Evaluation of Low Complexity Massive MIMO Techniques Under Realistic Channel Conditions

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University of York
  • University of Surrey
  • Samsung Cambridge Solution Centre

Abstract

In this paper, a low complexity massive multiple-input multiple-output technique is studied with a geometry-based stochastic channelmodel, calledCOST2100 model. We propose to exploit the discrete-time Fourier transform of the antenna correlation function to perform user scheduling. The proposed algorithm relies on a tradeoff between the number of occupied bins of the eigenvalue spectrum of the channel covariance matrix for each user and spectral overlap among the selected users. We next show that linear precoding design can be performed based only on the channel correlation matrix. The proposed scheme exploits the angular bins of the eigenvalue spectrum of the channel covariance matrix to build up an "approximate eigenchannels" for the users. We investigate the reduction of average system throughput with no channel state information at the transmitter (CSIT). Analysis and numerical results show that while the throughput slightly decreases due to the absence of CSIT, the complexity of the system is reduced significantly.

Details

Original languageEnglish
Pages (from-to)9297-9302
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Volume68
Issue number9
Publication statusPublished - Sep 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • COST 2100 channel model, massive MIMO, MMSE estimation, spatial correlation, user scheduling

ID: 37758991