Al-Tam, FaroqMazayev, AndriyCorreia, NoéliaRodriguez, J.2021-06-242021-06-242020978-1-7281-6339-02378-4865http://hdl.handle.net/10400.1/16611Developing artificial intelligence (AI) solutions for communication problems is one of the hottest topics nowadays. This article presents Deep PC-MAC, a novel deep reinforcement learning (DRL) solution to solve the fair coexistence problem (FCP) between heterogeneous nodes in the unlicensed bands. It is based on a hybrid architecture between pointer networks (Ptr-nets) and advantage actor-critic (A2C), i.e., pointer-critic architecture. The proposed model allows base stations to fairly share unlicensed bands with incumbent nodes. It jointly protects the incumbent nodes from spectrum starvation and improves key-performance indicators (KPIs). Deep PC-MAC is trained from scratch with zero-knowledge about FCP and experimental results demonstrate its efficiency when compared to a baseline method.eng5GDeep reinforcement learningMACUnlicensed bandsCSMA/CALTE-LAALTE-UWiFiCoexistenceComputer Science; Telecommunicationsconference object