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Among the challenges in VANETs networks there is power control, which permits to achieve better permformance in terms of Signal to Interference plus Noise Ratio (SINR), Energy Efficiency (EE), Energy Utilization (EU),... Another important point to address is the transmition delay, which we should adapt it to be under an acceptable threshold. For that, we model the network as a Cox Line-Point process, and the transmitter as an M/D/1 queue server. To solve this tradeoff problem, we use machine learning techniques, especialy Q learning algorithm. It is shown , via simulations, that through our algorithm, the vehicular transmitter be able to learn its transmit power in an autonomous way, and achieve better performance for the energy utilization rate, the system waiting time and the area spectral efficiency. |
