Cable Television Laboratories, Inc.

Karthik Sundaresan

Gregory Charles White

Vikas Sarawat

Jingjie Zhu

Christopher Grundemann

Christopher Jason Donley

James Kim

Thomas Kee

Luis Alberto Campos

Steve Glennon

Robert J Briscoe

Jun Tian

Jason W Rupe

Chris Donley

Chris Grundemann

Zhensheng Jia

Bernardo A Huberman

Junwen Zhang

Chrisopher J Donley

Christopher Grundeman

Christopher J Donely

A profile optimizing method is provided for a downstream channel transmission of active subcarriers to user devices. The method includes steps of receiving channel measurement data from each user device for each subcarrier, calculating a maximum bit-loading value for each user device per subcarrier, grouping the user devices into a plurality of clusters based on a proximity of the maximum bit-loading values of a first user device to those of a second user device within the particular cluster, assigning each user device within the particular cluster to a single cluster profile. A plurality of single cluster profiles for the plurality of clusters forms a set of cluster profiles. The method further includes steps of determining a channel capacity ratio for the set of cluster profiles, and combining at least two single profiles of the set of cluster profiles into a coalesced profile pair.

Systems and methods for profile management

The present disclosure generally relates to apparatus, software and methods for detecting anomalous elements in data. For example, the data can be any time series, such as but not limited to radio frequency data, temperature data, stock data, or production data. Each type of data may be susceptible to repeating phenomena that produce recognizable features of anomalous elements. In some embodiments, the features can be characterized as known patterns and used to train a machine learning model via supervised learning to recognize those features in a new data series.

Systems and methods for detecting and grouping anomalies in data

A method for traffic flow classification in a communication network includes (1) identifying a traffic flow in communication network traffic, (2) extracting features of the traffic flow, and (3) using a machine learning model, classifying the traffic flow at least partially based on the features of the traffic flow. The features of the traffic flow include, for example, a distribution of packet size values of a sliding window sample of the traffic flow, a distribution of inter-arrival time values of a sliding window sample of the traffic flow, standard deviation of data packet size, average of data packet size, standard deviation of data packet inter-arrival time, and average of data packet inter-arrival time.

Traffic flow classifiers and associated methods

Systems and methods presented herein provide for a software defined network (SDN) controller that virtualizes network elements to provide content delivery and data services through the virtualized network elements. In one embodiment, the SDN controller is operable in a cloud computing environment to balance data traffic through the virtualized network elements. For example, the SDN controller may abstract Layer 2 Control Protocol (L2CP) frame processing of the network elements into the cloud computing environment to relieve the network elements from the burdens of Ethernet frame processing. In this regard, the SDN controller comprises a L2CP decision module that determines how L2CP should be processed for the network elements.

Software defined networking

The present disclosure generally relates to systems, methods and software for quantitatively evaluating an improvement on an active communication network when an impairment, such as a developing impairment, is addressed by one or more repair options via proactive network maintenance.

Systems and methods for proactive network maintenance

Methods, systems, and devices for Content-Centric Networking (CCN) are described. In some cases, a node may receive a CCN packet from an upstream node and communicate the CCN packet to one or more downstream nodes (e.g., that previously requested the CCN packet). In a first case, the node may establish a persistent internet protocol (IP) tunnel with the downstream node and communicate the CCN packet to the downstream node by the persistent IP tunnel. Here, a cable modem between the node and the downstream node may not decode the CCN packet. In a second case, the node may append an identifier to the CCN packet prior to communicating the CCN packet to the one or more downstream nodes. Here, the identifier may indicate to the downstream nodes which CCN packets are relevant to the downstream node.

Content centric networking systems and methods

The present disclosure generally relates to systems, methods and software for selecting a spectral segment for important downlink and/or uplink transmissions. Particularly, the spectral segment may be a set of contiguous subcarriers within a plurality of subcarriers transmitting over a channel to a population of cable modems. In an embodiment, the systems, methods and software disclosed herein optimize placement of a physical link channel (PLC) within an OFDM channel. In an embodiment, the system, methods and software disclosed herein optimize placement of a ranging region within an OFDMA channel.

Systems and methods for selecting a spectral segment for a transmission

Systems and methods for DOCSIS profile management

The present disclosure generally relates to apparatus, software and methods for detecting and classifying anomalous features in one-dimensional data. The apparatus, software and methods disclosed herein use a YOLO-type algorithm on one-dimensional data. For example, the data can be any one-dimensional data or time series, such as but not limited to be power over time data, signal to noise ratio (SNR) over time data, modulation error ratio (MER) data, full band capture data, radio frequency data, temperature data, stock data, or production data. Each type of data may be susceptible to repeating phenomena that produce recognizable anomalous features. In some embodiments, the features can be characterized or labeled as known phenomena and used to train a machine learning model via supervised learning to recognize those features in a new data series.

Systems and methods for detecting and classifying anomalous features in one-dimensional data

A scheduling device for managing a packet queue of a communication gateway includes a receiving portion configured to receive data packets according to at least one communication protocol, a processor, and classification module configured to separate the received data packets into a first traffic queue and a second traffic queue separate from the first traffic queue. The first traffic queue includes a low latency service flow classified to have strict priority. The second traffic queue includes a primary service flow classified as having a classic priority. The classification module separates the received data packets so that those with a first indicator are sent to the first traffic queue and those without the first indicator are sent to the second traffic queue.

Systems and methods for queue protection

The present disclosure generally relates to systems, methods and software for quantitatively comparing an overall capacity improvement for a channel used for transmissions to a population of user devices when an existing profile is replaced with a proposed profile. Additionally, the quantitative comparison can be used to prioritize profile changes and/or network maintenance.

Systems and methods for profile management driven operations

Systems and methods presented herein provide for a software defined network (SDN) controller in a cable television system that virtualizes network elements in the cable television system to provide content delivery and data services through the virtualized network elements. In one embodiment, the SDN controller is operable in a cloud computing environment to balance data traffic through the virtualized network elements. For example, the SDN controller may abstract Layer 2 Control Protocol (L2CP) frame processing of the network elements into the cloud computing environment to relieve the network elements from the burdens of Ethernet frame processing. In this regard, the SDN controller comprises a L2CP decision module that determines how L2CP should be processed for the network elements in the cable television system.

Growing community of inventors

Boulder, CO, United States of America

Karthik Sundaresan