Systems and methods for reducing latency in the data path between a source and destination and managing resources in a 5G network are provided. The systems and methods described herein include dynamically selecting user plane functions (UPFs) and/or dynamically placing UPFs at particular locations within the 5G network. In some examples, each UPF in the 5G network is statically placed, and the UPF used for communication between a source and destination is dynamically selected based on control plane data. In other examples, UPFs are dynamically placed, and the UPF used for communication is dynamically selected to reduce latency and/or resource usage based on control plane data. In other examples, UPFs are dynamically placed, and the UPF used for communication is dynamically selected to reduce latency and/or resource usage based on user plane data after the flow of packets has started between a source and destination.

When a plurality of apparatuses each having a role of establishing a wireless network performs, in parallel, communication with a first apparatus having a role of joining a wireless network via a plurality of transmission channels, a communication apparatus controls, based on acquired information about a communication quality in communication between a second apparatus included in the plurality of apparatuses and the first apparatus, a transmission output of at least the second apparatus such that a difference between a communication quality in communication between an apparatus included in the plurality of apparatuses and different from the second apparatus and the first apparatus and a communication quality in communication between the second apparatus and the first apparatus becomes smaller. Alternatively, the communication apparatus determines at least one of a coding rate and a modulation scheme for use in communication between the second apparatus and the first apparatus based on the information.

A system described herein may provide for the use of modeling techniques to generate models associated with various locations or regions (e.g., sectors) associated with one or more radio access networks (“RANs”) of a wireless network, as well as for one or more mobile radio units (“MRUs”) that include wireless network infrastructure and which may be dynamically moved from one location to another. The system may identify attributes of a location within the network at which the MRU is located, and may automatically configure base stations of the RAN as well as the wireless network infrastructure of the MRU in order to optimize the operation of the RAN, as well as to gain optimal usage of the MRU.

Aspects of the subject disclosure may include, for example, collecting information about capabilities of a user equipment (UE) device operating on a radio communication network, collecting information about requirements and status of the UE device, collecting information about the radio communication network, selecting a carrier aggregation arrangement for the UE device based on at least the information about capabilities and the information about requirements and status, forming a selected carrier aggregation arrangement, and configuring network nodes of the radio communication network according to the selected carrier aggregation arrangement. Other embodiments are disclosed.

Capability of a user equipment to support machine learning adaptation by a base station of a reference signal pattern is signaled between the base station and the user equipment. Configuration information from the base station indicates one or more of enabling or disabling of machine learning adaptation of the reference signal pattern, a machine learning model used for machine learning adaptation of the reference signal pattern, updated model parameters for the machine learning model, or whether model parameters received from the user equipment will be used for machine learning adaptation of the reference signal pattern. Model training may be performed or model parameters received, and reference signals are received from the base station. Information on a reference signal pattern may be transmitted by the user equipment to the serving base station. Assistance information may be transmitted by the user equipment to the base station, which configures a reference signal pattern.

Capability of a user equipment to support machine learning adaptation by a base station of a reference signal pattern is signaled between the base station and the user equipment. Configuration information from the base station indicates one or more of enabling or disabling of machine learning adaptation of the reference signal pattern, a machine learning model used for machine learning adaptation of the reference signal pattern, updated model parameters for the machine learning model, or whether model parameters received from the user equipment will be used for machine learning adaptation of the reference signal pattern. Model training may be performed or model parameters received, and reference signals are received from the base station. Information on a reference signal pattern may be transmitted by the user equipment to the serving base station. Assistance information may be transmitted by the user equipment to the base station, which configures a reference signal pattern.

A method for accessing an access point by a station device, a device, and a system. A station device selects a master access point from multiple access points according to channel quality and accesses the master access point, and sends information about remaining access points to the master access point, so that the master access point selects a slave access point according to the information about the remaining access points.

A method for accessing an access point by a station device, a device, and a system. A station device selects a master access point from multiple access points according to channel quality and accesses the master access point, and sends information about remaining access points to the master access point, so that the master access point selects a slave access point according to the information about the remaining access points.

A channel selection method and a transmit end are provided. The method includes: ranking multiple channels, and generating a backoff count value; sequentially decrementing, from an initial timeslot, the backoff count value in each timeslot according to a ranking sequence of the channels and busy/idle states of all the channels until the backoff count value is 0; and selecting, from the multiple channels according to a result of the decrement performed on the backoff count value and a busy/idle state of at least one of the multiple channels, a channel that is used by the transmit end for sending data. The method and the transmit end can improve channel utilization.

A channel selection method and a transmit end are provided. The method includes: ranking multiple channels, and generating a backoff count value; sequentially decrementing, from an initial timeslot, the backoff count value in each timeslot according to a ranking sequence of the channels and busy/idle states of all the channels until the backoff count value is 0; and selecting, from the multiple channels according to a result of the decrement performed on the backoff count value and a busy/idle state of at least one of the multiple channels, a channel that is used by the transmit end for sending data. The method and the transmit end can improve channel utilization.