Saankhya Labs Pvt. Ltd.

Other

Texas Instruments Corporation

Saankhya Labs Private Limited

Tejas Networks Limited

Anindya Saha

Parag Naik

Gururaj Padaki

Vishwakumara Kayargadde

Arindam Chakraborty

Hemant Mallapur

Sandeep Pendharkar

Rubin Ajit Parekhji

Nikila Krishnamoorthy

Shrinivas Subramania Bhat

Susmit Kumar Datta

Sunil Hosur Ramesh

Santhosh Billava

Sunil Hr

Sudeep Mirpadi Anand

Makarand Kulkarni

Sudheer Prasad

Anmol Sharma

Mark Andrew Aitken

Venugopal Kolathur

Subrahmanya Kondageri Shankaraiah

Saurabh Mishra

Vivek Gorakhnath Pawar

Anupama Aniruddha Agashe

Suresh R Puthucode

Rakshit Kumar Singhal

Rakesh A Joshi

Viji Rajangam

Sudarshan V

Makarand Kulkami

Preetham Uthaiah

Deepak Samaga

Yogesh Singh

Santosh Billava

Smitha Banavikal Math Veerabhadresh

Manish Kumar

Vishwakumara Kayergadde

A virtual Radio Access Network (vRAN) system () for provisioning a virtual Radio Access Network (RAN) that is portable across one or more RAN hardware platforms is provided. The virtual Radio Access Network (vRAN) system () includes a waveform development kit (WDK) (), and a waveform execution environment (). The waveform development kit () defines at least one portable Radio Access Network (RAN) application into a form that is instantiated on a RAN hardware (). The waveform execution environment () (i) monitors real-time schedulable resources in real-time, and (ii) collects one or more statistics and monitors the one or more statistics for network automation. The waveform execution environment () includes a RAN hypervisor () that virtualizes at least one attribute of a spectral resource required to provision the RAN that is portable across at least one hardware platform of the one or more RAN hardware platforms in the RAN hardware.

System and method for provisioning a portable virtual RAN across a plurality of RAN hardware platforms

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.

Radio mapping architecture for applying machine learning techniques to wireless radio access networks

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.

System and method for offloading traffic from a cellular network to a broadcast network

A system and method for switching one or more User Equipment (UEs)A-N from a unicast mode to a broadcast or multicast mode to transmit a streaming media content to the UEsA-N is provided. The system includes, an Over-the-top (OTT) platform, a CDN, the UEsA-N, a cellular core network, one-to-many offload core, an analytics engine, a database, one-to-many transmitter, a real time switching module, a Cellular base stationand a user specified rules module. The analytics enginecontinuously analyzes real-time and historical data stored in the databaseto identify the UEsA-N that receive a streaming media content through the unicast mode and the streaming media content to be offloaded. An offload is a process by which certain portions of the streaming media content is shifted from the unicast mode to the broadcast or multicast mode.

System and method for out-of-order transmission stream of content via ambient intelligent one-to-many transmission mode

A system for deploying a Radio Access Network Containerized Network Function (RAN CNF) that is portable across a plurality of RAN hardware platforms is provided. The system includes a Software Development Kit (SDK), a schedule generator and a scheduler runtime unit. The SDK enables providing a RAN functionality in a physical layer (L1) software code in a platform-independent manner as a RAN pipeline of a plurality of RAN tasks. The RAN tasks include a first and second RAN task. The first RAN task invokes an Application programming interface (API) from a plurality of Application Programming Interfaces to call to the second RAN task. The schedule generator generates a schedule for allocating a node in the RAN pipeline to one or more processing elements. The scheduler runtime unit loads the RAN tasks corresponding to nodes in the RAN pipeline, based on the schedule generated by the schedule generator.

