Cell-free massive multiple-input multiple-output (CF mMIMO) requires effective power control, but centralized processing relies on global instantaneous channel state information (CSI) and creates heavy fronthaul load. This letter focuses on low-overhead distributed power control under limited fronthaul capacity. We propose an information bottleneck (IB)-based policy that exchanges compact latent messages instead of raw local observations, and we train it using cluster-based federated learning to keep raw CSI local. The IB penalty provides an explicit information-rate proxy for online coordination, while robustness under imperfect CSI and data locality are evaluated as supporting effects rather than formal guarantees. Simulations show that the proposed method approaches a centralized benchmark with much lower effective overhead and stable behavior under channel estimation errors.
Yukun Ma, Jiayi Zhang, Zih-Yi Liu et al.· IEEE Wireless Communications...· 0 citations
This paper investigates the rate-splitting multiple access (RSMA)-enabled integrated sensing, communication, and power transfer (ISCPT) network assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Particularly, considering the inherent heterogeneous quality-of-service (QoS) requirements, communication users (CUs) are granted priority in information reception, and the legitimate sensing targets (STs) are also regarded as potential eavesdroppers to intercept the information of CUs. In order to meet the service demands of heterogeneous users of such a system while ensuring the physical layer security of CUs against wiretapping, we formulate a secrecy energy efficiency (SEE) maximization problem via jointly optimizing the transmit beamforming matrix, sensing matrix, STAR-RIS reflection/transmission coefficient matrix, power splitting (PS) ratio vector, and common rate allocation vector. Due to the non-convexity of the formulated problem and the challenges caused by imperfect channel state information (CSI) and dynamic wireless environment, an agentic-AI enabled optimization approach (ISCPT-SEE-AA) is proposed, which works in a closed-loop perception-decision–reward adaptation manner. Specifically, a Transformer-based channel refinement module is developed to mitigate the uncertainty induced by imperfect CSI. Meanwhile, a mixture-of-experts group relative policy optimization (MoE-GRPO) scheme is adopted to achieve adaptive decision-making under time-varying network conditions. Furthermore, a large language model (LLM)-aided reward configuration module with retrieval-augmented generation (RAG) is integrated to automatically configure and update reward parameters, thereby reducing manual tuning overhead and enhancing training stability. Extensive simulation results verify that the proposed ISCPT-SEE-AA outperforms conventional learning-based schemes significantly in terms of SEE and exhibits stronger robustness against CSI imperfections. Notably, it achieves performance close to that of the convex optimization-based benchmark with substantially lower online computational complexity.
Wanle Zhang, Ke Xiong, Rui Dong et al.· IEEE Transactions on Cogniti...· 0 citations
Space–air–ground integrated networks (SAGINs) break through the coverage and capacity limitations of terrestrial networks, providing seamless, high-bandwidth, and highly reliable communication services in remote areas. For end-to-end transmission in the Internet of Things (IoT), the store-and-forward architecture transmits data packets in a best-effort way, resulting in unpredictable latency. The exclusive occupation of links by flows leads to a significant drop in resource utilization. To satisfy deterministic end-to-end communication, this article proposes a quality-of-service (QoS)-aware end-to-end transmission architecture for SAGINs. It comprises two core types of links: access links assisted by high-altitude platforms (HAPs) and backhaul links built around the low-Earth orbit (LEO) satellite. End-to-end flows will be allocated time slots and transmitted hop-by-hop within a single frame to meet deterministic QoS requirements. In this architecture, an optimization problem is designed to maximize the number of successfully scheduled flows with different QoS requirements. To solve the non-deterministic polynomial hard (NP-hard) mixed-integer nonlinear program in dynamic scheduling scenarios, a joint access and transmission heuristic algorithm is proposed. Specifically, to ensure efficient transmission and improve resource utilization, concurrent end-to-end flows are first processed with conflict filtering, followed by hop-by-hop scheduling with the priority based on the least number of required time slots. The simulation results show that, compared to other baseline schemes, the proposed scheme achieves a significant improvement in scheduling performance.
Chen-Yan Lei, Yong Niu, Zhu Han et al.· IEEE Internet of Things Jour...· 0 citations
As computing demands continue to grow, a single server is no longer sufficient to meet user requirements, leading to increasing interest in multiserver collaborative edge computing. However, load imbalance is a prevalent issue in multiserver edge computing systems, resulting in inefficient resource utilization and degraded service quality. To address this issue, a multiserver collaborative edge computing architecture is established, and a joint optimization problem is formulated to minimize task latency and energy consumption under latency constraints. Considering the dynamic nature of task arrivals and queue evolution, the problem is further modeled as a Markov Decision Process (MDP). To characterize more accurately the dynamic evolution of computation queue states in the MDP during task transmission, an arrival order-based queue state (AOBQS) model is introduced to capture the impact of transmission delay on task execution order. Furthermore, as transmission delay alters the task execution order in the computation queue and thus invalidates the system’s Markov property, the task waiting time and a virtual queue are introduced to reconstruct the queue state. Based on the reconstructed state representation, a queue-aware twin-delayed deep deterministic policy gradient (QATD3) algorithm is developed to solve the task scheduling and resource allocation problem, thereby achieving load balancing in multiserver collaborative edge computing systems. Extensive simulation results demonstrate that the proposed method effectively achieves joint optimization of task latency and energy consumption, significantly improving overall system performance. Compared with baseline algorithms, the proposed QATD3 reduces average task delay by 24.53%, reduces normalized energy consumption by 16.06%, and improves average reward by 5.27%.
Jingzhe Wang, Si-yu Lin, Qingqing Pan et al.· IEEE Internet of Things Jour...· 0 citations