Machine Learning Oriented Resource Allocation to Achieve Ultra Low Power, Low Latency and High Reliability Vehicular Communication Networks

With the advent of high-speed communication technologies, such as 5G and the Internet of Things, vehicular communication attracted numerous researchers. Vehicular networks are categorized under high mobility networks; therefore, power efficiency, low latency, and embedded intelligence are essential features. In the current paper, we propose a novel algorithm to reduce power, improve reliability, and minimize latency employing the machine learning algorithms. The proposed algorithm decouples itself to run both locally and globally to achieve low latency and power-Bit Error Rate (BER) optimization. We also offer an algorithm called the Field-Aware Hot-Potato routing algorithm, enhancing the classical Hot-Potato routing algorithm for shortest path routing. The algorithm also serves to optimize BER and data rate, employing a dynamic modulation scheme selection using machine learning. The simulation results high-light the algorithm's effectiveness in achieving power efficiency, reliability, and speed in vehicular communications.