To solve the energy efficiency problem in integrated communication, sensing and computing networks assisted by uncrewed aerial vehicles (UAVs), this article proposes a power minimization scheme based on energy harvesting. By incorporating an energy harvesting module into the system, both UAVs and mobile devices can dynamically capture radio frequency energy to replenish battery capacity, thereby reducing the dependence on external energy sources. To minimize the total system power consumption, this article designs a joint optimization framework. First, a task offloading strategy is constructed based on the Lyapunov optimization algorithm. By dynamically adjusting the tradeoff between task queue backlog and energy consumption delay, online optimization of task scheduling decisions is achieved. Second, the UAV trajectory optimization is modeled as a multistage decision problem and solved via dynamic programming by discretizing the flight path and applying the Bellman equation. Simulation results show that the proposed algorithm is significantly superior to greedy and genetic algorithms in minimizing total energy consumption. Moreover, the dynamic trajectory planning effectively balances flight energy consumption and energy harvesting efficiency, validating the robustness and practicability of the proposed scheme in complex scenarios.

Embodied Intelligence of Things (EIoT) is evolving Internet of Things (IoT) into intelligent agents that actively perceive and interact with physical environments. However, realizing self-sustaining EIoT systems faces critical bottlenecks in communication reliability, energy efficiency, and computational demands. This article presents a self-sustaining EIoT paradigm empowered by coordinated intelligent metasurfaces (IMs), which transform the wireless environment into a programmable service entity. We outline a three-layer architecture akin to a digital nervous system, including perception, communication, and decision layers for seamless agent-edge-cloud synergy. Furthermore, we develop a scalable channel acquisition framework based on tensor decomposition to overcome high-dimensional estimation challenges, and propose a SIM-enabled interference exploitation scheme for energy-efficient beamforming. We also highlight open challenges in semantic communication, security, and hardware impairments, charting future directions toward robust, autonomous EIoT ecosystems.

Stacked intelligent metasurfaces (SIMs) represent a breakthrough in wireless hardware by comprising multilayer, programmable metasurfaces capable of analog computing in the electromagnetic (EM) wave domain. By examining their architectural analogies, this article reveals a deeper connection between SIMs and artificial neural networks (ANNs). Leveraging this profound structural similarity, this work introduces a learnable SIM architecture and proposes a learnable SIM-based machine learning (ML) paradigm for sixth-generation (6G)-andbeyond systems. Then, we develop two SIM-empowered wireless signal processing schemes to effectively achieve multi-user signal separation and distinguish communication signals from jamming signals. The use cases highlight that the proposed SIM-enabled signal processing system can significantly enhance spectrum utilization efficiency and anti-jamming capability in a lightweight manner and pave the way for ultra-efficient and intelligent wireless infrastructures.

In this paper, we propose a secure content caching and delivery strategy to support multi-level video viewing services based on scalable video coding (SVC). With the appearance of eavesdroppers (Eves), the base layer (BL) and enhancement layers (ELs) encoded by SVC are cached in the macro-cell base station (MBS) and small-cell base stations (SBSs), respectively, using a probabilistic caching strategy. Power domain non-orthogonal transmission is utilized to deliver ELs for SBSs. We analyze the transmission and secrecy performance of the end user. Specifically, employing the tool of stochastic geometry, the successful transmission probabilities (STPs) from the serving MBS and SBS are derived, and the secrecy outage probability (SOP) in the worst case is also analyzed. All derived expressions are in complex forms, and the Gaussian-Chebyshev quadrature is adopted to approximate them in closed formsThe secrecy cache-aided data rate (SCADR) is derived and then maximized by jointly optimizing the random caching probabilities and power allocation coefficients for content caching and non-orthogonal transmission, respectively. The SCADR maximization problem with coupled variables is difficult to solve, and is divided into two sub-problems for tractability. The alternative optimization approach is adopted to obtain the optimal caching probabilities and power allocation coefficients. Simulation results validate the accuracy of derived STPs and SOP, and the approximation effectiveness of the Gaussian-Chebyshev quadrature is demonstrated. Results also show the proposed scheme has great potential to improve the system SCADR, as compared to existing caching and power allocation schemes.

