Millimeter Wave Channel Estimation via Exploiting Joint Sparse and Low-Rank Structures

Xingjian Li, Jun Fang, Hongbin Li, Pu Wang

Research output: Contribution to journalArticlepeer-review

135 Scopus citations

Abstract

We consider the problem of channel estimation for millimeter wave (mmWave) systems, where, to minimize the hardware complexity and power consumption, an analog transmit beamforming and receive combining structure with only one radio frequency chain at the base station and mobile station is employed. Most existing works for mmWave channel estimation exploit sparse scattering characteristics of the channel. In addition to sparsity, mmWave channels may exhibit angular spreads over the angle of arrival, angle of departure, and elevation domains. In this paper, we show that angular spreads give rise to a useful low-rank structure that, along with the sparsity, can be simultaneously utilized to reduce the sample complexity, i.e., the number of samples needed to successfully recover the mmWave channel. Specifically, to effectively leverage the joint sparse and low-rank structure, we develop a two-stage compressed sensing method for mmWave channel estimation, where the sparse and low-rank properties are respectively utilized in two consecutive stages, namely, a matrix completion stage and a sparse recovery stage. Our theoretical analysis reveals that the proposed two-stage scheme can achieve a lower sample complexity than a conventional compressed sensing method that exploits only the sparse structure of the mmWave channel. Simulation results are provided to corroborate our theoretical results and to show the superiority of the proposed two-stage method.

Original languageEnglish
Article number8122055
Pages (from-to)1123-1133
Number of pages11
JournalIEEE Transactions on Wireless Communications
Volume17
Issue number2
DOIs
StatePublished - Feb 2018

Keywords

  • angular spread
  • compressed sensing
  • jointly sparse and low-rank
  • mmWave channel estimation

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