Abstract: Wireless communications and/or systems (e.g., 100) and/or methods (e.g., 200, 300, 400) may be provided for predicting of potential undetected flows in a DPI system using a machine learning (ML) model. The system may include an input packet module which may be configured for verifying packet parameters from a network traffic flow, and a processor which can be configured for processing the extracted parameters to identify whether the network traffic flow is potentially detectable or undetectable using a trained machine learning (ML) model based on at least the extracted parameters and perform DPI processing for the detectable flows. Thus, the system may provide an optimized DPI flow processing for high rate traffic networks with decreasing processing time.

Abstract: The present disclosure relates to field of telecommunication network and discloses method and apparatus e.g., network entity for network traffic management. The network entity may be associated with a Deep Packet Inspection System (DPI), the network entity receives an encrypted Quick User Datagram Protocol (UDP) Internet Connection (QUIC) data packet flow from a source. Further, network entity predicts using an Artificial Intelligence (AI) model, a relevancy of decryption for an encrypted QUIC initial handshake packet of the QUIC data packet flow based on at least one of pre-stored User Equipment (UE) context parameters, pre-stored server context parameters and flow context parameters of the encrypted QUIC data packet flow. The network entity decrypts the encrypted QUIC initial handshake packet based on the relevancy of the decryption being predicted to be useful. The present disclosure helps to optimize Deep Packet Inspection (DPI) operation.

Abstract: Wireless communications and/or systems (e.g., 100) and/or methods (e.g., 200, 300, 400) may be provided for predicting of potential undetected flows in a DPI system using a machine learning (ML) model. The system may include an input packet module which may be configured for verifying packet parameters from a network traffic flow, and a processor which can be configured for processing the extracted parameters to identify whether the network traffic flow is potentially detectable or undetectable using a trained machine learning (ML) model based on at least the extracted parameters and perform DPI processing for the detectable flows. Thus, the system may provide an optimized DPI flow processing for high rate traffic networks with decreasing processing time.

Abstract: Methods and systems for simulating at least one room impulse response between two or more sound sources and two or more receivers positioned in an enclosure are provided. At least one early impulse response is generated that includes early reflections from the two or more sound sources to at least one of the receivers. At least one late impulse response is generated including a reverberation portion. The late impulse response is generated to spatially shape the reverberation portion corresponding to a spatial parameter of the enclosure. The at least one early impulse response is combined with the at least one late impulse response to form the at least one simulated room impulse response.