Deep Learning Techniques to Intelligently Allocate Network Resources in Wireless Communication Systems

Abstract

Using a deep learning methodology, this article conducts an exhaustive examination and study of intelligent resource allocation in wireless communication networks. The investigation begins with an examination of the methods and concepts behind CSCN architecture, in addition to the throughput of SBS (small base stations) included into this design. Thus, an LSTM (long short-term memory network) model is then built to forecast the mobile positions of users. User transmission conditions are assessed using two factors: the location of the users' mobile devices and the condition of the tiny base stations to which they are linked, ensuring that the cache settings are in the intended state. Upon careful examination of the scores, the tiny base station ascertains which users are in possession of the most advantageous transmission circumstances. Throughput optimization in networks is seen as a multi-agent, non-cooperative game problem that may be approached using game theory. The purpose of this study is to allow the tiny base station to autonomously learn and choose channel resources in line with the network environment so as to optimise performance by creating a deep augmented learning-based method for wireless resource allocation. When compared to the standard random-access approach and the algorithm reported in the literature, simulation findings suggest that the method presented in this study significantly boosts network throughput. We provide a framework-based resource control technique in this study by tackling the difficulty of user traffic distribution within fine-grained resource management. Despite having a processing cost similar to polynomials, the findings suggest that the resource management approach exhibits a performance that is unexpectedly comparable to that of a proportional fair user dual connection strategy based on matching. In order to allocate available resources and delegate duties, the subsequent course of action is to implement the optimized decision strategy. Once the intelligent entities have undergone training, they will independently execute these activities in accordance with the present condition of the system and the predetermined policy. The results of the simulations indicate that, in conclusion, the algorithm has the potential to reduce latency and energy consumption while improving user experience.