Abstract: A cellular wakeup receiver (C-WuRx) for reducing power consumption of a wireless wide area network (WWAN) radio of a user equipment (UE) includes receiver circuitry to receive a wakeup signal from a base station in response to the UE performing link aggregation by which downlink communications from the base station are offloaded to a wireless local area network (WLAN). The C-WuRx also includes processing circuitry to configure the receiver circuitry to periodically monitor at least a portion of a WWAN band for the wakeup signal and process the wakeup signal to cause the WWAN radio to resume receiving the downlink communications from the base station.

Abstract: A circuit arrangement includes a preprocessing circuit configured to obtain context information related to a user location, a learning circuit configured to determine a predicted user movement based on context information related to a user location to obtain a predicted route and to determine predicted radio conditions along the predicted route, and a decision circuit configured to, based on the predicted radio conditions, identify one or more first areas expected to have a first type of radio conditions and one or more second areas expected to have a second type of radio conditions different from the first type of radio conditions and to control radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.

Abstract: In various aspects, power saving host-modem interactions are described herein. For instance, a host of a communication device includes a host processor that receives, via an interface, information about the reduced activity state of a modem of the communication device including an indication of the RRC state of the modem. It also receives, via the interface, information about a projected expiration of a reduced activity period of the modem including an indication of a time remaining until a next event of the modem. The host processor controls a reduction of an operation of a component of the communication device during the reduced activity period of the modem, based on the indication of the RRC state of the modem and the indication of the time remaining until the next event of the modem. The reduced activity state of the modem corresponding to the reduced activity period of the modem.

Abstract: Systems and methods of reducing power consumption associated with paging or cDRX mode are described. A wake-up receiver (WUR) wakes up from an idle mode or cDRX state. Whether a wake-up signal (WUS) has been received by the WUR is determined. The WUS is a low-complexity signal that is less complicated than a PDCCH or PDSCH and is repeated multiple times at resource elements as indicated in a configuration from an eNB. If received, a baseband transceiver wakes up for reception of a PDCCH for the UE in a PO when the UE is in the idle mode or a PDSCH for the UE when the UE is in the cDRX state.

Abstract: A cellular wakeup receiver (C-WuRx) for reducing power consumption of a wireless wide area network (WWAN) radio of a user equipment (UE) includes receiver circuitry to receive a wakeup signal from a base station in response to the UE performing link aggregation by which downlink communications from the base station are offloaded to a wireless local area network (WLAN). The C-WuRx also includes processing circuitry to configure the receiver circuitry to periodically monitor at least a portion of a WWAN band for the wakeup signal and process the wakeup signal to cause the WWAN radio to resume receiving the downlink communications from the base station.

Abstract: Methods and apparatus to provide power management for multi-die stacks using artificial intelligence are disclosed. An example integrated circuit (IC) package includes a computer processor unit (CPU) die, a memory die, inference engine circuitry within the CPU die, the inference engine circuitry to infer, based on a first machine learning model, a workload for at least one of the CPU die or the memory die, and power management engine circuitry within the CPU die, the power management engine circuitry distinct from the inference engine circuitry, the power management engine circuitry to adjust, based on a second machine learning model different than the first machine learning model, operational parameters associated with the at least one of the CPU die or the memory die, the inferred workload to be an input to the second machine learning model.

Abstract: A wireless communication device includes a processor configured to select an offload processing task for performance by an edge computing device; cause a baseband modem to establish a direct wireless connection between the wireless communication device and the edge computing device; cause the baseband modem to send first data to the edge computing device via the direct wireless connection; and receive second data from the edge computing device, wherein the second data comprise a result of the offload processing task performed on the first data. The edge computing device includes a processor configured to receive, from a user device, offloaded data to be processed according to an offload processing task; execute the offload processing task on the offloaded data; and cause the radio via the interface to wirelessly send a result of the executed offload processing task via a direct wireless connection with the user device.

Abstract: In one embodiment, a road side unit (RSU) establishes a data offload session with a vehicle in the vicinity of the RSU based on a session establishment request sent by the vehicle, and stores data received from the vehicle during the data offload session in its memory. The RSU generates storage record information (including identifying information for the RSU) for the stored data, and transmits the storage record information to the vehicle.

Abstract: A telecommunication network may operate to enable a wake up signals (WUSs) within the telecommunication network. A mobility management entity (MME) may estimate a coverage enhancement (CE) level of a user equipment (UE), determine, based on the CE level, a number of repetitions for a wake up signal (WUS) for the UE, and cause a WUS for UE to be transmitted to a radio access network (RAN) node corresponding to the UE. The RAN node may inform the MME that the RAN node has disabled the WUS feature, and may cause system information to be broadcast to UEs in IDLE mode, indicating that the WUS feature of the RAN node has been disabled. A UE may determine a paging occasion (PO) determine a maximum WUS duration, minimum offset, and start location of the WUS.

