公开(公告)号：US12050608B2

公开(公告)日：2024-07-30

申请号：US17700008

申请日：2022-03-21

Applicant: Parallel Wireless, Inc.

Inventor： Nihar Nanda ,  Steven Paul Papa

IPC: G06F16/2455 ,  G06F16/25 ,  G06F16/28

CPC classification number: G06F16/24568 ,  G06F16/258 ,  G06F16/288

Abstract: Systems and methods are disclosed for performing computations on data at an intelligent data pipe en route to a data store. In one embodiment, a method is disclosed, comprising: receiving metadata regarding a data stream from a data source; performing an analysis of the metadata at a service orchestrator; creating at least one container instance based on the analysis; streaming the data stream from the data source to a data sink via the at least one container; and processing the data stream as it passes through the at least one container instance, thereby enabling application-aware processing of data streams in real time prior to arrival at the data store.

公开(公告)号：US20240022492A1

公开(公告)日：2024-01-18

申请号：US18351491

申请日：2023-07-12

Applicant: Parallel Wireless, Inc.

Inventor： Nihar Nanda

IPC: H04L43/091 ,  H04L41/16

CPC classification number: H04L43/091 ,  H04L41/16

Abstract: A method of providing warnings in a telecom network based on forecasting Key Performance Indicators (KPIs), the method comprising: receiving, at a data processing and preparation service, data; transforming, by the data processing and preparation service, the data and feeding transformed data to a forecasting model; predicting, by the forecasting model, a future KPI value for each cell, wherein each KPI has a pre-trained model for prediction that covers all cells; sending, by the forecasting model, predictions to a notification component; receiving, by the notification component, predicted KPI values; and matching, by the notification component, the predicted KPI value against a threshold to generate warnings for any predicted value that exceeds the threshold.

公开(公告)号：US10911266B2

公开(公告)日：2021-02-02

申请号：US16417492

申请日：2019-05-20

Applicant: Parallel Wireless, Inc.

Inventor： Yang Cao ,  Nihar Nanda

IPC: H04L25/02 ,  H04B7/0413

Abstract: Systems and methods are disclosed for performing training using superimposed pilot subcarriers to determine training data. The training includes starting with a training duration (T) equal to a number of antennas (M) and running a Convolutional Neural Network (CNN) model using training samples to determine if a testing variance meets a predefined threshold. When the testing variance meets a predefined threshold, then reducing T by one half and repeating the running Convolutional Neural Network (CNN) model until the testing variance fails to meet the predefined threshold. When the testing variance fails to meet the predefined threshold, then multiplying T by two and using the new value of T as the new training duration to be used. Generating a run-time model based on the training data, updating the run-time model with new feedback data received from a User Equipment (UE), producing a DL channel estimation from the run-time model; and producing an optimal precoding matrix from the DL channel estimation.

公开(公告)号：US20250036633A1

公开(公告)日：2025-01-30

申请号：US18789501

申请日：2024-07-30

Applicant: Parallel Wireless, Inc.

Inventor： Nihar Nanda ,  Steven Paul Papa

IPC: G06F16/2455 ,  G06F16/25 ,  G06F16/28

Abstract: Systems and methods are disclosed for performing computations on data at an intelligent data pipe en route to a data store. In one embodiment, a method is disclosed, comprising: receiving metadata regarding a data stream from a data source; performing an analysis of the metadata at a service orchestrator; creating at least one container instance based on the analysis; streaming the data stream from the data source to a data sink via the at least one container; and processing the data stream as it passes through the at least one container instance, thereby enabling application-aware processing of data streams in real time prior to arrival at the data store.

公开(公告)号：US20230403578A1

公开(公告)日：2023-12-14

申请号：US18333536

申请日：2023-06-12

Applicant: Parallel Wireless, Inc.

Inventor： Nihar Nanda

IPC: H04W24/02 ,  H04B17/336 ,  H04B17/318

CPC classification number: H04W24/02 ,  H04B17/336 ,  H04B17/328

Abstract: In one embodiment, a method of providing automatic configuration of cells includes training a Cell Configuration Bot (CCBot); wherein the training includes providing, by the CCBot, coverage optimization and capacity optimization; wherein the coverage optimization includes measuring Reference Signal Received Power (RSRP) and Signal to Inference and Noise Ratio (SINR) are measured as LTE coverage indicators; wherein the capacity optimization includes measuring a number of Radio Resource Control (RRC) connections and evolved Radio Access Bearer (eRAB) establishments per cell as a measure of the cell accessibility; and adjusting antenna tilt and reference power to impact the cell coverage and capacity.

公开(公告)号：US20220215028A1

公开(公告)日：2022-07-07

申请号：US17700008

申请日：2022-03-21

Applicant: Parallel Wireless, Inc.

