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Spectrum Sensing Using CNN With Attention on Switch of Channel States

Journal Article


Abstract


  • This work addresses the issue of spectrum sensing with random arrival and departure of primary signals. We first design a convolutional neural network (CNN) with outputs as the posterior probabilities of the arrival and departure of primary signals, leading to a CNN-based detector with the ratio of the posterior probabilities (i.e., the outputs of the CNN) as a test statistic. To further enhance the attention of the network on the switch feature of channel states, we design a switch attention module (SAM) that adaptively weights the received signals. Replacing the convolution plus maximum pooling block in the CNN detector with the SAM block leads to an SAM-CNN detector. Simulations show that the proposed CNN detector significantly outperforms existing detectors, and further improvement of detection probability by 19% is achieved by the SAM-CNN detector.

Publication Date


  • 2022

Citation


  • Cong, Z., Jin, M., Guo, Q., Zhou, Z., & Tian, Y. (2022). Spectrum Sensing Using CNN With Attention on Switch of Channel States. IEEE Communications Letters, 26(10), 2365-2369. doi:10.1109/LCOMM.2022.3193302

Scopus Eid


  • 2-s2.0-85135235567

Start Page


  • 2365

End Page


  • 2369

Volume


  • 26

Issue


  • 10

Place Of Publication


Abstract


  • This work addresses the issue of spectrum sensing with random arrival and departure of primary signals. We first design a convolutional neural network (CNN) with outputs as the posterior probabilities of the arrival and departure of primary signals, leading to a CNN-based detector with the ratio of the posterior probabilities (i.e., the outputs of the CNN) as a test statistic. To further enhance the attention of the network on the switch feature of channel states, we design a switch attention module (SAM) that adaptively weights the received signals. Replacing the convolution plus maximum pooling block in the CNN detector with the SAM block leads to an SAM-CNN detector. Simulations show that the proposed CNN detector significantly outperforms existing detectors, and further improvement of detection probability by 19% is achieved by the SAM-CNN detector.

Publication Date


  • 2022

Citation


  • Cong, Z., Jin, M., Guo, Q., Zhou, Z., & Tian, Y. (2022). Spectrum Sensing Using CNN With Attention on Switch of Channel States. IEEE Communications Letters, 26(10), 2365-2369. doi:10.1109/LCOMM.2022.3193302

Scopus Eid


  • 2-s2.0-85135235567

Start Page


  • 2365

End Page


  • 2369

Volume


  • 26

Issue


  • 10

Place Of Publication