Energy Efficient Power Allocation for Cell-Free mmWave Massive MIMO With Hybrid Precoder

This letter investigates the downlink of a cell-free millimeter wave (mmWave) massive multiple-input multiple-output (mMIMO) system, where many access points (APs) cooperatively serve a user. Although the intensive deployment of APs can dramatically improve the system capacity, it also increases the...

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Vydané v:IEEE communications letters Ročník 26; číslo 2; s. 394 - 398
Hlavní autori: He, Yun, Shen, Min, Zeng, Fanhui, Zheng, Huanping, Wang, Rui, Zhang, Meng, Liu, Xiangyan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Antenna arrays
Cell-free massive MIMO
Chains
Eigenvalues
Energy consumption
energy-efficient
Game theory
Hybrid power systems
hybrid precoder
Matching pursuit algorithms
Mathematical programming
Maximization
millimeter wave
Millimeter waves
Optimization
Phase shifters
power allocation
Power consumption
Power demand
Radio frequency
Resource management
ISSN:1089-7798, 1558-2558
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Shrnutí:This letter investigates the downlink of a cell-free millimeter wave (mmWave) massive multiple-input multiple-output (mMIMO) system, where many access points (APs) cooperatively serve a user. Although the intensive deployment of APs can dramatically improve the system capacity, it also increases the network energy consumption substantially. To track the non-concave global energy-efficiency (GEE) optimization problem, we decompose it into hybrid precoder design and power allocation design. A novel dynamic subarray with quantized phase shifters (DS-QPS) hybrid precoder is introduced, where each radio frequency (RF) chain only connects to a disjointed subset of antennas. The optimization problem of the number of RF chains is formulated as an eigenvalue maximization problem considering a realistic power consumption model. For power allocation, a new centralized framework is exploited to solve a sequence of simpler power allocation subproblems while still aiming at the GEE maximization by merging with fractional programming, non-cooperative game theory, and gradient-assisted binary search (GABS) algorithm. Simulations show that the joint design is more energy-efficient than the baselines.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2021.3110985