Artificial Intelligence with Machine Learning Model for Resource Management in Wireless Networks

Abstract

Artificial Intelligence (AI) improves resource management and energy conservation through machine learning, improved forecasting, and quicker data processing. Artificial intelligence (AI) and machine learning (ML) have become essential enablers of this integration in this context.  Machine learning and artificial intelligence are necessary for an organization’s survival and expansion, not just a choice. This research explores the growing significance of machine learning and artificial intelligence in resource management.  The proposed method, deep reinforcement learning (DRL) based on machine learning is an essential tool for addressing the issues of resource management.  Resource management can ensure that certain QoS criteria are met for various applications, such as information or real-time video transmission. Reducing total transmit power allows optimal resource management, while following Quality of Service (QoS) criteria permits a better relay choice. In this paper, the numerical results show that it performs competitively in terms of bandwidth of 97.5%, power consumption of 96%, spectrum efficiency of 91%, and network lifespan of 93 percent. Costs are decreased and environmental responsibility is enhanced through resource management.  This research examines how AI might increase bandwidth, power consumption, spectrum efficiency, and resource management, with a particular emphasis on automation, efficiency, and prediction via quality of service.