Resource Allocation Based on Deep Neural Networks for Automotive Radar Interference Optimization

Deep Neural Networks (DNNs) are going to be used in this study with the intention of achieving the objective of optimising the resource allocation and interference control of vehicle radar systems. Because contemporary automobiles depend heavily on radar technology for both their safety and their capacity to engage in autonomous driving, reliability is an essential characteristic of vehicular radar systems. Despite this, there is an increasing amount of congestion in the electromagnetic spectrum, which makes it more difficult for radar systems to perform their functions. Utilising DNNs as a tool for resource allocation is a method that shifts the paradigm by optimising accuracy, efficiency, and flexibility to the fullest extent possible. For the purpose of this study, a comprehensive review of the existing literature on a variety of topics, including smart grids, the energy harvesting potential of wireless networks, and the use of DNNs in automobile radar systems, is carried out. The article presents a comprehensive review of smart grids and coordinated multi-point communication, with the primary emphasis being placed on collaborative energy use and communication. This article investigates the fundamental ideas behind DNNs, as well as their applications, advantages, and challenges when applied to automobile radar systems.