Simultaneous data rate and transmission power adaptation in V2V communications: A deep reinforcement learning approach
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This work was supported in part by the AEI/FEDER/UE [Agencia Estatal de Investigación (AEI), Fondo Europeo de Desarrollo Regional (FEDER), and Unión Europea (UE)] under Grant PID2020-116329GB-C22 [ARISE2: Future IoT Networks and Nano-networks (FINe)] and Grant PID2020-112675RB-C41 (ONOFRE-3), in part by the Fundación Séneca, Región de Murcia, under Grant 20889/PI/18 (ATENTO), and in part by the LIFE project (Fondo SUPERA COVID-19 through 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) through the Formación de Personal Investigador (FPI) Predoctoral Scholarship under Grant BES-2017-081061Fecha de publicación
2021Editorial
IEEECita bibliográfica
Aznar-Poveda, J., Garcia-Sanchez, A. J., Egea-Lopez, E., and Garcia-Haro, J. (2021, August). Simultaneous Data Rate and Transmission Power Adaptation in V2V Communications: A Deep Reinforcement Learning Approach. IEEE Access 9, 122067-122081. DOI: 10.1109/ACCESS.2021.3109422Palabras clave
Vehicular ad-hoc networksConnected vehicles
Vehicle-to-Vehicle (V2V) communications
Congestion control
Power control
Data rate control
Deep reinforcement learning
Resumen
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 ...
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