Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systems

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PubDate: Dec 2019

Teams: Beijing University of Posts and Telecommunications;University of California at Davis

Writers: Zhenyu Liu, Lin Zhang, Zhi Ding

PDF: Overcoming the Channel Estimation Barrier in Massive MIMO Communication Systems

Abstract

A new wave of wireless services, including virtual reality, autonomous driving and internet of things, is driving the design of new generations of wireless systems to deliver ultra-high data rates, massive number of connected devices and ultra low latency. Massive multiple-input multiple-output (MIMO) is one of the critical underlying technologies that allow future wireless networks to meet these service needs. This article discusses the application of deep learning (DL) for massive MIMO channel estimation in wireless networks by integrating the underlying characteristics of channels in future high-speed cellular deployment. We develop important insights derived from the physical radio frequency (RF) channel properties and present a comprehensive overview on the application of DL for accurately estimating channel state information (CSI) with low overhead. We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research.

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