Energy Efficiency Enhancement in User-Centric and Cell-Free Millimeter-Wave Massive MIMO Systems With Hybrid Beamforming

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a new and scalable network architecture that can bring antennas closer to users. In addition, hybrid beamforming can greatly reduce the power consumption by limiting the number of radio frequency chains. To achieve higher energy efficien...

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Vydané v:IEEE transactions on vehicular technology Ročník 73; číslo 4; s. 1 - 16
Hlavní autori: Zheng, Qinyuan, Zhu, Pengcheng, Li, Jiamin, Wang, Dongming, You, Xiaohu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.04.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
access point selection
Algorithms
Array signal processing
Backhaul networks
Beamforming
Cell-free massive multiple-input multiple-out-put
Complexity
Continuity (mathematics)
Energy efficiency
hybrid bemforming
Hybrid systems
Millimeter wave communication
Millimeter waves
Mixed integer
Nonlinear programming
Optimization
Power consumption
Power demand
Quality of service
Radio frequency
Wireless networks
ISSN:0018-9545, 1939-9359
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Popis
Shrnutí:Cell-free massive multiple-input multiple-output (CF-mMIMO) is a new and scalable network architecture that can bring antennas closer to users. In addition, hybrid beamforming can greatly reduce the power consumption by limiting the number of radio frequency chains. To achieve higher energy efficiency (EE) of a wireless network, it is of practical interest to incorporate hybrid beamforming into CF-mMIMO systems to fully utilize their combined benefits. Moreover, user-centric scheme is considered so that idle access points (APs) can be turned into sleep mode for energy saving. In this paper, we formulate the EE maximization problem where user-AP association, hybrid beamformers and AP selection are jointly optimized with a realistic power consumption model. Due to the binary variables involved, the formulated problem is a mixed integer nonlinear programming problem. Given this setup, we first exploit the strong coupling between binary variables and continuous beamformers, then a group sparse beamforming method is used to induce group-sparsity of beamformers, and finally the original problem is transformed into a more tractable one. To reduce complexity, we propose another low-complexity method to solve the original problem by using an alternate optimization algorithm. Numerical results indicate the superiority of the two proposed algorithms over some well-known schemes.
Bibliografia:ObjectType-Article-1
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2023.3336071