2026· IEEE Transactions on Wireless Communications· Vol 25, pp. 20664-20680· 0 citations· 40 references
Computer Science
Abstract
Reconfigurable intelligent surface (RIS) is a promising technology for enhancing coverage and connectivity in Internet of Things (IoT) networks. However, the passive nature of RIS impedes the decoupling of the BS-RIS channel and RIS-user channels from the cascaded channel, making both channel estimation (CE) and active user detection (AUD) challenging in RIS-assisted IoT networks. To address this problem, this paper formulates the task of joint CE and AUD as a multi-layer sparse recovery problem by exploiting the sparsity structures in cascaded channels, as well as their joint scaling property whereby the cascaded channel of each user equipment (UE) can be normalized relative to that of an active reference UE through a diagonal matrix. Within the framework of variational Bayesian inference, the formulated problem is transformed into a Bethe free energy (BFE) minimization problem. To solve it effectively, we propose a low-complexity BFE-based hybrid message passing (BFE-HMP) algorithm that introduces various moment matching constraints. More importantly, two decentralized implementations of the proposed BFE-HMP algorithm are developed for the emerging decentralized base station (BS) architectures. Extensive simulation results validate the superiority of the proposed BFE-based algorithms in terms of normalized mean square error and the detection error probability over the existing state-of-the-art methods.
Integrated sensing and communication (ISAC) technology enables the joint integration of communication and sensing functions through the efficient utilization of spectrum/energy resources. By further incorporating with the reconfigurable intelligent surfaces (RISs), the wireless propagation environment of ISAC systems can be dynamically controlled, thereby enhancing the overall sensing and communication (SAC) performance. In this context, accurate channel estimation is a fundamental prerequisite for the efficient operation of the RIS-assisted ISAC systems. However, the strong coupling between SAC signals from direct and reflected channel links, as well as severe noise interference, limits the SAC channel estimation accuracy. This paper proposes a novel two-stage channel estimation scheme for RIS-assisted ISAC systems, where the direct and reflected SAC channels are respectively estimated in the first and second stages. To mitigate the negative effects of noise interference in each estimation stage, an extreme learning machine-based feature purification module is precisely designed, improving the quality of received SAC signals and generating purified channel features. Then, the dedicated deep neural network adopts the purified channel features to estimate SAC channels. Simulation results demonstrate that, under different signal-to-noise ratio conditions and channel dimensions, the proposed scheme achieves superior estimation accuracy and strong robustness compared to the benchmark methods.
Gang Liu, Yu Liu, Zelin Zheng· Electronics· 0 citations
This study proposes a novel FL framework where the CF-mMIMO participants are APs, and proposes a client selection strategy that prioritizes APs based on their average received signal power, showing competitive performance compared to baseline methods, while also addressing the scalability and privacy requirements of mMTC systems.
Ali Elkeshawy, W. Jaafar, H. Fares et al.· IEEE Transactions on Machine...· 0 citations
Reliable intelligent sensing over bandwidthlimited and noise-impaired wireless links is a key challenge for resource-constrained Internet-of-Things (IoT) agents. Existing semantic communication methods mainly optimize either image reconstruction or task inference, which limits their use in joint sensing scenarios under severe compression. This paper proposes R-SemCom, a robust joint sensing and task inference framework for resourceconstrained IoT agents. R-SemCom employs a heterogeneous dual-stream encoder with a Convolutional Neural Network (CNN) branch for local structural modeling and a Vision Transformer (ViT) branch for global semantic representation. An orthogonality-constrained decoupling mechanism is introduced to improve latent-space efficiency, while a serial reconstruction-guided inference strategy is designed to enhance task robustness under noisy channels. Experimental results on the CIFAR-10 dataset show that, at a compression ratio of 1/12 and under lowSNR conditions ranging from -5 dB to 5 dB, R-SemCom achieves the best overall performance among the compared methods and provides a more favorable trade-off between reconstruction quality and recognition accuracy.
Jie Liu, Yun Lin, Shihao Wang et al.· 2026 International Conferenc...· 0 citations
The integration of artificial intelligence (AI), reconfigurable intelligent surfaces (RIS), and integrated sensing and communication (ISAC) is emerging as a key enabler for intelligent, adaptive, and efficient wireless networks in the 6G era. This survey provides a comprehensive and well-structured overview of how AI is revolutionizing RIS-assisted ISAC by enabling dynamic, data-driven control over the radio environment. Through intelligent configuration of RIS, AI facilitates real-time adaptation of signal propagation paths, enhances sensing resolution, and ensures robust communication performance across diverse and dynamic network conditions. The discussion is organized across two major domains: terrestrial networks (TNs) and non-terrestrial networks (NTNs). In TNs, we examine four critical directions: (i) improving EE, (ii) strengthening security, (iii) achieving high sensing accuracy and low communication latency via joint optimization, and (iv) maximizing throughput. In NTN domain, especially within UAV-based platforms, we explore AI-RIS frameworks that support coordinated sensing and communication, reinforce link security in mobile and unpredictable environments, and enhance aerial network capacity through real-time RIS tuning. By bridging both terrestrial and aerial applications, this survey highlights how AI-driven RIS control enables environment-aware, low-latency, and secure ISAC operations. Finally, we identify and analyze key challenges such as managing trade-offs between sensing and communication, generalizing AI models across varied network scenarios, and ensuring scalable, low-complexity real-time deployment. The work concludes with forward-looking insights into future research directions essential for realizing robust and intelligent RIS-assisted ISAC architectures in 6G and beyond.
Shabeer Ahmad, Jinli Zhang, Manzoor Ahmed et al.· Journal of King Saud Univers...· 0 citations
To support Internet of Things (IoT) applications, next-generation networks are envisioned to provide both sensing functionality and low-power communications. This letter investigates the impact of co-channel interference (CCI) on a multi-user intelligent reconfigurable surface (IRS)-assisted non-orthogonal multiple access (NOMA) system with integrated sensing and backscatter communication (ISABC). Taking into account the effects of CCI signals on the communication users and the sensing receiver, as well as error propagation in successive interference cancellation (SIC), new closed-form expressions for communication performance, including ergodic rate, and sensing performance, including the probabilities of false alarm and detection, are developed. The analytical results are validated through Monte Carlo simulations. Numerical results demonstrate that CCI degrades both communication and sensing performance, which can be alleviated by increasing the number of reflecting elements and collected samples. Moreover, to gain deeper insights, asymptotic analysis is also obtained to evaluate the system performance. The effects of imperfect SIC and backscattered signals on the communication rate are also analyzed.
Abdulkarim Al Hanif, K. Altuwairgi, K. Hamdi· IEEE Wireless Communications...· 0 citations
Neural receivers have been recognized as a promising paradigm for the next-generation (NextG) communications. However, due to the reliance on a static network optimized for specific channel conditions, their generalization capability across diverse scenarios remains a significant challenge. To address this issue, this paper proposes a novel channel-adaptive neural receiver network (CARNet) based on the mixture-of-experts (MoE) framework. The proposed architecture employs multiple expert networks together with an efficient routing mechanism to enable signal detection in various scenarios. The experts are constructed via stacked ResNet blocks and specialize in robust signal detection within specific channel conditions, while the routing mechanism incorporates a lightweight representation learning module, which projects the coarse channel estimate into a low-dimensional latent embedding. The learned embedding characterizes task-relevant channel conditions and provides efficient guidance for accurate expert selection. Link-level simulation experiments demonstrate that the proposed CARNet achieves superior performance across diverse channel conditions.
Chao Jiang, Zhuo Xu, Yongli Yan· 0 citations
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