multiple access (RSMA). We specifically evaluate the impact of hardware impairments (HWIs) originating from non-ideal access points (APs), user equipments (UEs), and Eavesdroppers (Eves) on the system’s secrecy performance. The investigation encompasses scenarios with both colluding and non-colluding Eves orchestrating pilot spoofing attacks against a designated UE, subsequently intercepting transmissions from both common and private streams. By taking into account a spatially correlated Ricean fading channel model and imperfect channel state information, we derive closed-form expressions for both legitimate and secrecy rates. The secrecy performance is scrutinized across different system configurations, including varying HWI levels, power splitting ratios, AP/Eve transmission powers, spatial correlations, line-of-sight components, and the presence of colluding versus non-colluding Eves. To enhance the secrecy rate for the compromised UE, we propose a secure power control strategy for adjusting the downlink transmission powers of the common and private streams. A sequential convex approximation-based algorithm is introduced to iteratively address this non-convex problem. Through comprehensive simulations, we validate our theoretical propositions and extract pivotal insights for system design.

This paper investigates a robust resource allocation for reconfigurable intelligent surface (RIS) aided vehicle-to-everything (V2X) communications with imperfect channel state information (CSI). To satisfy the diverse quality-of-service (QoS) requirements of V2X communications, we aim at maximizing the sum capacity of cellular user equipments (CUEs) while guaranteeing the outage probability constraints of vehicular user equipments (VUEs). Then, the considered problem is decomposed into the subproblems of power, spectrum and RIS phase shift op-timization. A graph-based power allocation method is presented to transform the non-convex power allocation subproblem into a tractable one and obtain the closed-form solutions. A worst-case conditional value-at-risk (CVaR) approximation-based method is developed to convert the RIS phase optimization subproblem into a convex semidefinite programming (SDP) problem. We propose a low-complexity learning-based alternating optimization approach which alternately optimizes three subproblems to obtain a near-optimal solution. Simulation results demonstrate that the proposed approach outperforms other benchmark methods.

Grant-free random access (GFRA) becomes attractive in Internet of Things (IoT) due to its low signaling overhead and short access latency. In this article, we investigate GFRA in a multicel massive multiple-input-multiple-output (MIMO) system. As the IoT device usually has sporadic traffic, we set a packet buffer for each device to describe its temporal traffic, and also use the stochastic geometry to describe the randomness of devices’ spatial locations. With the backoff mechanism, only devices with a nonempty buffer and a successful backoff are activated and allowed to request access. Unlike previous works that regard all devices selecting the same pilot (i.e., the colliding devices) as undetectable, we give a more accurate model that the base station (BS) can detect colliding devices when they locate far away from each other, and we set a unique collision area for each device to quantify the boundary that the collision can be ignored. A tight approximation for the number of packets successfully transmitted at the unit area and time slot, named packet throughput, is derived. Based on it, we obtain the optimal backoff parameter that maximizes the packet throughput under devices’ delay constraints. It is shown that when pilots are insufficient or device packet traffic is heavy, a long backoff time is needed. However, as the pilot grows or the packet traffic turns light, devices should gradually reduce the backoff time. In particular, if pilots are surplus, cheap detectors can be equipped on the BS without an obvious packet throughput reduction.

Integrating reconfigurable intelligent surface (RIS) into cell-free massive multiple-input multiple-output (MIMO) is a promising approach to enhance the coverage quality, spectral efficiency (SE), and energy efficiency. In this paper, an RIS-assisted cell-free massive MIMO downlink system suffering from the transceiver hardware impairments (T-HWIs) is investigated. To improve the accuracy of the direct estimation (DE) scheme, a modified ON/OFF estimation (MOE) with moderate pilot overhead is proposed. Relying on the knowledge of imperfect channel state information, we derive closed-form expressions of the lower-bound achievable SE with T-HWIs under both DE and MOE schemes. The closed-form results facilitate the investigation of how RIS improves the downlink SE under various system settings and allow us to explore the trade-off strategies between using more hardware-impaired APs and low-cost RISs in terms of the downlink SE and power consumption. Numerical results validate the theoretical analysis and show that the proposed MOE scheme outperforms the DE scheme in terms of the downlink SE. Moreover, the benefits of introducing RIS into hardware-impaired cell-free massive MIMO systems are also illustrated.

