Modulation Classification for OTFS-NOMA in the presence of HPA Nonlinearity and Impulsive Noise
Maurya, Pawan; Bhatia, Vimal; Rajatheva, Nandana; Latva-Aho, Matti (2025-02-07)
IEEE
07.02.2025
P. Maurya, V. Bhatia, N. Rajatheva and M. Latva-Aho, "Modulation Classification for OTFS-NOMA in the presence of HPA Nonlinearity and Impulsive Noise," 2024 27th International Symposium on Wireless Personal Multimedia Communications (WPMC), Greater Noida, India, 2024, pp. 1-5, doi: 10.1109/WPMC63271.2024.10863670
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© 2025 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.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202504012317
https://urn.fi/URN:NBN:fi:oulu-202504012317
Tiivistelmä
Abstract
Orthogonal Time Frequency Space (OTFS) is designed for high-speed communication scenarios where high-mobility users strain traditional bandwidth resources. To address this, OTFS-NOMA, a protocol combining OTFS with nonorthogonal multiple access (NOMA), is introduced for users with varying mobility profiles. While most research assumes ideal systems characterized by linearity and Gaussian noise, real-world systems often involve high-power amplifiers that exhibit nonlinear behavior, significantly affecting communication performance. Additionally, impulsive noise is frequently encountered in industrial, transportation, and other high-mobility settings, making it crucial to consider both factors when evaluating modulation classification accuracy. This study evaluates modulation classification accuracy under these conditions, highlighting spectrum utilization efficiency by combining signals from different mobility profiles. By leveraging adaptive modulation based on channel conditions and employing machine learning to assess classification accuracy across various SNR levels, power ratios, and nonlinearity factors, the study underscores OTFS-NOMA’s resilience in practical, high-mobility scenarios, enhancing its potential for real-world applications.
Orthogonal Time Frequency Space (OTFS) is designed for high-speed communication scenarios where high-mobility users strain traditional bandwidth resources. To address this, OTFS-NOMA, a protocol combining OTFS with nonorthogonal multiple access (NOMA), is introduced for users with varying mobility profiles. While most research assumes ideal systems characterized by linearity and Gaussian noise, real-world systems often involve high-power amplifiers that exhibit nonlinear behavior, significantly affecting communication performance. Additionally, impulsive noise is frequently encountered in industrial, transportation, and other high-mobility settings, making it crucial to consider both factors when evaluating modulation classification accuracy. This study evaluates modulation classification accuracy under these conditions, highlighting spectrum utilization efficiency by combining signals from different mobility profiles. By leveraging adaptive modulation based on channel conditions and employing machine learning to assess classification accuracy across various SNR levels, power ratios, and nonlinearity factors, the study underscores OTFS-NOMA’s resilience in practical, high-mobility scenarios, enhancing its potential for real-world applications.
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