Abstract: A task modeling system, including a plurality of processing clients having a plurality of processing cores; a task modeler, including a memory storing an artificial neural network; and a processor, configured to receive input data representing a plurality of processing tasks to be completed by the processing client within a predefined time duration; and implement its artificial neural network to determine from the input data an assignment of the processing tasks among the processing cores for completion of the processing tasks within the predefined time duration, and determine a power management factor for each of the plurality of processing cores for power management during the predefined time duration; wherein the artificial neural network is configured to select the power management factor for each of the plurality of processing cores to achieve a power usage within a predefined threshold for the plurality of processing cores during the predefined time duration.

Abstract: A device may include a processor that includes a named data network (NDN) forwarding daemon (NFD) module. The NFD module may be configured to identify an accelerator on which to configure an offloaded NFD module based on a configuration table indicating a current configuration of the accelerator. The NFD module may be configured to configure the offloaded NFD module on the identified accelerator. The NFD module may be configured to receive an interest packet that includes a workload request. The interest packet may be configured according to an NDN protocol. The NFD module may be configured to determine that the offloaded NFD module is configured to perform the workload request using the identified accelerator based on the configuration table. The NFD module may be configured to offload, via an application programming interface, the workload request to the offloaded NFD module to perform the workload request using the identified accelerator.

Abstract: The present disclosure relates to a system for use in a wireless network, the system including: a processor configured to determine a performance parameter representative of a performance of a model of radio resource management, the model operating on a radio access network environment; and a radio resource manager configured to perform radio resource management dependent on the performance parameter.

Abstract: Devices and methods power configurations of radio communication devices. A device may include a memory storing communication activity data comprising information indicating a plurality of attributes with respect to communication activities over a radio connection between a user equipment (UE) and a base station (BS). The device may further include a processor that is configured to provide the communication activity data to a trained machine learning model configured to predict a communication activity for the radio connection between the UE and the BS and encode a power preference information for transmission to the BS based on the predicted communication activity.

Abstract: The present disclosure relates to a device for use in a wireless network, the device including: a processor configured to: provide input data to a trained machine learning model, the input data representative of a network environment of the wireless network, wherein the trained machine learning model is configured to provide, based on the input data, output data representative of an expected performance of a plurality of configurations of network components with respect to power consumption and performance of the wireless network; select a configuration of a network component from the plurality of configurations based on the output data of the trained machine learning model; and instruct an operation of the network component according to the selected configuration; and a memory coupled with the processor, the memory storing the input data provided to the trained machine learning model and/or the output data from the trained machine learning model.

Abstract: A circuit arrangement includes a preprocessing circuit configured to obtain context information related to a user location, a learning circuit configured to determine a predicted user movement based on context information related to a user location to obtain a predicted route and to determine predicted radio conditions along the predicted route, and a decision circuit configured to, based on the predicted radio conditions, identify one or more first areas expected to have a first type of radio conditions and one or more second areas expected to have a second type of radio conditions different from the first type of radio conditions and to control radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas.

Abstract: In one embodiment, a road side unit (RSU) establishes a data offload session with a vehicle in the vicinity of the RSU based on a session establishment request sent by the vehicle, and stores data received from the vehicle during the data offload session in its memory. The RSU generates storage record information (including identifying information for the RSU) for the stored data, and transmits the storage record information to the vehicle.

Abstract: An apparatus and system of providing energy saving in a network are described. A target node receives, from a source node, a handover request to handover a UE due to at least one of entry of the source node into a power saving state or mobility optimization. In response, the target node transmits a relative energy efficiency value of Bits per Joule and periodic feedback related to a status of the UE for training an AI/ML energy saving model and stops the feedback in response to a stop parameter being met. The stop parameter is a first of: a predicted period of the source node to stay in the power saving state or time for a UE to connect to the target node, a percentage of UEs handed over that enter an idle or inactive state, and a percentage of UEs handed over that are further handed to another node.

Abstract: A circuit arrangement includes a preprocessing circuit configured to obtain context information related to a user location, a learning circuit configured to determine a predicted user movement based on context information related to a user location to obtain a predicted route and to determine predicted radio conditions along the predicted route, and a decision circuit configured to, based on the predicted radio conditions, identify one or more first areas expected to have a first type of radio conditions and one or more second areas expected to have a second type of radio conditions different from the first type of radio conditions and to control radio activity while traveling on the predicted route according to the one or more first areas and the one or more second areas.

Abstract: Edge devices utilizing personalized machine learning and methods of operating the same are disclosed. An example edge device includes a model accessor to access a first machine learning model from a cloud service provider. A local data interface is to collect local user data. A model trainer is to train the first machine learning model to create a second machine learning model using the local user data. A local permissions data store is to store permissions indicating constraints on the local user data with respect to sharing outside of the edge device. A permissions enforcer is to apply permissions to the local user data to create a sub-set of the local user data to be shared outside of the edge device. A transmitter is to provide the sub-set of the local user data to a public data repository.