用神经演化方法联合优化RIS相位与用户功率，提升定位精度。
Joint Active RIS Configuration and User Power Control for Localization: A Neuroevolution-Based Approach
- 结合神经演化与监督学习，实现RIS和用户功率协同调控。
- 仅需单比特反馈，支持离散响应的RIS元件，定位误差更低。
- 适合需要低反馈开销的智能反射面定位系统设计者。
本文研究基于可重构智能表面（RIS）的用户定位问题。采用从基站到用户的反馈链路，实现上行导频传输的动态功率控制。提出一种基于神经演化（NE）与监督学习融合的多智能体联合优化算法，用于RIS相位配置与用户发射功率的协同控制。该方案仅需单比特反馈消息即可完成上行功率控制，适用于具有离散响应特性的RIS元件，数值结果表明其性能优于指纹匹配、深度强化学习基线以及基于反向传播的定位估计器。
原文摘要 · Abstract (English)
This paper studies user localization aided by a Reconfigurable Intelligent Surface (RIS). A feedback link from the Base Station (BS) to the user is adopted to enable dynamic power control of the user pilot transmissions in the uplink. A novel multi-agent algorithm for the joint control of the RIS phase configuration and the user transmit power is presented, which is based on a hybrid approach integrating NeuroEvolution (NE) and supervised learning. The proposed scheme requires only single-bit feedback messages for the uplink power control, supports RIS elements with discrete responses, and is numerically shown to outperform fingerprinting, deep reinforcement learning baselines and backpropagation-based position estimators.
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