A Predictive Interference Management Algorithm for URLLC in Beyond 5G Networks

TL;DR

This paper introduces a novel interference prediction algorithm for URLLC in beyond 5G networks that models interference as a Markov chain, enabling more accurate resource allocation to meet reliability and latency requirements.

Contribution

It presents a new interference management method that considers the entire interference distribution, improving reliability in URLLC systems over traditional average-based schemes.

Findings

Achieves target reliability with ~25% additional resources

Models interference as a Markov chain for better prediction

Effective in low-latency single-shot transmissions

Abstract

Interference mitigation is a major design challenge in wireless systems,especially in the context of ultra-reliable low-latency communication (URLLC) services. Conventional average-based interference management schemes are not suitable for URLLC as they do not accurately capture the tail information of the interference distribution. This letter proposes a novel interference prediction algorithm that considers the entire interference distribution instead of only the mean. The key idea is to model the interference variation as a discrete state space discrete-time Markov chain. The state transition probability matrix is then used to estimate the state evolution in time, and allocate radio resources accordingly. The proposed scheme is found to meet the target reliability requirements in a low-latency single-shot transmission system considering realistic system assumptions, while requiring only ~25% more resources than the optimum case with perfect interference knowledge.