Publication

Abstract : Efficient resource management is essential to maximizing 6 G communication networks performance, which are expected to revolutionize wireless communication with unprecedented speeds and connectivity. This paper proposes a system leveraging Logistic Regression and Convolutional Neural Networks (CNN) to tackle the difficulties associated with 6 G networks’ resource allocation and optimization. Logistic Regression is employed for dynamic resource allocation, optimizing spectrum utilization and energy efficiency. Meanwhile, CNN is utilized for spectrum sensing and allocation, enhancing network reliability and throughput. The proposed system aims to maximize resource utilization while minimizing interference and latency in 6 G networks. This research assesses the efficacy of existing literature with a thorough evaluation of Logistic Regression and CNN in improving 6 G networks’ resource management. Significant gains over conventional heuristic approaches are shown in performance tests employing accuracy (91.2%), precision (89.4%), recall (90.1%), latency reduction (30%), and energy efficiency improvement (7%). This study advances the understanding, and implementation of efficient resource management strategies in the context of next-generation communication networks.