MDPRP: A Q-learning approach for the joint control of beaconing rate and transmission power in VANETs
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This work was supported in part by the AIM Project [Agencia Estatal de Investigación (AEI)/Fondo Europeo de Desarrollo Regional (FEDER), Unión Europea (UE)] under Grant TEC2016-76465-C2-1-R, in part by the Fundación Séneca, Región de Murcia, through the ATENTO Project, under Grant 20889/PI/18, and in part by the LIFE (Fondo SUPERA Covid-19 funded by the Agencia Estatal Consejo Superior de Investigaciones Científicas CSIC, Universidades Españolas, and Banco Santander). The work of Juan Aznar-Poveda was supported by the Spanish Ministerio de Educación, Cultura y Deporte (MECD) for the FPI Grant BES-2017-081061.Fecha de publicación
2021Editorial
IEEECita bibliográfica
Aznar-Poveda, J., Garcia-Sanchez, A. J., Egea-Lopez, E., and Garcia-Haro, J. (2021, January). MDPRP: A Q-Learning Approach for the Joint Control of Beaconing Rate and Transmission Power in VANETs. IEEE Access, 9, 10166-10178. DOI: 10.1109/ACCESS.2021.3050625Palabras clave
Vehicular ad-hoc networksConnected vehicles
Vehicle-to-vehicle (V2V) communications
Congestion control
Power control
Rate control
Reinforcement learning
IEEE 802.11p,
SAE J2945/1
Resumen
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) ...
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