Provided is a communication device that favorably implements SR communication and MU communication. A communication device that operates as a base station includes a determination unit that determines a communication status in a BSS of the communication device, and a control unit that controls transmission and reception of a signal regarding connection change of a terminal according to the communication status to and from a surrounding base station. The determination unit determines the presence of a small number of terminals falling outside a distribution of implementation statuses of spatial reuse communication or multi user communication of entire subordinate terminals, and transmits a signal requesting the small number of terminals to be connected to the surrounding base station or transmits a signal inquiring of the surrounding base station acceptance of the small number of terminals.

The present disclosure is a research which has been conducted with the support of the “Cross-Departmental Giga KOREA Project” of the government (the Ministry of Science and ICT) in 2017 (No. GK17N0100, Development for mmWave-based 5G mobile communication system). The present disclosure relates to a communication technique for convergence of a 5G communication system for supporting a higher data transmission rate beyond a 4G system with an IoT technology, and a system therefor. The present disclosure may be applied to an intelligent service (for example, smart home, smart building, smart city, smart car or connected car, health care, digital education, retail business, security and safety-related service, etc.) on the basis of a 5G communication technology and IoT-related technology. The present disclosure provides a system and a control method therefor, which can prevent, when a dual connectivity scheme is used, delay and data loss due to an operation of releasing and adding a secondary node cell or a process of resetting a user plane and updating a security key and a procedure of forwarding data between user planes through a common anchored user plane and changing (updating) a path between the user plane and a core.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a neighbor cell list that indicates at least one neighbor cell. The UE may perform a cell selection procedure or a cell reselection procedure to switch to a serving cell without receiving an updated neighbor cell list from the serving cell. Numerous other aspects are provided.

Wireless communication devices may directly communicate within groups of wireless communication devices using Layer-2 communications to implement “push-to-talk” type applications. In one implementation, a method may include generating a floor request signaling message to take control of a communication channel for a group. After transmitting data relating to the communications, a floor release signaling message may be generated and transmitted a number of times.

Embodiments of this application provide a packet transmission method, a communication apparatus, and a communication system. A target transport layer proxy network element establishes a fourth transport layer connection based on a first context of a source transport layer proxy network element, where the first context is a context about packet transmission of the source transport layer proxy network element on a first path. If the target transport layer proxy network element receives first indication information, and the first indication information indicates that application context migration between a first application server and a second application server is completed, the target transport layer proxy network element performs transport layer processing on a packet on a second path based on a second context of the target transport layer proxy network element, and transmits, on the second path, a packet obtained through the transport layer processing. Hence, a packet loss can be avoided.

Machine-learning based techniques are described herein for determining and modifying handover parameters within multilayer wireless networks. Various communication session data, such as key performance indicators, may be analyzed and compared at multiple frequency layers to determine sets of custom parameters associated with one or more wireless networks. The sets of custom parameters and network performance data may be used to train one or more machine-learned models to improve and/or optimize the handover parameters used by the network nodes. In some examples, different trained models may be associated with different network performance metrics, such as throughput optimization, network speed, and/or dropped call minimization, etc. A trained machine-learned model may be used to analyze the session data from a set of network nodes, and to determine or tune the handover parameters used by the network nodes.

A communication method implemented by a network server, includes: obtaining, based on geographical location information of a plurality of base stations, a first connection relationship between the plurality of base stations; converting the first connection relationship into a second connection relationship based on a first correspondence between geographical location information of a base station and a network element ID of a network device connected to the base station, where the second connection relationship is a topological connection relationship between a plurality of network elements, and the plurality of base stations are respectively connected to the plurality of network elements in a one-to-one correspondence; and generating a service connection configuration of the bearer network based on the second connection relationship. As a result, the service connection configuration of the bearer network can be automatically generated, thereby reducing an error rate and improving work efficiency.

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 disclosure relates to a method of obtaining path information of an unmanned aerial vehicle (UAV). The method is applicable to a base station, and includes: receiving indication information from the UAV, where the indication information indicates whether the UAV has a capability of sending the path information; if the UAV has the capability of sending the path information according to the indication information, sending configuration information to the UAV, where the configuration information indicates whether the UAV is allowed to send the path information to the base station; and if the UAV is allowed to send the path information to the base station, receiving the path information from the UAV. According to examples of the present disclosure, the base station successfully receives the path information from the UAV.