Modulation Classification for OTFS-NOMA in the presence of HPA Nonlinearity and Impulsive Noise

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. © 2024 IEEE.