Balancing Queueing and Retransmission: Latency-Optimal Massive MIMO Design

TL;DR

This paper proposes a low-complexity, latency-optimal design for massive MIMO systems in 5G URLLC by jointly optimizing target error rate and transmission rate, balancing queueing and retransmission delays.

Contribution

It introduces LYRRC, a closed-form, asymptotically optimal policy for latency minimization in massive MIMO, considering practical system parameters.

Findings

LYRRC achieves latency and reliability targets in simulations.

The policy is asymptotically optimal as the number of antennas grows.

It effectively balances queueing and retransmission delays.

Abstract

One fundamental challenge in 5G URLLC is how to optimize massive MIMO systems for achieving low latency and high reliability. A natural design choice to maximize reliability and minimize retransmission is to select the lowest allowed target error rate. However, the overall latency is the sum of queueing latency and retransmission latency, hence choosing the lowest target error rate does not always minimize the overall latency. In this paper, we minimize the overall latency by jointly designing the target error rate and transmission rate adaptation, which leads to a fundamental tradeoff point between queueing and retransmission latency. This design problem can be formulated as a Markov decision process, which is theoretically optimal, but its complexity is prohibitively high for real-system deployments. We managed to develop a low-complexity closed-form policy named Large-arraY Reliability and Rate Control (LYRRC), which is proven to be asymptotically latency-optimal as the number of antennas increases. In LYRRC, the transmission rate is twice of the arrival rate, and the target error rate is a function of the antenna number, arrival rate, and channel estimation error. With simulated and measured channels, our evaluations find LYRRC satisfies the latency and reliability requirements of URLLC in all the tested scenarios.