Cisco Technology, Inc.

Xu Zhang

Khashayar Mirfakhraei

Ardalan Alizadeh

Matthew Aaron Silverman

Paul Jeffrey Stager

Gautam Dilip Bhanage

Santosh Ghanshyam Pandey

Amir Hosein Kamalizad

Abhishek Mukherji

Daniel Joseph Lyons

Brian Donald Hart

Jerome Henry

Qing Zhao

Huy Phuong Tran

Dantong Liu

Vishal Satyendra Desai

Pooya Monajemi

Hugo Latapie

Enzo Fenoglio

John Martin Blosco

Zhigang Gao

Danielle N Bane

Vinay S Raghuram

Xiangxiang Fang

Mukul Chandail

Daniel J Lyons

In one embodiment, an apparatus comprises a compressive sensing schedule generator configured to generate a plurality of compressive sensing schedules, wherein each of the plurality of compressive sensing schedules is for each of a plurality of frequency bands of a network, wherein the network comprises a plurality of access points and a plurality of clients, and a sensing matrix combiner configured to combine the plurality of compressive sensing schedules into a resulting schedule that comprises a spatial distribution and a scheduled time slot for each of the plurality of access points.

Multi-band compressed spatio-temporal logging of radio frequency (RF) telemetry data

Access Point (AP) placement using Fine Time Measurement (FTM) may be provided. First, a plurality of Time-of-Flight (ToF) values between a first service end point and a second service end point may be determined. Each one of the plurality of ToF values may be derived from packets transmitted via different beamforming vector patterns at the first service end point and the second service end point. Then a minimum ToF value of the plurality of ToF values may be determined. Next, a distance between the first service end point and the second service end point may be determined based on the minimum ToF value.

Access Point (AP) placement using Fine Time Measurement (FTM)

Systems and methods provide for improving the accuracy of a location system. The location system can capture partial phase vector data from one or more access points (APs). The location system can capture associated data associated with the partial phase vector data across multiple dimensions, such as identity data of the APs and client devices generating the partial phase vector data and frequency band data, location data, a time and date, and other data associated with the partial phase vector data. The location system can determine correlation data across the multiple dimensions using the first partial phase vector data and the associated data. The location system can a cause of the partial phase vector data based on the correlation data. The location system can perform one or more remediation actions based on the cause of the partial phase vector data.

Partial phase vectors as network sensors

Learning-based service migration in mobile edge computing may be provided. First, a service migration policy may be created for a network that includes a plurality of edge clouds configured to provide a service to users. Next, a movement of a user receiving the service from a source edge cloud may be detected. The source edge cloud may be associated with a first area and the detected movement may be from the first area to a second area. Then, the service migration policy may be applied to determine whether to migrate the service for the user from the source edge cloud. In response to determining to migrate the service, a target edge cloud may be identified and the service for the user may be migrated from the source edge cloud to the target edge cloud. The service migration policy may then be updated based on a success of the migration.

Learning-based service migration in mobile edge computing

Offloading of location computation from a location server to an access point through the use of projections on base phase vectors may be provided. First, an Access Point (AP) may receive a set of two or more base phase vectors from a location server. Next, the AP may measure a measured phase vector for a first signal from a user device. Then, the AP can determine projection values based on a comparison of the measured phase vector to each base phase vector. From these comparisons, the AP can determine a subset of base phase vectors with the highest projection values. The AP can then send the projection values and the subset of base phase vectors to the location server, wherein the location server determines the device location from these projection values and subset of base phase vectors.

Offloading location computation from cloud to access point (AP) with projection on base phase vectors

Access point (AP) placement using fine time measurement (FTM)

In one embodiment, a management entity monitors for a change in a convergence rate of spatio-temporal compressive sensing measurements from a plurality of sensors in a sensor network operating according to a measurement matrix up to a halting criterion, and if the change is below a given threshold, determines whether the change is due to impulse noise or due to continued sensed measurements. If continued sensed measurements, the management entity initiates a single-dimensional compressive sensing in a spatial domain at regular time intervals, and identifies and tracks gradient clusters. In response to a change in joint spatio-temporal sparsity of tracked nodes of the gradient clusters, the management entity can then determine an updated measurement matrix based on the joint spatio-temporal sparsity of tracked nodes while satisfying one or more operating parameters, and directs at least certain sensors of the plurality of sensors to operate according to the updated measurement matrix.

Adaptively calibrated spatio-temporal compressive sensing for sensor networks

Determining a device's location in a space in real time is computing intensive. To offload some of the workload in conducting this hyperlocation, the access points in the network conduct some of process in determining the location of a device. The cloud determines a restricted AoA search area based on previous client locations. After this determination, a three-dimensional (3D) AoA search is conducted by each AP in the restricted area (restricted by a range of azimuth directions) for a device. Finally, each AP reports a location(s) for the device, which comprises weights for selected angular sectors. The cloud can then construct a probability heat map for location computation from the weights provided from each AP for the device.

Scalability of location in the cloud with angle of arrival (AOA) search on the edge

In one embodiment, a service obtains spatial information regarding a physical area. The service estimates locations of a device within the physical area over time, based on wireless signals sent by the device. The service generates a set of images based on the spatial information regarding the physical area and on the estimated locations of the device within the physical area over time. The service updates an estimated location of the device by inputting the generated set of images to a machine learning model trained to minimize a location estimation error.

Image-based framework for improving location estimate accuracy in indoor positioning systems

In one embodiment, a supervisory service for a wireless network computes a compressive sensing schedule for a plurality of sensors in the wireless network. The service sends target wake time (TWT) messages to a subset of the plurality of sensors according to the computed compressive sensing schedule. The service receives, in response to the TWT messages, sensor readings from the subset of the plurality of sensors. The service performs compressive sensing on the received sensor readings.

Compressive sensing using target wake times

In one embodiment, a device in a wireless network receives a target wake time (TWT) request from a wireless client. The device computes TWT parameters based on the received request. The device predicts, using the computed TWT parameters as input to a machine learning model, whether the computed TWT parameters will be accepted by the wireless client. The device provides the computed TWT parameters to the wireless client, based on a prediction by the machine learning model that the client will accept the computed TWT parameters.

Machine learning-based target wake time negotiation optimization for wireless networks

In one embodiment, a device in a network receives, at its wireless receiver, a preamble of a spatially modulated packet. The device analyzes the preamble of the packet to identify a transmit antenna index of the packet. The device determines that the packet was not destined for the device, based on the transmit antenna index of the packet. The device depowers, prior to decoding the complete packet, the wireless receiver of the device, based on the determination that the packet was not destined for the device.

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Fremont, CA, United States of America

Xu Zhang