arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2104.11541eess.SPcs.ITmath.IT
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可重构智能表面辅助MIMO系统的深度多阶段CSI获取

Deep Multi-Stage CSI Acquisition for Reconfigurable Intelligent Surface Aided MIMO Systems

Shen Gao, Peihao Dong, Zhiwen Pan, Geoffrey Ye Li

摘要:

针对可重构智能表面(RIS)中继无线信道估计中导频开销过大的问题,提出了一种基于导频的RIS中继无线信道估计方法。受深度学习在解决非线性映射问题方面的令人信服的把握的启发,所提出的方法仅激活RIS元素的一部分,并利用相应的级联信道估计来预测另一部分。通过合成深度神经网络(DNN),首先依次估计直接信道和活动级联信道,然后对非活动RIS元件进行信道预测。一个三阶段的训练策略,开发了这种合成DNN。从仿真结果来看,所提出的基于深度学习的方法在减少导频开销和保证可靠的估计精度方面是有效的。

英文摘要:

This article aims to reduce huge pilot overhead when estimating the reconfigurable intelligent surface (RIS) relayed wireless channel. Motivated by the compelling grasp of deep learning in tackling nonlinear mapping problems, the proposed approach only activates a part of RIS elements and utilizes the corresponding cascaded channel estimate to predict another part. Through a synthetic deep neural network (DNN), the direct channel and active cascaded channel are first estimated sequentially, followed by the channel prediction for the inactive RIS elements. A three-stage training strategy is developed for this synthetic DNN. From simulation results, the proposed deep learning based approach is effective in reducing the pilot overhead and guaranteeing the reliable estimation accuracy.

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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