UnifSrv: AP Selection for Achieving Uniformly Good Performance of CF-MIMO in Realistic Urban Networks

摘要

Under the ideal assumption of uniform propagation, cell-free massive MIMO (CF-mMIMO) provides uniformly high throughput over the network by effectively surrounding each user with its serving access point (AP) set. However, in realistic non-uniform urban propagation environments, it is difficult to consistently select good limited serving AP sets, resulting in significantly degraded throughput, reintroducing "edge-effect" for the worst-served users. To restore the uniformly good performance of scalable CF-mMIMO in realistic urban networks, we formulate a novel multi-objective optimization problem to jointly achieve high throughput by maximizing the sum data rate, uniform throughput by maximizing Jain's fairness index of the throughput per user, and scalability by minimizing the serving AP set size. We then propose the UnifSrv AP selection algorithms to solve this optimization problem, consisting of a deep reinforcement learning (DRL)-based algorithm UnifSrv-DRL and a heuristic algorithm UnifSrv-heu. We conduct a comprehensive performance evaluation of scalable CF-mMIMO under realistic urban network distributions, propagation, and mobility patterns, showing that the prior benchmark AP selection schemes fail to provide uniformly high throughput in practice. By contrast, UnifSrv at least doubles the throughput compared to prior benchmarks, or achieves comparable throughput but with half of the serving AP set size. Importantly, our heuristic algorithm achieves equivalent throughput to our DRL one, but with orders of magnitude lower complexity. We thus for the first time propose an AP selection algorithm that achieves uniformly good CF-mMIMO performance in realistic urban networks with low complexity.