Bivalence Fuzzified Decision Stump Bootstrap Aggregation for Energy and Cost-Efficient 6G Communication

Abstract

Future Sixth generation (6G) wireless networks are anticipatedto offer entirecoverage, improved spectral, energyandcost-efficient communication.The 6G will enable a network collectivelyand offer seamless wireless connectionsbetween the devices. While the deployment of 5G is ongoing, mobile communication networks are still suffering many basic challenges such as high-energy consumption and operating costs. To address these issues, it is very important to consider and develop new technologies in next-generation mobile communication, namely 6G. Novel machine learning can potentially assist the 6G to obtain better communication. Bivalence Fuzzified Decision Stump Bootstrap Aggregating (BFDSBA) model is introduced for energy and cost efficient communication. The BFDSBA model considers the nodes i.e. devices in the forecasting process before the data communication in the 6G network. The Bootstrap Aggregative technique utilizes set of weak learners as Bivalence Fuzzified Decision Stump. For each device in the network, energy, signal strength, and bandwidth is measured. Based on the estimated resources, efficient devices are selected for the 6G network architectural design. This in turn helps to improvedata communication with lesser cost in6G networks. The result exposesimprovement of BFDSBA model than the conventional methods.