Outage Analysis of Sparse Vector Coding-Based Downlink Multicarrier NOMA for URLLC
Ultrareliable and low-latency communication (uRLLC) is a promising use case in the fifth generation and beyond systems for critical and delay-sensitive applications with aid of short-packet transmission. Nevertheless, multicarrier nonorthogonal multiple access (MCNOMA) is a promising technique to ac...
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Published in | IEEE internet of things journal Vol. 10; no. 14; pp. 12393 - 12400 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
IEEE
15.07.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | Ultrareliable and low-latency communication (uRLLC) is a promising use case in the fifth generation and beyond systems for critical and delay-sensitive applications with aid of short-packet transmission. Nevertheless, multicarrier nonorthogonal multiple access (MCNOMA) is a promising technique to achieve spectral efficiency and massive connectivity, however, intercarrier interference (ICI) stands out to be a major downside. Recently, sparse vector coding (SVC) is proposed to fulfill the tradeoff between reliability and latency, thereby achieving the benchmarks of uRLLC. In this article, SVC-based downlink MCNOMA (SVC-MCNOMA) system is considered and first it is shown that SVC-MCNOMA is free from ICI due to the sparse nature of the information. Further, compressed sensing (CS)-based recovery at the receiver end helps in reducing the latency as well as enhances reliability. Moreover, to study the system performance, this article derives the closed-form outage expressions of the SVC-MCNOMA system over the Rician fading channel, and through Monte Carlo simulation, the analytical results are validated. The analyses demonstrate that SVC-MCNOMA outperforms conventional MCNOMA (C-MCNOMA), thereby achieving spectral efficacy, ultrareliability with low latency, and eradicates the effect of ICI. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 2327-4662 2327-4662 |
DOI: | 10.1109/JIOT.2023.3246300 |