Interference Distribution Prediction for Link Adaptation in Ultra-Reliable Low-Latency Communications

TL;DR

This paper proposes a method to predict interference in URLLC scenarios by exploiting time correlation, improving link adaptation to meet strict latency and reliability requirements in 5G networks.

Contribution

It introduces a novel interference prediction approach based on interference time correlation, enhancing link adaptation for URLLC in 5G networks.

Findings

Interference prediction improves spectral efficiency.

Exploiting time correlation enhances reliability.

Outperforms existing interference prediction methods.

Abstract

The strict latency and reliability requirements of ultra-reliable low-latency communications (URLLC) use cases are among the main drivers in fifth generation (5G) network design. Link adaptation (LA) is considered to be one of the bottlenecks to realize URLLC. In this paper, we focus on predicting the signal to interference plus noise ratio at the user to enhance the LA. Motivated by the fact that most of the URLLC use cases with most extreme latency and reliability requirements are characterized by semi-deterministic traffic, we propose to exploit the time correlation of the interference to compute useful statistics needed to predict the interference power in the next transmission. This prediction is exploited in the LA context to maximize the spectral efficiency while guaranteeing reliability at an arbitrary level. Numerical results are compared with state of the art interference prediction techniques for LA. We show that exploiting time correlation of the interference is an important enabler of URLLC.