To address the challenge of co-channel interference in computation-intensive task offloading for vehicular edge computing (VEC), this paper proposes a novel task offloading and resource allocation algorithm based on interference-aware Soft Actor-Critic (IA-SAC). First, an interference aware (IA) module is designed to monitor the real-time signal-to-interference-plus-noise ratio (SINR) and load factor of each channel. This information is then integrated with the original state to construct a multi-dimensional decision space. On this basis, a multi-branch hybrid Actor network architecture is developed to enable coordinated sampling from a hybrid action space, which includes continuous power allocation, discrete channel and offloading mode selection. This structure overcomes the limitations of conventional reinforcement learning algorithms confined to a single action space. Ablation experimental results demonstrate the IA-SAC algorithm achieves a 7.72

Unmanned aerial vehicles (UAVs) experience random jitter due to mechanical disturbances and pressure variations during signal transmission, which severely degrades the performance of millimeter-wave (mmWave) systems. To address this challenge, this paper leverages the spatial beam-focusing property of lens by introducing a lens antenna array (LAA). We establish an LAA-based mmWave channel model for a UAV-enabled system and derive the complex channel gain in terms of error functions, enabling angle-dependent energy-focusing. Using this model, we obtain analytical expressions for the average received signal-to-noise ratio (SNR) when the LAA is employed at the receiver with maximum ratio combining (MRC). For benchmarking, we also develop the channel model for the conventional uniform linear array (ULA) employing a wide-beam receive combining scheme based on Zadoff-Chu sequences. Simulation results demonstrate that, compared to the ULA, the proposed LAA-based mmWave channel model achieves a significantly more concentrated energy distribution and a higher channel gain, indicating that the LAA-based model can effectively mitigate the impact of UAV jitter.

This article addresses the impact of asymmetric jitter, which arises from air turbulence or mechanical vibrations, on the 3-D attitude angles of an aerial intelligent reflecting surface (AIRS) mounted on an uncrewed aerial vehicle (UAV). To characterize these effects, a physics-based channel model based on the spherical wavefront assumption (SWA) is established. To mitigate the resulting performance degradation, we propose a novel continuous reflection phase based on the planar wavefront assumption (PWA), leveraging the concept of the Zadoff-Chu sequence. This design integrates a conventional reflection phase component with a spatial-frequency-bandwidth-dependent term, effectively broadening the bandwidth of the passive beam. Using the proposed continuous reflection phase, we analyze the normalized array gain function and average received signal power under UAV jitter, deriving approximate expressions for these metrics using Fresnel functions. The analysis demonstrates that the proposed reflection phase can expand the beam bandwidth to cover the potential range of jitter angles. Furthermore, a discrete reflection phase is designed based on the continuous version. Numerical results confirm that the beam bandwidth remains stable as the severity of asymmetric jitter increases, illustrating the effectiveness of the designed phases in mitigating UAV platform instability. Additionally, the results indicate that the proposed reflection phase can effectively compensate for performance degradation caused by UAV jitter.

This letter addresses the critical challenge of high-complexity channel estimation in extremely large-scale multiple-input multiple-output (XL-MIMO) systems operating under hybrid-field conditions, where both near-field and far-field propagation effects coexist. To overcome the computational bottlenecks of existing methods, we propose a linearized accelerated alternating direction method of multipliers (LADMM). By integrating linearization strategy into the ADMM framework, the proposed algorithm eliminates the need for computationally expensive high-dimensional matrix inversions that are typically associated with hybrid-field channel estimation. The algorithm features closed-form updates for all subproblems throughout the optimization process, leading to a substantial reduction in computational complexity. Simulation results demonstrate that the proposed approach achieves superior channel estimation performance, providing approximately 2 dB normalized mean square error (NMSE) gain compared with state-of-the-art techniques.