This paper investigates the uplink performance of a general cell-free massive multiple-input multiple-output (CF-mMIMO) system, in which all access points (APs) and user equipments (UEs) suffer from hardware impairments (HWIs). Besides, there are several reconfigurable intelligent surfaces (RISs) that aim to improve the coverage quality, spectral efficiency (SE), and energy efficiency (EE). Relying on the knowledge of only imperfect channel state information, a tight closed-form expression for the lower-bound achievable SE is derived. Based on this expression, we quantitatively investigate the impacts of different system parameters on uplink SE and EE, and conduct a tradeoff analysis between using more APs versus using more RISs with respect to the above performance metrics. In addition, we also design a max-min SE algorithm that takes into account both large-scale fading decoding weights and power control coefficients to guarantee UE fairness. Specifically, the proposed algorithm admits a closed-form solution and is therefore memory-efficient and time-saving. Both the theoretical analysis and the effectiveness of the proposed max-min SE algorithm are verified via extensive simulations.

Driven by the development of time-sensitive applications, short packet transmission (SPT) design has become the key point in the ultra-reliable and low-latency communications (URLLC) area. The primary challenge in it is that the delay caused by pilot overhead cannot be neglected. To deal with this issue, this paper presents a frame structure adopting partial-superimposed-pilot (PSP) scheme for SPT. The key of PSP scheme is that the number of data symbols is equal to the available blocklength, and the number of pilot symbols transmitted with data in the training stage needs to be optimized. Under the finite blocklength regime, we first derive a closed-form lower bound achievable rate of an uplink massive MIMO system with imperfect pilot removal for maximal-ratio-combining (MRC) receiver. Then, we formulate a weighted sum rate maximization problem by jointly optimizing the pilot length, pilot power, and data power. We derive a closed-form solution of optimal pilot length. Using the log-function and successive convex approximation (SCA) method, we develop an iterative optimization framework to find a locally optimal power solution. For comparison, the conventional frame structures based on complete-superimposed-pilot (CSP) and regular pilot (RP) schemes are also shown. Simulation results indicate that the proposed PSP scheme is superior to the existing CSP and RP schemes.

Mobile edge computing (MEC) is a promising paradigm to handle the mismatch between computation-intensive applications and resource-limited devices. Nevertheless, as most Internet of Things (IoT) terminals are battery-limited, the computation gain of MEC may be compromised due to insufficient battery energy for task offloading. Wireless power transfer (WPT) and full duplex (FD) communications are economical charging and transmission methods for battery-limited IoT terminals. However, when integrating wireless power transfer and FD into MEC, the incentive problem should be jointly addressed with task offloading, because the WPT facilities and their powered IoT nodes belong to different service operators. In this paper, we investigate the efficiency of WPT from the perspective of battery saving, and propose an efficient wireless powered task offloading and incentive mechanism in FD MEC-enabled cellular IoT networks. The battery saving efficiency, which addresses both the total cost of WPT and saved energy of battery, is proposed as the performance metric. By adopting this metric as the utility function of the network operator (NO), the task offloading and incentive problem are jointly formulated as a Stackelberg game. We then propose an efficient alternating direction iteration-based algorithm to solve its equilibrium efficiently. Simulation results demonstrate the benefits of our algorithm in battery saving by comparisons with utility oriented benchmarks. Moreover, it reveals the tradeoff between the utility of NO and battery saving, which verifies the positive effects of FD communications and WPT in improving the efficiency of battery saving.

With the rapid development of 5G, massive machine-type communication is expected to experience significant growth, leading to severe random access collisions. To address this issue, we first adopt deep neural networks to detect random access collisions by learning the features of the received signals. Based on the collision-detection results, we propose a double-contention random access (DCRA) scheme, with which the base station can schedule one more contention process for devices experiencing collisions. To fully harness the collision-resolution capability of the proposed DCRA scheme, we further analyze its performance and illustrate how to tune the backoff parameters to optimize the network throughput. It is revealed that the maximum throughput of the DCRA scheme depends on the number of random access preambles and the collision recognition accuracy. The corresponding optimal backoff parameters are then obtained, which greatly facilitates implementations in practice. Simulation results show that with a high collision recognition accuracy, the proposed scheme can achieve significant throughput improvement.

