Enhancing massive MIMO: A new approach for Uplink training based on heterogeneous coherence time

TL;DR

This paper proposes a novel massive MIMO uplink training approach that adapts to users' heterogeneous coherence times, aiming to improve spectral efficiency by optimizing training frequency based on individual channel conditions.

Contribution

It introduces a user scheduling algorithm that leverages coherence time diversity to enhance uplink training efficiency in TDD massive MIMO systems.

Findings

Significant spectral efficiency gains through adaptive training

Effective user grouping based on coherence intervals

Improved sum rate performance in simulations

Abstract

Massive multiple-input multiple-output (MIMO) is one of the key technologies in future generation networks. Owing to their considerable spectral and energy efficiency gains, massive MIMO systems provide the needed performance to cope with the ever increasing wireless capacity demand. Nevertheless, the number of scheduled users stays limited in massive MIMO both in time division duplexing (TDD) and frequency division duplexing (FDD) systems. This is due to the limited coherence time, in TDD systems, and to limited feedback capacity, in FDD mode. In current systems, the time slot duration in TDD mode is the same for all users. This is a suboptimal approach since users are subject to heterogeneous Doppler spreads and, consequently, different coherence times. In this paper, we investigate a massive MIMO system operating in TDD mode in which, the frequency of uplink training differs among users based on their actual channel coherence times. We argue that optimizing uplink training by exploiting this diversity can lead to considerable spectral efficiency gain. We then provide a user scheduling algorithm that exploits a coherence interval based grouping in order to maximize the achievable weighted sum rate.