Motivated by the transformative potential of integrating uncrewed aerial vehicles (UAVs) and stacked intelligent metasurfaces (SIMs) into low-altitude aerial intelligent networks (LAINs), this article provides a comprehensive investigation of UAV-SIM technologies within integrated sensing and communication (ISAC) systems. By synergizing physical mobility with real-time electromagnetic wavefront manipulation, the UAV-SIM paradigm is poised to significantly enhance system performance across diverse operational scenarios. Despite these promising prospects, deploying UAV-SIM technology in LAINs faces substantial bottlenecks, particularly in the accurate characterization of complex air-to-ground propagation environments and the design of efficient optimization algorithms. To address these challenges, we propose a unified channel model-driven optimization framework tailored to ensure reliable quality of service for UAV-SIM-enabled LAINs. Utilizing the 3GPP TR 38.901 channel model as a realistic benchmark, this framework establishes a rigorous foundation for synthesizing and validating resource allocation strategies, ultimately yielding superior system capacity and reliability. Furthermore, to improve the robustness and efficiency of end-to-end information processing, we develop a deep reinforcement learning (DRL) scheme based on the mixture-of-experts architecture. This specialized approach facilitates the optimal management of network resources, thereby enabling highly efficient signal transmission. Finally, we outline open research directions and emerging trends to stimulate future investigations in the realm of UAV-SIM-enabled LAINs.

This paper develops a physically consistent precoding framework for extremely large antenna arrays (ELAAs), incorporating structural mutual coupling through a two-dimensional impedance network. To maintain scalability, we introduce a Neumann series approximation for the inverse coupling operator. Our analysis reveals that coupling-aware received power maximization reduces to a Hermitian rank-one quadratic form, whose optimum aligns with the dominant eigendirection of the effective coupling-shaped channel. This result indicates that both eigen-decomposition-based optimization and coupling-aware maximum ratio transmission (MRT) enhance power efficiency under mutual coupling, with the eigenmode design achieving superior performance. In addition, we further extend the analysis from the free-space path to the multipath scenario, demonstrating the robustness and adaptability of the proposed method under practical propagation conditions. Simulations confirm that structural coupling severely degrades conventional MRT, whereas the proposed eigenmode method with Neumann approximated coupling attains the highest received power among all considered schemes. The framework is interpretable, numerically stable, and readily implementable, offering practical guidance for energy-efficient near-field beamforming on ultra-large apertures.

Covert communication assisted by unmanned aerial vehicles (UAVs) can achieve a low detection probability in complex environments through auxiliary strategies, including dynamic trajectory planning and power management, etc. This paper proposes a dual-UAV scheme, where one UAV transmits covert information while the other one generates stochastic jamming to disrupt the eavesdropper and reduce the probability of detection. We propose a dual-mode jamming scheme which can efficiently enhance the average covert rate (ACR). A joint optimization of the dual UAVs’ flight speeds, accelerations, transmit power, and trajectories is conducted to achieve the maximum ACR. Given the high complexity and non-convexity, we develop a dedicated algorithm to solve it. To be specific, the optimization is decomposed into three sub-problems, and we transform them into tractable convex forms using successive convex approximation (SCA). Numerical results verify the efficacy of dual-mode jamming in boosting ACR and confirm the effectiveness of this algorithm in enhancing CC performance.

To effectively capture the inherent near-field effects and spatial non-stationarity across extremely large antenna arrays (ELAAs), this letter develops a novel analytical channel model tailored for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. In the proposed framework, spatial non-stationarity is first characterized using a 0-1 diagonal matrix, after which the composite XL-MIMO channel matrix is formulated as a linear combination of structured matrix components. Leveraging this representation, we perform a comprehensive performance analysis, evaluating key metrics including the downlink ergodic capacity, an efficiently computable upper bound derived from eigenvalue matrix, and the symbol error probability (SEP). The analysis demonstrates that the proposed scheme not only achieves performance comparable to benchmark methods but also substantially reduces computational complexity. Furthermore, the analysis reveals a pronounced performance degradation in the presence of increasing channel estimation errors.

Massive multiuser multiple-input multiple-output (MU-MIMO) is a cornerstone of 5G technology, offering significant gains in data rate and network capacity. However, its deployment is challenged by the high energy consumption and hardware costs of large antenna arrays at base stations. The use of 1-bit constant envelope (CE) precoding has emerged as an effective solution to reduce both power consumption and hardware complexity. Designing 1-bit CE precoding involves a non-convex combinatorial optimization problem with tightly coupled variables, which is computationally challenging. This paper proposes a novel algorithm based on the alternating direction method of multipliers (ADMM) and the heavy-ball (HB) method to efficiently solve this problem. Our approach provides an exact solution with notably lower computational complexity. Simulations demonstrate that the proposed algorithm outperforms existing schemes, achieving improved bit error rate (BER) and faster convergence.