The sixth-generation (6G) integrated sensing and communication (ISAC) paradigm is aimed at simultaneously delivering high-capacity communication and high-accuracy sensing for emerging intelligent network services. A major challenge arises from the separation of sensing measurements and processing nodes, as performing quantization during data transmission fundamentally limits the accuracy of sensing; however, this topic has not been adequately modeled in prior studies. To address this issue, we derive analytical approximations of the quantization noise introduced under uniform and non-uniform quantization schemes. Afterward, we establish a closed-form expression for the target detection probability that explicitly incorporates both the sensing signal-to-noise ratio (SSNR) and signal-to-quantization noise ratio. On the basis of these results, we propose an adaptive quantization transmission mechanism (AQTM) that dynamically selects quantization strategies and bit resolutions to balance sensing performance with spectrum efficiency (SE). The simulation results match the quantizer approximation results when the number of uniform and non-uniform quantization bits is greater than 3 and 5, respectively. The target detection probability achieved in the simulation is consistent with the corresponding closed-form expression. Compared with a 16-bit baseline, the introduction of the AQTM in the prototype reduces sensing data transmission resources by 62.54% at an uplink communication signal-to-interference-plus-noise ratio (SINR) of 15 dB and an SSNR of 6.8 dB and by 68.7% at an uplink communication SINR of 5 dB and an SSNR of 7.5 dB. These results validate our theoretical framework and show the practical value of the AQTM in significantly increasing spectral efficiency.

Unmanned aerial vehicles (UAVs) have emerged as pivotal components in next-generation communication systems due to their broad coverage and flexible deployment capabilities, enabling efficient connectivity with multiple ground users. Although the integration of nonorthogonal multiple access (NOMA) and multiple-input multiple-output (MIMO) technologies in UAV communications has attracted growing research interest, existing studies remain insufficient for jointly optimizing beamforming and power allocation, particularly in terms of fully capturing the complex coupling among decision variables. This study investigates the joint optimization problem of beamforming, power allocation, and UAV position optimization in UAV systems, where the UAV communicates with multiple ground users using NOMA and MIMO technologies. The core objective is to maximize the achievable transmission rate of the system while complying with a total power budget constraint. Owing to the inherent nonconvexity of the formulated problem and the intricate coupling among decision variables, the original problem is decomposed into three subproblems: beamforming, power control, and UAV placement optimization. To address these subproblems efficiently, we propose an angle-sector-based iterative optimization framework by invoking the alternating optimization technique under the NOMA-MIMO system. This strategy not only enhances overall spectral efficiency but also ensures reliable connectivity for users at greater distances while maintaining high communication quality for those in proximity. The simulation results demonstrate that the adopted user grouping strategy, which incorporates a group matching mechanism, yields notable improvements in resource utilization. Compared with other solution strategies, the proposed alternating optimization algorithm exhibits superior performance in terms of achievable rate enhancement, thereby validating its effectiveness and practical value in complex UAV-enabled NOMA-MIMO systems.

This paper presents a novel approach for wireless federated learning (WFL) that, for the first time, enables the aggregation of local models with mild to moderate errors under practical communication settings, which has to date been pre-vented by floating-point standards, e.g., IEEE binary32, and encryption. Specifically, we propose a new conversion from floating-point local models to fixed-point models on a layer basis, eliminating the need to transmit error-intolerant sign and exponent bits of floating-point numbers while accommodating variations in model layer widths and magnitudes. We also quantify how bit errors in the ciphertext affect the plaintext when symmetric encryption is employed for local model uploading, e.g., under Rayleigh, Rician, and Nakagami-m fading channels. Notably, these bit errors are leveraged to enhance the privacy of local models. We interpret the local model transmission process as a (lambda,is an element of)-Renyi Differential Privacy (DP) mechanism, where bit errors induced by noisy channels, controlled via transmit powers, and exacerbated by decryption act as DP perturbations. Experiments show the superiority of the new WFL to the status quo with higher training accuracy and lower communication overhead

