Abstract: Embodiments herein provide a method for managing Quality of Service (QoS) of applications in a wireless network. Detecting a plurality of data traffic of plurality of applications based on current activities of a user equipment (UE), hardware components of UE, a plurality traffic parameter, a QoS report received from an access point (AP) associated with UE in a wireless network. Detecting an enhanced distributed channel access (EDCA) function based on plurality of data traffic. The EDCA function includes current mapping between access category of each data structure and traffic category of plurality of data traffic based on association. Classifying plurality of data traffic of the plurality of applications into priority data traffic category and non-priority data traffic category based on current mapping of the EDCA function.

Abstract: A method implemented at one or more base stations for a power management of the one or more base stations in a wireless communication network is disclosed. The method includes transmitting, by the one or more base stations, a plurality of Key Performance Indicator (KPI) parameters associated with the one or more base stations to a federal server controller. The method includes receiving, by the one or more base stations from the federal server controller, a KPI relationship predicting a future load based on the plurality of KPI parameters associated with the one or more base stations. The method includes adjusting, by the one or more base stations, a Central Processing Unit (CPU) frequency associated with the one or more base stations autonomously based on the KPI relationship for managing the future load associated with the one or more base stations.

Abstract: A method and an electronic device for managing network resources among application traffic are provided. The method comprises identifying a real time application that is running on the electronic device and consuming network resources, determining whether the real time application belongs to a prioritized class, based on the real time application belonging to the prioritized class, determining a User Identifier (UID) of the real time application, and prioritizing the network resources for the real time application based on the UID.

Abstract: Methods and systems for reporting CSI and selecting optimal beams using ML. The CSI report is sent to a gNB, which includes feedback parameters, computed and predicted using ML. The feedback parameters are computed using measurements performed using CSI-RS. Values of the feedback parameters likely at future, based on channel variation and the measurements, are pre-dieted using ML. The computed and predicted feedback parameters are included in the CSI report. Optimal CSI-RS resource allocation and optimal CSI reporting periodicity are determined using ML and sent to the gNB. The CSI report is encoded using the ML based model. The RSRP of the beams are predicted using ML for beam selection.