基于深度学习的无线电信号解调

K. Chia, Vishnu Monn Baskaran
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引用次数: 0

摘要

正交调幅(M-QAM)调制信号在数字通信系统中被广泛使用,因为它具有任意高的频谱效率,仅受通信信道的噪声水平和线性度的限制。典型的M-QAM信号解调技术利用相干解调的变体。本文旨在利用深度学习的鲁棒性,特别是通过使用神经网络解调M-QAM符号。这是通过模拟时域基带M-QAM信号实现的,这些信号跨越一系列信道损伤,即加性高斯白噪声、直流偏移和I/Q不平衡。所提出的结果表明,在最优接收器上使用深度学习是有改进的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning-Based Demodulation of Radio Signal
M-ary quadrature amplitude modulation (M-QAM) modulated signal is commonly used in digital telecommunication systems for its arbitrarily high spectral efficiencies limited only by the noise level and linearity of the communications channel. Typical demodulation techniques for M-QAM signal utilize variants of coherent demodulation. This paper aims to exploit the robustness of deep learning, specifically by using neural networks to demodulate M-QAM symbols. This is achieved with simulated time-domain baseband M-QAM signals across a range of channel impairments namely additive white Gaussian noise, DC offset and I/Q imbalance. The presented results show an improvement when utilizing deep learning over optimal receiver.
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