Joint Demodulation and Decoding with Multi-Label Classification Using Deep Neural Networks

I. Ahmed, Wenjie Xu, R. Annavajjala, Woo-Sung Yoo
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引用次数: 1

Abstract

In this paper, we leverage the power of artificial intelligence in the receiver design for joint baseband demodulation and channel decoding. We consider a point-to-point communication system and develop a deep neural network (DNN) based joint demodulator and decoder (DeModCoder) that accomplishes the tasks of demodulation and decoding in a single operational block. We incorporate a multi-label classification (MLC) scheme for the considered DNN framework, which is trained offline over a wide-range of signal-to-noise ratios (SNRs) in a supervised learning manner and deployed online in real-time applications. Simulation results demonstrate that our developed DeModCoder outperforms the conventional block-based sequential demodulation and decoding schemes. We also observe that the MLC DeModCoder shows better performance than conventional multiple output classifier in high SNR region while incurring lower computational complexity.
基于深度神经网络的多标签分类联合解调与解码
在本文中,我们利用人工智能的力量在接收器设计联合基带解调和信道解码。我们考虑了一个点对点通信系统,并开发了一个基于深度神经网络(DNN)的联合解调器和解码器(DeModCoder),它在单个操作块中完成解调和解码任务。我们将多标签分类(MLC)方案纳入考虑的深度神经网络框架,该框架以监督学习的方式在广泛的信噪比(SNRs)范围内离线训练,并在线部署在实时应用中。仿真结果表明,我们开发的DeModCoder优于传统的基于块的顺序解调和解码方案。我们还观察到MLC DeModCoder在高信噪比区域比传统的多输出分类器表现出更好的性能,同时产生更低的计算复杂度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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