Satellite-terrestrial integrated networks with simultaneous wireless information and power transfer (SWIPT) provide wide-area connectivity and sustainable service support, but they also face serious security challenges due to the broadcast nature of satellite links and the possibility that an energy receiver may act as potential eavesdropper. To address this issue, this paper proposes a secure precoding design for a high-altitude platform (HAP)-assisted rate-splitting multiple access (RSMA) architecture under a quasi-static transmission model. Specifically, a cooperative direct and relay transmission (CDRT) framework is developed, in which the HAP assists the satellite transmission to improve the physical layer security for multi-user SWIPT services. By assuming the energy receiver near the target user as potential eavesdropper, we formulate a sum secrecy rate maximization problem subject to energy harvesting and transmit power constraints. To transform the original nonconvex optimization problem into a tractable convex problem, we employ techniques such as first-order Taylor expansion approximation, rank-one constraint relaxation, successive convex approximation, and semidefinite relaxation. Numerical results demonstrate that the proposed CDRT-RSMA scheme significantly outperforms conventional non-orthogonal and time-division multiple access schemes in terms of security performance.
Mengyan Huang, Xingwang Li, Chengjun Jiang et al.· IEEE Journal on Selected Are...· 0 citations
With the growing number of antennas in massive multiple-input multiple-output (MIMO) systems, robust and fast channel estimation becomes increasingly critical yet remains highly challenging. In this work, we propose a lightweight zero-shot self-supervised (ZS-SS) learning framework. It leverages non-local self-similarity in wireless channels to construct a channel-coefficient bank and generate training pairs via randomized and non-contiguous spatial permutations to decorrelate noise. These pairs then train a compact convolutional neural network (CNN) with a specially designed composite loss for robust channel estimation. To further improve adaptability and efficiency, we incorporate a meta-learning approach for fast inference time to dynamic channel environments. Simulations under Gaussian and representative non-Gaussian scenarios show that our method achieves up to 90% gains over traditional estimators and consistent improvements over state-of-the-art baselines, while running nearly 100 times faster. This demonstrates its practicality and suitability for real-time deployment in resource-limited massive MIMO systems.
Zijun Gao, Wenqiang Yi, Fatma Benkhelifa et al.· IEEE Transactions on Wireles...· 0 citations
Semi-grant-free non-orthogonal multiple access (SGF-NOMA) schemes group one grant-based (GB) user with multiple grant-free (GF) users into one time/frequency resource block (RB) to enhance spectral efficiency. Due to the sporadic traffic of GF users and the stringent quality of service (QoS) requirement of the GB user, the access collision problem becomes severe in SGF-NOMA. To solve this problem, this paper firstly designs an RB-based power pool (PP), which directs GF users to adjust their transmit power without disrupting the ongoing transmission of the internal GB user. After that, this work proposes an efficient multi-agent deep reinforcement learning (MA-DRL) framework to jointly optimize the PP and access strategy for maximizing the network throughput. In particular, this work exploits the fast-response feature of the traditional competitive MA-DRL and the increased-performance feature of the traditional cooperative MA-DRL to redesign a mixed reward system, which contributes to a hybrid MA-DRL mode for enhancing the learning efficiency of agents, i.e., GF users. We investigate the performance of the proposed algorithm at the network level and the NOMA-cluster level. We show that the proposed hybrid MA-DRL at the cluster level converges faster to an optimal solution than that at the network level but at an extra cost of user clustering. The numerical results show that the proposed scheme increases the successful decoded users by 42.38% when compared to the traditional schemes without learning capability. The proposed hybrid MA-DRL mode performs better than the pure competitive and cooperative MA-DRL modes, especially under a heavy-load network. It is able to achieve a 69% success rate of access in a time-varying environment with high packet arrival rates.
M. Fayaz, Sohail Abbas, Abdullah Alajmi et al.· IEEE Transactions on Cogniti...· 0 citations
A pinching-antenna system (PASS)-enabled multi-UAV integrated sensing and communication (ISAC) framework is proposed for adaptive downlink communications and UAV sensing. By jointly optimizing the pinching antenna (PA) activation, waveguide-level baseband precoding, and PA-level radiation power, the weighted sum of communication rates and sensing information rates is maximized, subject to the minimum-rate requirements of communication users (CUs) and sensing targets (STs). To address the resulting mixed-integer, high-dimensional, and strongly coupled non-convex problem, a genetic algorithm (GA)-based two-layer optimization (TLO) framework is developed. The PA activation is inferred by a GA-trained MLP policy in the outer layer, while the waveguide-level baseband precoding and PA-level radiation power are alternately optimized using weighted minimum mean-square error (WMMSE) and successive convex approximation (SCA) in the inner layer. Numerical results demonstrate that the proposed GA-TLO significantly improves both weighted sum rate and constraint satisfaction compared with conventional multiple-antenna architectures. Moreover, it achieves up to a 35% higher weighted sum rate than the fixed-activation PASS benchmark with BCD-based continuous optimization, while also substantially outperforming the fully uniform PASS and MIMO baselines.
Yanglin Hu, Tiankui Zhang, Xiaoxia Xu et al.· IEEE Transactions on Wireles...· 0 citations
The escalating complexity of deep neural networks introduces substantial challenges to deploying federated learning (FL) in resource-limited edge environments. To address these limitations, split federated learning (SFL) has emerged as a promising paradigm, alleviating client-side computational and communication burdens via strategic model splitting, and periodically aggregating client-side and server-side models consistent with the principles of FL. Nevertheless, existing SFL frameworks encounter significant performance degradation arising from data heterogeneity and imbalance, client heterogeneity, as well as constrained wireless resources. To overcome these issues, this paper introduces a novel data distribution deviation-aware split federated learning (DA-SFL) framework. DA-SFL dynamically adjusts aggregation weights according to the deviation of clients’ data distributions from a global distribution, effectively mitigating biases induced by data imbalance and heterogeneity. Furthermore, we theoretically establish the convergence bound of DA-SFL under a non-convex loss function setting, demonstrating that minimizing the data deviation in each training round enhances learning efficacy. Motivated by this, we formulate a mixed-integer nonlinear programming to optimize learning performance under long-term energy constraints. Leveraging the Lyapunov optimization framework, we decompose the problem into a series of tractable subproblems in each learning round, and propose efficient algorithms to find the client scheduling, adaptive cut layer selection, bandwidth allocation, and aggregation weighting policies. Extensive experimental evaluations conducted on Fashion-MNIST, CIFAR-10, and CINIC-10 datasets across diverse scenarios of data heterogeneity and imbalance demonstrate that DA-SFL significantly outperforms baselines regarding test accuracy, time and energy efficiency, while exhibiting notable robustness and scalability.
Chunfeng Xie, Zhixiong Chen, Wenqiang Yi et al.· IEEE Transactions on Communi...· 1 citation