Federated learning (FL) framework enables user devices collaboratively train a global model based on their local datasets without privacy leak. However, the training performance of FL is degraded when the data distributions of different devices are incongruent. Fueled by this issue, we consider a clustered FL (CFL) method where the devices are divided into several clusters according to their data distributions and are trained simultaneously. Convergence analysis is conducted, which shows that the clustered model performance depends on cosine similarity, device number per cluster, and device participation probability. Then, aiming at optimizing the model training performance, a joint problem of resource allocation and device clustering is formulated, which is solved by decoupling it into two sub-problems. Specifically, a coalition formation algorithm is proposed for the device clustering sub-problem, and the sub-problem of bandwidth allocation and transmit power control is solved directly due to its convexity. Finally, simulation experiments are conducted on the MNIST dataset to validate the performance of the proposed algorithm in terms of test accuracy.

Hierarchical federated edge learning (H-FEEL) has been recently proposed to enhance the federated learning model. Such a system generally consists of three entities, i.e., the server, helpers, and clients. Each helper collects the trained gradients from users nearby, aggregates them, and sends the result to the server for model update. Due to limited communication resources, only a portion of helpers can upload their aggregated gradients to the server, thereby necessitating a well design for helper scheduling and communication resources allocation. In this paper, we develop a training algorithm for H-FEEL which involves local gradient computing, weighted gradient uploading, and model updating phases. By characterizing these phases mathematically and analyzing the one-round convergence bound of the training algorithm, we formulate a problem to achieve the scheduling and resource allocation scheme. To solve the problem, we first transform it into an equivalent problem and then decompose the transformed problem into two subproblems: bit and sub-channel allocation problem and helper scheduling problem. For the first subproblem, we obtain a low-complexity suboptimal solution by using a four-stage method. For the second subproblem, we obtain a stationary point by using the penalty convex-concave procedure. The efficacy of our scheme is demonstrated via simulations, and the analytical framework is shown to provide valuable insights for the design of practical H-FEEL system.

To meet the demanding need for global connectivity, non-terrestrial networks can provide essential support to complement and extend the terrestrial infrastructure. However, the presence of non-terrestrial networks comes up with new demands. For example, a critical problem is satisfying the latency constraints of delay-sensitive applications while reducing the signaling consumption to save valuable channel resources in the random access stage. To address this issue, we propose a distributed access class barring (ACB) factor determination algorithm in this paper to satisfy the specific latency constraints of delay-sensitive applications and reduce the signaling exchange between user equipments (UEs) and the base station simultaneously. With this algorithm, UEs can estimate the required ACB factor only by their previous experiences in an estimation period rather than relying on the base station. Simulations show that the proposed distributive ACB factor determination algorithm can satisfy the requirements of delay-sensitive applications well when the delay constraint is not too strict. Besides, the influence of the variation of UEs and the estimation period is also discussed. It is found that the estimation period plays an important role in the accurate estimation of the ACB factor and needs to adapt to the changes in the number of UEs.

In this paper, we study a distributed reconfigurable intelligent surface-aided downlink multiple-input multiple-output non-orthogonal multiple access systems with random distributed users, where the signal alignment can be achieved within the paired users by controlling discrete phase shifts. In particular, a unified precoder and decoder are provided to cancel inter-cluster interference. To evaluate the performance of proposed framework, the near and far users’ channel statistics with Nakagami-m fading are derived. Further, the approximate expressions of average outage probability are obtained by utilizing the proposed channel statistics, and the corresponding diversity orders can also be obtained to acquire more insights. Finally, simulation results reveal that not only discrete phase shifts design by selecting a 3bit resolution can realize the near optimal performance but also the diversity gain can be significantly improved by increasing the number of reflection elements.

In this article, the performance of simultaneous wireless information and power transfer (SWIPT) in downlink (DL) Internet of Things (IoT) networks relying on the cell-free massive multiple-input–multiple-output (CF-mMIMO) technique is investigated. In such a network, the access points (APs) beam the radio-frequency (RF) energy toward IoT sensors during the DL wireless power transfer phase. Tight closed-form expressions for DL harvested energy (HE) and achievable rate with conjugate beamforming (CB) and normalized CB (NCB) are, respectively, derived, which enable us to analyze the behaviors of CB and NCB schemes in terms of both HE and achievable rate. Apart from this, to guarantee sensor fairness with respect to the HE and achievable rate, a max–min power control strategy based on the accelerated projected gradient (APG) method is proposed. Specifically, the proposed APG-based power control is able to determine the optimal solution in closed form and is more memory efficient than the convex-solver-based counterpart. These analytical results as well as the effectiveness of the proposed power control policy are verified by experimental simulations.

