结构化无线电通信信号的无监督表示学习

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE) Pub Date : 2016-04-24 DOI:10.1109/SPLIM.2016.7528397

Tim O'Shea, Johnathan Corgan, T. Clancy

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引用次数: 80

摘要

我们在原始采样时间序列表示中探索无线电通信信号的无监督表示学习。我们证明了我们可以使用卷积自编码器学习调制基函数，并直观地识别它们与数字通信中使用的分析基的关系。我们还提出并评估了使用领域相关性能指标的编码质量定量指标。

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Unsupervised representation learning of structured radio communication signals

We explore unsupervised representation learning of radio communication signals in raw sampled time series representation. We demonstrate that we can learn modulation basis functions using convolutional autoencoders and visually recognize their relationship to the analytic bases used in digital communications. We also propose and evaluate quantitative metrics for quality of encoding using domain relevant performance metrics.

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来源期刊

2016 First International Workshop on Sensing, Processing and Learning for Intelligent Machines (SPLINE)

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