Parallel Wireless, Inc.

A5g Networks, Inc.

Mushroom Networks, Inc.

Fuze, Inc.

Communications Acquisitions, LLC

Whaleback Systems Corporation

Telefonaktiebolaget Lm Ericsson (publ)

Powerwave Cognition, Inc.

Rajesh Kumar Mishra

Kaitki Agarwal

Steven Paul Papa

Sridhar Donepudi

Yang Cao

Sumit Garg

Kartik Shashikant Raval

Prashanth Rao

Jitender Arora

Murali Talluri

Pratik Vinod Mehta

Zeev V Lubenski

Michael Yasuhiro Saji

Eugina Jordan

Julian Wray West

Zahid Ghadialy

Anupam Goyal

Abhijit Navalekar

Randy Rutherford

Rene L Cruz

Steven Beaudette

Christopher Simmonds

Eric J Martin

Babak Jafarian

Mustafa Arisoylu

Harish Kumar Lohar

Jason Paul Jeffords

David Johnston

Darryl Dietz

Praveen Kumar

Babu Rajagopal

Pradeep Kumar Dakshayani Raghavan

H Cahit Akin

Gaurav Kumar

David Ruffen

Mohit Chugh

Simon Mellor

Fernando Cerioni

Sharique Qureshi

Praveen Puvvadi

Anoop Kumar

Syed Intekhab Anjum

Santosh Kumar Pandey

Arun Seshadri

David Cooke

Louis Paquette

Robert Sullivan

Arun Prasath

Michael C Silva

Gregory Copeland

Scott Y Seidel

Per Johansson

Ofir Ben Ari Katzav

Ludger Schlicht

Baruch Navon

Peter Atwal

Praneet Chivate

Huseyin Cahit Akin

Victor M T Pataca

Jeffrey E Smith

Steven Brigmann

Systems, methods and computer software are disclosed for providing an OpenRAN solution suite. In one embodiment, a method is disclosed, the method including communicating, by an all G COTS (Commercial off the Shelf) Base Band Unit (BBU), with a plurality of different G user devices; communicating, by a software platform, with the all G COTS BBU, wherein the software platform includes virtualized software providing open RAN controller functionality, network orchestrator functionality, and SON edge core functionality; and communicating, by the software platform, with a plurality of different G core networks.

OpenRAN solution suite

A system and method for dynamically creating distributed, self-adjusting and optimizing core network with machine learning is disclosed. The method includes receiving a request to access one or more services and establishing a secure real time communication session with one or more client devices and a set of service layers based on the received request. The method further includes determining one or more service parameters based on the received request and sending one or more handshake messages to each of the set of service layers. Further, the method includes determining one or more environmental parameters and determining best possible service layer capable of processing the received request by using a trained service based ML model. The method includes processing the request at the determined best possible service layer and terminating or transferring the secure real time communication session after the request is processed.

Distributed, self-adjusting and optimizing core network with machine learning

A gateway for X2 interface communication is provided, including: an X2 internal interface for communicating with, and coupled to, a plurality of radio access networks (RANs); and an X2 external interface for communicating with, and coupled to, a destination outside of the plurality of RANs, the X2 external interface further including a single X2 endpoint for the plurality of radio access networks, such that the X2 external interface provides a single interface for an external macro cell or core network to interact with the plurality of radio access networks. The gateway may further include a handover module for maintaining X2 signaling associations and transactions for incoming and outbound handovers, including X2 to S1 and S1 to X2 translation.

Federated X2 gateway

A system and method for autonomous data and signalling traffic management in a distributed infrastructure is disclosed. The method includes a distributed multi-cloud computing system with machine learning based intelligence across heterogenous computing platforms hosting mobile network functions, capable of leveraging AI based distribution across all the resources to creates autonomous network operations and intelligently work around any impairments. The method includes determining one or more service nodes by using a trained traffic management based ML model and establishing one or more cloud mesh links between the one or more service nodes at multiple levels of hierarchy based on the system, environment and network parameters and the current network demand. Further, the method includes processing the request by providing access of the one or more services hosted on the one or more external devices to the one or more electronic devices via the one or more cloud mesh links.

System and method for autonomous data and signalling traffic management in a distributed infrastructure

A method for scheduling resources in a network where the scheduling activity is split across two nodes in the network is disclosed, comprising: receiving, from a local scheduler in a first radio access network, access network information at a global scheduler; accessing information regarding a second radio access network allocating, at the global scheduler, resources for secondary allocation by the local scheduler; applying a hash function to map the allocated resources for secondary allocation to a set of hash values; and sending, from the global scheduler, the set of hash values to the local scheduler.

Elastic scheduling

A method for utilizing quality of service information in a network with tunneled backhaul is disclosed, comprising: establishing a backhaul bearer at a base station with a first core network, the backhaul bearer established by a backhaul user equipment (UE) at the base station, the backhaul bearer having a single priority parameter, the backhaul bearer terminating at a first packet data network gateway in the first core network; establishing an encrypted internet protocol (IP) tunnel between the base station and a coordinating gateway in communication with the first core network and a second core network; facilitating, for at least one UE attached at the base station, establishment of a plurality of UE data bearers encapsulated in the secure IP tunnel, each with their own QCI; and transmitting prioritized data of the plurality of UE data bearers via the backhaul bearer and the coordinating gateway to the second core network.

