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arXiv 2601.03427eess.SP
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双Transformer辅助的分层深度强化学习用于鲁棒的RIS辅助近场通信

Dual-Transformer Aided Hierarchical Deep Reinforcement Learning for Robust RIS-Assisted Near-Field Communications

University of Ottawa（渥太华大学）；Ericsson（爱立信）

Mohammad Ghassemi, Han Zhang, Ali Afana, Akram Bin Sediq, Melike Erol-Kantarci

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17 pages, 11 figures,
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AI总结：

针对6G超大孔径阵列在近场通信中易受物理遮挡导致非视距的问题，提出双Transformer分层深度强化学习框架，集成光线追踪数字孪生和视觉Transformer预测遮挡，实现鲁棒的波束聚焦与RIS相位优化。

摘要：

第六代（6G）网络中部署超大孔径阵列（ELAA）预计将通信推向近场区域，其中球面波传播能够实现距离感知的波束聚焦，但极易受到物理遮挡导致非视距（NLoS）条件的影响。为解决这一固有脆弱性，可利用可重构智能表面（RIS）绕过这些遮挡，有效建立可靠的NLoS通信链路。在预期的部署场景中，由于大规模无源阵列带来的过高导频开销以及环境散射体的不可预测移动性，准确获取瞬时CSI并预测突发遮挡极具挑战性。为此，我们提出双Transformer分层深度强化学习（DT-HDRL）框架，该框架将两个专用Transformer模型与一个双时间尺度分层控制智能体相结合。第一个Transformer将光线追踪数字孪生先验与距离感知几何校正特征相结合，以实现快速精确的CSI估计；而互补的视觉Transformer（ViT）处理连续相机帧，以在链路质量下降之前预测即将发生的遮挡。这些预测输出随后直接馈入分层控制智能体。在该架构中，高层控制器处理慢时间尺度的遮挡预测，共同决定用户传输路径（视距（LoS）或RIS辅助NLoS）以及RIS的激活/休眠调度；而低层控制器利用快时间尺度的CSI估计，执行联合基站（BS）波束聚焦和RIS相位优化。

英文摘要：

The deployment of extremely large aperture arrays (ELAAs) in sixth-generation (6G) networks is expected to shift communications into the near-field regime, where spherical-wave propagation enables distance-aware beamfocusing but remains highly vulnerable to physical blockages that cause non-line-of-sight (NLoS) conditions. To resolve this inherent vulnerability, reconfigurable intelligent surfaces (RIS) can be utilized to circumvent these blockages and effectively establish reliable NLoS communication links. In envisioned deployment scenarios, accurately acquiring instantaneous CSI and predicting sudden blockages is profoundly challenging due to the prohibitive pilot overhead associated with massive passive arrays and the unpredictable mobility of environmental scatterers. To address this, we propose the Dual-Transformer Hierarchical Deep Reinforcement Learning (DT-HDRL) framework, which integrates two specialized transformer models with a two-timescale hierarchical control agent. The first transformer integrates a ray-tracing digital twin prior with distance-aware geometric correction features to yield rapid and precise CSI estimates, while a complementary vision transformer (ViT) processes sequential camera frames to forecast impending blockages prior to link degradation. These predictive outputs are then fed directly into the hierarchical control agent. Within this architecture, a high-level controller processes the slow-timescale blockage predictions to jointly dictate the user transmission path (line-of-sight (LoS) or RIS-assisted NLoS) and RIS active/sleep scheduling, whereas a low-level controller employs the fast-timescale CSI estimates to perform joint base station (BS) beamfocusing and RIS phase-shift optimization.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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