This paper investigates cellular base station (BS)-enabled wireless systems featuring downlink integrated sensing and communications, a critical enabler for the low-altitude economy. To serve a sensing target and multiple communication users simultaneously, a hybrid active-passive reconfigurable intelligent surface (RIS) is employed. A key challenge arises from the inherent downtilt of cellular BS antennas, which often results in high-altitude targets (e.g., uncrewed aerial vehicles) being sensed predominantly via antenna sidelobes, thereby substantially constraining sensing performance. To jointly enhance both communication and sensing capabilities, we formulate a radar signal-to-noise ratio maximization problem. This formulation involves the co-optimization of beamforming vectors and RIS phase shifts, subject to constraints on transmit power, hybrid RIS power consumption, discrete RIS phase shifts, and communication user quality of service requirements. Addressing the non-convexity and NP-hardness of this problem, we propose a novel mixture-of-experts (MoE)-based proximal policy optimization (PPO) approach. Specifically, the MoE architecture is integrated within PPO to alleviate the learning complexity and mitigate gradient interference inherent in a single policy network. This is achieved by employing expert networks that specialize in distinct regions of the state space, orchestrated by a gating network that dynamically weights their contributions, thereby promoting accelerated convergence and enhanced optimization efficiency. Numerical results demonstrate that even for a target flying above the cellular BS, the deployment of a hybrid RIS with as few as 4 active elements, each providing an amplitude amplification of 2, yields a remarkable 18.5% improvement in sensing SNR, which translates to substantially enhanced sensing accuracy and reliability for low-altitude targets.

In this work, we investigate covert communication with a cognitive jammer (CJ) embedded in a public communication system. Specifically, while the transmitter continuously transmits public messages to the receiver, it may also transmit its covert messages opportunistically, and a CJ is utilized to assist the covert communication. To evaluate the covert performance, we derive the detection error probability (DEP) for both the CJ and the warden, as well as the average covert rate (ACR). Our analysis indicates that applying a CJ to assist covert communication embedded in a public communication system can outperform the scheme with an uninformed jammer or without a jammer in terms of covertness. A high public transmission rate can degrade the covertness; however, improving the detection accuracy of the CJ can contribute to enhanced performance in terms of both covertness and ACR.

In this paper, we examine covert communication in two-way relaying networks. We consider a scenario that two legitimate users forward messages to each other via a relay, while the relay will opportunistically transmit its own message to a covert user. To prevent covert communication, a legitimate user assumes the role of a monitor to detect relay's covert transmission. Through theoretical analysis, we derive expressions for the probability of detection error (PDE) and average covert rate (ACR), facilitating a thorough examination of the impact of system parameters on covert performance. Findings indicate that utilizing low, fixed-rate transmission, coupled with pronounced large-scale fading in the legitimate channel, markedly improves communication covertness. However, an ACR gain can be obtained from low to high fixed-rate region, when the large-scale fading of the legitimate channel is low.

In this paper, we propose an efficient joint precoding design method to maximize the weighted sum-rate in wideband intelligent reflecting surface (IRS)-assisted cell-free networks by jointly optimizing the active beamforming of base stations and the passive beamforming of IRS. Due to employing wideband transmissions, the frequency selectivity of IRSs has to been taken into account, whose response usually follows a Lorentzian-like profile. To address the high-dimensional non-convex optimization problem, we employ a fractional programming approach to decouple the non-convex problem into subproblems for alternating optimization between active and passive beamforming. The active beamforming subproblem is addressed using the consensus alternating direction method of multipliers (CADMM) algorithm, while the passive beamforming subproblem is tackled using the accelerated projection gradient (APG) method and Flecher-Reeves conjugate gradient method (FRCG). Simulation results demonstrate that our proposed approach achieves significant improvements in weighted sum-rate under various performance metrics compared to primal-dual subgradient (PDS) with ideal reflection matrix. This study provides valuable insights for computational complexity reduction and network capacity enhancement.