The unmanned aerial vehicle (UAV) technique provides a potential solution to scalable wireless edge networks. This paper uses two UAVs, with accelerated motions and fixed altitudes, to realize a wireless edge network, where one UAV forwards downlink signals to user terminals (UTs) distributed over an area while the other one collects uplink data. The conditional average achievable rates, as well as their lower bounds, of both the uplink and downlink transmission are derived considering the active probability of UTs and the service queues of two UAVs. In addition, a problem aiming to maximize the energy efficiency of the whole system is formulated, which takes into account communication related energy and propulsion energy consumption. Then, we develop a novel multi-agent Q-learning (MA-QL) algorithm to maximize the energy efficiency, through optimizing the trajectory and transmit power of the UAVs. Finally, simulation results are conducted to verify our analysis and examine the impact of different parameters on the downlink and uplink achievable rates, UAV energy consumption, and system energy efficiency. It is demonstrated that the proposed algorithm achieves much higher energy efficiency than other benchmark schemes.

The combination of reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) has been recognized as a critical method to improve the sixth generation networks performance. In this paper, a distributed RIS-aided downlink multiple-input multiple-output (MIMO) NOMA systems with discrete phase shifts are studied, where the channel directions from base station to paired users can be manipulated with the assistance of RISs by employing the concept of signal alignment. In order to ensure base station can flexibly serve some users with NOMA and others users with spatial division multiple access, a unified precoder and decoder are provide to cancel inter-cluster interference. Subsequently, the channel statistics over Nakagami-$m$fading channels are derived for near and far users. In particular, considering the cascade channel gain may exists two different cases, Beaulieu series is further adopted to characterize their corresponding cumulative distribution function. In what follows, the outage probability and ergodic rate for three situations within one cluster are derived by utilizing the obtained channel statistics, respectively. Based on the derived results, we also analyze the diversity order and high signal-to-noise ratio slope to provide essential insights into the considered systems. Finally, simulation results are presented to reveal that: 1) selecting the setting of 3-bits resolution can realize a near-aligned performance for our proposed systems; 2) the cascade channel statistic caused by RIS can be evaluated with any number of RIS element and any channel gain via Beaulieu series.

This paper investigates the secure transmission in downlink cell-free massive multiple-input multiple-output (MIMO) systems in the presence of an active multi-antenna eavesdropper (Eve) over Rician fading channels, assuming that each access point (AP) possesses multiple antennas which are connected with low-resolution digital-to-analog converters (DACs). Closed-form expressions of the achievable secrecy rate relied on the additive quantization noise model are derived. Based on these analytical results, we quantify the impacts of key system parameters, such as the antenna array number, DAC resolution, Rician$\mathcal K$-factor, and balance factor between data and artificial noise power on secrecy enhancement. Several interesting insights are attained by assuming that Eve can or cannot perfectly remove inter-mobile-terminal interference. Moreover, we also propose a power control algorithm that maximizes the achievable secrecy rate, which can be represented as a series of second-order-cone programs for which efficient solvers exist. All the theoretical analyses and the effectiveness of the proposed algorithm are corroborated by simulation experiments.

The concept of hierarchical federated edge learning (H-FEEL) has been recently proposed as an enhancement of federated learning model. Such a system generally consists of three entities, i.e., the server, helpers, and clients, in which each helper collects the trained gradients from clients nearby, aggregates them, and sends the result to the server for global model update. Due to limited communication resources, only a portion of helpers can be scheduled to upload their aggregated gradients in each round of the model training. And that necessitates a well-designed scheme for the joint helper scheduling and communication resources allocation. In this paper, we develop a training algorithm for the H-FEEL system which involves local gradient computing, weighted gradient uploading, and machine learning model updating phases. By characterizing these phases mathematically and analyzing one-round convergence bound of the training algorithm, we formulate an optimization problem to achieve the scheduling and resource allocation scheme. The problem simultaneously captures the uncertainty of the wireless channel and the importance of the weighted gradient. To solve the problem, we first transform it into an equivalent problem and then decompose the transformed problem into two subproblems:bit and sub-channel allocationandhelper scheduling, which are mixed integer nonlinear programming and continuous nonlinear problems, respectively. For the first subproblem, we obtain an optimal solution of exponential complexity and a suboptimal solution that has polynomial complexity. For the second subproblem, we obtain a closed-form optimal solution in a special case and a suboptimal solution in the general case. The efficacy of our scheme is amply demonstrated via simulations and the analytical framework is shown to provide valuable design insights for the practical implementation of the H-FEEL system.

