Performance analysis of cell-free massive MIMO systems with massive connectivity

Mangqing Guo, M. Cenk Gursoy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we investigate the performance of cell-free massive MIMO systems with massive connectivity. With the generalized approximate message passing (GAMP) algorithm, we obtain the minimum mean-squared error (MMSE) estimate of the effective channel coefficients from all users to all access points (APs) in order to perform joint user activity detection and channel estimation. Subsequently, using the decoupling properties of MMSE estimation for large linear systems and state evolution equations of the GAMP algorithm, we obtain the variances of both the estimated channel coefficients and the corresponding channel estimation error. Finally, we study the achievable uplink rates with zero-forcing (ZF) detector at the central processing unit (CPU) of the cell-free massive MIMO system. With numerical results, we analyze the impact of the number of pilots used for joint activity detection and channel estimation, the number of APs, and signal-to-noise ratio (SNR) on the achievable rates.

Original languageEnglish (US)
Title of host publication2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728197944
DOIs
StatePublished - Jan 9 2021
Event18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021 - Virtual, Las Vegas, United States
Duration: Jan 9 2021Jan 13 2021

Publication series

Name2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021

Conference

Conference18th IEEE Annual Consumer Communications and Networking Conference, CCNC 2021
CountryUnited States
CityVirtual, Las Vegas
Period1/9/211/13/21

Keywords

  • Achievable rates
  • Activity detection
  • Cell-free massive MIMO systems
  • Generalized approximate message passing (GAMP)
  • Minimum mean-squared error estimation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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