Journal
IEEE COMMUNICATIONS LETTERS
Volume 24, Issue 10, Pages 2196-2200Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2020.3002073
Keywords
Feature extraction; Detectors; Covariance matrices; Hidden Markov models; Data models; Deep learning; CNN; cognitive radio; deep learning; LSTM; spectrum sensing
Categories
Funding
- National Natural Science Foundation of China [61934008, 61871091, 61801082]
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For most existing spectrum sensing detectors, the design of their test statistics relies on certain signal-noise model assumptions and hence, their detection performance heavily depends on the accuracy of the assumed models. Therefore, recently, much attention in the research of spectrum sensing is focused on deep learning which is free from model assumptions. Note that, in deep learning, the convolutional neural networks (CNNs) and the long-short term memory (LSTM) networks have the powerful capabilities in extracting spatial and temporal features of the input, respectively. In this letter, we propose a CNN-LSTM detector which first uses the CNN to extract the energy-correlation features from the covariance matrices generated by the sensing data, then the series of energy-correlation features corresponding to multiple sensing periods are input into the LSTM so that the PU activity pattern can be learned. The purpose of learning PU activity pattern is to further promote the detection probability. With sufficient simulations, the superiority of the CNN-LSTM detector is proven in scenarios with and without noise uncertainty.
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