Deep PC-MAC: a deep reinforcement learning pointer-critic media access protocol

Al-Tam, Faroq; Mazayev, Andriy; Correia, Noélia; Rodriguez, J.

http://hdl.handle.net/10400.1/16611

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Resumo(s)

Developing 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.

Palavras-chave

5G Deep reinforcement learning MAC Unlicensed bands CSMA/CA LTE-LAA LTE-U WiFi Coexistence

URI

http://hdl.handle.net/10400.1/16611

Projetos de investigação

Center for Electronics, Optoelectronics and Telecommunications

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Editora

IEEE

Coleções

FCT2-Artigos (em revistas ou actas indexadas)

Licença CC

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