A Two-Phase Transfer Learning-Based Power Spectrum Maps Reconstruction Algorithm for Underlay Cognitive Radio Networks

Abstract

In the underlay cognitive radio networks, the power spectrum maps (PSMs) estimation is the main challenge in sensing the idle wireless radio resources. Traditional deep learning-based algorithms achieve good estimation performance, under the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the PSMs in the target region. However, collecting the PSMs training data is not an easy task, which is time-consuming and requires a numerous number of sensing devices. For this reason, we propose a two-phase transfer learning generative adversarial network (TPTL-GAN) for the PSMs reconstruction task. The proposed algorithm relaxes the i.i.d. assumption in traditional deep learning-based algorithms, allowing us to estimate the PSMs based on the simulated or previously collected training data, which share similar rather than strictly identical distribution with the target data. In the first phase of the TPTL-GAN algorithm, we design a domain projecting (DP) framework to project the source domain to the adjacent domain. In the second phase, we propose a domain completing (DC) framework, which extracts helpful radio environment features from the adjacent domain and reconstructs the PSMs in the target domain. Through the above two phases, the proposed algorithm provides a more accurate PSMs reconstruction performance than the traditional methods, as verified by the simulation results.