A Weighted MMSE Approach to Amorphous Cell for Mixed-ADC Distributed Massive MIMO

Jide Yuan, Qi He, Michail Matthaiou, Yuyang Wang, Tony Q.S. Quek, Shi Jin

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

1 Citation (Scopus)

Abstract

Distributed massive multi-input-multi-output (mMIMO) is a promising architecture which has potential to satisfy the strick latency requirement in Internet of Things (IoT). To further meet the low-cost and low-latency demand in IoT, this paper provides a low-complexity scheme to the access phase for mixed analog-to-digital convertors (ADC) distributed mMIMO. which consists of two steps. In the first step, the clustering behavior among users is detected using large scale fading information, which aims to reduce the complexity. In the second step, with the number of clusters as a priori, a weighted minimum mean square error (WMMSE) clustering algorithm that can provide stable and robust results is proposed. The clustering algorithm aims to maximize the achievable sum rate, in which the nonconvex objective function and constraints are modeled using ell1-norm approximation. Numerical results show that the proposed algorithm has strong convergence, and significant gain can be obtained in various scenarios.

Original languageEnglish
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages969-974
Number of pages6
ISBN (Electronic)9781538692189
DOIs
Publication statusPublished - 2019 Feb 19
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: 2018 Oct 282018 Oct 31

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2018-October
ISSN (Print)1058-6393

Conference

Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
CountryUnited States
CityPacific Grove
Period18-10-2818-10-31

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Networks and Communications

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