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Optimal massive-MIMO-aided clustered base-station coordination

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.
0018-9545
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
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. (In Press)

Record type: Article

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.

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Accepted/In Press date: 1 February 2021

Identifiers

Local EPrints ID: 446754
URI: http://eprints.soton.ac.uk/id/eprint/446754
ISSN: 0018-9545
PURE UUID: 0f8baec2-e26b-41ac-8c36-47bdd76d63d7
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 19 Feb 2021 17:33
Last modified: 20 Feb 2021 02:32

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Contributors

Author: Xueru Li
Author: Xu Zhang
Author: Yongxin Zhou
Author: Lajos Hanzo ORCID iD

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