Interference Prediction for Low-Complexity Link Adaptation in Beyond 5G Ultra-Reliable Low-Latency Communications

TL;DR

This paper introduces a kernel-based interference prediction method to improve link adaptation in beyond 5G URLLC networks, aiming to meet strict latency and reliability requirements.

Contribution

A novel interference prediction approach using kernel density estimation, with a low-complexity variant suitable for practical deployment in b5G URLLC scenarios.

Findings

Outperforms existing link adaptation solutions on 3GPP-calibrated channels.

Provides a statistically analyzed, low-complexity interference prediction algorithm.

Enhances reliability and reduces latency in URLLC applications.

Abstract

Traditional link adaptation (LA) schemes in cellular network must be revised for networks beyond the fifth generation (b5G), to guarantee the strict latency and reliability requirements advocated by ultra reliable low latency communications (URLLC). In particular, a poor error rate prediction potentially increases retransmissions, which in turn increase latency and reduce reliability. In this paper, we present an interference prediction method to enhance LA for URLLC. To develop our prediction method, we propose a kernel based probability density estimation algorithm, and provide an in depth analysis of its statistical performance. We also provide a low complxity version, suitable for practical scenarios. The proposed scheme is compared with state-of-the-art LA solutions over fully compliant 3rd generation partnership project (3GPP) calibrated channels, showing the validity of our proposal.