Systems and methods are described herein for pre-steering traffic within a telecommunications network. In some embodiments, the systems and methods pre-steer traffic by steering user devices to optimal or suitable frequency bands of a network before the user devices begin streaming content and/or performing other actions via the network.

On-demand quality of service guarantees are provided in a wireless network environment. The system determines an on-demand quality of service for a segment of a communication path between a user equipment communicating with a radio access network connected to a core network and an external network connected to the core network. The system then determines if the on-demand quality of service for the segment meets a quality of service requirement. If the on-demand quality of service for the segment does not meet the quality of service requirement, the system identifies an alternate communication path between the user equipment and the external network, wherein the alternate communication path differs from the communication path. The system can then setup the alternate communication path for traffic between the user equipment and the external network.

On-demand quality of service guarantees are provided in a wireless network environment. The system determines an on-demand quality of service for a segment of a communication path between a user equipment communicating with a radio access network connected to a core network and an external network connected to the core network. The system then determines if the on-demand quality of service for the segment meets a quality of service requirement. If the on-demand quality of service for the segment does not meet the quality of service requirement, the system identifies an alternate communication path between the user equipment and the external network, wherein the alternate communication path differs from the communication path. The system can then setup the alternate communication path for traffic between the user equipment and the external network.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may monitor one or more first conditions pertaining to non-cellular communications between the UE and a non-cellular network while the UE is operating in a dual networking mode for steering, switching, or splitting traffic (e.g., an access traffic steering, switching, and splitting (ATSSS) mode) between the non-cellular network and a cellular network. The UE may predict an availability status of at least the non-cellular network based on at least one of the one or more first conditions. In some cases, the UE may determine whether to change dual networking modes based on the availability status and may communicate in accordance with the same or a different dual networking mode using at least one of the cellular network, the non-cellular network, or a combination thereof based on the prediction.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a transmitter node may predict a future state associated with a wireless channel at a future time instance using a machine learning model, wherein the future state is predicted based at least in part on one or more of weights associated with the machine learning model, a current state associated with the wireless channel, or one or more previous states associated with the wireless channel. The transmitter node may select one or more parameters for a transmission to occur at the future time instance based at least in part on the future state associated with the wireless channel. The transmitter node may perform the transmission using the one or more parameters. Numerous other aspects are described.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a transmitter node may predict a future state associated with a wireless channel at a future time instance using a machine learning model, wherein the future state is predicted based at least in part on one or more of weights associated with the machine learning model, a current state associated with the wireless channel, or one or more previous states associated with the wireless channel. The transmitter node may select one or more parameters for a transmission to occur at the future time instance based at least in part on the future state associated with the wireless channel. The transmitter node may perform the transmission using the one or more parameters. Numerous other aspects are described.

A system and method of propagating signal links by using artificial neural networks using a relay link selection protocol to predict an optimal link or path, providing a reliable mechanism to meet 5G-new radio requirements. The artificial neural networks used in the method classify training and testing datasets into sufficient signal strengths and insufficient signal strengths, such that paths are evaluated for predicted propagation links, and such that the strongest propagation link can be selected. Specifically, a multilayer perceptron method is used to identify and characterize new link candidates using the path loss parameter or the received signal strength, such that optimal links can be selected and updated. To determine the sufficiency of a signal, a threshold energy strength is determined (for example, a threshold of −120 dBm can be used; any energy strength below the threshold is considered a poor propagation and is classified as an insufficient signal).

A system and method of propagating signal links by using artificial neural networks using a relay link selection protocol to predict an optimal link or path, providing a reliable mechanism to meet 5G-new radio requirements. The artificial neural networks used in the method classify training and testing datasets into sufficient signal strengths and insufficient signal strengths, such that paths are evaluated for predicted propagation links, and such that the strongest propagation link can be selected. Specifically, a multilayer perceptron method is used to identify and characterize new link candidates using the path loss parameter or the received signal strength, such that optimal links can be selected and updated. To determine the sufficiency of a signal, a threshold energy strength is determined (for example, a threshold of −120 dBm can be used; any energy strength below the threshold is considered a poor propagation and is classified as an insufficient signal).

A network administration device may include one or more processors to receive operational information regarding a plurality of network devices; receive flow information relating to at least one traffic flow; input the flow information to a model, where the model is generated based on a machine learning technique, and where the model is configured to identify predicted performance information of one or more network devices with regard to the at least one traffic flow based on the operational information; determine path information for the at least one traffic flow with regard to the one or more network devices based on the predicted performance information; and/or configure the one or more network devices to implement the path information for the traffic flow.

A network administration device may include one or more processors to receive operational information regarding a plurality of network devices; receive flow information relating to at least one traffic flow; input the flow information to a model, where the model is generated based on a machine learning technique, and where the model is configured to identify predicted performance information of one or more network devices with regard to the at least one traffic flow based on the operational information; determine path information for the at least one traffic flow with regard to the one or more network devices based on the predicted performance information; and/or configure the one or more network devices to implement the path information for the traffic flow.