The Ergodic Capacity of the Multiple Access Channel Under Distributed Scheduling - Order Optimality of Linear Receivers

TL;DR

This paper analyzes a distributed user selection algorithm for large MIMO MACs, showing it achieves optimal scaling laws with minimal coordination, thus offering a scalable solution for user scheduling in massive networks.

Contribution

It introduces and analyzes a distributed user selection algorithm that attains order-optimal capacity scaling without centralized coordination.

Findings

Distributed algorithm achieves same scaling as centralized schemes.

Algorithm performs well with both ZF and MMSE receivers.

Scalability is maintained with increasing number of users.

Abstract

Consider the problem of a Multiple-Input Multiple-Output (MIMO) Multiple-Access Channel (MAC) at the limit of large number of users. Clearly, in practical scenarios, only a small subset of the users can be scheduled to utilize the channel simultaneously. Thus, a problem of user selection arises. However, since solutions which collect Channel State Information (CSI) from all users and decide on the best subset to transmit in each slot do not scale when the number of users is large, distributed algorithms for user selection are advantageous. In this paper, we analyse a distributed user selection algorithm, which selects a group of users to transmit without coordinating between users and without all users sending CSI to the base station. This threshold-based algorithm is analysed for both Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) receivers, and its expected sum-rate in the limit of large number of users is investigated. It is shown that for large number of users it achieves the same scaling laws as the optimal centralized scheme.