A Power Spectrum Maps Estimation Algorithm Based on Generative Adversarial Networks for Underlay Cognitive Radio Networks

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 1; p. 311
• Main Authors: Han, Xu, Xue, Lei, Shao, Fucai, Xu, Ying
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 06.01.2020; MDPI
• Subjects: Algorithms; Cartography; Deep learning; generative adversarial networks; image reconstruction; Licenses; Mapping; Mineral reserves; Neural networks; Power; power spectrum maps estimation; Radio networks; Spectrum allocation; underlay cognitive radio networks; Wireless networks; generative adversarial networks; deep learning; underlay cognitive radio networks; image reconstruction; power spectrum maps estimation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20010311

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’ 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’s 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.