Journal
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume 68, Issue -, Pages 2645-2659Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2020.2986391
Keywords
MIMO communication; Downlink; Optimization; Covariance matrices; Resource management; Wireless communication; Antenna arrays; Energy efficiency; spectral efficiency; tradeoff; resource efficiency; massive MIMO; statistical CSI; power allocation
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Funding
- National Key R&D Program of China [2018YFB1801103]
- National Natural Science Foundation of China [61801114, 61761136016, 61631018]
- Jiangsu Province Basic Research Project [SBK2019050020]
- Natural Science Foundation of Jiangsu Province [BK20170688]
- Fundamental Research Funds for the Central Universities
- MIUR under the PRIN Liquid_Edge contract
- Italian Ministry of Education and Research, under the program Dipartimenti di Eccellenza 2018-2022
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As a key technology for future wireless networks, massive multiple-input multiple-output (MIMO) can significantly improve the energy efficiency (EE) and spectral efficiency (SE), and the performance is highly dependant on the degree of the available channel state information (CSI). While most existing works on massive MIMO focused on the case where the instantaneous CSI at the transmitter (CSIT) is available, it is usually not an easy task to obtain precise instantaneous CSIT. In this paper, we investigate EE-SE tradeoff in single-cell massive MIMO downlink transmission with statistical CSIT. To this end, we aim to optimize the system resource efficiency (RE), which is capable of striking an EE-SE balance. We first figure out a closed-form solution for the eigenvectors of the optimal transmit covariance matrices of different user terminals, which indicates that beam domain is in favor of performing RE optimal transmission in massive MIMO downlink. Based on this insight, the RE optimization precoding design is reduced to a real-valued power allocation problem. Exploiting the techniques of sequential optimization and random matrix theory, we further propose a low-complexity suboptimal two-layer water-filling-structured power allocation algorithm. Numerical results illustrate the effectiveness and near-optimal performance of the proposed statistical CSI aided RE optimization approach.
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