Abstract: A method managing multipath communication includes acquiring a plurality of network addresses respectively corresponding to a plurality of servers associated with a domain, sending a data request to at least one server of the plurality of servers based on the acquired plurality of network addresses, receiving a first data packet transmitted from a first server of the plurality of servers in response to the data request, the first data packet being received first in order among a plurality of data packets received from the plurality of servers in response to the data request, accepting the first data packet, and rejecting data packets transmitted from each server of the plurality of servers other than the first server.

Abstract: A method of joining a first conductor of a power cable with an essentially cylindrical metal element, the method including: a) arranging an end section of the first conductor and a portion of the metal element in a metal fixture, b) penetrating the metal fixture with a rotating friction stir welding, FSW, tool, c) moving the rotating FSW tool, while penetrating the metal fixture, towards and through an interface between the end section of the first conductor and the metal element, joining the end section, the metal element, and the metal fixture, and d) removing excess metal fixture material from the joined end section and metal element to obtain an FSW joint between the first conductor and the metal element.

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: Embodiments herein disclose a method and a device for embedding neural networks as a matrix for a network device in wireless networks. The method includes receiving s from the network device. Further, the method also includes determining the KPI among the plurality of KPIs as target KPIs that related to a network anomaly using a ML model. Further, the method also includes determining a correlation of the target KPI with the plurality of KPIs for the network anomaly using the ML model. Further, the method also includes determining the matrix indicating a relation of the target KPI with the plurality of KPIs. Furthermore, the method includes optimizing a resource of the network device by embedding the matrix in the network device.

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: The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method performed by an operation, administration and maintenance (OAM) controller for facilitating continuous integration and continuous deployment (CI-CD) in a wireless communication system is provided. The method includes transmitting, to a network slice management service (NSMS) consumer, a request to trigger a test upgraded technique for a network function (NF), wherein the request to trigger the test upgraded technique comprises at least one parameter indicated as part of a test network slice information object class (IOC); receiving, from the NSMS consumer, a trigger test response based on the request to trigger the test upgraded technique; and configuring at least one user equipment (UE) with at least one test slice identifier based on the trigger test response.

Abstract: The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. A method performed by an operation, administration and maintenance (OAM) controller for facilitating continuous integration and continuous deployment (CI-CD) in a wireless communication system is provided. The method includes transmitting, to a network slice management service (NSMS) consumer, a request to trigger a test upgraded technique for a network function (NF), wherein the request to trigger the test upgraded technique comprises at least one parameter indicated as part of a test network slice information object class (IOC); receiving, from the NSMS consumer, a trigger test response based on the request to trigger the test upgraded technique; and configuring at least one user equipment (UE) with at least one test slice identifier based on the trigger test response.

Abstract: Embodiments of the disclosure disclose a method and a network node for selecting QUIC streams in wireless communication. As such, current values associated with network parameters related to a data session in real-time are received by network node in a wireless communication system. The network parameters include at least one of: connection metrics, network condition and a type of service. As such, new values of the network parameters for the data session are predicted by the network node using an artificial intelligence (AI) model based on the current values. A plurality of QUIC streams related to the data session are determined based on new network parameters. Each QUIC stream of the plurality of QUIC streams is selected from at least: reliable QUIC stream, semi-reliable QUIC stream, and unreliable QUIC stream. The plurality of QUIC streams are streamed in the wireless communication system.

Abstract: A method for sharing data between machine learning (ML) applications with a network training platform. The method includes: receiving a request to register a first ML application with the network training platform, wherein the request comprises first one or more parameters related to the first ML application; identifying at least one second ML application registered with the network training platform based on the first one or more parameters; identifying second one or more parameters related to the at least one second ML application; comparing the first one or more parameters with the second one or more parameters related to the at least one second ML application; and sharing, with the first ML application, predicted data corresponding to the at least one second ML application based on the comparing.

Abstract: Methods for optimizing training of a data driven model in a wireless network by data driven model validation controller running in electronic device. The method may include obtaining and selecting a candidate data driven model from a plurality of candidate data driven models. The method may include determining whether the selected candidate data driven model meets a predefined prediction. The method may include deploying the selected candidate data driven model to a target deployment environment upon determining that the selected candidate data driven model meets the prediction. The method may include sending the selected candidate data driven model to a data driven model optimizer running in the electronic device for tuning a hyper-parameter and the data driven technique running in the data driven model upon determining that the selected candidate data driven model does not meet the prediction.

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: A method of providing congestion control and reducing latency of data incoming to a core network, the method performed by a control plane gateway, includes: monitoring values of key performance indicators (KPIs) associated with a plurality of user plane gateways in the core network; predicting, using a machine learning (ML) model, an optimal window size respectively for each of the plurality of user plane gateways, based on the monitored values of the KPIs; and transmitting the optimal window size to the respective user plane gateway in the plurality of user plane gateways.

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 managing multipath communication includes acquiring a plurality of network addresses respectively corresponding to a plurality of servers associated with a domain, sending a data request to at least one server of the plurality of servers based on the acquired plurality of network addresses, receiving a first data packet transmitted from a first server of the plurality of servers in response to the data request, the first data packet being received first in order among a plurality of data packets received from the plurality of servers in response to the data request, accepting the first data packet, and rejecting data packets transmitted from each server of the plurality of servers other than the first server.

Abstract: A system and/or method for automated ML model retraining by an electronic device. The system and/or method may include one or more of: running a first ML model and a second ML model, predicting an accuracy degradation of the first ML model using the second ML model, determining whether the predicted accuracy degradation meets a pre-defined threshold, and/or retraining the first ML model when the predicted accuracy degradation meets the pre-defined threshold.

Abstract: Accordingly the embodiments herein provide a method for dynamically controlling a TCP congestion window. The method includes estimating, by an electronic device 100, a real time available bandwidth for an available network. Further, the method includes deriving, by the electronic device 100, a dynamic congestion window control factor from the estimated real time available bandwidth. Further, the method includes modifying, by the electronic device 100, the congestion window based on the derived dynamic congestion window control factor. In an embodiment, the congestion window is modified by passing the real time available bandwidth information from a lower layer of a modem to a higher layer of a TCP Stack and adjusting the congestion window.

Abstract: Embodiments herein disclose a method and a device for embedding neural networks as a matrix for a network device in wireless networks. The method includes receiving s from the network device. Further, the method also includes determining the KPI among the plurality of KPIs as target KPIs that related to a network anomaly using a ML model. Further, the method also includes determining a correlation of the target KPI with the plurality of KPIs for the network anomaly using the ML model. Further, the method also includes determining the matrix indicating a relation of the target KPI with the plurality of KPIs. Furthermore, the method includes optimizing a resource of the network device by embedding the matrix in the network device.

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 tensioner comprising a base, a ring engaged with the base, the ring rotatable about a center “C” within a base opening, a pulley journalled to the ring, a pivot arm pivotally engaged with the ring, a pulley journalled to the pivot arm, a torsion spring disposed between the ring and the pivot arm for urging the pivot arm, a damping assembly frictionally disposed between the base and the ring, a spring applying a normal load to the damping material, and the damping material having a resistance in the range of greater than 0? up to approximately 10,000?.