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A Q-Learning approach with collective contention estimation for bandwidth-efficient and fair access control in IEEE 802.11p vehicular networks
Version 2 2023-06-07, 08:25
Version 1 2023-06-07, 06:38
journal contribution
posted on 2023-06-07, 08:25 authored by Andreas Pressas, Zhengguo ShengZhengguo Sheng, Falah AliFalah Ali, Daxin TianVehicular Ad hoc Networks (VANETs) are wireless networks formed of moving vehicle-stations, that enable safety-related packet exchanges among them. Their infrastructure-less, unbounded nature allows the formation of dense networks that present a channel sharing issue, which is harder to tackle than in conventional WLANs, due to fundamental differences of the protocol stack. Optimising channel access strategies is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. We present a Q-Learning-based approach to wirelessly network a big number of vehicles and enable the efficient exchange of data packets among them. More specifically, this work focuses on a IEEE 802.11p-compatible contention-based Medium Access Control (MAC) protocol for efficiently sharing the wireless channel among multiple vehicular stations. The stations feature algorithms that "learn" how to act optimally in a network in order to maximise their achieved packet delivery and minimise bandwidth wastage. Additionally, via a Collective Contention Estimation (CCE) mechanism which we embed on the Q-Learning agent, faster convergence, higher throughput and short-term fairness are achieved.
Funding
Doing More with Less Wiring: Mission-Critical and Intelligent Communication Protocols for Future Vehicles Using Power Lines; G2132; EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCIL; EP/P025862/1
Bionic communications and networking for connected vehicles; G2114; ROYAL SOCIETY; IE160920
Intelligent and decentralized cyber security and privacy for 5G vehicular networks; G2628
History
Publication status
- Published
File Version
- Accepted version
Journal
IEEE Transactions on Vehicular TechnologyISSN
0018-9545Publisher
Institute of Electrical and Electronics EngineersExternal DOI
Department affiliated with
- Engineering and Design Publications
Research groups affiliated with
- Advanced Communications, Mobile Technology and IoT (ACMI) Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2019-07-12First Open Access (FOA) Date
2019-07-16First Compliant Deposit (FCD) Date
2019-07-11Usage metrics
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