Inventor： Nihar Nanda ,  Steven Paul Papa

IPC: G06F16/2455 ,  G06F16/28 ,  G06F16/25

Abstract: Systems and methods are disclosed for performing computations on data at an intelligent data pipe en route to a data store. In one embodiment, a method is disclosed, comprising: receiving metadata regarding a data stream from a data source; performing an analysis of the metadata at a service orchestrator; creating at least one container instance based on the analysis; streaming the data stream from the data source to a data sink via the at least one container; and processing the data stream as it passes through the at least one container instance, thereby enabling application-aware processing of data streams in real time prior to arrival at the data store.

公开(公告)号：US20250105959A1

公开(公告)日：2025-03-27

申请号：US18893960

申请日：2024-09-23

Applicant: Parallel Wireless, Inc.

Inventor： Nihar Nanda ,  Erez Biton ,  David Khemelevsky

IPC: H04L5/00 ,  H04W24/02

Abstract: A method for providing a Real-Time PHY model at a RAN edge involves running an Artificial Intelligence (AI) model on a general-purpose processor in the cloud using telecommunications data as inputs to generate outputs. These inputs and outputs are stored in a lookup format to facilitate lookup without concurrently storing the AI model. The stored inputs and outputs are then deployed to a Virtual Base Band Unit (VBBU), where in-memory lookup of parameters is used for real-time telecommunications data. The method may also include storing the inputs and outputs with compression and periodically refreshing them by re-running the AI model and deploying a new lookup format to the VBBU. The telecommunications data can be radio frequency physical layer data or 4G/5G media access control (MAC) layer data.

公开(公告)号：US20240031863A1

公开(公告)日：2024-01-25

申请号：US18329575

申请日：2023-06-05

Applicant: Parallel Wireless, Inc.

Inventor： Nihar Nanda

IPC: H04W28/02

CPC classification number: H04W28/0289 ,  H04W28/0231

Abstract: Dynamic Traffic Control (DTC) aims at balancing network capacity demand and supply at a finer grain to minimize capacity buffer always comfortably meeting the demand trends. Resiliency of DTC enables handling traffic management of network regions that have gone thru equipment replacement, or upgrades, or service expansions. DTC should be a fully automated close-loop control process in managing regional traffic without human intervention but reporting benefits measuring improvements from a baseline. In one embodiment, a method of providing dynamic traffic control includes forecasting, using a model, network traffic build up over a region ahead of time; and adjusting network capacity to match a forecasted demand from the model, allowing a precise control of capacity and demand closely matched all times. In another embodiment a system for dynamic traffic control includes a traffic predictor; a controller in communication with the traffic predictor; a Key Performance Indicator (KPI) collector in communication with the traffic predictor; a region manager in communication with the traffic predictor and the controller; a model manager in communication with the traffic predictor and the controller; an action recommender in communication with the controller; and wherein the system forecasts network buildup over a region ahead of time and recommends network capacity adjustments.

公开(公告)号：US20190356516A1

公开(公告)日：2019-11-21

申请号：US16417492

申请日：2019-05-20

Applicant: Parallel Wireless, Inc.

Inventor： Yang Cao ,  Nihar Nanda

IPC: H04L25/02

Abstract: Systems and methods are disclosed for performing training using superimposed pilot subcarriers to determine training data. The training includes starting with a training duration (T) equal to a number of antennas (M) and running a Convolutional Neural Network (CNN) model using training samples to determine if a testing variance meets a predefined threshold. When the testing variance meets a predefined threshold, then reducing T by one half and repeating the running Convolutional Neural Network (CNN) model until the testing variance fails to meet the predefined threshold. When the testing variance fails to meet the predefined threshold, then multiplying T by two and using the new value of T as the new training duration to be used. Generating a run-time model based on the training data, updating the run-time model with new feedback data received from a User Equipment (UE), producing a DL channel estimation from the run-time model; and producing an optimal precoding matrix from the DL channel estimation.

公开(公告)号：US20230127116A1

公开(公告)日：2023-04-27

申请号：US17973125

申请日：2022-10-25

Applicant: Parallel Wireless, Inc.

Inventor： Nihar Nanda ,  David Khemelevsky ,  Erez Biton

IPC: H04W52/02 ,  H04W24/08 ,  H04W24/10

Abstract: A method may be disclosed for energy savings in cellular networks, comprising: collecting performance data from the cellular network; sharing the performance data with a batch process to train a prediction model; loading the prediction model into a closed-loop process; using the performance data in combination with the trained prediction model to predict cellular access network load for a particular cell; using the predicted cellular access network load to determine a recommended action; and executing the recommended action. The method may be implemented using a public cloud. The trained prediction model may be used to classify different cells and determine different recommended actions for the different cells.