A1 Journal article (refereed)
Energy Efficiency Optimization for Multi-cell Massive MIMO : Centralized and Distributed Power Allocation Algorithms (2021)

You, L., Huang, Y., Zhang, D., Chang, Z., Wang, W., & Gao, X. (2021). Energy Efficiency Optimization for Multi-cell Massive MIMO : Centralized and Distributed Power Allocation Algorithms. IEEE Transactions on Communications, 69(8), 5228-5242. https://doi.org/10.1109/TCOMM.2021.3081451

JYU authors or editors

Publication details

All authors or editors: You, Li; Huang, Yufei; Zhang, Di; Chang, Zheng; Wang, Wenjin; Gao, Xiqi

Journal or series: IEEE Transactions on Communications

ISSN: 0090-6778

eISSN: 1558-0857

Publication year: 2021

Volume: 69

Issue number: 8

Pages range: 5228-5242

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1109/TCOMM.2021.3081451

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/77893


This paper investigates the energy efficiency (EE) optimization in downlink multi-cell massive multiple-input multiple-output (MIMO). In our research, the statistical channel state information (CSI) is exploited to reduce the signaling overhead. To maximize the minimum EE among the neighbouring cells, we design the transmit covariance matrices for each base station (BS). Specifically, optimization schemes for this max-min EE problem are developed, in the centralized and distributed ways, respectively. To obtain the transmit covariance matrices, we first find out the closed-form optimal transmit eigenmatrices for the BS in each cell, and convert the original transmit covariance matrices designing problem into a power allocation one. Then, to lower the computational complexity, we utilize an asymptotic approximation expression for the problem objective. Moreover, for the power allocation design, we adopt the minorization maximization method to address the non-convexity of the ergodic rate, and use Dinkelbach’s transform to convert the max-min fractional problem into a series of convex optimization subproblems. To tackle the transformed subproblems, we propose a centralized iterative water-filling scheme. For reducing the backhaul burden, we further develop a distributed algorithm for the power allocation problem, which requires limited inter-cell information sharing. Finally, the performance of the proposed algorithms are demonstrated by extensive numerical results.

Keywords: energy efficiency; optimisation; algorithms; modelling (creation related to information)

Free keywords: energy efficiency; statistical CSI; multi-cell MIMO; max-min fairness; distributed processing

Contributing organizations

Ministry reporting: Yes

Preliminary JUFO rating: 2

Last updated on 2021-23-09 at 11:43