Decentralized Coordinated Precoding Design in Cell-Free Massive MIMO Systems for URLLC

TL;DR

This paper develops decentralized precoding algorithms for cell-free massive MIMO systems to enhance URLLC performance, addressing short-packet communication challenges with algorithms that balance reliability, latency, and computational complexity.

Contribution

It introduces path-following algorithms for decentralized precoding in cell-free massive MIMO tailored for URLLC, optimizing user rates under short-packet constraints.

Findings

Decentralized PFA precoding achieves 80% of the centralized URLLC rate.

Decentralized PFA precoding attains 89% of the average URLLC rate.

Decentralized approach reduces computational complexity by 88% compared to centralized methods.

Abstract

Cell-free massive multiple-input multiple-output (MIMO) is a promising network to offer huge improvement of the achievable rate compared with conventional cellular massive MIMO systems. However, the commonly adopted Shannon-type achievable rate is only valid in the long block length regime that is not applicable to the emerging short-packet communication. To realize ultra-reliable and low-latency communication (URLLC) in cell-free massive MIMO systems, we optimize the precoding vector at the access points (APs) to maximize the minimum user rate in both the centralized and decentralized fashion. The design takes into account the impact of URLLC and we propose path-following algorithms (PFA) to address the considered problem which generates a sequence of advanced feasible points and converges to at least a locally optimal solution of the design problem. Moreover, we investigate the requirement of the precoding schemes, the length of the transmission duration, the number of antennas equipped at each AP, and the size of each AP cluster on the URLLC rate. Numerical results show that the decentralized PFA precoding can achieve 80\% of the 95\%-likely URLLC rate of the centralized precoding and 89\% of the average URLLC rate with only 12\% computational complexity of the centralized precoding.