Deep Learning-based Modulation Detection for NOMA Systems

TL;DR

This paper introduces a deep learning-based blind modulation detection algorithm for NOMA systems that uses wavelet denoising and a residual network to accurately identify modulation modes without extra signaling.

Contribution

It proposes a novel blind detection method utilizing joint constellation density diagrams and deep residual networks to improve modulation recognition in NOMA systems.

Findings

Achieves high detection accuracy in noisy environments

Wavelet denoising improves constellation quality and detection performance

Analyzes factors influencing recognition accuracy

Abstract

Since the signal with strong power should be demodulated first for successive interference cancellation (SIC) demodulation in non-orthogonal multiple access (NOMA) systems, the base station (BS) should inform the near user terminal (UT), which has allocated higher power, of modulation mode of the far user terminal. To avoid unnecessary signaling overhead in this process, a blind detection algorithm of NOMA signal modulation mode is designed in this paper. Taking the joint constellation density diagrams of NOMA signal as the detection features, deep residual network is built for classification, so as to detect the modulation mode of NOMA signal. In view of the fact that the joint constellation diagrams are easily polluted by high intensity noise and lose their real distribution pattern, the wavelet denoising method is adopted to improve the quality of constellations. The simulation results represent that the proposed algorithm can achieve satisfactory detection accuracy in NOMA systems. In addition, the factors affecting the recognition performance are also verified and analyzed.