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    SYSTEM AND METHOD FOR OFFLOADING TRAFFIC FROM A CELLULAR NETWORK TO A BROADCAST NETWORK
    2.
    发明公开
    SYSTEM AND METHOD FOR OFFLOADING TRAFFIC FROM A CELLULAR NETWORK TO A BROADCAST NETWORK 审中-公开

    公开(公告)号：US20240107376A1

    公开(公告)日：2024-03-28

    申请号：US17767407

    申请日：2021-03-18

    Applicant: Saankhya Labs Pvt Ltd.

    Inventor： Arindam Chakraborty ,  Makarand Kulkarni ,  Anindya Saha ,  Gururaj Padaki ,  Parag Naik ,  Preetham Uthaiah

    IPC: H04W28/08

    CPC classification number: H04W28/0867

    Abstract: A system for offloading traffic from a cellular network to a broadcast network is provided. The offloading mechanism caters to both unicast and broadcast traffic. The system includes a converged cellular core network, World Wide Web, a CDN, a Broadcast Offload Packet Core (BO-PC), a cellular base station, a Broadcast Radio Head, and a converged UE. The converged cellular core network includes an enhanced packet core, a policy rules engine and a packet inspection and steering unit. The BO-PC includes a Broadcast Proxy, a subscriber database, a Broadcast Offload Service Center, a Broadcast Offload Gateway and an analytics engine. For offloading the unicast traffic, the packet inspection and steering unit identifies sessions that are offloaded for supporting offload of the traffic from the converged cellular core network to the broadcast network.

    RADIO MAPPING ARCHITECTURE FOR APPLYING MACHINE LEARNING TECHNIQUES TO WIRELESS RADIO ACCESS NETWORKS
    4.
    发明申请
    RADIO MAPPING ARCHITECTURE FOR APPLYING MACHINE LEARNING TECHNIQUES TO WIRELESS RADIO ACCESS NETWORKS 有权

    公开(公告)号：US20230062443A1

    公开(公告)日：2023-03-02

    申请号：US17281218

    申请日：2020-05-20

    Applicant: Saankhya Labs Pvt. Ltd.

    Inventor： Arindam Chakraborty ,  Parag Naik ,  Anindya Saha ,  Vishwa Kayargadde ,  Susmit Kumar Datta

    IPC: H04W16/22 ,  H04W24/08

    Abstract: A radio mapping architecture for applying machine learning techniques to mobile wireless radio access networks, including a base station, a user equipment (UE), and a network is provided. The radio mapping architecture includes a spectrum monitoring unit and a server and utilizes the UE. The server includes a radio mapping database and a Machine Learning module. The UE or the spectrum monitoring unit captures Radio parameters to derive an input schema for the radio mapping database. The spectrum monitoring unit extracts the Radio parameters that correspond to the base station and the UE and updates them in the radio mapping database periodically. The input schema for the radio mapping database is updated with the Radio parameters sensed by the spectrum monitoring unit and the UE.

    FRAMEWORK FOR DEVELOPMENT AND DEPLOYMENT OF PORTABLE SOFTWARE OVER HETEROGENOUS COMPUTE SYSTEMS
    10.
    发明公开
    FRAMEWORK FOR DEVELOPMENT AND DEPLOYMENT OF PORTABLE SOFTWARE OVER HETEROGENOUS COMPUTE SYSTEMS 审中-公开

    公开(公告)号：US20240192934A1

    公开(公告)日：2024-06-13

    申请号：US18064251

    申请日：2022-12-10

    Applicant: Saankhya Labs Pvt Ltd

    Inventor： Parag Naik ,  Anindya Saha ,  Vineet Gupta ,  Pramit Biswas ,  Makarand Kulkarni

    IPC: G06F8/41 ,  G06F8/73

    CPC classification number: G06F8/427 ,  G06F8/73

    Abstract: Configurations of a system and a method for implementing a framework that optimizes the execution and deployment of operations in a computationally intensive software applications including varying complexity workloads, are described. In one aspect, a portable framework (PF) may transform an algorithmic routine developed via an IDE into an intermediate form. The PF may enable adding constraint definitions to the intermediate form of the algorithmic software routine. The PF may further enable or provision including constraint definitions, hardware architecture description and multiple optimization metrics to the intermediate form. Based on the constraint definitions, the hardware architecture description, and the multiple optimization metrics the PF may determine computing resources from multiple heterogenous hardware resources deployed on a hardware platform. The execution of the operations may be optimized by deploying the operations to be executed on the determined computing resources.

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