A predictive interference management algorithm for URLLC in beyond 5G networks
Mahmood, Nurul Huda; López, Onel Alcaraz; Alves, Hirley; Latva-aho, Matti (2020-10-30)
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
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2020110589335
Tiivistelmä
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 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.
Kokoelmat
- Avoin saatavuus [38840]