Abstract: Devices, networks, systems, methods, and processes for facilitating power conservation in wireless devices are described herein. An access point (AP) in a wireless network may predict sensor data associated with a wireless device based on historical sensor data and various parameters associated with the wireless device. The AP predicts the sensor data when a transceiver of the wireless device is operating in a sleep mode. A wake-up radio of the wireless device that consumes less power than the transceiver is awake when the transceiver is operating in the sleep mode. The AP transmits the predicted sensor data to the wake-up radio. The wake-up radio maintains the transceiver in the sleep mode in response to the predicted sensor data deviating from actual sensor data, that is generated by a sensor of the wireless device, below a threshold value, thereby conserving power of the wireless device.

Abstract: In one embodiment, a machine learning server in a computer network determines a plurality of computing features shared across a given set of computing products, and collects, from each computing product of the given set, problem-solution data for each computing feature of the plurality of computing features. Problem-solution data is indicative of problems related to a respective computing feature, attempted solution actions for the problems, and outcomes of the attempted solutions on the problem. The machine learning server updates a machine learning model of suggested solutions for computing-feature-specific problems based on the collected problem-solution data, and provides, based on the machine learning model, a particular suggested solution for a particular computing-feature-specific problem to a particular computing product.

Abstract: In one embodiment, a machine learning server in a computer network determines a plurality of computing features shared across a given set of computing products, and collects, from each computing product of the given set, problem-solution data for each computing feature of the plurality of computing features. Problem-solution data is indicative of problems related to a respective computing feature, attempted solution actions for the problems, and outcomes of the attempted solutions on the problem. The machine learning server updates a machine learning model of suggested solutions for computing-feature-specific problems based on the collected problem-solution data, and provides, based on the machine learning model, a particular suggested solution for a particular computing-feature-specific problem to a particular computing product.

Abstract: Methods and systems are disclosed which may provide MultiGig capability to a system where a physical layer device (PHY) or a network device does not have the capacity to support all available line speeds while operating in a single system-interface mode between MAC and PHY devices.

Abstract: Methods and systems are disclosed which may provide MultiGig capability to a system where a physical layer device (PHY) or a network device does not have the capacity to support all available line speeds while operating in a single system-interface mode between MAC and PHY devices.

Abstract: Disclosed herein are systems and methods of negotiating an acceptable speed for communication between at least two Ethernet devices connected by a link. In one aspect, the method monitors the link health and status and makes a speed choice decision based on the error rate and link partner capability.