Affirmative data analytics based data processing method for 6G wireless network applications

Sixth generation (6G) wireless network infrastructure makes use of terahertz communication interfaces and latency service. The growth of real-time applications and service support increases the data handling capacity and processing requirements. The data processing rate must be comparatively high with the available resources to meet the users' quality of service (QoS) requirements. This article proposes an affirmative data analytics (ADA) method to improve data processing consistency in 6G wireless networks. The consistency of the ADA method relies on the 6G service features in the service-rendering environment. The affirmation process is provided using support vector machine (SVM) learning to achieve consistency in handling diverse-attributed data. Then the attribute and association between data and services are achieved with best-fit processing time and minimum complexity. The performance of the proposed ADA is verified for heterogeneous service applications in a wireless network using the metrics analysis time (723.629 ms), complexity (11.311%), and response time (1.034 s).