用多智能体强化学习控制反射阵列，无需信道信息就能精准聚焦信号。
Bypassing the CSI Bottleneck: MARL-Driven Spatial Control for Reflector Arrays
- 通过多智能体强化学习将机械约束映射到虚拟焦点空间，实现自主控制。
- 在动态非视距环境下，信号增益最高达26.86 dB，优于传统方法。
- 对定位误差有强鲁棒性，适合实际部署的智能无线网络场景。
可重构智能表面（RIS）是下一代智能无线环境的核心，但其部署受限于信道状态信息（CSI）估计带来的巨大计算开销。为突破这一物理层瓶颈，本文提出一种原生人工智能、数据驱动的新范式，以空间智能替代复杂信道建模。论文构建了一个完全自主的多智能体强化学习（MARL）框架，用于控制机械可调金属反射阵列。通过将高维机械约束映射至低维虚拟焦点空间，采用中心化训练、分散执行（CTDE）架构，并使用多智能体近端策略优化（MAPPO），各智能体基于用户坐标学习协同波束聚焦策略，实现无须CSI的运行。在动态非视距（NLOS）环境中的高保真射线追踪仿真表明，该多智能体方法能快速适应用户移动，相比静态平面反射器信号增益提升最高达26.86 dB，且在空间选择性和时间稳定性上优于单智能体及受硬件限制的深度强化学习基线。关键的是，所学策略具备良好部署鲁棒性，在1.0米定位噪声下仍保持稳定信号覆盖。结果验证了MARL驱动的空间抽象是一种可扩展、高度实用的AI赋能无线网络路径。
原文摘要 · Abstract (English)
Reconfigurable Intelligent Surfaces (RIS) are pivotal for next-generation smart radio environments, yet their practical deployment is severely bottlenecked by the intractable computational overhead of Channel State Information (CSI) estimation. To bypass this fundamental physical-layer barrier, we propose an AI-native, data-driven paradigm that replaces complex channel modeling with spatial intelligence. This paper presents a fully autonomous Multi-Agent Reinforcement Learning (MARL) framework to control mechanically adjustable metallic reflector arrays. By mapping high-dimensional mechanical constraints to a reduced-order virtual focal point space, we deploy a Centralized Training with Decentralized Execution (CTDE) architecture. Using Multi-Agent Proximal Policy Optimization (MAPPO), our decentralized agents learn cooperative beam-focusing strategies relying on user coordinates, achieving CSI-free operation. High-fidelity ray-tracing simulations in dynamic non-line-of-sight (NLOS) environments demonstrate that this multi-agent approach rapidly adapts to user mobility, yielding up to a 26.86 dB enhancement over static flat reflectors and outperforming single-agent and hardware-constrained DRL baselines in both spatial selectivity and temporal stability. Crucially, the learned policies exhibit good deployment resilience, sustaining stable signal coverage even under 1.0-meter localization noise. These results validate the efficacy of MARL-driven spatial abstractions as a scalable, highly practical pathway toward AI-empowered wireless networks.
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