Abstract
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.
Highlights
Vehicular ad hoc networks (VANETs) are becoming one of the most active research fields
We propose a reinforcement learning based algorithm to handle the transmission power control that aims to optimize the transmission energy while guaranteeing that the transmitted messages are arriving with a limited delay
Where λ is the average number of active transmitting nodes per unit area, as we are intersted by the special case in which our model converges to a 1D PPP scenario, the equation of λ is given by λ = pλv, and: 2.1 Cox vehicular ad hoc networks (VANETs) Network
Summary
Vehicular ad hoc networks (VANETs) are becoming one of the most active research fields. The papers [17, 18] gave a survey on multi-layer techniques and MAC protocols respectively, to improve message transmission delay Both these issues can be solved with reinforcement learning (RL). We propose a reinforcement learning based algorithm to handle the transmission power control that aims to optimize the transmission energy while guaranteeing that the transmitted messages are arriving with a limited delay. Where λ is the average number of active transmitting nodes per unit area, as we are intersted by the special case in which our model converges to a 1D PPP scenario, the equation of λ is given by λ = pλv, and: 2.1 Cox VANET Network. As the transmission probability p impact the overall spectral efficiency with a tradeoff manner, there is an optimum value of transmission probability p∗ that maximizes the ASE of the network, in the case of 1D PPP: p∗
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