Fairness Scheduling in Dense User-Centric Cell-Free Massive MIMO Networks

TL;DR

This paper investigates the optimal operation regime of user-centric cell-free massive MIMO networks, proposing a scheduler to improve fairness and efficiency by controlling active user count and throughput distribution.

Contribution

It introduces a new perspective on rate allocation and user density regimes, proposing a scheduler that enhances fairness and system performance in dense cell-free massive MIMO networks.

Findings

Maximum sum throughput occurs when UE count is about half of total antennas.

Reducing active UEs improves fairness and overall throughput.

Tunable system achieves desired throughput distribution.

Abstract

We consider a user-centric scalable cell-free massive MIMO network with a total of $LM$ distributed remote radio unit antennas serving $K$ user equipments (UEs). Many works in the current literature assume $LM\gg K$, enabling high UE data rates but also leading to a system not operating at its maximum performance in terms of sum throughput. We provide a new perspective on cell-free massive MIMO networks, investigating rate allocation and the UE density regime in which the network makes use of its full capability. The UE density $K$ approximately equal to $\frac{LM}{2}$ is the range in which the system reaches the largest sum throughput. In addition, there is a significant fraction of UEs with relatively low throughput, when serving $K>\frac{LM}{2}$ UEs simultaneously. We propose to reduce the number of active UEs per time slot, such that the system does not operate at ``full load'', and impose throughput fairness among all users via a scheduler designed to maximize a suitably defined concave componentwise non-decreasing network utility function. Our numerical simulations show that we can tune the system such that a desired distribution of the UE throughput, depending on the utility function, is achieved.