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Gongpu Wang

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Open access 2026

Physical Layer Security for STAR-RIS-Assisted NOMA Backscatter Communications Against Multiple Eavesdroppers

Simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) have emerged as a transformative technology for achieving omnidirectional coverage in smart radio environments, enabling energy-efficient and spectrally efficient wireless communications. When integrated with ambient backscatter communication (AmBC) and non-orthogonal multiple access (NOMA), STAR-RIS facilitates the concurrent exploitation of ambient radio frequency signals and spectrum resources, offering significant potential for scalable Internet of Things (IoT) networks. This paper conducts a comprehensive analysis of the physical layer security performance of a STAR-RIS-assisted AmBC system employing NOMA in the presence of multiple eavesdroppers. Specifically, the STAR-RIS serves as an active backscatter device to enhance the backscatter link by mitigating direct link interference, while NOMA optimizes spectrum utilization through power-domain multiplexing. We derive closed-form expressions for critical performance metrics, including outage probability (OP), intercept probability, throughput, and energy efficiency, under realistic channel fading models. Asymptotic analysis of the OP is provided to reveal insights into high signal-to-noise ratio regimes. Furthermore, we investigate the impact of key system parameters. Numerical results validate that the proposed STAR-RIS-assisted AmBC-NOMA framework significantly enhances secrecy performance compared to conventional AmBC systems, demonstrating its robustness against eavesdropping threats and its suitability for secure IoT applications.

Yuhui Zhou, Gaojian Huang, Xingwang Li et al. · 0 citations
2026

Decentralized Cascaded Channel Estimation and Active User Detection for RIS-Assisted IoT Networks

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.

Yufei Cao, Heng Liu, Tao Yu et al. · 0 citations