Deploying a radio access network containerized network function (RAN CNF) that is portable across a plurality of RAN hardware platforms

A system and method for dynamically switching transmission of selected data from cellular core network to unidirectional point-to-multipoint downlink network or from unidirectional point-to-multipoint downlink network to cellular core network based on traffic flow analysis is provided. The system includes a cellular packet core, a broadcast offload packet core (BO-PC), and a load manager. The cellular packet corecontrols a cellular radio access network (RAN)for providing bidirectional connectivity to a converged user equipment (UE) () to transmit or receive selected data through the cellular packet coreand the RAN. The BO-PCcontrols a broadcast radio access network (RAN). The broadcast radio access network (RAN) includes at least one Broadcast Radio Head (BRH)for providing unidirectional downlink path to the converged user equipment (UE)to receive selected data through the at least one Broadcast Radio Heads (BRH)

System and method for dynamically switching transmission of data from cellular to unidirectional point-to-multipoint network

A system for improving indoor coverage of cellular reception is provided. The system includes an Intelligent receiver and a Pico transmitter. The intelligent receiver demodulates a signal received from a Broadcast radio head (BRH) with a HPHT or a LPLT toplogy through an outdoor high gain rooftop antenna that is externally connected to the intelligent receiver. The intelligent receiver includes an artificial intelligence (AI) or Machine learning (ML) based indoor coverage monitoring unit and a Pico transmitter application. The AI/ML based indoor coverage monitoring unit continuously monitors cellular reception factors of indoor user devices. The AI/ML based indoor coverage monitoring unit predicts an optimal indoor modulation profile and selects a required modulation index required for the indoor user devices. The Pico transmitter application re-broadcasts or relays the demodulated signal, based on an optimal indoor modulation profile required for the indoor user devices.

System and method for improving indoor coverage of cellular reception using a smart television

A system for dynamically offloading data and video traffic to a broadcast offload core network from a cellular network or to the cellular network from the broadcast offload core network is provided. The system includes an analytics engine, a load manager, and a radio access network (RAN). The analytics engine captures geographical radio frequency (RF) information from a geographical RF information database. The geographical RF information includes an operator infrastructure information, a physical terrain information, a subscriber information, a coverage information, a signal quality information, and telecom traffic patterns. The analytics engine determines whether to offload the data and the video traffic to at least one of a unidirectional downlink network from a unicast network or the unicast network from the unidirectional downlink network by analyzing a hybrid cellular user equipment from a particular geographical location trying to access the data or video content.

System and method for offloading data and video traffic to a supplemental downlink overlay network

Disclosed is a system for mitigating co-channel interference (CCI) caused due to narrow multiband signal in white space (WS) modems. The system includes a base-station that includes a base-station transmitter and a base-station receiver. The base-station receiver receives an orthogonal frequency division multiplexing signal (OFDM) from one or more customer premises equipment (CPE) transmitters. An interference detection module detects a presence of co-channel interference in carriers of the OFDM signal and determines co-channel interference affected carriers. A dynamic notch filter module receives the central interference carriers from interference detection module and mitigates the co-channel interference of co-channel interference affected carriers without affecting frame detection capability of the system. The base-station transmitter includes an interference aware frequency interleaver that spreads co-channel interference affected carriers based on interference specific interleaving parameters and frequency de-interleaving is performed corresponding to frequency interleaving in the base-station receiver.

System and method for mitigating co-channel interference in white space modems

Disclosed is a system for mitigating co-channel interference (CCI) in white space (WS) modems. The system includes a base-station that includes a base-station transmitter and a base-station receiver. The base-station receiver receives an OFDM signal from one or more CPE transmitters. An interference detection module detects presence of co-channel interference in carriers of the OFDM signal and determines co-channel interference affected carriers. A dynamic notch filter module (i) receives central interference carriers from interference detection module and (ii) mitigates the co-channel interference of co-channel interference affected carriers without affecting frame detection capability of the system. An interference aware signal to noise ratio (SNR) estimation module determines an average signal to noise ratio (SNR) of (i) interference unaffected carriers and (ii) co-channel interference affected carriers. An interference aware de-mapper module processes average signal to noise ratio of (i) interference unaffected carriers and (ii) co-channel interference affected carriers.

