MDPRP: A Q-Learning Approach for the Joint Control of Beaconing Rate and Transmission Power in VANETs Article Swipe
Juan Aznar-Poveda
,
Antonio-Javier García-Sánchez
,
Esteban Egea-López
,
Joan Garcı́a-Haro
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3050625
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3050625
Vehicular ad-hoc communications rely on periodic broadcast beacons as the basis for most of their safety applications, allowing vehicles to be aware of their surroundings. However, an excessive beaconing load might compromise the proper operation of these crucial applications, especially regarding the exchange of emergency messages. Therefore, congestion control can play an important role. In this article, we propose joint beaconing rate and transmission power control based on policy evaluation. To this end, a Markov Decision Process (MDP) is modeled by making a set of reasonable simplifying assumptions which are resolved using Q-learning techniques. This MDP characterization, denoted as MDPRP (indicating Rate and Power), leverages the trade-off between beaconing rate and transmission power allocation. Moreover, MDPRP operates in a non-cooperative and distributed fashion, without requiring additional information from neighbors, which makes it suitable for use in infrastructureless (ad-hoc) networks. The results obtained reveal that MDPRP not only balances the channel load successfully but also provides positive outcomes in terms of packet delivery ratio. Finally, the robustness of the solution is shown since the algorithm works well even in those cases where none of the assumptions made to derive the MDP model apply.
Related Topics
Concepts
Computer science
Beacon
Markov decision process
Robustness (evolution)
Computer network
Power control
Network packet
Wireless ad hoc network
Transmission (telecommunications)
Markov process
Control channel
Vehicular ad hoc network
Wireless
Real-time computing
Power (physics)
Telecommunications link
Telecommunications
Gene
Physics
Chemistry
Biochemistry
Mathematics
Statistics
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3050625
- https://ieeexplore.ieee.org/ielx7/6287639/9312710/09319141.pdf
- OA Status
- gold
- Cited By
- 26
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3118465984
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3118465984Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2021.3050625Digital Object Identifier
- Title
-
MDPRP: A Q-Learning Approach for the Joint Control of Beaconing Rate and Transmission Power in VANETsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Juan Aznar-Poveda, Antonio-Javier García-Sánchez, Esteban Egea-López, Joan Garcı́a-HaroList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3050625Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09319141.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09319141.pdfDirect OA link when available
- Concepts
-
Computer science, Beacon, Markov decision process, Robustness (evolution), Computer network, Power control, Network packet, Wireless ad hoc network, Transmission (telecommunications), Markov process, Control channel, Vehicular ad hoc network, Wireless, Real-time computing, Power (physics), Telecommunications link, Telecommunications, Gene, Physics, Chemistry, Biochemistry, Mathematics, Statistics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
26Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 4, 2023: 6, 2022: 10, 2021: 3Per-year citation counts (last 5 years)
- References (count)
-
50Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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