Systems and methods for a scalable test model for a cellular communications system having multiple different bandwidth and subcarrier combinations are disclosed. Embodiments of a method performed by a test node and corresponding embodiments of a test node are disclosed. In some embodiments, a method performed by a test node comprises generating a test signal for a particular bandwidth and subcarrier spacing combination, the test signal being in accordance with a test model that is scalable for a plurality of different bandwidth and subcarrier spacing combinations. By using the scalable test model, the test model can be flexibly used to test for different bandwidth and subcarrier spacing combinations.

Aspects of the subject disclosure may include, for example, obtaining, over an uplink (UL) using an aggregation of modular antenna arrays, a modulated signal that includes feedback transmitted by a user equipment (UE), wherein the aggregation of modular antenna arrays comprises multiple groups of antenna elements, after the obtaining the modulated signal, performing a demodulation of the modulated signal, determining demodulator constellation errors from the demodulation of the modulated signal, performing an error gradient weight adaptation responsive to the determining the demodulator constellation errors to derive revised weights for various antenna elements of the multiple groups of antenna elements, and applying the revised weights to the various antenna elements of the multiple groups of antenna elements to adjust signals received over the UL. Other embodiments are disclosed.

A network device may receive multi-access edge computing (MEC) proximity service information that identifies a set of MEC devices and a set of network services supported by respective MEC devices of the set of MEC devices. The network device may transmit, to a base station, at least a portion of the MEC proximity service information for broadcasting by the base station. The network device may receive, from a user device, a request to establish a peer-to-peer connection with a MEC device for the network service. The network device may authenticate the user device for access to the network service. The network device may transmit, to the user device and the MEC device, connection information that enables the user device and the MEC device to establish the peer-to-peer connection, based on authenticating the user device for access to the network service.

A network device may receive multi-access edge computing (MEC) proximity service information that identifies a set of MEC devices and a set of network services supported by respective MEC devices of the set of MEC devices. The network device may transmit, to a base station, at least a portion of the MEC proximity service information for broadcasting by the base station. The network device may receive, from a user device, a request to establish a peer-to-peer connection with a MEC device for the network service. The network device may authenticate the user device for access to the network service. The network device may transmit, to the user device and the MEC device, connection information that enables the user device and the MEC device to establish the peer-to-peer connection, based on authenticating the user device for access to the network service.

A vehicle determines that a stop is upcoming along a route and determines that the upcoming stop will place the vehicle within range of a wireless transceiver. The vehicle approximates a predicted transfer rate of the wireless transceiver and determines if data onboard the first vehicle is to be uploaded, based on at least a size of the data, a priority assigned to the data and the predicted transfer rate. Additionally, the vehicle determines a predicted duration of communication between the vehicle and the transceiver based on the route and the upcoming stop, allowing the vehicle to determine a first predicted amount of data able to be transferred. The vehicle designates first data for upload, based on the first data having a size that is below the first predicted amount and begins transferring the designated data responsive to establishing communication with the wireless transceiver.

A method includes obtaining wireless traffic statistics indicating current conditions and future trends for at least one of throughput, latency, and device power consumption. The method also includes generating penalty functions that reflect the wireless traffic statistics. The method further includes determining a target wakeup time (TWT) interval based on the penalty functions. The method also includes adapting the TWT interval based on a change in the wireless traffic statistics.

A method includes obtaining wireless traffic statistics indicating current conditions and future trends for at least one of throughput, latency, and device power consumption. The method also includes generating penalty functions that reflect the wireless traffic statistics. The method further includes determining a target wakeup time (TWT) interval based on the penalty functions. The method also includes adapting the TWT interval based on a change in the wireless traffic statistics.

Embodiments of this application provide a data analytics method and data analytics apparatus. The method includes obtaining, by a user plane data processing network element, at least one matching condition that is from a data analytics network element, where each of the at least one matching condition corresponds to at least one service type or at least one execution rule. The method further includes obtaining, by the user plane data processing network element based on the at least one matching condition, a service type associated with user plane data or an execution rule associated with the user plane data. Embodiments of this application implement data analytics by using the data analytics network element in a communications network.

Embodiments of this application provide a data analytics method and data analytics apparatus. The method includes obtaining, by a user plane data processing network element, at least one matching condition that is from a data analytics network element, where each of the at least one matching condition corresponds to at least one service type or at least one execution rule. The method further includes obtaining, by the user plane data processing network element based on the at least one matching condition, a service type associated with user plane data or an execution rule associated with the user plane data. Embodiments of this application implement data analytics by using the data analytics network element in a communications network.

According to an example aspect of the present invention, there is provided a method comprising, receiving, from a first data endpoint of a radio access network, a representation of a local model of the first data endpoint of the radio access network, determining multiple common models for endpoints of the radio access network, selecting, based on the representation of the local model of the first data endpoint, one of said multiple common models for the first data endpoint and transmitting the selected common model to the first data endpoint, any other data endpoint or any other external system which utilizes the selected common model.