System and method for mitigating co-channel interference in white space modems using interference aware techniques

A method and system of dynamically designing and operating an optimal communication network configuration is provided. The method includes the steps of (a) gathering a pre-saved data associated with communication network and/or network devices from a database, or a real-time data associated with at least one of the communication network and the network devices, (b) generating an optimal network configuration by selecting a first and a second network optimization parameters based on gathered data, designing an optimal network configuration to optimize the first network optimization parameter with respect to the second network optimization parameter based on gathered data, and generating network implementation instructions for implementing the designed optimal network configuration, and (c) adapting at least one network device to operate in compliance with the designed optimal network configuration based on the generated instructions by transmitting the instructions to the network devices and by executing the instructions at the network devices.

Method and system of dynamically designing and operating an optimal communication network configuration

Various methods and systems for establishing a connection between a user device and a base station (BS) in a dynamically self-optimizing communication network and methods and systems for handover of the user device from a first BS to a second BS is disclosed. In one embodiment, the method for establishing connection includes initializing user device, identifying a channel and a modulation scheme, receiving virtual machine (VM) primitives and registering by user device with the network based on the VM primitives. The method of handover includes generating by the first BS, a network implementation instruction for user device to adapt to second BS, downloading the network implementation instruction to user device to provide information regarding when to start executing the instruction, communicating by first BS to second BS an intimation regarding completion of handover of user device and informing second BS as to when to take over the user device.

Method and system of registering a user device with a dynami-cally self-optimizing communication network

A vector slot processor that is capable of supporting multiple signal processing operations for multiple demodulation standards is provided. The vector slot processor includes a plurality of micro execution slot (MES) that performs the multiple signal processing operations on the high speed streaming inputs. Each of the MES includes one or more n-way signal registers that receive the high speed streaming inputs, one or more n-way coefficient registers that store filter coefficients for the multiple signal processing, and one or more n-way Multiply and Accumulate (MAC) units that receive the high speed streaming inputs from the one or more n-way signal registers and filter coefficients from one or more n-way coefficient registers. The one or more n-way MAC units perform a vertical MAC operation and a horizontal multiply and add operation on the high speed streaming inputs.

Vector slot processor execution unit for high speed streaming inputs

A method for implementing a digital filter is provided. The method includes (a) determining a bit-width of an incoming data sample of an incoming signal by measuring a distance between a leading zero or one of the incoming data sample and a trailing zero of the incoming data sample. The incoming data sample is obtained by sampling the incoming signal at a pre-defined time interval, (b) obtaining bit-width multipliers with variable bit-widths based on a first probability distribution function (PDF) of bit-widths of incoming data samples, (c) allocating the incoming data sample and a filter coefficient based on the bit-width of the incoming data sample and a bit-width of the filter coefficient to one bit-width multiplier of the bit-width multipliers, and (d) performing a multiply operation of a Multiply and Accumulate (MAC) operation on the one bit-width multiplier to generate an output of the digital filter.

Digital filter implementation for exploiting statistical properties of signal and coefficients

A system for computing a block floating point scaling factor by detecting a dynamic range of an input signal in a central processing unit without additional overhead cycles is provided. The system includes a dynamic range monitoring unit that detects the dynamic range of the input signal by snooping outgoing write data and incoming memory read data of the input signal. The dynamic range monitoring unit includes a running maximum count unit that stores a least value of a count of leading zeros and leading ones, and a running minimum count that stores a least value of the count of trailing zeros. The dynamic range is detected based on the least value of the count of leading zeros and leading ones and the count of trailing zeros. The system further includes a scaling factor computation module that computes the block floating point (BFP) scaling factor based on the dynamic range.

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Bengaluru, India

Anindya Saha