Policy Learning based Cognitive Radio for Unlicensed Cellular Communication
Peihao Yang, Jiale Lei, Linghe Kong, Chenren Xu, Peng Fei Zeng, Evgeny Khorov · GLOBECOM 2022 - 2022 IEEE Global Communications Conference · 2022
With the fast evolution in the cellular communication, the unlicensed spectrum is exploited to resolve the shortage of band resources. The sharing of the unlicensed spectrum extends the applications of LTE and 5G NR techniques, especially in the industrial Internet of Things (IIoT). However, the coexistence problem among various communication technologies in the unlicensed spectrum arises great concerns due to the different communication mechanisms. The existing solutions are either not compatible with the LTE/NR standards or not flexible enough for complex and dynamic IIoT environments. In this paper, we propose a policy learning based unlicensed communication (PLUC) framework to directly learn coexistence policies from the spectrogram of frequency channels. A recurrent neural network (RNN) is built to deal with the observations from time-variant spectrogram and extract deep learning features. We further verify this framework under the duty cycle mechanism and the listen before talk mechanism in 3GPP standards, respectively. The experiments reveal the effectiveness of the proposed framework in the dynamic environment.