Disclosed is a way to expand the range of Internet of Things devices in a home, office, or structure to the range of a local WiFi network. This is accomplished by generating a network bridge for the devices using machine-to-machine protocols to communicate using the WiFi network backhaul channel. Transmissions in machine-to-machine protocol are tunneled through WiFi communications and extracted by the closest access point. Access points include radios for both WiFi and machine-to-machine protocols.

Disclosed is a way to expand the range of Internet of Things devices in a home, office, or structure to the range of a local WiFi network. This is accomplished by generating a network bridge for the devices using machine-to-machine protocols to communicate using the WiFi network backhaul channel. Transmissions in machine-to-machine protocol are tunneled through WiFi communications and extracted by the closest access point. Access points include radios for both WiFi and machine-to-machine protocols.

A handover method, a network device, and a terminal device are provided. The method includes: a first network device acquires measurement results of links, wherein the links include a link between the first network device and a first terminal device and a link between the first network device and a second terminal device; and the first network device determines a first handover scheme of the first terminal device and/or a second handover scheme of the second terminal device according to the measurement results.

A Machine-to-machine (M2M) terminal (11) comprises a radio communication unit (111) and a controller (112). The radio communication unit (111) is configured to communicate with a base station (13). The controller (112) is configured to change at least one of a cell selection operation, a cell reselection operation, and a handover operation according to whether a specific coverage enhancement processing is required or according to whether the specific coverage enhancement processing is supported by at least one of a cell (13) in which the M2M terminal (11) camps on and a neighbouring cell (14) of the cell (13) which the M2M terminal (11) camps on. It is thus possible to provide an improved technique for allowing the M2M terminal that is supporting a special coverage enhancement processing for M2M terminals to camp on an appropriate cell.

A Machine-to-machine (M2M) terminal (11) comprises a radio communication unit (111) and a controller (112). The radio communication unit (111) is configured to communicate with a base station (13). The controller (112) is configured to change at least one of a cell selection operation, a cell reselection operation, and a handover operation according to whether a specific coverage enhancement processing is required or according to whether the specific coverage enhancement processing is supported by at least one of a cell (13) in which the M2M terminal (11) camps on and a neighbouring cell (14) of the cell (13) which the M2M terminal (11) camps on. It is thus possible to provide an improved technique for allowing the M2M terminal that is supporting a special coverage enhancement processing for M2M terminals to camp on an appropriate cell.

The present disclosure provides a voice call processing method and a terminal device. The method includes: when a terminal meets at least one of two preset conditions, performing handover from a Long Term Evolution LTE network currently accessed by the terminal to a non-LTE standard network, and performing a circuit switched CS voice call process on the non-LTE standard network. The voice call processing method provided in embodiments of the present invention increases a voice call completion rate of a terminal and meets a user experience requirement.

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.

The present application relates to the field of communications technologies, and provides an inter-small cell handover method, a device, and a system. When UE is in a coverage hole of a serving small cell, the UE needs to perform synchronous measurement only on each available beam pair in a set of available small cells determined by the UE, thereby reducing a delay of re-accessing a high frequency network by the UE, and improving QoS of receiving a high frequency service by the UE. the present application includes: performing, by UE, synchronous measurement, to determine a set of small cells available for the UE; sending the set of available small cells to a macro base station; when the UE is in a coverage hole of a current serving small cell, receiving, by the UE, a first synchronization indication sent by the macro base station.

A method includes obtaining client information from a set of access points. The client information indicates client devices detected by each access point in the set of access points. In some implementations, the method includes grouping the set of access points into a plurality of access point groups based on the client information. The grouping allows each client device to associate with an access point from at least two different access point groups. The method includes, for a first access point group of the plurality of access point groups, migrating the client devices associated with access points in the first access point group to access points in a second access point group of the plurality of access point groups. In some implementations, the method includes configuring the access points in the first access point group while maintaining configuration of the access points in the second access point group.