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arXiv 2202.11997eess.SP
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基于监督学习的RIS辅助通信稀疏信道估计

Supervised Learning based Sparse Channel Estimation for RIS aided Communications

Dilin Dampahalage, K. B. Shashika Manosha, Nandana Rajatheva, Matti Latva-aho

摘要:

可重构智能表面(RIS)可用于在直接路径受损时建立视距(LoS)通信,这在毫米波(mmWave)网络中是常见的。在本文中,我们专注于这样的网络的上行链路信道估计。我们制定这作为一个稀疏信号恢复问题,通过离散的到达角(AoAs)在基站(BS)。单独考虑并网和离网AoA。在并网的情况下,我们提出了一种算法来估计直接和RIS信道。基于监督学习训练的神经网络用于估计离网情况下的剩余角度以及这两种情况下的AoA。数值结果表明,在这两种情况下,所提出的算法的性能增益。

英文摘要:

An reconfigurable intelligent surface (RIS) can be used to establish line-of-sight (LoS) communication when the direct path is compromised, which is a common occurrence in a millimeter wave (mmWave) network. In this paper, we focus on the uplink channel estimation of a such network. We formulate this as a sparse signal recovery problem, by discretizing the angle of arrivals (AoAs) at the base station (BS). On-grid and off-grid AoAs are considered separately. In the on-grid case, we propose an algorithm to estimate the direct and RIS channels. Neural networks trained based on supervised learning is used to estimate the residual angles in the off-grid case, and the AoAs in both cases. Numerical results show the performance gains of the proposed algorithms in both cases.

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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