Optimal massive-MIMO-aided clustered base-station coordination
Optimal massive-MIMO-aided clustered base-station coordination
A large-scale clustered massive MIMO network is proposed for improving the spectral efficiency of the nextgeneration wireless infrastructure by maximizing its sum-rate. Our solution combines the advantages of the centralized processing architecture and massive MIMO. Explicitly, the network is divided into multiple clusters; each cluster is handled by a centralized processing unit, which connects to a certain number of massive MIMO-aided BSs, where only limited information is exchanged among the clusters; each user of a cluster can be served by several nearby BSs in a user-centric way. We analyze the maximum sum-rate of the network with multiple antennas at BSs and UEs, relying on the optimal transmit precoder matrix of each BS configured for each user, and on the optimal frequency-domain power sharing scheme of each cluster. The optimizations are conceived for multiple coordination schemes that were widely studied in literature, namely the coherent-joint-transmission (CJT) scheme, the noncoherent- joint-transmission (NCJT) scheme and the coordinatedbeamfoming/
scheduling (CBF/CS) scheme. Our simulations show that the optimal CJT achieves 2.2 – 4.5 times higher average sumrate than its non-cooperative massive MIMO network counterpart, while the optimal NCJT and the optimal CBF/CS achieve at most a factor 1.3 average sum-rate gain. The popular signalto-leakage-and-noise-ratio (SLNR) scheme is also extended to the multi-antenna UE scenario and achieves a factor 1.1 – 1.2 gain.
Large-scale clustered MIMO network, cell free MIMO, optimal power sharing, optimal precoding matrix, weighted sum rate maximization
2699-2712
Li, Xueru
ee102447-3555-4a17-9827-5c4fdb430bcd
Zhang, Xu
50925f96-e46c-475d-9059-07a0fb66702b
Zhou, Yongxin
dabba47f-c214-4f70-b694-e06ffbb08554
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
3 March 2021
Li, Xueru
ee102447-3555-4a17-9827-5c4fdb430bcd
Zhang, Xu
50925f96-e46c-475d-9059-07a0fb66702b
Zhou, Yongxin
dabba47f-c214-4f70-b694-e06ffbb08554
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Li, Xueru, Zhang, Xu, Zhou, Yongxin and Hanzo, Lajos
(2021)
Optimal massive-MIMO-aided clustered base-station coordination.
IEEE Transactions on Vehicular Technology, 70 (3), , [9347738].
(doi:10.1109/TVT.2021.3056875).
Abstract
A large-scale clustered massive MIMO network is proposed for improving the spectral efficiency of the nextgeneration wireless infrastructure by maximizing its sum-rate. Our solution combines the advantages of the centralized processing architecture and massive MIMO. Explicitly, the network is divided into multiple clusters; each cluster is handled by a centralized processing unit, which connects to a certain number of massive MIMO-aided BSs, where only limited information is exchanged among the clusters; each user of a cluster can be served by several nearby BSs in a user-centric way. We analyze the maximum sum-rate of the network with multiple antennas at BSs and UEs, relying on the optimal transmit precoder matrix of each BS configured for each user, and on the optimal frequency-domain power sharing scheme of each cluster. The optimizations are conceived for multiple coordination schemes that were widely studied in literature, namely the coherent-joint-transmission (CJT) scheme, the noncoherent- joint-transmission (NCJT) scheme and the coordinatedbeamfoming/
scheduling (CBF/CS) scheme. Our simulations show that the optimal CJT achieves 2.2 – 4.5 times higher average sumrate than its non-cooperative massive MIMO network counterpart, while the optimal NCJT and the optimal CBF/CS achieve at most a factor 1.3 average sum-rate gain. The popular signalto-leakage-and-noise-ratio (SLNR) scheme is also extended to the multi-antenna UE scenario and achieves a factor 1.1 – 1.2 gain.
Text
pure
- Accepted Manuscript
More information
Accepted/In Press date: 1 February 2021
e-pub ahead of print date: 4 February 2021
Published date: 3 March 2021
Additional Information:
Funding Information:
Manuscript received March 23, 2020; revised December 14, 2020; accepted January 31, 2021. Date of publication February 4, 2021; date of current version April 2, 2021. The work of Lajos Hanzo was supported by Engineering and Physical Sciences Research Council Projects EP/N004558/1, EP/P034284/1, EP/P034284/1, and EP/P003990/1 (COALESCE) as well as of the European Research Council’s Advanced Fellow Grant QuantCom. The review of this article was coordinated by Prof. A. Hamouda. (Corresponding author: Lajos Hanzo.) Xueru Li, Xu Zhang, and Yongxing Zhou are with the Huawei Technologies Company, Ltd., Shenzhen, Guandong 310051, China (e-mail: lix-ueru2@huawei.com; criss.zhang@huawei.com; yongxing.zhou@huawei.com).
Publisher Copyright:
© 1967-2012 IEEE.
Keywords:
Large-scale clustered MIMO network, cell free MIMO, optimal power sharing, optimal precoding matrix, weighted sum rate maximization
Identifiers
Local EPrints ID: 446754
URI: http://eprints.soton.ac.uk/id/eprint/446754
ISSN: 0018-9545
PURE UUID: 0f8baec2-e26b-41ac-8c36-47bdd76d63d7
Catalogue record
Date deposited: 19 Feb 2021 17:33
Last modified: 18 Mar 2024 02:36
Export record
Altmetrics
Contributors
Author:
Xueru Li
Author:
Xu Zhang
Author:
Yongxin Zhou
Author:
Lajos Hanzo
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics