Deep Reinforcement Learning for Energy Efficient Routing and Throughput Maximization in Various Networks

Large bandwidth and more mobility are only two reasons why wireless and mobile networks are fast overtaking wired ones as the preferred mode of connectivity. Heterogeneous networks refer to systems that consist of many independent networks, each of which has its own unique set of protocols and characteristics. Due to their density and complexity, such dense small-cell heterogeneous networks currently consume a lot of power; thus, in order to tackle climate change, we require power information security. A Modified Deep Reinforcement Learning (MDRL) approach may offer an on-demand automated approach with short inference time for NP-hard network communication problems including radio resource distribution, identification, and battery preservation. We examine the DRL algorithm’s applicability to a multi-objective issue. A paradigm for hopeful nonlinear assistance that is founded on the entertainer paradigm and explores repeatedly for potential answers to the multiobjective issue we have given. Throughput and energy savings achieved by our algorithm are equivalent to those of currently used approaches, according to the findings of our tests.