ML-assisted resource allocation outage probability: simple, closed-form approximations

Rashika Raina*, Nidhi Simmons, David E. Simmons, Michel Daoud Yacoub

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
12 Downloads (Pure)

Abstract

In this paper, we establish simple and efficient approximations for the outage probability of a single-user multi-resource allocation system that consists of a machine learning (ML) based outage predictor whose task is to assign resources to the user while minimizing outages. We begin by presenting the outage probability expressions for this system. We then propose the approximations to this system’s outage probability using both the sinc function and the zeroth-order Bessel function of the first kind. These approximations are based on naive upper and lower bounds and stem from understanding how the channel samples de-correlate over time. Our results demonstrate that the outage probability indeed lies within the range defined by the bounds. Moreover, the effectiveness of the proposed outage probability approximations are evident as they exhibit a strong alignment with the trend of the outage probability curve. Finally, because of the simplicity of our approximations, they can be calculated in a computationally efficient manner.
Original languageEnglish
Title of host publication2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS): Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350307672
ISBN (Print)9798350307689
DOIs
Publication statusPublished - 25 Mar 2024
Event2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) - Jaipur, India
Duration: 17 Dec 202420 Dec 2024

Publication series

NameIEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS): Proceedings
ISSN (Print)2153-1676
ISSN (Electronic)2153-1684

Conference

Conference2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)
Country/TerritoryIndia
CityJaipur
Period17/12/202420/12/2024

Keywords

  • single-user multi-resource allocation system
  • ML
  • Machine learning (ML)
  • outages

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  • Best Workshop Paper Award

    Raina, R. (Recipient), Simmons, N. (Recipient), Simmons, D. E. (Recipient) & Yacoub, M. D. (Recipient), 2023

    Prize: Prize (including medals and awards)

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