End-to-end prioritization for mobile base station

Systems, methods and computer software are disclosed for providing a Diameter multifold message. In one embodiment a method is disclosed, comprising: providing a multifold-command Attribute Value Pair (AVP), the multifold-command AVP including an AVP code, a set of VMP flags, an AVP length and a vendor ID; including the AVP in a Capabilities Exchange Request (CER) command for a Diameter stack supporting multiplexing of commands in one message; and using the AVP to combine messages from multiple applications running on a single Diameter node and multiple commands from a single application.

VoIP and native carrier call integration

A method may be disclosed in accordance with some embodiments, comprising: receiving, at a virtualizing gateway, a first service request from a first user equipment (UE) via a first eNodeB; creating, at the virtualizing gateway, an association from each of a plurality of UE identifiers to a desired core network; applying, at the virtualizing gateway, a first filter using a first UE identifier of the first UE, based on the association; forwarding, at the virtualizing gateway, based on the applied first filter, the first service request from the first UE to the first core network; receiving, at the virtualizing gateway, via a second eNodeB, a second service request from a second user equipment (UE); applying, at the virtualizing gateway, a second filter using a second UE identifier of the second UE, based on the association; and forwarding, at the virtualizing gateway, based on the applied second filter, the second service request from the second UE to the second core network.

SIM whitelisting and multi-operator core networks

Systems, methods and computer software are disclosed for demand-based dynamic carrier scaling. In one embodiment a method is disclosed, comprising: determining, at a gateway supporting dynamically created cells in a wireless network, whether there is a requirement for additional capacity; when there is a requirement for additional capacity, then providing, by the gateway, dynamically created cells as needed to handle the requirement for additional capacity; determining, at the gateway, whether there is a requirement for less capacity; and when there is a requirement for less capacity, then turning off, by the gateway, dynamically created cells as needed to handle the requirement for less capacity.

Demand-based dynamic carrier scaling

Described herein are systems and methods for providing software provisioning of functionality in a wireless communications device. Software-enabling functionality may include systems for granting a license to intellectual property or other pre-embedded functionality within a device. Communications to and from the device may be used to send or receive activation messages and/or licensing messages. Network capabilities may be provisioned using activation messages sent over the network. Activation messages may be sent in-band or out-of-band, for a device connected to the Internet and/or a mobile operator core network. Licenses may be required for any functions or intellectual property present on a given device. Activation may enable logical modules of a system-on-chip (SOC), functions of a software-defined radio (SDR), baseband, or DSP core. The disclosed systems and methods could thereby provide a new, flexible paradigm, namely, 'Silicon as a Service (SaaS).'

Software-enabled remote licensing and provisioning

Systems, methods and computer software are disclosed for providing a 5G native architecture. In one embodiment a method includes providing an all G software platform, including a core virtualization stack capable of communication with all hauls, and a radio virtualization stack capable of communication with all cores; and wherein the core virtualization stack communicates with an analytics layer, the analytics layer communicates with an orchestration layer, the orchestration layer communicates with a consolidation layer, and the consolidation layer communicates with the radio virtualization stack such that any G core is able to communicate with any G radio access network.

5G native architecture

Systems, method sand computer readable medium are provided for proving real-time Self Optimizing Network (SON) Virtual Network Function (VRF) included as part of the HNG. In one embodiment data from connected devices is forwarded to the HNG; the data is organized into virtualized containers; and the data is processed by agile analytics and results are displayed to a user.

Real-time any-G SON

A 5G enhanced HetNet Gateway (HNG) is presented. In one embodiment the 5G HNG includes at least one non-5G virtual network function (VNF); a 5G Core (5GC) option selection VNF; a 5GC default VNF; a 5G local breakout VNF; at least one interface for any G base station; and at least one interface for any core network. Core functionality is abstracted for the EPC and for the 5GC.

5G enhanced HetNet gateway

A method for Xx/Xn interface communication is disclosed, comprising: at an Xx/Xn gateway for communicating with, and coupled to, a first and a second radio access network (RAN), receiving messages from the first RAN according to a first Xx/Xn protocol and mapping the received messages to a second Xx/Xn protocol for transmission to the second RAN; maintaining state of one of the first RAN or the second RAN at the Xx/Xn gateway; executing executable code received at an interpreter at the Xx/Xn gateway as part of the received messages; altering the maintained state based on the executed executable code; and receiving and decoding an initial Xx/Xn message from the first RAN; identifying specific strings in the initial Xx/Xn message; matching the identified specific strings in a database of stored scripts; and performing a transformation on the initial Xx/Xn message, the transformation being retrieved from the database for stored scripts, the stored scripts being transformations.

Xx/Xn protocol programmability

The present disclosure describes an artificial intelligence (AI)/machine learning (ML) based distributed, hybrid, and multi-cloud software fabric-based system that unifies the communication infrastructure across hybrid and multi clouds. This mobile connectivity software fabric allows operators to modernize their networks to bring significant operational savings while rolling out new mobile services. This fabric can enable small independent networks and allow them to seamlessly connect with public networks, and it can enable network of networks while keeping the underlying compute and heterogeneity unified.

Multi cloud connectivity software fabric for autonomous networks

Growing community of inventors

Westford, MA, United States of America

Rajesh Kumar Mishra