Federated learning (FL) enables devices to update a global model while keeping the training data local, so that data privacy is protected. However, the local data of devices is usually non-independent and identically distributed (non-i.i.d.), which leads to performance degradation. This paper aims to address this issue by a client-selection approach. In particular, in consideration of balancing the label distribution of the selected clients, a new client selection method called grouping based scheduling (GS) scheme is proposed, with which clients are divided into several groups based on a new metric called group earth mover’s distance (GEMD). Experiment results show that the GS can improve the performance of FL algorithms, compared to the random scheduling scheme. An encryption method is further proposed to enhance privacy protection, which facilitates the application of the proposed GS scheme.

In this paper, we consider a hierarchical federated learning system and formulate a joint problem of edge aggregation interval control and time allocation to minimize the weighted sum of training loss and training latency. To quantify the learning performance, an upper bound of the average global gradient deviation, in terms of the edge aggregation interval, the time allocated for training, and the number of successfully participating devices, is derived. Then an alternative problem is formulated, which can be decoupled into two sub-problems and solved with two steps. In the first step, given the time allocation strategy, a relaxation and rounding method is proposed to optimize the edge aggregation interval. In the second step, with the results of the obtained edge aggregation interval and based on the convex optimization theory, an optimal time allocation can be evaluated. Simulation results show that the proposed scheme, compared to the benchmarks, can achieve higher learning performance with lower training latency.

Federated learning (FL) has become a promising solution to train a shared model without exchanging local training samples. However, in the traditional cloud-based FL framework, clients suffer from limited energy budget and generate excessive communication overhead on the backbone network. These drawbacks motivate us to propose an energy-aware hierarchical federated learning framework in which the edge servers assist the cloud server to migrate the local models from the clients. Then a joint local computing power control and client association problem is formulated in order to minimize the training loss and the training latency simultaneously under the long-term energy constraints. To solve the problem, we recast it based on the general Lyapunov optimization framework with the instantaneous energy budget. We then propose a heuristic algorithm, which takes the importance of local updates into account, to achieve a suboptimal solution in polynomial time. Numerical results demonstrate that the proposed algorithm can reduce the training latency compared to the scheme with greedy client association and myopic energy control, and improve the learning performance compared to the scheme in which the associated clients transmit their local models with the maximal power.

By offloading tasks from the mobile device (MD) to its nearby deployed access points (APs), each of which is connected to one server for task processing, computation offloading can strike a balance between MD's task execution delay and energy consumption in mobile edge computing (MEC) systems. Considering communication and computation dynamics in MEC systems, we aim to design online computation offloading mechanisms in this paper to minimize the time average expected task execution delay under the constraint of average energy consumption. Firstly, with known current channel gains between the MD and APs as well as available computing capability at MEC servers, we leverage the Lyapunov optimization framework to make an optimal one-slot decision on MD's transmit power allocation and MEC server selection. On this basis, we then consider a more realistic scenario, where it is difficult to capture current available computing capability at MEC servers, and combine the multi-armed bandit framework for an online learning based MEC server selection algorithm. Finally, through theoretical analyses and extensive simulations, we demonstrate the near-optimality and feasibility of our proposed algorithms, and present that our proposed algorithms fully explore the interplay between communication and computation with enriched user experience and reduced energy consumption.

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for real-time implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.

By exploiting the computing power and local data of distributed clients, federated learning (FL) features ubiquitous properties such as reduction of communication overhead and preserving data privacy. In each communication round of FL, the clients update local models based on their own data and upload their local updates via wireless channels. However, latency caused by hundreds to thousands of communication rounds remains a bottleneck in FL. To minimize the training latency, this work provides a multi-armed bandit-based framework for online client scheduling (CS) in FL without knowing wireless channel state information and statistical characteristics of clients. Firstly, we propose a CS algorithm based on the upper confidence bound policy (CS-UCB) for ideal scenarios where local datasets of clients are independent and identically distributed (i.i.d.) and balanced. An upper bound of the expected performance regret of the proposed CS-UCB algorithm is provided, which indicates that the regret grows logarithmically over communication rounds. Then, to address non-ideal scenarios with non-i.i.d. and unbalanced properties of local datasets and varying availability of clients, we further propose a CS algorithm based on the UCB policy and virtual queue technique (CS-UCB-Q). An upper bound is also derived, which shows that the expected performance regret of the proposed CS-UCB-Q algorithm can have a sub-linear growth over communication rounds under certain conditions. Besides, the convergence performance of FL training is also analyzed. Finally, simulation results validate the efficiency of the proposed algorithms.

