Abstract

The last few years have seen the proliferation of wireless devices as well as the congestion of the network and the need to have effective mechanisms of controlling the power consumption with in recent years, the proliferation of wireless devices has led to network congestion and a greater need for effective power control mechanisms. With the increase in high-density local area networks (WLANs), security and performance challenges have become more prominent. performance challenges. Conventional methods of power control are effective in more basic contexts but in the context of dense network using conditions, they are not adaptable to the dynamic and complex environment, which results in inefficiencies and security threats such as signal interference and congestion. The paper will provide a reinforcement learning (RL)-driven power control system, which allows access points (APs) to change the transmission power independently of environmental factors. The RL agent can solve problems of optimal power control strategies by modeling the power control problem as a Markov decision process (MDP), where the strategies are learned by balancing coverage, intervention, energy efficiency and security issues like interference management. To assess the significance of the proposed approach, it is tested in the simulated dense WLAN environments and it is observed that the alternative approach is much better in terms of the throughput, fairness, energy efficiency and the network resilience to interference. Flow and architectural charts are provided to illustrate the structure of operations and technical design of the system. A comparative performance study of a dynamic set of real-time data and benchmark metrics shows the efficiency of the RL-based method of ensuring network security and performance under dynamic circumstances. These findings establish the fact that reinforcement learning offers a scalable, adaptive and secure method of smart power management in dense WLAN settings, which is the path to smarter and self-sufficient network systems. This study will help in the process of developing smarter and more secure wireless communication systems by resolving the issue of power control and security in the network in the future dense network infrastructures.