Abstract: A method for managing scheduling performance of bearers by a base station in a wireless network is provided. The method includes determining a configured packet delay budget (PDB) of non-guaranteed bit rate (NGBR) bearers and a configured PDB of guaranteed bit rate (GBR) bearers; monitoring a PDB of the NGBR bearers and a PDB of the GBR bearers during data communication; determining an average of quality-of-service class identifier (QCI) divergence of the NGBR bearers and an average of QCI divergence of the GBR bearers, wherein the QCI divergence of a bearer is a percentage variation between a monitored PDB and a configured PDB of the bearer; and controlling resource allocation parameters for minimizing the average of QCI divergence of the NGBR bearers and the average of QCI divergence of the GBR bearers.

Abstract: A method and system for multi-core load scheduling in an operating system (OS) less communication network is disclosed. The method comprises initializing a plurality of threads for processing corresponding functionalities of incoming packets. The method further comprises synchronizing the plurality of initialized threads with each other for simultaneous processing of the one or more incoming packets. The method further comprises determining central processing unit (CPU) load on each of the plurality of cores and an ingress data-rate of one or more incoming data packets. The method further comprises enabling or disabling at least one flag based on the determined load and the ingress data-rate and determining at least one thread based on the enabled flag by the RL agent. The method further comprises processing the corresponding functionalities associated with the one or more incoming packets based on the at least one determined thread.

Abstract: The present disclosure discloses system and method for managing operations of one or more applications on an electronic device. The method includes monitoring at predefined instants, device parameters associated with the electronic device and user parameters associated with usage of a plurality of applications in the electronic device. Application usage pattern is identified based on the device parameters and the user parameters using predefined techniques. Further, the one or more applications may be clustered into one or more groups using a real-time learning model stored in the electronic device. The learning model is trained dynamically based on the application usage pattern for clustering. The operations of the one or more applications are managed on the electronic device based on the one or more clustered groups. The disclosure provides a method and a system for improving throughput in wireless communication.

Abstract: The present disclosure provides a method for radio-resource scheduling in a telecommunication network. The method comprises selecting at least one objective associated with a radio-resource scheduling from a plurality of objectives; prioritizing at least one flow from a plurality of flows for the selected at least one objective; identifying at least one state parameter from a plurality of state parameters associated with at least one of an active bearers from a plurality of active bearers; inputting at least one of the plurality of state parameters for the at least one of the active bearers to be scheduled during a current transmission time interval (TTI) to a reinforcement machine learning (ML) network, the reinforcement ML network being configured for a reward in accordance with the selected at least one objective; and receiving, from the reinforcement ML network, a radio resource allocation for each of the active bearers for the current TTI.

Abstract: The present disclosure provides a method for radio-resource scheduling in a telecommunication network. The method comprises selecting at least one objective associated with a radio-resource scheduling from a plurality of objectives; prioritizing at least one flow from a plurality of flows for the selected at least one objective; identifying at least one state parameter from a plurality of state parameters associated with at least one of an active bearers from a plurality of active bearers; inputting at least one of the plurality of state parameters for the at least one of the active bearers to be scheduled during a current transmission time interval (TTI) to a reinforcement machine learning (ML) network, the reinforcement ML network being configured for a reward in accordance with the selected at least one objective; and receiving, from the reinforcement ML network, a radio resource allocation for each of the active bearers for the current TTI.