University of Oulu

N. H. Mahmood, O. A. López, H. Alves and M. Latva-Aho, "A Predictive Interference Management Algorithm for URLLC in Beyond 5G Networks," in IEEE Communications Letters, vol. 25, no. 3, pp. 995-999, March 2021, doi: 10.1109/LCOMM.2020.3035111

A predictive interference management algorithm for URLLC in beyond 5G networks

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Author: Mahmood, Nurul Huda1; López, Onel Alcaraz1; Alves, Hirley1;
Organizations: 16G Flagship, Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2020-11-05


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 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.

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Series: IEEE communications letters
ISSN: 1089-7798
ISSN-E: 2373-7891
ISSN-L: 1089-7798
Volume: 25
Issue: 3
Pages: 995 - 999
DOI: 10.1109/LCOMM.2020.3035111
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work is supported by the Academy of Finland 6Genesis Flagship program (grant no. 318927).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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