Simultaneous Data Rate and Transmission Power Adaptation in V2V Communications: A Deep Reinforcement Learning Approach
In Vehicle-to-Vehicle (V2V) communications, channel load is key to ensuring the appropriate operation of safety applications as well as driver-assistance systems. As the number of vehicles increases, so do their communication messages. Therefore, channel congestion may arise, negatively impacting ch...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9526602/ |
| _version_ | 1818608329530802176 |
|---|---|
| author | Juan Aznar-Poveda Antonio-Javier Garcia-Sanchez Esteban Egea-Lopez Joan Garcia-Haro |
| author_facet | Juan Aznar-Poveda Antonio-Javier Garcia-Sanchez Esteban Egea-Lopez Joan Garcia-Haro |
| author_sort | Juan Aznar-Poveda |
| collection | DOAJ |
| description | In Vehicle-to-Vehicle (V2V) communications, channel load is key to ensuring the appropriate operation of safety applications as well as driver-assistance systems. As the number of vehicles increases, so do their communication messages. Therefore, channel congestion may arise, negatively impacting channel performance. Through suitable adjustment of the data rate, this problem would be mitigated. However, this usually involves using different modulation schemes, which can jeopardize the robustness of the solution due to unfavorable channel conditions. To date, little effort has been made to adjust the data rate, alone or together with other parameters, and its effects on the aforementioned sensitive safety applications remain to be investigated. In this paper, we employ an analytical model which balances the data rate and transmission power in a non-cooperative scheme. In particular, we train a Deep Neural Network (DNN) to precisely optimize both parameters for each vehicle without using additional information from neighbors, and without requiring any additional infrastructure to be deployed on the road. The results obtained reveal that our approach, called NNDP, not only alleviates congestion, leaving a certain fraction of the channel available for emergency-related messages, but also provides enough transmission power to fulfill the application layer requirements at a given coverage distance. Finally, NNDP is thoroughly tested and evaluated in three realistic scenarios and under different channel conditions, demonstrating its robustness and excellent performance in comparison with other solutions found in the scientific literature. |
| first_indexed | 2024-12-16T14:40:55Z |
| format | Article |
| id | doaj.art-e4a629dde4374712ad9536b55ffceb03 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T14:40:55Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e4a629dde4374712ad9536b55ffceb032022-12-21T22:27:57ZengIEEEIEEE Access2169-35362021-01-01912206712208110.1109/ACCESS.2021.31094229526602Simultaneous Data Rate and Transmission Power Adaptation in V2V Communications: A Deep Reinforcement Learning ApproachJuan Aznar-Poveda0https://orcid.org/0000-0002-0879-6651Antonio-Javier Garcia-Sanchez1https://orcid.org/0000-0001-5095-3035Esteban Egea-Lopez2https://orcid.org/0000-0002-6926-4923Joan Garcia-Haro3https://orcid.org/0000-0003-0741-7530Department of Information and Communications Technologies, Universidad Politécnica de Cartagena, Cartagena, SpainDepartment of Information and Communications Technologies, Universidad Politécnica de Cartagena, Cartagena, SpainDepartment of Information and Communications Technologies, Universidad Politécnica de Cartagena, Cartagena, SpainDepartment of Information and Communications Technologies, Universidad Politécnica de Cartagena, Cartagena, SpainIn Vehicle-to-Vehicle (V2V) communications, channel load is key to ensuring the appropriate operation of safety applications as well as driver-assistance systems. As the number of vehicles increases, so do their communication messages. Therefore, channel congestion may arise, negatively impacting channel performance. Through suitable adjustment of the data rate, this problem would be mitigated. However, this usually involves using different modulation schemes, which can jeopardize the robustness of the solution due to unfavorable channel conditions. To date, little effort has been made to adjust the data rate, alone or together with other parameters, and its effects on the aforementioned sensitive safety applications remain to be investigated. In this paper, we employ an analytical model which balances the data rate and transmission power in a non-cooperative scheme. In particular, we train a Deep Neural Network (DNN) to precisely optimize both parameters for each vehicle without using additional information from neighbors, and without requiring any additional infrastructure to be deployed on the road. The results obtained reveal that our approach, called NNDP, not only alleviates congestion, leaving a certain fraction of the channel available for emergency-related messages, but also provides enough transmission power to fulfill the application layer requirements at a given coverage distance. Finally, NNDP is thoroughly tested and evaluated in three realistic scenarios and under different channel conditions, demonstrating its robustness and excellent performance in comparison with other solutions found in the scientific literature.https://ieeexplore.ieee.org/document/9526602/Vehicular ad-hoc networksconnected vehiclesVehicle-to-Vehicle (V2V) communicationscongestion controlpower controldata rate control |
| spellingShingle | Juan Aznar-Poveda Antonio-Javier Garcia-Sanchez Esteban Egea-Lopez Joan Garcia-Haro Simultaneous Data Rate and Transmission Power Adaptation in V2V Communications: A Deep Reinforcement Learning Approach IEEE Access Vehicular ad-hoc networks connected vehicles Vehicle-to-Vehicle (V2V) communications congestion control power control data rate control |
| title | Simultaneous Data Rate and Transmission Power Adaptation in V2V Communications: A Deep Reinforcement Learning Approach |
| title_full | Simultaneous Data Rate and Transmission Power Adaptation in V2V Communications: A Deep Reinforcement Learning Approach |
| title_fullStr | Simultaneous Data Rate and Transmission Power Adaptation in V2V Communications: A Deep Reinforcement Learning Approach |
| title_full_unstemmed | Simultaneous Data Rate and Transmission Power Adaptation in V2V Communications: A Deep Reinforcement Learning Approach |
| title_short | Simultaneous Data Rate and Transmission Power Adaptation in V2V Communications: A Deep Reinforcement Learning Approach |
| title_sort | simultaneous data rate and transmission power adaptation in v2v communications a deep reinforcement learning approach |
| topic | Vehicular ad-hoc networks connected vehicles Vehicle-to-Vehicle (V2V) communications congestion control power control data rate control |
| url | https://ieeexplore.ieee.org/document/9526602/ |
| work_keys_str_mv | AT juanaznarpoveda simultaneousdatarateandtransmissionpoweradaptationinv2vcommunicationsadeepreinforcementlearningapproach AT antoniojaviergarciasanchez simultaneousdatarateandtransmissionpoweradaptationinv2vcommunicationsadeepreinforcementlearningapproach AT estebanegealopez simultaneousdatarateandtransmissionpoweradaptationinv2vcommunicationsadeepreinforcementlearningapproach AT joangarciaharo simultaneousdatarateandtransmissionpoweradaptationinv2vcommunicationsadeepreinforcementlearningapproach |