Efficient task offloading in dynamic, collaborative multiagent networks is critical for latency-sensitive and energy-constrained applications, yet it presents a significant optimization challenge due to its combinatorial complexity and the non-Euclidean nature of network topologies. While traditional methods struggle with scalability and dynamics, existing learning-based approaches often fail to generalize across diverse network structures. The primary aim of this study is to propose and validate the hierarchical graph transformer (HGT), a novel neural architecture designed to learn powerful and generalizable policies for such complex, mixed-variable optimization problems. Instead of decoupling the problem, the HGT approaches the joint optimization in a holistic, end-to-end manner. By representing the network as a graph, HGT first employs edge-aware graph attention networks (EAGANs) to encode rich and localized physical-layer information into node embeddings. The graph transformer (GT) subsequently processes these embeddings to capture the global context and latent interagent competitive relationships. The resulting context-aware representations are then decoded into a complete action through a multihead architecture that is designed to handle hybrid discrete–continuous action spaces with global constraints. We demonstrate the efficacy of the HGT on the problem of joint latency and energy optimization in collaborative multiagent networks. Compared with strong heuristic, iterative, and recent learning-based baselines, the proposed policy achieves the best weighted utility and latency across the tested task-composition range while remaining highly competitive in energy, and it also shows strong zero-shot transferability to networks of varying scales and topologies. Our work signifies a tangible step towards creating more autonomous and efficient wireless systems, with direct implications for fields like swarm robotics and the Internet of Things.

Satellite-based Internet of Things (S-IoT) faces a fundamental trilemma: propagation delay, dynamic fading, and bandwidth scarcity. While layer-coded hybrid automatic repeat request (L-HARQ) enhances reliability, its backtracking decoding introduces age ambiguity, undermining the standard age of information (AoI) metric and obscuring the critical tradeoff between data freshness and transmission efficiency. To bridge this gap, we propose a novel cross-layer optimization framework centered on a new metric, the cross-layer age of error information (C-AoEI). We derive a closed-form expression for C-AoEI, explicitly linking freshness to system parameters, establishing an explicit analytical connection between freshness degradation and channel dynamics. Building on this, we develop a packet-level encoded L-HARQ scheme for multi-ground base station (GBS) scenarios and an adaptive algorithm that jointly optimizes coding and decision thresholds. Extensive simulations demonstrate the effectiveness of our proposed framework: it achieves 31.8% higher transmission efficiency and 17.2% lower C-AoEI than conventional schemes. The framework also proves robust against intercell interference and varying channel conditions, providing a foundation for designing efficient, latency-aware next-generation S-IoT protocols.

Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL). However, pushing topology-agnostic aggregation into heterogeneous, multi-task environments creates a fundamental bottleneck: it drives negative transfer and overconsensus bias (OCB). This paper introduces a personalized DSC framework that cuts off this cross-task interference. At the node level, a policy-driven multi-path routing mechanism separates task-specific features from shared representations to preserve local fidelity. Across the network, we deploy a "communicationwhile- aggregation" protocol. It calibrates a column-stochastic consensus matrix using task affinities. This limits the system to absorbing complementary knowledge while actively blocking mismatched parameter updates. To bound the convergence, we derive a unified Lyapunov drift analysis. We reveal a strict Ushaped trade-off: deeper topological mixing reduces variance but amplifies structural OCB. Resolving this tension yields a closed-form expression for the optimal aggregation depth. We evaluate the proposed framework on NYU-v2, where the results reveal a clear trade-off between insufficient aggregation and excessive topological mixing. At the analytically derived optimal aggregation depth, our method achieves a 4.77

