Abstract: A technique for performing time synchronization between a master device (200) and a slave device (210) is disclosed. A method implementation of the technique is performed by the master device (200) and comprises transmitting, using optical wireless signals, a timestamp representative of a clock time of the master device (200) to the slave device (210), wherein the timestamp is encoded using a color-based optical coding scheme and is to be used by the slave device (210) to set a clock time of the slave device (210) in accordance with the timestamp to thereby synchronize the clock time of the slave device (210) with the clock time of the master device (200).

Abstract: There is provided a method for managing services in a network. The method is performed by a first node. The method is performed in response to receiving a service for a user equipment (UE) from at least one second node that is incapable of handling network slices for providing the service. The method comprises assigning (100) the service to one of a plurality of network slices in the network based on a quality of service (QoS) indicator assigned to the service.

Abstract: There is provided a method for handling packets in a network. The method is performed by a first entity. The method is performed in response to receipt of a first packet, from a first network slice in the network, to be scheduled for a first service. A first priority value is assigned to the first packet by a provider of the first service. The method comprises setting (100) a second priority value for the first packet based on a comparison of the first priority value with at least one reference priority value. The second priority value is indicative of a priority with which the first packet is to be scheduled relative to at least one other packet.

Abstract: A system, method and non-transitory computer readable media for effectuating ML-based fault analysis in a network (102A, 102B) comprising a plurality of nodes (104-N, 120-M). An example method (200A) comprises determining (202) that at least one of a topological configuration of the network (102 A, 102B) and one or more key performance indicator (KPI) requirements associated with the network (102A, 102B) have changed; and responsive to the determining, selecting (204) a machine language (ML) engine optimally adapted to facilitate root cause determination of any faults detected in the network (102 A, 102B) after the topological configuration or any KPI requirements of the network (102A, 102B) have changed.