Reducing energy consumption while maintaining tracking accuracy is a significant challenge in the context of underwater target tracking using wireless sensor networks. The allocation of digitalizing bits for each sensor node is pivotal in influencing both tracking accuracy and energy efficiency. However, traditional optimization methods for bit allocation in sensing quantization are constrained by environmental dependencies, inflexibility, and prolonged computation times. To address these issues, we propose an innovative bit allocation algorithm based on deep reinforcement learning (DRL) for target tracking in underwater sensor networks. This algorithm focuses on adaptively quantizing and transmitting sensor data to optimize energy usage. Initially, we establish a quantitative relationship between bit allocation, tracking accuracy, and energy consumption within these sensor nodes. Building on this foundation, we develop a bit allocation model to minimize energy consumption while adhering to accuracy constraints. We present a novel transformer-based DRL network, the transformer-double dueling deep Q-network, to optimize bit allocation strategies. This network facilitates the offline-trained reinforcement learning agent to derive optimal sensor quantization bit allocations in real time. Our experimental results demonstrate that the proposed algorithm effectively achieves a balance between target tracking precision and digital bit consumption, significantly enhancing real-time computational efficiency.

This paper investigates an intelligent reflective surface (IRS)-assisted cellular network, designed to enhance the downlink coverage for an unmanned aerial vehicle (UAV). We model a composite communication link from a terrestrial base station (BS) to the UAV, which is comprised of a direct path and a secondary path established via the IRS. To capture the impact of practical antenna directionality, we adopt a model developed by the third generation partnership project for the BS antenna radiation pattern. Furthermore, we formulate the optimization of the IRS as a passive beamforming problem, with the objective of maximizing the achievable rate by configuring the phase shifts of its reflective elements. To tackle this nonconvex problem, we propose an efficient iterative algorithm based on successive refinement to find a near-optimal solution for the IRS phase shifts. Numerical results validate that our framework significantly improves UAV communication quality. Our numerical results demonstrate that an IRS, with merely 16 elements and a UAV height of 300 m, can achieve a remarkable 100% improvement in the achievable rate. This finding underscores the potential of IRS technology to provide a robust and scalable solution for integrating UAVs into future low-altitude networks.

This paper investigates a UAV-assisted wireless communication system that integrates optical wireless communication (LiFi) with conventional RF links to enhance network capacity in crowd-gathering scenarios. While the unmanned aerial vehicle (UAV) serves as a flying base station providing downlink transmission to mobile ground users, the study places particular emphasis on the role of LiFi as a complementary physical layer technology within heterogeneous networks—an aspect closely connected to optical and photonics advancements. The proposed system is designed for environments such as theme parks and public events, where user groups move collectively toward points of interest (PoIs). To maintain quality of service (QoS) under dynamic mobility, we develop a joint optimization framework that simultaneously designs the UAV’s flight path and resource allocation over time. Given the problem’s non-convexity, a block coordinate descent (BCD) based approach is introduced, which decomposes the problem into power allocation and path planning subproblems. The power allocation step is solved using convex optimization techniques, while the path planning subproblem is handled via successive convex approximation (SCA). Simulation results demonstrate that the proposed algorithm achieves rapid convergence within 3–5 iterations while guaranteeing 100% heterogeneous QoS satisfaction, ultimately yielding nearly 15.00 bps/Hz system capacity enhancement over baseline approaches. These findings motivate the integration of coordinated three-dimensional trajectory planning for multi-UAV cooperation as a promising direction for further enhancement. Although LiFi is implemented in free-space optics rather than fiber-based sensing, this work highlights a relevant optical technology that may inspire future cross-domain applications, including those in optical sensing, where UAVs and reconfigurable optical links play a role.

