{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:37:50Z","timestamp":1781714270892,"version":"3.54.5"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T00:00:00Z","timestamp":1578268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the underlay cognitive radio networks, the main challenge in detecting the idle radio resources is to estimate the power spectrum maps (PSMs), where the radio propagation characteristics are hard to obtain. For this reason, we propose a novel PSMs estimation algorithm based on the generative adversarial networks (GANs). First, we constructed the PSMs estimation model as a regression model in deep learning. Then, we converted the estimation task into an image reconstruction task by image color mapping. We fulfilled the above task by designing an image generator and an image discriminator in the proposed maps\u2019 estimation GANs (MEGANs). The generator is trained to extract the radio propagation characteristics and generate the PSMs images. However, the discriminator is trained to identify the generated images and help to improve the generator\u2019s performance. With the training process of MEGANs, the abilities of the generator and the discriminator are enhanced continually until reaching a balance, which means a high-accuracy PSMs estimation is achieved. The proposed MEGANs algorithm learns and utilizes accurate radio propagation features from the training process rather than making direct imprecise or biased propagation assumptions as in the traditional methods. Simulation results demonstrate that the MEGANs algorithm provides a more accurate estimation performance than the conventional methods.<\/jats:p>","DOI":"10.3390\/s20010311","type":"journal-article","created":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T10:34:46Z","timestamp":1578306886000},"page":"311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":59,"title":["A Power Spectrum Maps Estimation Algorithm Based on Generative Adversarial Networks for Underlay Cognitive Radio Networks"],"prefix":"10.3390","volume":"20","author":[{"given":"Xu","family":"Han","sequence":"first","affiliation":[{"name":"Electronic Countermeasure College, National University of Defense Technology, Shushan District, Hefei 230037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xue","sequence":"additional","affiliation":[{"name":"Electronic Countermeasure College, National University of Defense Technology, Shushan District, Hefei 230037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fucai","family":"Shao","sequence":"additional","affiliation":[{"name":"Beijing Military Representative Office, Beijing 100191, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Xu","sequence":"additional","affiliation":[{"name":"Electronic Countermeasure College, National University of Defense Technology, Shushan District, Hefei 230037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Han, B., Luo, Y., and Zeng, M. 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