A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: You, Li; Huang, Yufei; Zhang, Di; Chang, Zheng; Wang, Wenjin; Gao, Xiqi

Lehti tai sarja: IEEE Transactions on Communications

ISSN: 0090-6778

eISSN: 1558-0857

Julkaisuvuosi: 2021

Volyymi: 69

Lehden numero: 8

Artikkelin sivunumerot: 5228-5242

Kustantaja: Institute of Electrical and Electronics Engineers (IEEE)

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

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

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/77893

Tiivistelmä

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.

YSO-asiasanat: energiatehokkuus; optimointi; algoritmit; mallintaminen

Vapaat asiasanat: energy efficiency; statistical CSI; multi-cell MIMO; max-min fairness; distributed processing

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2021

JUFO-taso: 2