Spectrum sensing with transmit power identification can greatly facilitate the application of the hybrid spectrum access strategy in cognitive radio (CR) networks. Conventional model-driven methods suffer from severe performance degradation in low signal-to-noise ratio (SNR) regime. In this paper, we propose a multiple transmit power levels identification network (TPIN) which consists of three components. In the data preprocessing components, the covariance matrix (COV) of the received data is first employed as the observation data. Then, the residual network (ResNet) based feature extractor components is used to construct the test statistic by extracting high-dimensional features of the observation data. Furthermore, the likelihood ratio test (LRT) criterion is leveraged to design the cost function for obtaining the maximum posterior probability in the classifier components. Different from the assumption in conventional method, the prior probability of each transmit power levels is unknown to the TPIN, and the array of training set is randomly disturbed. In addition, in order to verify the ability of TPIN in data features extraction, a comparison reference experiment using a general test statistic (e.g., higher-order cumulative) as the observation data is introduced. Finally, simulation results demonstrate the identification performance of the COV-based (COV-TPIN) scheme.
|Title of host publication||2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||E-pub ahead of print - 11 Jan 2023|
|Event||2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil|
Duration: 4 Dec 2022 → 8 Dec 2022
|Name||2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings|
|Conference||2022 IEEE Global Communications Conference, GLOBECOM 2022|
|Period||4/12/22 → 8/12/22|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This work was supported in part by the National Key R&D Program of China 2021YFC2203503; in part by the National Natural Science Foundation of China 61901328, 11973077 and 12003061; in part by the Young Talent fund of University Association for Science and Technology in Shaanxi, China 20210111.
© 2022 IEEE.
- cognitive radio
- convolutional neural network
- multiple transmit power levels identification
- non-gaussian signal