We investigate the challenges of user pairing, power allocation, and bandwidth allocation problems in unmanned aerial vehicle (UAV) systems that employ nonorthogonal multiple access (NOMA) for communication with multiple ground users. The primary objective is to maximize the system’s achievable transmission rate while ensuring the users’ quality of service (QoS) requirements under a constrained total power budget. Considering the nonconvexity of the original problem and the interdependencies among multiple optimization variables, the problem is decomposed into three subproblems to optimize power and bandwidth allocation. To increase resource utilization and address user pairing challenges, a serial-optimized communication scheme is proposed, which leverages an optimized block coordinate descent (OP-BCD) method to sequentially solve the subproblems. Specifically, the power allocation strategy is optimized using an optimized deep Q-network (DQN) combined with a gradient ascent approach, whereas the intergroup bandwidth is optimized via a sequential least squares programming (SLSQP). Simulation results demonstrate that the proposed group matching method significantly enhances resource utilization and fairness compared to other user pairing strategies. Moreover, the proposed scheme effectively increases the system transmission rate and resource efficiency.

Orthogonal time frequency space (OTFS) modulation has emerged as a promising solution to mitigate the severe Doppler shift in low Earth orbit (LEO) satellite communications. However, the frequency-dependent Doppler shift induced by the high mobility of LEO satellites leads to the Doppler squint effect (DSE). This effect compromises the channel sparsity in the delay-Doppler (DD) domain, rendering existing channel estimation methods ineffective. To overcome this challenge, this paper proposes a DSE-resilient transmission scheme for cyclic prefix OTFS (CP-OTFS)-based LEO satellite systems. Specifically, we analyze the input-output relationship of the CPOTFS- based LEO satellite communication system and derive a DSE-aware representation of the satellite-terrestrial channel in the DD domain. To efficiently capture DSE-aware channel characteristics, we propose a novel OTFS frame structure that allows the energy distribution of the received signal to serve as prior information for channel estimation. Meanwhile, this frame structure strategically allocates pilot symbols to achieve uniform energy distribution and reduce the peak-to-average power ratio (PAPR), while imposing a time-domain waveform continuity constraint to suppress out-of-band emission (OOBE) caused by rectangular pulses. Based on the frame structure, we propose a prior-aided iterative channel reconstruction (PAICR) algorithm to mitigate the severe power leakage induced by DSE. The proposed algorithm iteratively extracts and removes dominant channel components using Doppler-domain received signal energy observations, with a convergence criterion ensuring reliable termination. Furthermore, a Cramer-Rao lower bound is derived to provide a theoretical benchmark for evaluating the algorithm's performance.

Accurate cascaded channel state information is pivotal for extremely large-scale intelligent reflecting surfaces (XL-IRS) in next-generation wireless networks. However, the large XL-IRS aperture induces spherical wavefront propagation due to near-field (NF) effects, complicating cascaded channel estimation. Conventional dictionary-based methods suffer from cumulative quantization errors and high complexity, especially in uniform planar array (UPA) systems. To address these issues, we first propose a tensor modelization method for NF cascaded channels by exploiting the tensor product among the horizontal and vertical response vectors of the UPA-structured base station (BS) and the incident-reflective array response vector of the IRS. This structure leverages spatial characteristics, enabling independent estimation of factor matrices to improve efficiency. Meanwhile, to avoid quantization errors, we propose an off-grid cascaded channel estimation framework based on sparse Tucker decomposition. Specifically, we model the received signal as a Tucker tensor, where the sparse core tensor captures path gain-delay terms and three factor matrices are spanned by BS and NF IRS array responses. We then formulate a sparse core tensor minimization problem with tri-modal log-sum sparsity constraints to tackle the NP-hard challenge. Finally, the method is accelerated via higher-order singular value decomposition preprocessing, combined with majorization-minimization and a tailored tensor over-relaxation fast iterative shrinkage-thresholding technique. We derive the Cramér-Rao lower bound and conduct convergence analysis. Simulations show the proposed scheme achieves a 13.6 dB improvement in normalized mean square error over benchmarks with significantly reduced runtime.

