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arXiv 1807.00747cs.ITeess.SPmath.IT
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基于纠错码的深度学习通信在线标签恢复

Online Label Recovery for Deep Learning-based Communication through Error Correcting Codes

Stefan Schibisch, Sebastian Cammerer, Sebastian Dörner, Jakob Hoydis, Stephan ten Brink

摘要:

我们证明,纠错码(ECC)可以用来构建一个标记的数据集微调的“可训练”的通信系统,而不牺牲已知符号的传输资源。这使得自适应系统能够进行实时训练,以补偿信道条件的缓慢波动或变化的硬件损伤。我们研究了损坏的训练数据的影响,并表明基于正确的标签进行训练至关重要。所提出的方法可以应用于完全端到端训练的通信系统(自动编码器),以及只有一些可训练的组件的系统。这通过用可在运行时间优化的可训练预均衡器神经网络(NN)扩展常规OFDM系统来例示。

英文摘要:

We demonstrate that error correcting codes (ECCs) can be used to construct a labeled data set for finetuning of "trainable" communication systems without sacrificing resources for the transmission of known symbols. This enables adaptive systems, which can be trained on-the-fly to compensate for slow fluctuations in channel conditions or varying hardware impairments. We examine the influence of corrupted training data and show that it is crucial to train based on correct labels. The proposed method can be applied to fully end-to-end trained communication systems (autoencoders) as well as systems with only some trainable components. This is exemplified by extending a conventional OFDM system with a trainable pre-equalizer neural network (NN) that can be optimized at run time.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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