Repository logo
 
Loading...
Thumbnail Image
Publication

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

Use this identifier to reference this record.

Advisor(s)

Abstract(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.

Description

Keywords

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

Citation

Organizational Units

Journal Issue