Deep PC-MAC: A deep reinforcement learning pointer-critic media access protocol
| Autor: | Noélia Correia, Faroq AL-Tam, Andriy Mazayev, Jonathan Rodriguez |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
02 engineering and technology Unlicensed bands Computer Science Telecommunications Base station 0203 mechanical engineering LTE-LAA 0202 electrical engineering electronic engineering information engineering LTE-U Reinforcement learning Architecture Deep reinforcement learning MAC Unlicensed band business.industry WiFi 020206 networking & telecommunications 020302 automobile design & engineering CSMA/CA Pointer (computer programming) Performance indicator business 5G Coexistence Carrier sense multiple access with collision avoidance Computer network |
| Zdroj: | CAMAD |
| DOI: | 10.1109/camad50429.2020.9209306 |
| Popis: | 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. European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Programme (COMPETE 2020) Fundacao para a ciencia e TecnologiaPortuguese Foundation for Science and TechnologyEuropean Commission [POCI-01-0145FEDER-030500] Fundacao para a ciencia e Tecnologia, Portugal, within CEOT (Center for Electronic, Optoelectronic and Telecommunications) [UID/MULTI/00631/2020] info:eu-repo/semantics/publishedVersion |
| Databáze: | OpenAIRE |
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