Dynamic Backhaul Clustering for Enhanced Scalability in Cell-Free Massive MIMO Networks

Mahnoor Ajmal, Ayesha Siddiqa, Muhammad Ashar Tariq, Malik Muhammad Saad, Dongkyun Kim

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

1 Scopus citations

Abstract

Cell-free massive multiple-input multiple-output (CF-mMIMO) networks are emerging as a promising technology for next-generation wireless communication. However, as the number of users increases in a CF-mMIMO network, scalability and optimal network performance become challenging. To tackle this issue, we propose a novel approach of dynamically clustering access points (APs) based on central processing unit (CPU) resources. The proposed method optimizes AP clustering by considering CPU resources, including bandwidth and power, distance, channel conditions, and APs' data demands. The joint optimization framework aims to resolve scalability issues and maximize network performance by balancing channel conditions, CPUs' computational strengths, and the user's varying data demands. The results from the simulations confirm that the proposed method effectively enhances both the network's scalability and performance.

Original languageEnglish
Title of host publication39th Annual ACM Symposium on Applied Computing, SAC 2024
PublisherAssociation for Computing Machinery
Pages1735-1741
Number of pages7
ISBN (Electronic)9798400702433
DOIs
StatePublished - 8 Apr 2024
Event39th Annual ACM Symposium on Applied Computing, SAC 2024 - Avila, Spain
Duration: 8 Apr 202412 Apr 2024

Publication series

NameProceedings of the ACM Symposium on Applied Computing

Conference

Conference39th Annual ACM Symposium on Applied Computing, SAC 2024
Country/TerritorySpain
CityAvila
Period8/04/2412/04/24

Keywords

  • B5G
  • backhaul
  • cell free massive MIMO
  • clustering
  • CPU resources
  • scalability

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