Intelligent Signal Identification of NOMA Signal with 256-QAM Modulation Using SVM Algorithm
Keywords:
SVM, NOMA, 5G, ZFE, MMSE, BFAbstract
Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technique to enhance the spectral efficiency and connectivity in wireless communication systems. This paper presents a novel approach for the detection of NOMA signals using Support Vector Machines (SVM), aiming to improve the efficiency and reliability of NOMA-enabled communication networks. The inherent challenge in NOMA lies in decoding multiple signals transmitted simultaneously on the same frequency channel. Conventional methods often struggle with the interference between these signals, leading to degraded performance. In this study, SVM, a machine learning algorithm known for its robust classification capabilities, is applied to effectively distinguish and demodulate NOMA signals. The proposed SVM-based detection system leverages the capability of SVM to find optimal hyperplanes in a high-dimensional space, enabling the classification of NOMA signals even in the presence of interference. The training phase involves the use of labelled datasets, where the SVM learns to differentiate between NOMA signals and potential interference patterns the parameters such as bit error rate (BER), Peak to average power ratio (PAPR) and power spectral density (PSD) are evaluated and analysed. Simulation results demonstrate the superior performance of the SVM-based NOMA signal detection compared to traditional methods. The SVM model exhibits high accuracy, robustness, and adaptability to varying signal conditions, making it a promising solution for the challenges posed by NOMA communication systems.
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