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Max-Min Fairness Optimization in Uplink Cell-Free Massive MIMO using Meta-Heuristics

Conceição, F. ; Antunes, C. H. Antunes ; Gomes, M. ; Silva, V. ; Dinis, R.

IEEE Transactions on Communications Vol. 70, Nº 3, pp. 1792 - 1807, January, 2022.

ISSN (print): 0090-6778
ISSN (online): 1558-0857

Scimago Journal Ranking: 3,11 (in 2021)

Digital Object Identifier: 10.1109/TCOMM.2022.3144989

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Abstract
One of the most promising technologies to be employed in beyond 5G networks is based on the concept of cell-free (CF) massive MIMO, in which a predetermined set of access points (APs) jointly cooperate in the data transmission and reception to/from the user equipment (UE). In order to efficiently manage radio resources, the CF central processing unit implement uplink power control policies. These policies aim to optimize a given network utility function. In this paper, we investigate the max-min fairness optimization problem, in which the spectral efficiency performance of the UE with the worst channel conditions is prioritized, taking into account a per-UE power constraint and assuming linear maximum ratio combining at APs. Existing solutions have typically relied on second-order cone programming with convex approximations, which exhibit high computational complexity and scalability issues. Therefore, meta-heuristics (MHs) are explored as alternative optimization schemes, capable of providing (near)-optimal solutions with reasonable computational effort. Three MH approaches with different operation principles are compared. Numerical results show that the differential evolution algorithm exhibits the best trade-off between solution quality and run time, being able to reach (near)-optimal solutions faster than the bisection approach while coping with the scalability issues of the geometric programming-based algorithm.