This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is no efficient solution available. We first design a fast subgradient algorithm that can achieve near-optimal solution with reduced complexity. We then propose a deep neural network structure to learn the optimal beamforming based on convolutional networks and exploitation of the duality of the original problem. Two strategies of learning various dual variables are investigated with different accuracies, and the corresponding recovery of the original solution is facilitated by the subgradient algorithm. We also develop a generalization method of the proposed algorithms so that they can adapt to the varying number of users and antennas without re-training. We carry out intensive numerical simulations and testbed experiments to evaluate the performance of the proposed algorithms. Results show that the proposed algorithms achieve close to optimal solution in simulations with perfect channel information and outperform the alleged theoretically optimal solution in experiments, illustrating a better performance-complexity tradeoff than existing schemes.

Traffic delay is a key metric to measure the quality-of-service of next-generation wireless communication networks. In this paper, we consider a cloud radio access network architecture with a hierarchical structure of virtual controllers and multiple clusters of remote radio heads (RRHs). A high-level controller coordinates control plane decisions among local controllers and each local controller is in charge of a cluster of RRHs. Moreover, each local controller is equipped with one server for creating virtual machines (VMs) to execute the users' baseband processing tasks. Then, under the considered architecture, we aim to minimize the average delay consisting of task execution delay and signal transmission delay under total power constraint, by joint optimization of task scheduling and resource allocation, including VM allocation and RRH assignment. Due to the non-deterministic polynomial-time hardness (NP-hardness) of the joint optimization problem, we translate it into a matroid constrained submodular maximization problem and propose heuristic algorithms to find solutions with 0.5-approximation. Besides, both centralized and distributed control schemes are considered. In the centralized control scheme, all decisions about task scheduling, VM allocation, and RRH assignment are made in the high-level controller. But in the distributed control scheme, the high-level controller is only in charge of task scheduling based on graph theory and the local controllers are responsible for their respective VM allocation and RRH assignment. The simulation results show that the proposed algorithms can achieve better performance than the separate optimization of VM allocation and RRH assignment.

In this paper, we aim to minimize the network power consumption (NPC) in a downlink cloud radio access network. Not only the powers consumed at remote radio heads and fronthaul links for transmission, but also the power consumed at the baseband unit pool for computation is considered. We formulate a joint NPC minimization problem as a mixed timescale issue which can be regarded as a combination of two power minimization problems for computation and transmission, where the former is a slow timescale issue since task scheduling and computation resource allocation are usually executed in a large time space whereas the latter is a fast timescale issue due to the dependence on small-scale fading. To deal with timescale challenge, we introduce approximate results of the joint NPC minimization problem according to large system analysis and turn it into a slow timescale issue because the approximations are only dependent on statistical channel information. We propose an iterative coordinate descent algorithm based on branch-and-bound algorithm to find solutions to the joint NPC minimization problem. Numerical results show that the NPC decreases as the delay constraint increases but increases if the execution efficiency or computing capability of servers is degraded.

In this paper, we consider an uplink heterogeneous cloud radio access network (H-CRAN), where a macro base station (BS) coexists with many remote radio heads (RRHs). For cost savings, only the BS is connected to the baseband unit (BBU) pool via fiber links. The RRHs, however, are associated with the BBU pool through wireless fronthaul links, which share the spectrum resource with radio access networks. Due to the limited capacity of fronthaul, the compress-and-forward scheme is employed, such as point-to-point compression or Wyner-Ziv coding. Different decoding strategies are also considered. This paper aims to maximize the uplink ergodic sum-rate (SR) by jointly optimizing quantization noise matrix and bandwidth allocation between radio access networks and fronthaul links, which is a mixed time-scale issue. To reduce computational complexity and communication overhead, we introduce an approximation problem of the joint optimization problem based on large-dimensional random matrix theory, which is a slow time-scale issue, because it only depends on statistical channel information. Finally, an algorithm based on Dinkelbach's algorithm is proposed to find the optimal solution to the approximate problem. In summary, this paper provides an economic solution to the challenge of constrained fronthaul capacity and also provides a framework with less computational complexity to study how bandwidth allocation and fronthaul compression can affect the SR maximization problem.

