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We consider the problem of max-min fairness for uplink cell-free massivemultiple-input multiple-output which is a potential technology for beyond 5Gnetworks. More specifically, we aim to maximize the minimum spectral efficiencyof all users subject to the per-user power constraint, assuming linear receivecombining technique at access points. The considered problem can be furtherdivided into two subproblems: the receiver filter coefficient design and thepower control problem. While the receiver coefficient design turns out to be ageneralized eigenvalue problem, and thus, admits a closed-form solution, thepower control problem is numerically troublesome. To solve the power controlproblem, existing approaches rely on geometric programming (GP) which is notsuitable for large-scale systems. To overcome the high-complexity issue of theGP method, we first reformulate the power control problem intro a convexprogram, and then apply a smoothing technique in combination with anaccelerated projected gradient method to solve it. The simulation resultsdemonstrate that the proposed solution can achieve almost the same objectivebut in much lesser time than the existing GP-based method.

A Low-Complexity Approach for Max-Min Fairness in Uplink Cell-Free Massive MIMO