A study on UE clustering and antenna selection for distributed MIMO cooperative transmission using multi-user MMSE-SVD

Yuta Seki, Tomoyuki Saito, Fumiyuki Adachi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

Distributed multiple-input multiple-output (MIMO) is a type of the centralized radio access network (C-RAN) to achieve a high link capacity uniformly over the service area. A large-scale distributed MIMO requires prohibitively high computational complexity. Clustering of user equipments (UEs) can reduce the computational complexity, however, this produces the inter-cluster interference (ICI). In this paper, we propose the UE clustering and antenna selection beyond the cluster boundary assuming multi-user minimum mean square error filtering combined with singular value decomposition (MMSE-SVD). We exploit the multiplexing-diversity tradeoff in MMSE-SVD to suppress effectively the ICI while keeping the multi-user multiplexing capability. It is shown that the proposed UE clustering and antenna selection can achieve 6 times higher sum capacity and 1.6 times higher UE capacity, compared to the without clustering.

Original languageEnglish
Title of host publicationProceedings of 2019 25th Asia-Pacific Conference on Communications, APCC 2019
EditorsVo Nguyen Quoc Bao, Tran Thien Thanh
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages73-77
Number of pages5
ISBN (Electronic)9781728136790
DOIs
Publication statusPublished - 2019 Nov
Event25th Asia-Pacific Conference on Communications, APCC 2019 - Ho Chi Minh City, Viet Nam
Duration: 2019 Nov 62019 Nov 8

Publication series

NameProceedings of 2019 25th Asia-Pacific Conference on Communications, APCC 2019

Conference

Conference25th Asia-Pacific Conference on Communications, APCC 2019
Country/TerritoryViet Nam
CityHo Chi Minh City
Period19/11/619/11/8

Keywords

  • Antenna selection
  • Cooperative transmission
  • Distributed MIMO
  • Multi-user mulltiplexing
  • OFDM
  • UE clustering

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