Channel Quality Feedback Enhancements for Accurate URLLC Link Adaptation in 5G Systems

Abstract

Accurate downlink link adaptation is a major challenge for ultra-reliable and low-latency communications (URLLC) as a consequence of the random and unpredictable load variations at the interfering cells. To address this problem, this paper introduces enhancements to the channel quality indicator (CQI) measurement and reporting procedures for 5G New Radio (NR). The goal is to accurately estimate and report the lower percentiles of the user channel quality distribution. First, a simple and efficient technique is proposed for filtering the channel quality samples collected at the user equipment and, accordingly, estimating tail signal-to-interference-and-noise (SINR) performance. Second, a new CQI reporting format is introduced which better guides downlink scheduling and link adaptation decisions of small URLLC payloads at the gNB. The benefits of the proposed solutions are evaluated via advanced system-level simulations, where it is shown that the proposed solutions significantly outperform existing CQI measurement and reporting schemes. For instance, the 99.999% percentile of the experienced latency is reduced from 1.3 ms to 0.86 ms for the case when URLLC traffic is multiplexed with enhanced mobile broadband (eMBB) traffic.