Optimal Data Rate and Power Estimation for Channel Congestion Control in V2V Communication

The prospects for vehicle mobility will focus on complete automation. Connected systems is the technology which is booming, making a common man’s life simple, be it in home environment, industries, public spaces or in transportation. The technology which is currently gaining pace in research is on Vehicle-to-Vehicle (V2V) Technology. This involves a network of motor vehicles that continuously access information from or sharing information to the peer vehicles which are driving along. This data exchange can help drivers be alerted to such as possibility of accidents, obstacle detection and presence of traffic gridlocks during travel. Researchers are facing certain hurdles while developing optimum algorithms related to sub-domains of V2V. Amidst these challenges, one is on adjusting the channel capacity. When data is sent, it must be transmitted in a way that no data gets corrupted or lost. Since the environment of vehicle movement is rapidly dynamic, there involves a lot of decision making at every small instances of time. Hence, with the help of previously observed scenarios, performance parameters and Reinforcement Learning (RL) concepts, the decision making could be manageable. The project focuses on managing data rate and transmission power value for every message sent for various scenarios that aids in channel being adequately used. The vehicle traffic is simulated running on an intersection scenario with DSRC Communication. Certain performance metrics such as Packet Delivery Ratio, Channel Busy Ratio is considered to verify the efficiency of the RL model and compared with existing one.