This paper introduces an interference-free multi-stream transmission architecture leveraging stacked intelligent metasurfaces (SIMs), from a new perspective of interference exploitation. Unlike traditional interference exploitation precoding (IEP) which relies on computational hardware circuitry, we perform the precoding operations within the analog wave domain provided by SIMs. However, the benefits of SIM-enabled IEP are limited by the nonlinear distortion (NLD) caused by power amplifiers. A hardware-efficient interference-free transmitter architecture is developed to exploit SIM’s high and flexible degree of freedom (DoF), where the NLD on modulated symbols can be directly compensated in the wave domain. Moreover, we design a frame-level SIM configuration scheme and formulate a max-min problem on the safety margin function. With respect to the optimization of SIM phase shifts, we propose a recursive oblique manifold (ROM) algorithm to tackle the complex coupling among phase shifts across multiple layers. A flexible DoF-driven antenna selection (AS) scheme is explored in the SIM-enabled IEP system. Using an ROM-based alternating optimization (ROM-AO) framework, our approach jointly optimizes transmit AS, SIM phase shift design, and power allocation (PA), and develops a greedy safety margin-based AS algorithm. Simulations show that the proposed SIM-enabled frame-level IEP scheme significantly outperforms benchmarks. Specifically, the strategy with AS and PA can achieve a 20 dB performance gain compared to the case without any strategy under the 12 dB signal-to-noise ratio, which confirms the superiority of the NLD-aware IEP scheme and the effectiveness of the proposed algorithm.

This paper proposes a novel adaptive dual-path framework for covert semantic communication (SemCom), which integrates covert information transmission with task-oriented semantic coding. Unlike conventional covert communication methods that embed hidden messages through power-domain signal superposition, our framework embeds covert data within task-specific features via semantic-level intrinsic encoding. This new architecture introduces dual encoding paths with adaptive block selection: an Explicit path for public task execution and a Stego path that jointly encodes both public and covert information through contrastive representation alignment. A Gumbel-Softmax enabled adaptive path selection mechanism dynamically activates network blocks based on task requirements. We formulate a multi-objective optimization framework that simultaneously ensures accurate semantic understanding and reliable covert transmission. We rigorously evaluate our framework’s security against a powerful, independently trained attacker. Experimental results on the Cityscapes dataset demonstrate a state-of-the-art level of covertness: our method suppresses the attacker’s detection accuracy to a near-random guessing level of 56.12%. This robust security is achieved while simultaneously maintaining superior performance on the primary semantic tasks compared to the baselines.

In sixth-generation (6G) networks, collaborative robotic networks enable connected robotic agents to perform tasks cooperatively, yet efficiently offloading tasks is critical to limit resource consumption in large-scale deployments. Compared to conventional mobile edge computing (MEC) approaches, collaborative robotic networks encounter unique challenges, including the high optimization computational complexity for large-scale robotic deployments and the necessity for coordinated, autonomous task assignment among agents. To address this, we formulate a multi-agent multitask offloading problem that jointly considers task assignment, resource management, offloading decisions, and computation frequency assignment, resulting in an NP-hard, nonconvex mixed-integer nonlinear programming (MINLP) problem. We then decompose the problem into resource allocation and task offloading subproblems, which are tackled through proximal policy optimization (PPO) and minimum-cost strategies respectively within a block coordinate descent (BCD) framework. Simulation results indicate that the proposed method achieves stable convergence, reducing latency by 61.95% and energy consumption by 11.82%, demonstrating strong applicability for large-scale collaborative robotic networks in future 6G scenarios, such as the industrial metaverse, autonomous swarm systems, and intelligent transportation.

This letter proposes an active reconfigurable intelligent surface (ARIS) assisted rate-splitting multiple access (RSMA) integrated sensing and communication (ISAC) system to overcome the fairness bottleneck in multi-target sensing under obstructed line-of-sight environments. Beamforming at the transceiver and ARIS, along with rate splitting, are optimized to maximize the minimum multi-target echo signal-to-interference-plus-noise ratio under multi-user rate and power constraints. The intricate non-convex problem is decoupled into three subproblems and solved iteratively by majorization-minimization (MM) and sequential rank-one constraint relaxation (SROCR) algorithms. Simulations show our scheme outperforms nonorthogonal multiple access, space-division multiple access, and passive RIS baselines, approaching sensing-only upper bounds.

