The present disclosure relates to a congestion control in wireless communications. According to an embodiment of the present disclosure, a method performed by a wireless device in a wireless communication system comprises: obtaining density information of neighboring wireless devices with respect to a reference wireless device; receiving a block from a neighboring wireless device; determining a probability that the wireless device will relay the received block based on the density information; and determining whether to relay the received block based on the probability.

The present disclosure relates to a congestion control in wireless communications. According to an embodiment of the present disclosure, a method performed by a wireless device in a wireless communication system comprises: obtaining density information of neighboring wireless devices with respect to a reference wireless device; receiving a block from a neighboring wireless device; determining a probability that the wireless device will relay the received block based on the density information; and determining whether to relay the received block based on the probability.

An apparatus for radio communications. The apparatus includes: a processor to determine at least one parameter characterizing a present QoS at a first apparatus of a radio communications network; a procesor to determine at least one first QoS prediction that characterizes a future QoS at the first apparatus at least on the at least one determined parameter; and a transmitter to transmit the at least one first prediction.

A method for managing a first node in a communication network is disclosed, wherein the first node is operable to exchange traffic flows with other nodes in the communication network. The method includes using a Variational Autoencoder (VAE) to generate a predicted traffic distribution for the first node, wherein the VAE has been trained using information about historical data flows exchanged by the first node with at least one other node in the communication network, and configuring at least one radio resource parameter of the first node based on the obtained predicted traffic distribution for the first node. Also disclosed are a method including training a VAE and nodes and a computer program product suitable for carrying out such methods.

Disclosed embodiments are related to techniques for implementing Vehicle-to-Everything (V2X) communications in Multi-access Edge Computing (MEC) systems and networks. V2X system scenarios characterized by high mobility and dynamic topologies, where the accuracy and timeliness of radio network information, location information may be hampered by environmental conditions and deployment density of network infrastructure. The disclosed embodiments provide a V2X Information Service (VIS) framework for cooperative acquisition, partitioning, and distribution of information for efficient, journey-specific quality-of-service (QoS) predictions. The VIS framework identifies space/time correlations between radio condition/quality data collected in V2X system(s) and a vehicle's planned journey for better prediction of the radio conditions/quality of the communication network along the designated route. As a consequence, the VIS may expose journey-specific information about the QoS prediction to authorized devices. Other embodiments may be described and/or claimed.

This application provides communication methods and communications apparatuses. In an example method, a first communications apparatus determines whether a first channel learning model is applicable, where the first channel learning model is used to determine first channel information based on target channel information, and a data amount of the first channel information is less than a data amount of the target channel information. The first communications apparatus sends a first message in response to determining that determining that the first channel learning model is not applicable, where the first message is used to indicate that the first channel learning model is not applicable. According to the example method, the first communications apparatus can determine applicability of the first channel learning model without assistance of a second communications apparatus.

A method performed by a first sensor device registered with a processing node via a network comprises steps of: i. detecting a trigger event, ii. broadcasting a signal to a second sensor device and establishing a direct connection between the first sensor device and the second sensor device, iii. transferring identity information and connection information to the second sensor device via the direct connection, the former identifying the first sensor device to the processing node, the latter being used by the first sensor device to connect to the processing node, and iv. receiving confirmation that the second sensor device has been registered with the processing node as a replacement for the first sensor device and removing the connection information from the first sensor device.

Techniques for improving data transmission in teleoperation systems including a method for dynamic packet routing. The method includes identifying an optimal channel of a plurality of channels based on a network connectivity status of a system and historical connectivity data related to a current location of the system, wherein the system includes a plurality of network authorization devices, wherein each network authorization device is configured to enable communications via an associated channel; and routing packets to the optimal channel using a network authorization device of the plurality of network authorization devices that is associated with the optimal channel.

Aspects of data re-direction are described, which can include software-defined networking (SDN) data re-direction operations. Some aspects include data re-direction operations performed by one or more virtualized network functions. In some aspects, a network router decodes an indication of a handover of a user equipment (UE) from a first end point (EP) to a second EP, based on the indication, the router can update a relocation table including the UE identifier, an identifier of the first EP, and an identifier of the second EP. The router can receive a data packet for the UE, configured for transmission to the first EP, and modify the data packet, based on the relocation table, for rerouting to the second EP. In some aspects, the router can decode handover prediction information, including an indication of a predicted future geographic location of the UE, and update the relocation table based on the handover prediction information.

Aspects of data re-direction are described, which can include software-defined networking (SDN) data re-direction operations. Some aspects include data re-direction operations performed by one or more virtualized network functions. In some aspects, a network router decodes an indication of a handover of a user equipment (UE) from a first end point (EP) to a second EP, based on the indication, the router can update a relocation table including the UE identifier, an identifier of the first EP, and an identifier of the second EP. The router can receive a data packet for the UE, configured for transmission to the first EP, and modify the data packet, based on the relocation table, for rerouting to the second EP. In some aspects, the router can decode handover prediction information, including an indication of a predicted future geographic location of the UE, and update the relocation table based on the handover prediction information.