XL-RIS辅助毫米波MIMO系统的混合场稀疏信道表示与恢复

中文摘要 AI 辅助

极大规模可重构智能表面（XL-RIS）辅助通信被视为未来6G网络的关键使能技术。然而，由于级联信道维度极高，且远场与近场传播共存，XL-RIS辅助系统的混合场信道估计颇具挑战性。传统全维稀疏恢复方法需要庞大的级联字典，且存在严重的计算与存储负担。为应对这些挑战，本文开发了一种解耦稀疏字典表示与恢复的双时间尺度信道估计框架。利用基站（BS）与RIS侧信道的准静态特性，本文提出一种基于狄利克雷核的离网字典压缩（DK-ODC）方案用于稀疏表示，该方案可降低对应字典的维度，同时缓解BS侧的角度离网误差。此外，针对用户设备（UE）与RIS侧的动态信道，本文提出一种子空间感知增量变分贝叶斯学习（SI-VBL）算法，该算法可利用已识别的低维子空间与剪枝阈值实现稀疏信道的增量学习。分析与仿真结果证实，所提框架避免了全维贝叶斯恢复，在估计精度、计算复杂度与存储开销之间实现了良好的权衡。

英文摘要

Extremely large-scale reconfigurable intelligent surface (XL-RIS)-assisted communication is regarded as a key enabling technology for future 6G networks. However, hybrid-field channel estimation for XL-RIS-assisted systems is challenging due to the high-dimensional cascaded channel and the coexistence of far-field and near-field propagation. In this case, traditional full-dimensional sparse recovery methods require a large cascaded dictionary and suffer from severe computational and storage burdens. To address these challenges, we develop a double-timescale channel estimation framework that decouples sparse dictionary representation and recovery. Then, by exploiting the quasi-static property of the channel at the base station (BS) and RIS side, we propose a Dirichlet kernel-based off-grid dictionary compression (DK-ODC) scheme for sparse representation, which reduces the dimension of the corresponding dictionary as well as mitigates BS-side angular off-grid error. Furthermore, for the dynamic channel at the user equipment (UE) and RIS side, we propose a subspace-aware incremental variational Bayesian learning (SI-VBL) algorithm, which enables incremental learning of sparse channels by exploiting the identified low-dimensional subspace and pruning threshold. Analysis and simulation results confirm that the proposed framework avoids full-dimensional Bayesian recovery and achieves a favorable tradeoff among estimation accuracy, computational complexity, and storage overhead.

发表机构

• Beijing University of Posts and Telecommunications（北京邮电大学）
• Peng Cheng Laboratory（鹏城实验室）
• ZGC Institute of Ubiquitous-X Innovation and Applications（中关村泛在X创新与应用研究院）

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