Multivariate Kernel Regressive Opportunistic Wilcoxon Gradient Descent for Resource and Connectivity Aware Mobile Communication in 5G Networks

Mobile computing refers to employ of portable computing tools, namely smartphones, tablets, laptops, to entrance as well as transmit information and information wirelessly over a network. These tools typically utilize wireless technologies such as cellular networks (e.g., 3G, 4G, 5G) and Bluetooth to connect to internet as well as additional devices. Among these networks, 5G represent the fifth generation of mobile communication, succeeding po and dynamically connect with other devices. Several fundamental challenges were tackled in 5G development, including low data transfer rates, high latency, intermittent connectivity, energy efficiency, and robustness. In order to enhance efficient communication in 5G, a novel Multivariate Kernel Regressive Opportunistic Wilcoxon Gradient Descent (MKROWGD) method has been developed. Major aim of MKROWGD technique is to enhance data communication among mobile devices within 5G networks by achieving minimal latency and data loss, higher throughput, and energy efficiency. MKROWGD comprises two major processes namely Multivariate kernel regressive Opportunistic Diversity Scheduling and Wilcoxon signed-rank Gradient Descent method. Firstly, Multivariate kernel regressive Opportunistic Diversity Scheduling is employed to find resource-efficient mobile devices in 5G networks based on energy and bandwidth, improving data delivery and minimizing loss rates. Secondly, the Wilcoxon signed-rank Gradient Descent method is used for seamless connectivity by applying the soft handover technique based on received signal strength estimation, enhancing throughput and minimizing latency in data communication within 5G networks. Comprehensive simulations are performed to estimate result of proposed technique by different parameters. Quantitatively explained outcomes denote MKROWGD method improves performance of 5G networks, achieving higher throughput and minimal latency as well as data loss compared to conventional methods.