Intelligent reflecting surface (IRS) technology is emerging as a major innovation in wireless communications due to its unique advantages. It takes advantage of a large number of low-cost passive elements with adjustable phase-shift capabilities, which can reflect incident signals independently. When these elements work together, IRSs can achieve 3-D passive beamforming without the use of any transmit radio frequency (RF) link. This mechanism not only enhances spectrum efficiency but also reduces the energy consumption of communication systems. Based on this advantage of IRSs, this article explores IRS-assisted multiuser wireless systems, where IRSs are cleverly deployed between a multiantenna access point (AP) and multiple single-antenna users. By jointly optimizing the transmission beamforming of active antenna array of the AP and the passive phase-shift beamforming of the IRSs, the objective is to minimize the total transmit power of APs, while ensuring that each user's signal-to-interference-plus-noise ratio (SINR) requirement is met. The optimization problem is challenging to solve, as it is a nonconvex quadratically constrained quadratic programming problem, and the optimization variables are highly coupled with each other. To address this challenge, a low-complexity and efficient optimization algorithm, known as the linearized alternating direction multiplier method (LADMM) algorithm is proposed to address the transmit power minimization problem. The simulation results indicate that the LADMM algorithm provides superior system performance and significantly lower complexity compared to other existing methods.

Millimeter wave (mmWave) communication and intelligent reflecting surface (IRS) are both promising solutions for the next generation of wireless communication technology. In this article, the near-field channel models based on the spherical wave assumption and parabolic wave assumption are proposed for double-IRS assisted mmWave communication systems, where the parabolic wave model serves as an approximation of the spherical wave model. Under the parabolic wave assumption, the directional-dependant rayleigh distances and near-field reflection phases are investigated, and explicit expressions for the normalized array gains and path power gains are obtained using the sine integral. Based on the obtained path power gains, the suboptimal IRS rotation angles are also explored. We first consider the range limits of the activation conditions for the rotation angles, and then take the derivative of the explicit expression for the amplitude of IRS-assisted link to obtain the suboptimal rotation angles for different links. Using the designed near-field reflection phases, the approximate achievable rate is obtained and verified by numerical results. From these results, an interesting finding emerges: under reasonable IRS dimensions, the performance gains of two IRSs working noncooperatively are significantly greater than those of two IRSs working together. In conclusion, this work highlights the importance of IRS rotation angles and the intrinsic nature of double-IRS assisted communications.

In this paper, we study the comprehensive effect of outdated channel state information (CSI) on the performance of covert communication in a relay network with relay selection. We will examine the scenario in which the selected relay secretly transmits its own message to the destination while forwarding the source’s message. We first derive the calculation formulas of detection error probability (DEP) and average covert rate (ACR) respectively, and then reveal the influence of different system parameters on the performance of the covert communication system through theoretical analysis. Our analysis indicates that the outdated CSI causes an increase in DEP, which can facilitate the covert communication. Applying relay selection can also cause an increase in DEP when the correlation coefficient is low. With the increase of the fixed rate of the source, the application of relay selection can increase the ACR.

Reconfigurable intelligent surfaces (RISs) have attracted significant attention due to their capability of establishing virtual line-of-sight (VLoS) links. This article proposes a channel model for RIS-assisted millimeter wave (mmWave) communication systems that incorporates the effective aperture (EA) of RIS elements, the horizontal and vertical rotation angles of the RIS, the servomechanism limitations associated with these rotation angles, and the activation criteria to constrain the feasible range of these rotation angles. To enhance the system performance, we jointly optimize the horizontal and vertical rotation angles of the RIS with the objective of maximizing the signal-to-noise ratio (SNR) based on the proposed model. An alternating optimization (AO) algorithm is developed to solve this problem efficiently. Specifically, the original optimization problem is decomposed into two subproblems corresponding to independent optimization of the horizontal and vertical angles, and closed-form optimal solutions are derived for each subproblem. Updating iteratively these closed-form solutions yields suboptimal horizontal and vertical rotation angles for the RIS. Moreover, a global optimal solution of closed-form to the original optimization problem is derived for the special case where the base station (BS) is positioned directly in front of the RIS. Numerical results demonstrate that the suboptimal rotation angles obtained by the AO algorithm closely approximate the optimal solutions. Furthermore, the proposed AO algorithm, which jointly optimizes both rotation angles, significantly outperforms the methods that individually optimize either the horizontal or vertical angle.