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Energy and spectral efficiency tradeoff in massive MIMO systems with multi-objective adaptive genetic algorithm

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Abstract

Spectral efficiency (SE) and energy efficiency (EE) are both important metrics in massive multiple-input multiple-output (MIMO) systems. However, maximizing EE and SE is always conflicting with each other, and they can hardly be achieved simultaneously. In this paper, we focus on the tradeoff optimization between EE and SE in multiuser massive MIMO systems in terms of the number of transmit antennas and the transmit power. Different from the previous EE-oriented or SE-oriented method, the EE–SE tradeoff problem is formulated into a multi-objective optimization problem. To efficiently attain the Pareto optimal front (POF) of EE–SE tradeoff, a multi-objective adaptive genetic algorithm, inspired by the non-dominated sorting genetic algorithm (NSGA-II), is proposed to improve the convergence speed. Experimental comparisons against several well-known multi-objective algorithms show that the proposed algorithm can quickly adapt to the true POF of EE–SE tradeoff and maintain good performance on benchmark functions in terms of the adopted performance metrics.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61201135), in part by the Chinese Postdoctoral Science Foundation (3464), in part by the Shaanxi Natural Science Foundation (No. 2015JQ6245), in part by the Fundamental Research Funds for the Central Universities (No. 7214569601) and in part by the 111 Project (B08038).

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Authors and Affiliations

  1. State Key Laboratory of ISN, Xidian University, Xi’an, People’s Republic of China

    Yongqiang Hei, Cong Zhang, Weiting Song & Yanchun Kou

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  1. Yongqiang Hei
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  2. Cong Zhang
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  3. Weiting Song
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  4. Yanchun Kou
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Correspondence to Yongqiang Hei.

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Hei, Y., Zhang, C., Song, W. et al. Energy and spectral efficiency tradeoff in massive MIMO systems with multi-objective adaptive genetic algorithm. Soft Comput 23, 7163–7179 (2019). https://doi.org/10.1007/s00500-018-3356-x

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