TY - GEN
T1 - Dynamic Backhaul Clustering for Enhanced Scalability in Cell-Free Massive MIMO Networks
AU - Ajmal, Mahnoor
AU - Siddiqa, Ayesha
AU - Tariq, Muhammad Ashar
AU - Saad, Malik Muhammad
AU - Kim, Dongkyun
N1 - Publisher Copyright:
© 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.
PY - 2024/4/8
Y1 - 2024/4/8
N2 - 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.
AB - 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.
KW - B5G
KW - backhaul
KW - cell free massive MIMO
KW - clustering
KW - CPU resources
KW - scalability
UR - http://www.scopus.com/inward/record.url?scp=85197686428&partnerID=8YFLogxK
U2 - 10.1145/3605098.3635914
DO - 10.1145/3605098.3635914
M3 - Conference contribution
AN - SCOPUS:85197686428
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 1735
EP - 1741
BT - 39th Annual ACM Symposium on Applied Computing, SAC 2024
PB - Association for Computing Machinery
T2 - 39th Annual ACM Symposium on Applied Computing, SAC 2024
Y2 - 8 April 2024 through 12 April 2024
ER -