Sparse signals are encountered in many modern technologies. Compressed sensing methods create the opportunity of sampling sparse signals significantly lower than the Nyquist rate. However, sparse signal recovery still remains a challenge. In this work, we show that detecting the support of the sparse signal can be viewed as a classification problem, and hence efficiently be solved using neural networks (NN). We find the best NN configuration in support detection for random data sets, where an accuracy of 98% is achieved in highly sparse signals. After detecting the support of the signal, we show that signal recovery can be done efficiently. In this work, we develop two recovery methods. The simulation studies confirm that our proposed methods outperform counterpart algorithms in both accuracy and convergence speed.
NN-based Support Detection of Sparse Signals
2020-11-01
979573 byte
Conference paper
Electronic Resource
English
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