Learning-based Tracking of AoAs and AoDs in mmWave Networks

H. S. Ghadikolaei, H. Ghauch, C. Fischione
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引用次数: 5

Abstract

This paper considers a millimeter-wave communication system and proposes an efficient channel estimation scheme with a minimum number of pilots. We model the dynamics of the channel's second-order statistics by a Markov process and develop a learning framework to obtain these dynamics from an unlabeled set of measured angles of arrival and departure. We then find the optimal precoding and combining vectors for pilot signals. Using these vectors, the transmitter and receiver will sequentially estimate the corresponding angles of departure and arrival, and then refine the pilot precoding and combining vectors to minimize the error of estimating the channel gains.
毫米波网络中基于学习的aoa和aod跟踪
本文以毫米波通信系统为研究对象,提出了一种最少导频的有效信道估计方案。我们通过马尔可夫过程对通道的二阶统计动态建模,并开发了一个学习框架,从一组未标记的测量到达和离开角度中获得这些动态。然后找到导频信号的最优预编码和组合向量。利用这些矢量,发送端和接收端依次估计出相应的出发角和到达角,然后对导频预编码和组合矢量进行细化,使信道增益估计误差最小化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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