Joint user scheduling and antenna selection in distributed massive MIMO systems with limited backhaul capacity

Massive MIMO systems offer a high spatial resolution that can drastically increase the spectral and/or energy efficiency by employing a large number of antennas at the base station (BS). In a distributed massive MIMO system, the capacity of fiber backhaul that links base station and remote radio heads is usually limited, which becomes a bottleneck for realizing the potential performance gain of both downlink and uplink. To solve this problem, we propose a joint antenna selection and user scheduling which is able to achieve a large portion of the potential gain provided by the massive MIMO array with only limited backhaul capacity. Three sub-optimal iterative algorithms with the objective of sum-rate maximization are proposed for the joint optimization of antenna selection and user scheduling, either based on greedy fashion or Frobenius-norm criteria. Convergence and complexity analysis are presented for the algorithms. The provided Monte Carlo simulations show that, one of our algorithms achieves a good tradeoff between complexity and performance and thus is especially fit for massive MIMO systems.