Deep Reinforcement Learning for Resource Allocation in Wireless Communication Systems

In wireless communication systems, deep reinforcement learning (DRL) methods are used to find the best way to use resources so that the system works at its best. When it comes to cellular networks, the old ways of doing things aren't working well enough to meet the growing need for efficient use of limited resources. The reason for this is that demand is rising very quickly. With this , the authors hope to come up with a new way to use DRL's features to distribute resources in real-time based on how the network is performing. That's what thisis all about. Also, deep neural networks are built into the system. This lets it learn how to adapt and improve strategies for allocating resources, which eventually makes the system work better overall. For the purpose of finding out if DRL is both possible and useful in handling the difficulties that come with wireless contact, this is being done. Changing traffic loads, the state of the route, and the needs of the users are just a few of the other things that are taken into account.