Application of deep reinforcement learning techniques to optimise resource allocation in wireless communication Systems

Deep reinforcement learning (DRL) techniques are applied in wireless communication systems to ascertain the optimal resource allocation strategy for maintaining optimal system performance. The conventional ways of doing things about cellular networks are not working well enough to meet the growing demand for efficiently using the available scarce resources. This is due, in part, to the demand growing at an unsettling rate. The authors hope that this will lead to the creation of a fresh method for exploiting DRL's capabilities to allocate resources in real-timeby network performance. Stated differently, that is the purpose of this. Deep neural networks are also a part of the system's architecture. This allows the system to learn how to improve and adjust resource allocation strategies, which eventually leads to the system operating more efficiently as a whole. This is now being done to ascertain whether or not DRL is useful in addressing the difficulties related to wireless contact in addition to being possible. Only a few of the extra variables that are taken into account are changes in the volume of traffic, the state of the route, and user requirements.