End-to-End Deep Learning-Based Compressive Spectrum Sensing in Cognitive Radio Networks
End-to-End Deep Learning-Based Compressive Spectrum Sensing in Cognitive Radio Networks
复制标题
认知无线电网络中基于端到端深度学习的压缩频谱感知
DOI：
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发表时间：
2020
期刊：
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通讯作者：
B. Krongold
中科院分区：
文献类型：
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作者：
Xiangyue Meng;Hazer Inaltekin;B. Krongold
在认知无线电网络中，压缩感知有可能允许辅助用户在不需要复杂硬件的情况下以亚奈奎斯特采样率有效地监控宽带频谱。一般而言，压缩感知技术利用宽带频谱稀疏性的假设，通过求解一组不适定的线性方程来恢复频谱。本文在深度学习中采用生成性对抗神经网络(GAN)的框架，提出了一种深度压缩频谱感知GAN(DCS-GAN)，通过训练两个神经网络来竞争以从时间域中的欠采样样本中恢复频谱。所提出的DCS-GAN是一种数据驱动的学习方法，不需要关于无线电环境的先验统计。此外，它是一种端到端算法，可以直接从原始样本中恢复频谱占用信息，而不需要进行能量检测。各种模拟结果表明，与传统的LASSO方法相比，在压缩比为1/8的情况下，DCS-GAN的预测精度提高了12.3%~16.2%。
In cognitive radio networks, compressive sensing has the potential to allow a secondary user to efficiently monitor a wideband spectrum at a sub-Nyquist sampling rate without complex hardware. In general, compressive sensing techniques leverage the assumption of sparsity of the wideband spectrum to recover the spectrum by solving a set of ill-posed linear equations. In this paper, we adopt the framework of a generative adversarial neural network (GAN) in deep learning and propose a deep compressive spectrum sensing GAN (DCSS-GAN), where two neural networks are trained to compete with each other to recover the spectrum from undersampled samples in the time domain. The proposed DCSS-GAN is a data-driven learning approach that does not require a priori statistics about the radio environment. In addition, it is an end-to-end algorithm that directly recovers the information of spectrum occupancy from raw samples and without the need of energy detection. Various simulations show that the proposed DCSS-GAN has a 12.3% to 16.2% performance gain on prediction accuracy at a 1/8th compression ratio compared to the conventional LASSO approach.