Abstract: One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

Abstract: One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

Abstract: One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

Abstract: One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

Abstract: One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

Abstract: Described herein are systems and methods for telecommunications spectrum sharing between multiple heterogeneous users, which leverage a hybrid approach that includes both distributed spectrum sharing, spectrum-sensing, and use of geo-reference databases.

Abstract: One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

Abstract: One or more processors control processing of radio frequency (RF) signals using a machine-learning network. The one or more processors receive as input, to a radio communications apparatus, a first representation of an RF signal, which is processed using one or more radio stages, providing a second representation of the RF signal as. Observations about, and metrics of, the second representation of the RF signal are obtained. Past observations and metrics are accessed from storage. Using the observations, metrics and past observations and metrics, parameters of a machine-learning network, which implements policies to process RF signals, are adjusted by controlling the radio stages. In response to the adjustments, actions performed by one or more controllers of the radio stages are updated. A representation of a subsequent input RF signal is processed using the radio stages that are controlled based on actions including the updated one or more actions.

Abstract: Methods and systems described herein relate to enhancing security on a device by enforcing security and access control policies on privileged code execution.

Abstract: Methods and systems described herein relate to enhancing security on a device by configuring one or more software functions in a trusted zone of a processor using object firewalls, IPC mechanisms, and/or a policy engine. An inter-process communication mechanism and inter-process communication bus enable secure inter-process communication between inter-process communication applications within the trusted zone and inter-process communication applications external to the trusted zone. Adapting, filtering, blocking, redirecting, or otherwise modifying inter-process communications is enabled by the inter-process communications mechanism. Modifications may be controlled by a policy engine within the trusted zone.

Abstract: Methods and systems described herein relate to enhancing security on a device by configuring one or more software functions in a trusted zone of a processor using object firewalls, IPC mechanisms, and/or a policy engine.

Abstract: Methods and systems described herein relate to enhancing security on a device by enforcing one or more policies on the loading, linking, and/or executing of native code by one or more applications executing on the device.

Abstract: Described herein are systems and methods for sharing public safety network resources between public safety devices and commercial devices. A request for network resources is received from a commercial device for an application running on the commercial device. It is determined that the request is for a first type of traffic that is tolerant to time delays caused by resource fluctuations. Resources are allocated from a public safety network instead of resources from a commercial network, so that the commercial device can use resources from the public safety network.

Abstract: A method, system, and computer-readable medium for synchronizing policy data on a device based on device usage context. By synchronizing policy data on the device based on device usage context, security, bandwidth and energy efficiency concerns associated with the current data synchronization art by intelligently organizing and prioritizing the updating of policy data in compliance with policy data.

Abstract: Described herein are systems and methods for sharing public safety network resources between public safety devices and commercial devices. A request for network resources is received from a commercial device for an application running on the commercial device. It is determined that the request is for a first type of traffic that is tolerant to time delays caused by resource fluctuations. Resources are allocated from a public safety network instead of resources from a commercial network, so that the commercial device can use resources from the public safety network.

Abstract: Described herein are systems and methods for telecommunications spectrum sharing between multiple heterogeneous users, which leverage a hybrid approach that includes both distributed spectrum sharing, spectrum-sensing, and use of geo-reference databases.

Abstract: Described are systems and methods for managing data traffic to a plurality of mobile devices over a wireless network. For each mobile device, a facility is provided that is capable of: (i) assigning a utility function to mathematically represent the value of a plurality of applications running on that mobile device with respect to a factor, (ii) aggregating the plurality of utility functions to generate an aggregated utility function corresponding to that mobile device, and (ii) communicating at least one of the associated aggregate utility function and a bid price based on the associated aggregate utility function over a network. A resource allocation is determined among a plurality of mobile devices based on a bidding process which accounts for the aggregate utility functions, and the data traffic between the mobile devices and the resource is managed using a network traffic shaping element according to the resource allocation.

Abstract: Methods and systems described herein relate to enhancing security on a mobile device. Systems and methods for mobile device security include restricting access to network resources via an in-location access point device, based on whether the mobile device is in proximity of the in-location access point device.

Abstract: Described are systems and methods for managing data traffic to a plurality of mobile devices over a wireless network. For each mobile device, a facility is provided that is capable of: (i) assigning a utility function to mathematically represent the value of a plurality of applications running on that mobile device with respect to a factor, (ii) aggregating the plurality of utility functions to generate an aggregated utility function corresponding to that mobile device, and (ii) communicating at least one of the associated aggregate utility function and a bid price based on the associated aggregate utility function over a network. A resource allocation is determined among a plurality of mobile devices based on a bidding process which accounts for the aggregate utility functions, and the data traffic between the mobile devices and the resource is managed using a network traffic shaping element according to the resource allocation.

Abstract: Described herein are systems and methods for telecommunications spectrum sharing between multiple heterogeneous users, which leverage a hybrid approach that includes both distributed spectrum sharing, spectrum-sensing, and use of geo-reference databases.