Scalable User Rate and Energy-Efficiency Optimization in Cell-Free Massive MIMO

This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper...

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Bibliographic Details
Published in:IEEE transactions on communications 2022-09, Vol.70 (9), p.6050-6065
Main Authors: Tuan, H. D., Nasir, A. A., Ngo, H. Q., Dutkiewicz, E., Poor, H. V.
Format: Article
Language:English
Subjects:
Algorithms
Beamforming
Cell-free massive MIMO (cfm-MIMO)
Channel estimation
Closed form solutions
conjugate beamforming (CB)
energy efficiency
Exact solutions
Gaussian beams (optics)
geometric mean
Indexes
Manganese
MIMO communication
nonconvex optimization
Optimization
Power consumption
Quality of service
Quality of service architectures
Random variables
Resource management
scalable algorithms
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Summary:This paper considers a cell-free massive multiple-input multiple-output network (cfm-MIMO) with a massive number of access points (APs) distributed across an area to deliver information to multiple users. Based on only local channel state information, conjugate beamforming is used under both proper and improper Gaussian signalings. To accomplish the mission of cfm-MIMO in providing fair service to all users, the problem of power allocation to maximize the geometric mean (GM) of users' rates (GM-rate) is considered. A new scalable algorithm, which iterates linear-complex closed-form expressions and thus is practical regardless of the scale of the network, is developed for its solution. The problem of quality-of-service (QoS) aware network energy-efficiency is also addressed via maximizing the ratio of the GM-rate and the total power consumption, which is also addressed by iterating linear-complex closed-form expressions. Intensive simulations are provided to demonstrate the ability of the GM-rate based optimization to achieve multiple targets such as a uniform QoS, a good sum rate, and a fair power allocation to the APs.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2022.3194046