In this paper, we consider the network power minimization problem in a downlink cloud radio access network (C-RAN), taking into account the power consumed at the baseband unit (BBU) for computation and the power consumed at the remote radio heads and fronthaul links for transmission. The power minimization problem for transmission is a fast time-scale issue, whereas the power minimization problem for computation is a slow time-scale issue. Therefore, the joint network power minimization problem is a mixed time-scale problem. To tackle the time-scale challenge, we introduce large system analysis to turn the original fast time-scale problem into a slow time-scale one that only depends on the statistical channel information. In addition, we propose a bound improving branch-and-bound algorithm and a combinational algorithm to find the optimal and suboptimal solutions to the power minimization problem for computation, respectively, and propose an iterative coordinate descent algorithm to find the solutions to the power minimization problem for transmission. Finally, a distributed algorithm based on hierarchical decomposition is proposed to solve the joint network power minimization problem. In summary, this paper provides a framework to investigate how execution efficiency and computing capability at BBU as well as delay constraint of tasks can affect the network power minimization problem in C-RANs.

In this paper, we consider the uplink transmission of a heterogeneous cloud radio access network, where a macro base station (BS) and many remote radio heads (RRHs) coexist to serve user equipment units. For cost-savings, only the BS is connected to the baseband unit (BBU) pool via fiber links, whereas the RRHs are associated with the BBU pool through wireless fronthaul links with limited capacities. By employing Wyner-Ziv (WZ) coding scheme, the RRHs first compress the received signal and then transmit the corresponding quantized version to the BBU pool. We derive deterministic equivalent for ergodic uplink sum rate and use this result to jointly optimize quantization noise matrix and bandwidth allocation between radio access networks and fronthaul links. An algorithm based on Dinkelbach's algorithm is also proposed to determine the optimal solutions. Numerical results show that as the normalized fronthaul capacity increases, more bandwidth is allocated to radio access networks. Besides, uniform quantization with WZ coding across RRHs can achieve near-optimal performance under high signal-to-quantization-noise ratio.

Small-cell networks and massive multiple-input multiple-output (MIMO) systems are regarded as important candidate techniques for 5G communication systems. This paper considers a heterogeneous network composed of a macrocell tier overlaid with an extremely dense tier of small-cells. In the network, the macrocell base station (BS), which applies massive MIMO, does not only serve macro user equipment units but also provides wireless backhaul for small-cell access points (APs). The wireless backhaul shares the same spectrum resource with radio access networks without creating extra spectrum resources. However, due to the densification of small-cells, the inter- and intra-tier interferences become severe. To mitigate the interferences, we use the regularized zero-forcing precoding combined with a projection technique is used at the BS in downlink (DL) to avoid interference to the APs in uplink (UL). Meanwhile, the joint linear minimum mean square error detection is applied in UL to mitigate the inter-tier interference. We derive deterministic expressions for ergodic UL and DL sum rates (SRs) by leveraging the large-dimensional random matrix theory. The expressions only depend on statistical channel information and can be used to optimize the bandwidth division between radio access links and wireless backhaul, as well as the time allocation between DL and UL operation intervals. Numerical results show that the deterministic SR equivalents are accurate and that the proposed resource allocation method can significantly improve system performance.

In this paper, we consider a heterogeneous network in which a macro-cell tier is overlaid with a very dense tier of small cells. The macro-cell base station (BS) that applies a massive MIMO scheme not only serves the macro user equipment but also provides a wireless backhual for small-cell access points (APs). These APs serve their associated small-cell user equipment. A reverse time division duplex transmission protocol is utilized. To avoid interference toward the APs in the uplink (UL), regularized zero-forcing precoding combined with a projection technique is utilized at the BS in the downlink (DL). We derive deterministic expressions for ergodic UL and DL sum rates (SRs) under the assumption that perfect channel state information is available and use these results to optimize the spectrum division between radio access links and the wireless backhaul. Simulation results suggest that the deterministic SR approximations are accurate and that system performance can be significantly improved through the optimization of spectrum division.