Learning to Decode Trellis Coded Modulation

Jayant Sharma, V. Lalitha
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Abstract

Trellis coded modulation (TCM) is a technique combining modulation with coding using trellises designed with heuristic techniques that maximize the minimum Euclidean distance of a codebook. We propose a neural networks based decoder for decoding TCM. We show experiments with our decoder that suggest the use of Convolutional Neural Network (CNN) with Recurrent Neural Network (RNN) can improve decoding performance and provide justification for the same. We show the generalization capability of the decoder by training it with small block length and testing for larger block length. We also test our decoder for its performance on noise model unseen in the training.
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学习解码格子编码调制
网格编码调制(TCM)是一种结合调制和编码的技术,使用启发式技术设计的网格,最大化码本的最小欧几里得距离。提出了一种基于神经网络的中药解码器。我们展示了解码器的实验,表明卷积神经网络(CNN)与循环神经网络(RNN)的使用可以提高解码性能,并为相同的解码器提供了理由。我们通过小块长度的训练和大块长度的测试来展示解码器的泛化能力。我们还测试了解码器在训练中未见的噪声模型上的性能。
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