Abstract: 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.

Abstract: 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.

Abstract: Inter-protocol interference reduction for hidden nodes may be provided. A first service end point may determine that an inter-protocol interference is present on a channel. Next, an initial packet failure count value on the channel may be determined. A transmit (Tx) power for selected packets may then be increased until a subsequent packet failure count value on the channel is less than the initial packet failure count value.

Abstract: Aspects described herein include a method for use with an access point (AP). The method comprises inspecting control frames received from a rogue AP, characterizing an intrusion by the rogue AP using one or more spatial reuse parameters included in the control frames, and selecting a defensive posture for the AP based on the characterization.

Abstract: Embodiments for preliminary antenna configuration for a radio in an access point are described. A preliminary antenna configuration with a first number of antennas transmitting at a first power level based on a maximum transmission power for the radio is used to determine a second antenna configuration based on network level factors, where the second antenna configuration includes a second number of antennas. The second antenna configuration is used to determine a third antenna configuration from the second antenna configuration, where the third antenna configuration includes a third number of antennas transmitting at a third power level based on a client transmission factors for a plurality of client devices connected to the access point. The third antenna configuration is used to transmit network traffic from the access point to the plurality of client devices using the third antenna configuration.

Abstract: The present technology enables inter-network routing by dynamically optimizing data paths for traffic destined for wired clients attached to a wireless access point in a wireless mesh network with a plurality of wireless access points. The present technology can also influence steering of wireless device connections within the wireless mesh network when high amounts of data traffic are exchanged between nearby mesh wireless access points.

Abstract: 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.

Abstract: The present technology enables inter-network routing by dynamically optimizing data paths for traffic destined for wired clients attached to a wireless access point in a wireless mesh network with a plurality of wireless access points. The present technology can also influence steering of wireless device connections within the wireless mesh network when high amounts of data traffic are exchanged between nearby mesh wireless access points.

Abstract: 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.

Abstract: 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.

Abstract: Inter-protocol interference reduction for hidden nodes may be provided. A first service end point may determine that an inter-protocol interference is present on a channel. Next, an initial packet failure count value on the channel may be determined. A transmit (Tx) power for selected packets may then be increased until a subsequent packet failure count value on the channel is less than the initial packet failure count value.

Abstract: 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.

Abstract: 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.

Abstract: The present disclosure provides for distortion cancellation by receiving a collided signal, the collided signal comprising a plurality of signals that each carry a corresponding packet; for a first signal of the plurality of signals that includes a first packet: amplifying and digitizing the collided signal into a first digital signal at a first gain; and decoding the first packet from the first digital signal; for each signal of the plurality of signals other than the first signal, carrying a given packet: estimating a given linear interference component and a given nonlinear interference component of one or more prior packets to the given packet on the collided signal; removing the given linear interference component and the given nonlinear interference component from the collided signal to produce a given de-interfered signal; and decoding the given packet of the plurality of packets from the given de-interfered signal.

Abstract: In one embodiment, an apparatus comprises an enhanced distributed channel access (EDCA) selection agent configured to receive a plurality of measurements pertaining to a plurality of clients, compute a set of optimal EDCA parameters using the plurality of measurements, and provide an EDCA configuration for the plurality of clients, and a client behavior predictor configured to receive the plurality of measurements pertaining to the plurality of clients, to receive the set of optimal EDCA parameters, and to compute a plurality of client mode predictions. Client mode predictions may be evaluated and potentially used for additional EDCA parameter optimization by the EDCA selection agent.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.