Artificial intelligence-agent communication networks (ACNs) in the sixth-generation (6G) enable collaborative task execution among agents and butler. However, compared with traditional mobile edge computing (MEC), in ACNs, a large number of agents possess comparable computing capabilities, and task proportions need to be jointly determined rather than being predefined, causing high optimization complexity in large-scale deployment scenarios. In this article, we propose an effective framework to solve the large-scale coupled optimization problem under acceptable complexity. Specifically, we model the joint task allocation, resource allocation, task offloading, and computation frequency adjustment problem as an NP-hard nonconvex mixed-integer nonlinear programming (MINLP) problem and derive its lower bound through Lagrangian relaxation. We decompose the problem into resource allocation and task offloading subproblems, which are solved via proximal policy optimization (PPO) and minimum-cost models within a block coordinate descent (BCD) framework. Simulations demonstrate the tightness of the lower bound, achieving stable convergence for 30 agents while reducing latency from 150 to 50 ms and the energy consumption by 28.18%. Our algorithm has great potential for ACNs with many agents deployed in future 6G scenarios, such as autonomous vehicles, robotic swarms, and precision telemedicine.

Satellite systems have emerged as a key component of future 6G networks, where beam hopping is widely adopted to improve resource utilization under limited onboard power and bandwidth. However, due to the inherent timescale mismatch between beam scheduling and resource allocation, as well as the increased system complexity caused by wide-area coverage and flexible resource demands, efficient resource scheduling requires coordinated decision-making across multiple timescales while ensuring user-level QoS provisioning. This paper aims to address the three-dimensional coupling of resources in the beam, power, and frequency domains while satisfying heterogeneous user QoS requirements. The key idea is to establish a multitime-scale framework that decouples the original optimization problem in the temporal domain. Specifically, beam scheduling is performed over beam hopping time slots(BHTSs), while resource allocation are conducted over finer-grained user scheduling time slots. A verification approach is developed to provide theoretical justification for the proposed timescale decomposition and to ensure the consistency. Based on this framework, a weight-based search strategy is employed for beam hopping pattern (BHP) selection, and a block coordinate descent (BCD) method is adopted to iteratively optimize user-level resource allocation. The simulation results demonstrate that the proposed scheme significantly improves system throughput and service satisfaction quality compared to existing methods, while maintaining robust performance under heterogeneous traffic and delay-sensitive scenarios.

In this paper, we investigate joint covert communications and physical layer security (PLS) in non-orthogonal multiple access (NOMA) networks based on simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and friendly jamming. In the scenario with obstacles between the transmitter and users, a novel covert and secure transmission scheme, utilizing the reflection and transmission functions of STAR-RIS to serve the covert user (CU) and secrecy user (SU), respectively, is proposed. The average detection error probability (AMDEP) and detection threshold of Willie, are theoretically derived to characterize the overall performance of covert communications. Owing to the proposed active jamming mechanism, the detection accuracy of Willie is further reduced, and the system covertness is significantly improved. In addition, the transmission reliability of the system is evaluated by deriving the outage probabilities of the CU and SU. Aiming to characterize the secrecy performance, a closed-form expression for the secrecy outage probability (SOP) of the SU is derived, and the impact of different parameters on the secrecy performance is analyzed. Moreover, a power allocation strategy is designed to maximize the covert rate, while satisfying the covertness constraint of the CU, the secrecy outage constraint of the SU, and the quality of service (QoS) constraints of both the CU and SU. We solve this optimization by first narrowing the feasible range of the power allocation factor, then using bisection search to find the optimal value. Simulation results validate our analysis, and confirm that the proposed scheme outperforms existing schemes in terms of improving the covertness, secrecy and reliability of the system.