University of Oulu

C. Jayawardhana, T. Sivalingam, N. H. Mahmood, N. Rajatheva and M. Latva-Aho, "Predictive Resource Allocation for URLLC using Empirical Mode Decomposition," 2023 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Gothenburg, Sweden, 2023, pp. 174-179, doi: 10.1109/EuCNC/6GSummit58263.2023.10188327

Predictive resource allocation for URLLC using empirical mode decomposition

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Author: Jayawardhana, Chandu1; Sivalingam, Thushan1; Mahmood, Nurul Huda1;
Organizations: 16G Flagship, Centre for Wireless Communications, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-09-06


Effective resource allocation is a crucial requirement to achieve the stringent performance targets of ultra-reliable low-latency communication (URLLC) services. Predicting future interference and utilizing it to design efficient interference management algorithms is one way to allocate resources for URLLC services effectively. This paper proposes an empirical mode decomposition (EMD) based hybrid prediction method to predict the interference and allocate resources for downlink based on the prediction results. EMD is used to decompose the past interference values faced by the user equipment. Long short-term memory and auto-regressive integrated moving average methods are used to predict the decomposed components. The final predicted interference value is reconstructed using individual predicted values of decomposed components. It is found that such a decomposition-based prediction method reduces the root mean squared error of the prediction by 20–25%. The proposed resource allocation algorithm utilizing the EMD-based interference prediction was found to meet near-optimal allocation of resources and correspondingly results in 2–3 orders of magnitude lower outage compared to state-of-the-art baseline prediction algorithm-based resource allocation.

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Series: European Conference on Networks and Communications
ISSN: 2475-6490
ISSN-E: 2575-4912
ISSN-L: 2475-6490
ISBN: 979-8-3503-1102-0
ISBN Print: 979-8-3503-1103-7
Pages: 174 - 179
DOI: 10.1109/EuCNC/6GSummit58263.2023.10188327
Host publication: 2023 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
Conference: Joint European Conference on Networks and Communications & 6G Summit
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the Academy of Finland 6Genesis Flagship (grant no. 346208).
Academy of Finland Grant Number: 346208
Detailed Information: 346208 (Academy of Finland Funding decision)
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