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arXiv 2602.06313eess.SPcs.ITmath.IT
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混合场联合信道和可见区域估计用于RIS辅助通信

Hybrid-Field Joint Channel and Visible Region Estimation for RIS-Assisted Communications

Zhejiang University(浙江大学);Air Force Engineering University(空军工程大学);Southern University of Science and Technology (SUSTech)(南方科技大学)

Xiaokun Tuo, Ming-Min Zhao, Xiang Wang, Changsheng You, Min-Jian Zhao

AI总结:

本文提出了一种混合场联合信道和可见区域估计方法,用于RIS辅助通信中的非平稳级联信道估计问题,通过降维稀疏双线性表示和涡轮结构联合贝叶斯估计提升估计精度。

摘要:

在可重构智能表面(RIS)辅助毫米波(mmWave)通信系统中,大规模RIS引入了显著的几何效应,导致远场和近场传播共存。此外,随机阻塞在RIS阵列上诱导了空间非平稳性,导致不同散射体的信号仅照亮部分区域,称为可见区域(VRs)。这使得传统远场和全可见阵列基的信道模型不足,并使信道估计特别具有挑战性。本文研究了混合场传播环境中非平稳级联信道估计问题,其中RIS基站(BS)链路在远场运行,而用户-RIS链路表现出近场特性并具有部分可见性。为了解决由此产生的高维和耦合估计问题,通过利用级联信道的结构特性,开发了一种降维稀疏双线性表示。特别是,提出了一种字典压缩技术,用低维极域字典加权可见矩阵来表示高维耦合字典,从而显著减少问题规模。基于此表示,提出了一种涡轮结构联合贝叶斯估计(TS-JBE)方法,以同时估计信道增益、VRs和非网格参数,从而避免现有顺序方法中固有的误差传播。仿真结果表明,所提出的方法在估计精度方面显著优于现有方法。

英文摘要:

In reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems, the large-scale RIS introduces pronounced geometric effects that lead to the coexistence of far-field and near-field propagation. Furthermore, random blockages induce spatial non-stationarity across the RIS array, causing signals from different scatterers to illuminate only partial regions, referred to as visible regions (VRs). This renders conventional far-field and fully visible array-based channel models inadequate and makes channel estimation particularly challenging. In this paper, we investigate the non-stationary cascaded channel estimation problem in a hybrid-field propagation environment, where the RIS-base station (BS) link operates in the far-field, while the user-RIS link exhibits near-field characteristics with partial visibility. To address the resulting high-dimensional and coupled estimation problem, a reduced-dimensional sparse bilinear representation is developed by exploiting the structural characteristics of the cascaded channel. In particular, a dictionary compression technique is proposed to represent the high-dimensional coupled dictionary using a low-dimensional polar-domain dictionary weighted by a visibility matrix, thereby significantly reducing the problem scale. Based on this representation, a turbo-structured joint Bayesian estimation (TS-JBE) approach is proposed to simultaneously estimate the channel gains, VRs, and off-grid parameters, thereby avoiding error propagation inherent in existing sequential methods. Simulation results demonstrate that the proposed method significantly improves the estimation accuracy compared with existing approaches.

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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