Wenchao Xia

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.

Wireless power transfer (WPT) is a promising technology to solve the charging issue for autonomous aerial vehicle (AAV). However, it makes charging incentive coupled with the task offloading, which increases the difficulty of performance optimization in multiple-AAV multiple WPT transmitter networks. To alleviate this problem, we propose a deep reinforcement learning (DRL) based algorithm to optimize task offloading and charging incentive. We firstly utilize Stackelberg game to formulate the interaction between offloading and charging incentive. Then we simplify the model and transform it into a Markov decision process (MDP) task. On the basis of that, we propose a double deep Q network based algorithm to obtain the optimal policies of offloading and charging incentive. Finally, we examine the effectiveness of the proposed algorithm by extensive experiments.

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 nonuniform distributions, which impose significant challenges for achieving optimal UAV placement. Conventional fixed or predetermined 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 multiobjective 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 (MINLP) 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 (BCD) structure to decouple the highly coupled optimization variables into two subproblems, which are efficiently solved via a differential evolution (DE) algorithm and a successive convex approximation (SCA) 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.

This article 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 (QoS), this article 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 used for hypothesis testing to filter out ghost targets. Finally, simulation results show that the proposed algorithm achieves more precise angle-of-arrival (AoA) estimation of multiple targets than conventional single-BS sensing and enables high-precision localization by eliminating ghost targets.

With the rapid evolution of wireless communication networks, it has become increasingly desirable for base stations to provide not only data transmission but also environmental perception capabilities. To meet such dual demands, integrated sensing technologies have attracted growing attention, among which bistatic sensing provides enhanced target detection through bistatic geometric configuration. However, bistatic sensing performance critically depends on the sampling alignment between transceivers. In practice, frequency discrepancies between crystal oscillators lead to clock asynchronism, causing sampling misalignment and inter‐symbol aliasing, thereby inducing systemic biases in target localization accuracy. To address this issue, this paper establishes a clock‐asynchronism sensing model that incorporates inter‐symbol aliasing effects and reveals how sampling shifts distort range measurements without affecting angle estimation. A cross‐attention‐based neural network is further developed to accurately estimate the sampling offsets by fusing expert knowledge derived from cross‐correlation peak shifts with raw signal features. Simulation results demonstrate that the proposed expert‐guided cross‐attention network (ECA‐Net) achieves an exact‐match offset prediction accuracy of 96.3% and a remarkably low offset prediction mean absolute error (MAE) of 0.08 over a signal‐to‐noise ratio (SNR) range of −5 to 10 dB. Furthermore, the implementation of cyclic‐shift compensation effectively diminishes the localization error to a sub‐meter level in bistatic sensing scenarios.

In this paper, a cell-free massive multiple-input multiple-output (CF-mMIMO) system coexisting with device-to-device (D2D) pairs is investigated under ultra-dense wireless networks (UDWNs), aiming to accommodate the growing number of devices and escalating data traffic demands in future sixth-generation (6G) networks. To overcome the challenge of pilot shortage in UDWNs, non-orthogonal multiple-access (NOMA) and successive interference cancellation (SIC) technologies are employed, enabling users within the same cluster to share pilots efficiently. A closed-form expression for the spectral efficiency of conjugate beamforming receivers is derived, revealing that pilot contamination caused by pilot sharing severely degrades system performance. A novel hypergraph coloring (HGC)-based pilot assignment algorithm is proposed to address this issue, effectively capturing the complex cumulative interference among multiple users and D2D pairs. The algorithm constructs users’ interference hypergraphs using a user-centric access point (AP) selection strategy and allocates pilots based on interference weights within these hyperedges. Numerical results demonstrate that the proposed scheme significantly improves spectral efficiency, offering a promising solution for interference management in CF-mMIMO systems under UDWNs.

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 a K-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.

Monostatic backscattered system (MBS) networks are emerging as a key for Internet of Things (IoT), due to their passive and cost-effective nature. However, in multiple-device MBS networks, the superposition of backscattered signals leads to interference, reducing the overall sum rate. Additionally, broadcast backscattered communication is also vulnerable to eavesdroppers, posing significant security risks. This paper addresses these challenges by formulating an optimization problem that simultaneously maximizes the sum rate of reflected signals and ensures secure communication. We model both independent and cumulative interference using an oriented hypergraph and apply a channel assignment strategy based on hypergraph coloring. Furthermore, artificial noise (AN) is introduced to enhance the physical layer security. Numerical results validate our approach, demonstrating significant improvements in the sum rate of backscattered signals while maintaining secure transmission.

This study investigates a fluid antenna (FA)-enabled multi-static integrated sensing and communication (ISAC) system. Within this system, multiple transmit access points (TAPs) are employed to transmit ISAC signals, enabling simultaneous downlink communication and target sensing. At the same time, there is a receive access point (RAP) to capture the reflected sensing signals for target detection. All of the TAPs are equipped with FAs to leverage the spatial degree of freedom. We formulate a maximization problem of the worst-case detection probability of designated targets subject to the communication constraints of the users, to optimize both the beamforming matrix and antenna position vector (APV) of each TAP. To address the non-convexity of the problem, we use an alternating optimization approach over two subproblems: 1) beamforming optimization, and 2) optimization of APVs. For the first subproblem, we use the semi-definite relaxation (SDR) method to derive the optimal beamforming vectors given fixed APVs. Then the latter is solved by the second-order Taylor series expansion to approximate the function related to the APVs in a convex form. Simulation results demonstrate that the adoption of FA significantly enhances the sensing performance of our proposed system. Also, the proposed cooperative multi-static ISAC system coordinates communication and sensing tasks across various TAPs effectively, underscoring its potential for improving overall system performance.