DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choe, Chungjae | ko |
dc.contributor.author | Choi, Junsung | ko |
dc.contributor.author | Ahn, Jangyong | ko |
dc.contributor.author | Park, Dongryul | ko |
dc.contributor.author | Ahn, Seungyoung | ko |
dc.date.accessioned | 2023-08-17T12:00:25Z | - |
dc.date.available | 2023-08-17T12:00:25Z | - |
dc.date.created | 2023-07-06 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.citation | 91st IEEE Vehicular Technology Conference, VTC Spring 2020 | - |
dc.identifier.issn | 1550-2252 | - |
dc.identifier.uri | http://hdl.handle.net/10203/311655 | - |
dc.description.abstract | Vehicular Ad-hoc Network (VANET) is a standard protocol for wireless vehicular communication that enables Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communications. VANET safety applications aim to prevent traffic accidents and require a high Packet Delivery Ratio (PDR) and low latency of safety packet broadcast. When a large number of vehicles simultaneously access a limited channel resource for the safety broadcast, the safety requirements impose more challenges; the communication performance will significantly degrade due to network congestion. Especially, infrastructureless VANETs, which only allow V2V communication, vehicles are supposed to overcome the congestion problem using a self-adaptation scheme without the aid of infrastructures. In this paper, we propose a self-adaptive MAC layer algorithm employing Deep Q Network (DQN) with a novel contention information-based state representation to improve the performance of the V2V safety packet broadcast. The proposed algorithm operates a fully distributed manner, and it is evaluated by simulations considering various levels of traffic congestion. | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Multiple Channel Access using Deep Reinforcement Learning for Congested Vehicular Networks | - |
dc.type | Conference | - |
dc.identifier.scopusid | 2-s2.0-85088291650 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 91st IEEE Vehicular Technology Conference, VTC Spring 2020 | - |
dc.identifier.conferencecountry | BE | - |
dc.identifier.conferencelocation | Antwerp | - |
dc.identifier.doi | 10.1109/VTC2020-Spring48590.2020.9128853 | - |
dc.contributor.localauthor | Ahn, Seungyoung | - |
dc.contributor.nonIdAuthor | Choe, Chungjae | - |
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