In an embodiment, an apparatus is provided that may include circuitry to generate, at least in part, and/or receive, at least in part, at least one request that at least one network node generate, at least in part, information. The information may be to permit selection, at least in part, of (1) at least one power consumption state of the at least one network node, and (2) at least one time period. The at least one time period may be to elapse, after receipt by at least one other network node of at least one packet, prior to requesting at least one change in the at least one power consumption state. The at least one packet may be to be transmitted to the at least one network node. Of course, many alternatives, modifications, and variations are possible without departing from this embodiment.

A wireless communication network manages a wireless access node. The wireless access node wirelessly exchanges user data with wireless User Equipment (UEs) and exchanges the user data with one or more network elements. The wireless access node generates status indicators that characterize wireless access node operation during the user data exchanges. An Element Management System (EMS) determines EMS load based on EMS operation and transfers load data that indicates the EMS load for delivery to the wireless access node. The wireless access node receives the load data transferred by the EMS. The wireless access node identifies individual priorities for individual ones of the status indicators. The wireless access node determines individual reporting times for the individual ones of the status indicators based on the load data and the individual priorities. The wireless access node transfers the individual ones of the status indicators to the EMS per the individual reporting times.

Systems and methods are disclosed herein that relate to Time Sensitive Communication (TSC) to Fifth Generation (5G) Quality of Service (QoS) mapping and associated QoS binding. In one embodiment, a method for QoS mapping in a 5G System (5GS) for a virtual Time Sensitive Networking (TSN) bridge comprises, at a first network function, obtaining information from a TSN Application Function (AF) comprising baseline TSC QoS parameters and one or more additional parameters comprising either or both of: (a) one or more additional TSC QoS attributes and (b) one or more additional traffic attributes. The method further comprises, at the first network function, generating one or more Policy and Charging Control (PCC) rules based on the obtained information and providing the one or more PCC rules to a second network function. The method further comprises, at the second network function, performing QoS binding based on the one or more PCC rules.

In example implementations, methods for selecting a network connection for paired endpoint devices and an apparatus for performing the same is provided. The method includes establishing a connection to a mobile endpoint device. A first quality score associated with a wireless connection of the mobile endpoint device is calculated based on a parameter associated with the wireless connection between the mobile endpoint device and a wireless network. A network selection is made based on a comparison of the first quality score and a second quality score. The second quality score is associated with a connection between the computer and a communication network.

Various techniques and approaches described herein may be applied to control congestion at a session management (SM) function (SMF) based on at least one back-off timer maintained at a user equipment (UE). In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may be a UE. The UE may be configured to establish a protocol data unit (PDU) session with a SMF. The UE may be configured to determine whether a back-off timer associated with the PDU session is terminated. The UE may refrain from sending, to the SMF, data over a control plane associated with the PDU session when the back-off timer is unterminated. The UE may send, to the SMF, the data over the control plane associated with the PDU session when the back-off timer is terminated.

A method of optimizing Overload Control Information (OCI) handling for a Control Plane (CP) that has a first association with a first User Plane (UP 1) and a second association with a second User Plane (UP 2) includes: upon receiving from UP 1 an OCI containing i) overload reduction metric defining a target number of sessions for the first association, and ii) a specified period of validity for the OCI handling, initiating a timer for the period of validity for the OCI handling; periodically checking the number of sessions currently being handled by the first association; initiating new sessions with UP 1 if the number of sessions currently being handled by the first association is below the target number of sessions; and not initiating new sessions with the UP 1 if the number of sessions currently being handled by the first association is above the target number of sessions.

Techniques for distributed computation are provided. A plurality of edge computing devices available to execute a computing task for a client device is identified, and a first latency of transmitting data among the plurality of edge computing devices is determined. A second latency of transmitting data from the client device to the plurality of edge computing devices is determined, and a set of edge computing devices, from the plurality of edge computing devices, is determined to execute the computing task based at least in part on the first and second latencies. Execution of the computing task is facilitated using the set of edge computing devices, where the client device transmits a portion of the computing task directly to each edge computing device of the set of edge computing devices.

Certain aspects of the present disclosure provide techniques for packet buffering. A method that may be performed by a receiving node includes dynamically determining one or more time durations to buffer packets. The one or more time durations can be different than a time duration of a configured timer for buffering the packets. The receiving node may input one or more parameters to a machine learning algorithm and obtain, as output of the machine learning algorithm based on the input one or more parameters, one or more time durations to buffer packets. The receiving node buffers packets for the determined one or more time durations. The receiving node may use machine learning to dynamically determine the one or more time durations to buffer packet. The buffering may be at a radio link control (RLC) reassembling buffer and/or a packet data convergence protocol (PDCP) buffer.

The invention relates to a method for efficiently discarding data packets destined to a mobile station connected to both a master base station and a secondary base station. The master base station configures a secondary discard function in a lower layer of the secondary base station, based on the master discard function in the higher layer of the master base station. The master base station forwards the data packet from the higher layer to the lower of the secondary base station. The secondary discard function of the lower layer at the secondary base station discards the received data packet upon expiry of the secondary timer started by the lower layer upon reception of the data packet from the higher layer at the master base station.

Embodiments are directed towards systems and methods for user plane function (UPF) and network slice load balancing within a 5G network. Example embodiments include systems and methods for load balancing based on current UPF load and thresholds that depend on UPF capacity; UPF load balancing using predicted throughput of new UE on the network based on network data analytics; UPF load balancing based on special considerations for low latency traffic; UPF load balancing supporting multiple slices, maintaining several load-thresholds for each UPF and each slice depending on the UPF and network slice capacity; and UPF load balancing using predicted central processing unit (CPU) utilization and/or predicted memory utilization of new UE on the network based on network data analytics.