A device and a method for reducing power consumption of an electronic device is provided. The electronic device includes a display device, a battery, and at least one processor configured to be operatively connected to the display device, wherein the processor may determine whether to perform a handover to a second communication network, based on whether the display device is activated and a data throughput, in a state of connection to a first communication network among a plurality of communication networks supportable by the electronic device, and perform a handover to the second communication network when it is determined to perform the handover to the second communication network.

A device and a method for reducing power consumption of an electronic device is provided. The electronic device includes a display device, a battery, and at least one processor configured to be operatively connected to the display device, wherein the processor may determine whether to perform a handover to a second communication network, based on whether the display device is activated and a data throughput, in a state of connection to a first communication network among a plurality of communication networks supportable by the electronic device, and perform a handover to the second communication network when it is determined to perform the handover to the second communication network.

The disclosed methods and systems use artificial intelligence (AI) and machine learning (ML) technologies to model the usage and interference on each channel. For example, units of the system can measure channel interference regularly over the time of day on all radios. The interference information is communicated to the base unit or a cloud server for pattern analysis. Interference measurements include interference from units within the system as well as interference from nearby devices. The base unit or the cloud server can recognize the pattern of the interference. Further, connected devices have a number of network usage characteristics observed and modeled including bitrate, and network behavior. These characteristics are used to assign channels to connected devices.

The disclosed methods and systems use artificial intelligence (AI) and machine learning (ML) technologies to model the usage and interference on each channel. For example, units of the system can measure channel interference regularly over the time of day on all radios. The interference information is communicated to the base unit or a cloud server for pattern analysis. Interference measurements include interference from units within the system as well as interference from nearby devices. The base unit or the cloud server can recognize the pattern of the interference. Further, connected devices have a number of network usage characteristics observed and modeled including bitrate, and network behavior. These characteristics are used to assign channels to connected devices.

This application provides a multichannel data transmission method performed at a proxy server when communicating with a mobile terminal and a target service server. The multichannel data transmission method includes: receiving, from the mobile terminal, a plurality of uplink data packets in parallel through a plurality of channels, parsing the plurality of uplink data packets to obtain a plurality of target service data packets and a plurality of uplink packet headers, performing deduplication processing on the plurality of target service data packets according to the plurality of uplink packet headers, and transmitting the target service data packets reserved after the deduplication processing to the target service server, each uplink packet headers including a packet sequence number of each uplink data packet. The uplink data packets can be transmitted in parallel through the plurality of channels, and reliable and effective transmission of target service data packets is implemented through deduplication processing.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may identify first set of metrics associated with a radio link monitoring procedure on a radio link and identify a second set metrics associated with a procedure for monitoring whether communications of a first service type or having a first quality of service requirement are supportable. The UE may monitor the radio link based on the first set of metrics to assess for radio link failure and monitor at least a portion of the radio link based on the second set of metrics. The UE may determine that a component of the radio link fails to satisfy the second set of metrics, and the UE may transmit an indication of the determination to the base station. The UE and the base station may update the radio link without declaring radio link failure.

The disclosed technology teaches detecting abnormal behavior of a UE mobile device, including a network data analytics function component, in communication with core network components of a cellular network, subscribing to location change-related events that report a change event for a UE device connection to and/or drop or handover from a cell. Included is analyzing location change-related events to detect abnormal handover behavior when the UE device changes its selection of a base station or cell more than N times in not more than M minutes, and reporting the detected abnormal handover behavior with an identifier of the UE mobile device involved and the involved cell's ID. The technology also applies to a group of UE devices selected for analysis, by device, geography or custom-defined affinity, with selection changes among a set of base stations or neighboring cells, each selected at least twice by the UE device in M minutes.

Aspects of the subject disclosure may include, for example, detecting a transit speed of a device, resulting in a detected transit speed; responsive to the detected transit speed satisfying a first threshold, including information in a message related to a handover request between a first access point of a network and a second access point of the network, the information being indicative of an amount of time that the device has been in a Discontinuous Reception (DRX) mode; and sending, to the first access point with which the device communicates and is transitioning away from, the message including the information that is indicative of the amount of time that the device has been in the DRX mode. Other embodiments are disclosed.

One example of a method of managing battery usage by a wireless mobile communication device includes reading a start time, an end time, and other information, for each of a number of extents, and identifying one or more handovers, using the start time, end time, and other information, wherein the one or more handovers are handover activity. Next, the handover activity is compared to an allowable threshold, and a determination made as to when the number of idle mode handovers exceeds the allowable threshold. Finally, battery usage by the wireless mobile communication device is reduced by taking action to reduce the number of idle mode handovers that occur.

One example of a method of managing battery usage by a wireless mobile communication device includes reading a start time, an end time, and other information, for each of a number of extents, and identifying one or more handovers, using the start time, end time, and other information, wherein the one or more handovers are handover activity. Next, the handover activity is compared to an allowable threshold, and a determination made as to when the number of idle mode handovers exceeds the allowable threshold. Finally, battery usage by the wireless mobile communication device is reduced by taking action to reduce the number of idle mode handovers that occur.