Abstract
This paper studies the energy efficiency (EE) oriented precoding design in multi-cell massive multiple-input multiple-output (MIMO) systems, with only statistical channel state information (CSI) at the transmitter. During the transmission, as the channel varies dynamically with time and the previously obtained CSI becomes outdated, the base stations must adjust their transmit policies accordingly. To tackle this issue, we propose an online EE maximization algorithm that can achieve a no-regret transmission; i.e., the performance of this online method gradually approaches that of the fixed offline method which has full knowledge of the future CSI. Specifically, we first construct the online EE optimization problem in a distributed way to reduce the information required to be exchanged between cells. Then, we apply the large-dimensional random matrix theory to lower the calculation complexity, and the Charnes-Cooper transform to address the nonconvexity of the problem, respectively. The online gradient ascent method is utilized to perform this no-regret power allocation strategy based on all past CSI. We also assess the robustness of the algorithm to estimation error of statistical CSI under some mild conditions which can usually be satisfied in practice. Numerical results demonstrate the no-regret property and the robustness of the proposed online algorithm for energy efficient multi-cell massive MIMO transmission.
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Acknowledgements
This work was supported by National Key Research and Development Program of China (Grant No. 2018YFB1801103), Jiangsu Province Basic Research Project (Grant No. BK20192002), National Natural Science Foundation of China (Grant No. 61801114), Fundamental Research Funds for the Central Universities (Grant 2242021R41148), and Young Elite Scientist Sponsorship Program by China Institute of Communications.
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You, L., Huang, Y., Zhong, W. et al. Robust online energy efficiency optimization for distributed multi-cell massive MIMO networks. Sci. China Inf. Sci. 66, 132302 (2023). https://doi.org/10.1007/s11432-021-3437-8
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DOI: https://doi.org/10.1007/s11432-021-3437-8