Energy Efficiency Optimization for Multi-cell Massive MIMO : Centralized and Distributed Power Allocation Algorithms
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
Published inIEEE Transactions on Communications
© 2021, IEEE
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. ...
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
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Additional information about fundingThis work was supported in part by the National Key R&D Program of China under Grant 2018YFB1801103; in part by the National Natural Science Foundation of China under Grants 61801114, 61631018, and 61761136016; in part by the Jiangsu Province Basic Research Project under Grant BK20192002; and in part by the Fundamental Research Funds for the Central Universities. The work of D. Zhang is supported by the National Science Foundation of China (NSFC) under grant 62001423; Henan Provincial Key Research, Development and Promotion Project under grant 212102210175; Henan Provincial Key Scientific Research Project for Colleges and Universities under grant 21A510011. The work of Z. Chang is partly supported by NSFC No. 62071105. ...
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