Method for Multi-Channel Resource Allocation in Wireless Networks Based on Genetic Perception Optimization Algorithm

Abstract

This paper presents a novel approach for multi-channel resource allocation in wireless networks, using a Genetic Perception Optimization Algorithm. We frame the resource allocation problem as a multi-objective optimization task, aiming to maximize throughput, minimize energy consumption, and balance network load. The constraints of the problem are defined in terms of channel capacity, user demand, and interference limitations. Our approach utilizes the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to effectively address these multiple objectives. A distinctive feature of our method is the integration of real-time wireless network feedback as a dynamic adjustment mechanism. This allows the algorithm to adaptively modify resource allocation strategies based on changes in network conditions. Experimental results indicate a significant improvement in both resource utilization efficiency and user satisfaction compared to traditional allocation methods. The proposed method is anticipated to enhance the performance and adaptability of wireless networks, providing a robust solution for dynamic and complex network.