论文标题
基于深度学习的调制检测
Deep Learning-based Modulation Detection for NOMA Systems
论文作者
论文摘要
由于应首先将具有强力的信号解调,以便在非正交多访问(NOMA)系统中连续的干扰取消(SIC)解调,因此基站(BS)应告知近用户终端的近用户终端(UT),该端子已分配了较高的较高功率,是远用户终端的调制模式。为避免在此过程中的不必要的信号传导开销,本文设计了NOMA信号调制模式的盲目检测算法。以Noma信号的关节星座密度图为检测特征,深度残留网络是用于分类的,以检测NOMA信号的调制模式。鉴于联合星座图很容易被高强度噪声污染并失去其真实分布模式,因此采用了小波降解方法来提高星座的质量。模拟结果表明,所提出的算法可以在NOMA系统中实现令人满意的检测准确性。此外,还验证和分析了影响识别性能的因素。
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.