Efficient Approximation of SINR and Throughput in 5G NR via Sparsity and Interference Aggregation

Research output: Contribution to book/Conference proceedings/Anthology/ReportConference contributionContributedpeer-review

Contributors

Abstract

This paper presents a novel approach to scheduling resources in a multi-beam next-generation Node B (gNB) that enables efficient resource reuse across beams within a transmission time interval (TTI). Unlike traditional medium access control (MAC) scheduling, which focuses on resource allocation within a single beam, our approach considers the simultaneous scheduling of multiple beams. We leverage a recently introduced sparse model and propose an algorithm that avoids exhaustive Monte Carlo (MC) simulation while approximating signal-to-interference-plus-noise ratio (SINR) and achievable throughput parameters in snapshot-based simulations. This approximation significantly reduces computational complexity while maintaining negligible error. We validate our approach through extensive simulations, demonstrating its effectiveness in approximating SINR and achievable throughput.

Details

Original languageEnglish
Title of host publication2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
PublisherIEEE
Pages1-7
Number of pages7
ISBN (electronic)978-1-6654-6483-3
ISBN (print)978-1-6654-6484-0
Publication statusPublished - 8 Sept 2023
Peer-reviewedYes

Conference

Title2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications
Abbreviated titlePIMRC 2023
Conference number34
Duration5 - 8 September 2023
Website
Degree of recognitionInternational event
LocationThe Westin Harbour Castle
CityToronto
CountryCanada

External IDs

Scopus 85174984897
ORCID /0000-0002-0738-556X/work/177360502

Keywords

ASJC Scopus subject areas

Keywords

  • 5G mobile communication, Approximation algorithms, Interference, Monte Carlo methods, Processor scheduling, Radio frequency, Throughput, Multi-beam, linear programming, scheduler, sparse solution, Monte Carlo, 5G NR