Effective Reinforcement Learning based Routing in Urban Vehicular Communication using mm Wave Beamforming Techniques

Vehicular communication facing a lot of entanglements for effective and reliable data transmission between two vehicles in an urban area. Various machine learning approaches are involved in vehicle to vehicle (V2V) routing, in this scenario mmWave (millimetre Wave) beamforming techniques integrated Reinforcement Learning based routing is proposed, where it adjusts its beam patterns based on its learned policies. Here a machine learning algorithm is used for prediction of vehicle mobility and mmWave technique is used for carrying the signal towards a specific area with higher rate of signal strength using beamforming technique. Proposed algorithm optimizes wireless transmission and routing path between two vehicle nodes in an intelligent transportation system. This algorithm ensures a reliable and adaptive system for vehicular communication in dynamic environments.