Abstract: Various embodiments herein achieve method for optimizing a performance of a customer premises equipment (CPE) in a wireless network by a network apparatus. The method includes receiving a measurement information from at least one CPE. Further, the method includes receiving a measurement information from at least one RAN node. Further, the method includes detecting whether a performance associated with at least one CPE is degraded based on the received measurement information from the at least one CPE. Further, the method includes identifying a RAN UE ID for the at least one CPE whose performance is degraded by correlating the measurement information received from the at least one CPE and the measurement information received from the at least one RAN node. Further, the method includes applying an action based on the identified RAN UE ID.

Abstract: Embodiments herein disclose a method and network apparatus for network performance evaluation using AI-based network cloning. The method includes constructing one or more AI-based network clones of one or more network nodes. The one or more AI-based network clones mimics a data pattern and cell behavior of the one or more network nodes. Further, the method includes receiving a solution predicted by an AI server to mitigate one or more problems associated with one or more services of the one or more network nodes. Further, the method includes evaluating a performance of the one or more AI-based network clones by deploying the solution on the one or more AI-based network clones. Further, the method includes sending the solution to the one or more network nodes for deployment or retraining based on the performance of the one or more AI-based network clones.

Abstract: The embodiments herein disclose a method for managing machine learning (ML) services in a wireless communication network. The method includes: storing a plurality of ML packages, each executing a network service request; receiving a trigger based on the network service request from a server; determining a plurality of parameters corresponding to the network service request, on receiving the trigger from the server; determining an ML package based on the trigger and the plurality of parameters corresponding to the network service request; and deploying the determined at least one ML package for executing the network service request.