有限块长 IRS 辅助系统中完美与非完美信道状态信息下的保密和速率最大化
Secrecy Sum-Rate Maximization in Finite Blocklength IRS-aided Systems With Perfect and Imperfect CSI
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中文总结 AI 辅助
针对 IRS 辅助 URLLC 系统，研究在完美、非完美及未知窃听者 CSI 下，联合优化波束成形与 IRS 反射元件以最大化总保密速率，提出基于 SCA 的低复杂度算法处理非完美 CSI 不确定性。
中文摘要 AI 辅助
物联网（IoT）的快速增长需要高效且安全的通信技术，以支持超可靠低时延（URLLC）通信应用。智能反射面（IRS）已成为一种有前景的解决方案，通过提升合法设备（Bob）的信号质量来增强物联网网络的保密性能，同时阻止窃听者（Eve）的截获。然而，考虑到多用户的不同信道和位置，确保多用户间的保密性具有挑战性，尤其是在物联网网络中常见的有限块长（FBR）约束下。本文研究了 IRS 辅助的 URLLC 系统在三种信道状态信息（CSI）场景下的保密性能：完美 CSI、非完美 CSI 以及窃听者 CSI 未知。我们首先构建了一个非凸优化问题，通过联合优化发射机的波束成形和 IRS 的无源反射元件，在满足 FBR 相关的时延和传输持续时间约束下，最大化系统的总保密速率（SSR）。对于非完美 CSI，通过基于逐次凸逼近（SCA）的方法将半无限不确定性约束转化为有限线性矩阵不等式（LMI），并证明所提算法能够以低计算复杂度收敛到局部最优解。
英文摘要
The rapid growth of the Internet of Things (IoT) requires efficient and secure communication technologies to enable ultra-reliable low-latency (URLLC) communication applications. Intelligent reflecting surfaces (IRS) have emerged as a promising solution to enhance IoT network secrecy performance by improving signal quality for legitimate devices (Bob), while thwarting the eavesdropper (Eve) interception. However, ensuring secrecy across multiple users, given their diverse channels and locations, is challenging, especially under finite blocklength (FBR) constraints, which are common in IoT networks. This paper investigates the secrecy performance of IRS-aided URLLC systems under three channel state information (CSI) scenarios: perfect CSI, imperfect CSI, and unknown eavesdropper's CSI. We first formulate a non-convex optimization problem to maximize the system's sum secrecy rate (SSR) by jointly optimizing the transmitter's beamforming and the IRS's passive reflective elements, while maintaining FBR-related latency and transmission duration constraints. For imperfect CSI, the semi-infinite uncertainty constraints are transformed into finite linear matrix inequalities (LMIs) via a successive convex approximation (SCA)-based approach, and the proposed algorithm is proven to converge to a locally optimal solution with low computational complexity.
发表机构
- University of Technology Sydney（悉尼科技大学）
- University of Liverpool（利物浦大学）
机构由 AI 辅助整理，请以论文原文为准。
