Satellite based spectrum sensing is studied for a system consisting of multiple satellites and a gateway (GW), where these satellites perform spectrum sensing and send mass spectrum-sensing data to the GW. To address the challenges of mass spectrum-sensing data and limited transmission capacity of the links from spectrum-sensing satellites to the GW, we propose a method called joint anomalous data repairing and deep convolutional neural network based spectrum reconstruction (ADRD-SR), which can reconstruct the original spectrum-sensing data from the incomplete data. Specifically, the GW preprocesses the incomplete data using the anomalous data repairing algorithm. A deep convolutional neural network is constructed and well trained, then it is activated to reconstruct the preprocessed spectrum data. Additionally, to sustain good reconstruction performance by tracing the dynamical spectrum-sensing data, we design a real-time evaluation oriented spectrum reconstruction framework, through seeking the events when the mean absolute error (MAE) becomes larger than a predefined threshold. Furthermore, the ADRD-SR method can reduce the MAE by more than 68% over the conventional reconstruction methods. Moreover, the reconstructed spectrum data can be used to assist spectrum sensing, and the corresponding probability of correct detection is only degraded by 5% even when 75% of the data is discarded.

中文翻译：

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卫星通信系统中通过深度卷积神经网络进行频谱重建


基于卫星的频谱感知研究了由多颗卫星和网关（GW）组成的系统，其中这些卫星执行频谱感知并向GW发送质谱感知数据。为了解决质谱传感数据和频谱传感卫星到GW链路传输容量有限的挑战，我们提出了一种称为联合异常数据修复和基于深度卷积神经网络的频谱重建（ADRD-SR）的方法，该方法可以从不完整的数据中重建原始的光谱传感数据。具体地，GW利用异常数据修复算法对不完整数据进行预处理。构建深度卷积神经网络并对其进行良好训练，然后激活它来重建预处理的频谱数据。此外，为了通过跟踪动态频谱传感数据来维持良好的重建性能，我们设计了一个面向实时评估的频谱重建框架，通过寻找平均绝对误差（MAE）大于预定义阈值时的事件。此外，ADRD-SR方法比传统重建方法可以降低MAE超过68%。此外，重建的频谱数据可以用于辅助频谱感知，即使丢弃75%的数据，相应的正确检测概率也仅降低5%。