Abstract: Optimizing link adaptation for a communication session with a user device is disclosed herein. In one embodiment, a method performed by a network node of a telecommunications network to optimize link adaptation for a communication session with a user device comprises receiving a link adaptation state update message from a user device, wherein the link adaptation state update message comprises one or more link adaptation state information elements associated with a communication link between the network node and the user device. The method further comprises determining one or more link adaptation parameters for communicating with the user device based on the link adaptation state update message.

Abstract: Systems and method for inter-node exchange of link adaptation assistance information in a communications system are disclosed. In one embodiment, a method performed by a first network node comprises obtaining link adaptation assistance information associated to at least one communication device served by the first network node and transmitting a link adaptation assistance information message comprising the link adaptation assistance information to a second network node. In this manner, more efficient and better performing link adaptation algorithms in the communications system are enabled. Corresponding embodiments of a first network node are also disclosed herein. Corresponding embodiments of a second network node and a method of operation thereof are also disclosed.

Abstract: A method (900) performed in a radio access network (RAN) (200) for Physical Uplink Control Channel (PUCCH) format configuration of a user equipment (UE) (102) currently being served by a network node (104) in the RAN. The method includes obtaining (902) information, the information comprising at least one of: UE information about the UE currently being served by the network node in the RAN or network information about the RAN currently serving the UE. The method includes processing (904) the obtained information using a machine learning model (300, 400A, 400B, 500). The method includes selecting (906) a PUCCH format configuration from a plurality of PUCCH format configurations based on the processing. The method includes determining (908) whether to initiate a configuration of the UE to the selected PUCCH format configuration.

Abstract: Systems and methods are disclosed for inter-node exchange of data formatting configuration related to formatting of data for execution of a at least a machine learning (ML) or artificial intelligence (AI) process, or ML or AI model thereof. In one embodiment, a method performed by a first network node comprises receiving a first message from a second network node, the first message comprising information about how to format data for execution of at least a ML or AI process, or ML or AI model thereof, that is available for execution at the first network node. The method further comprises executing the at least a ML or AI process, or the ML or AI model thereof, based on the information comprised in the first message.

Abstract: Providing user device assisted link adaptation in communication networks is disclosed herein. In one embodiment, a method performed by a network node of a telecommunications network for optimizing selection of radio resources and transmission format for a communication session with a user device comprises receiving, by the network node, a link adaptation report message from the user device, wherein the link adaptation report message comprises a link adaptation report. The method further comprises determining a scheduling grant comprising an allocation of radio resources for the user device based on the link adaptation report. The method also comprises transmitting the scheduling grant to the user device.

Abstract: Methods and apparatuses for a network node are disclosed. According to an example, there is provided a method implemented in a network node of a communication network, the method including: obtaining communication device context information related to a current status of a communication device, and network context information related to a current status of the communication network; inputting the communication device context information and the network context information to a machine-learning model, wherein the machine-learning model outputs a score for at least one candidate handover parameter value based on the communication device context information and the network context information; and selecting at least one handover parameter value for a handover procedure involving the communication device based on the output from the machine-learning model, wherein the selected at least one handover parameter value is specific to the communication device.

Abstract: Systems and methods are disclosed herein for Deep Reinforcement Learning (DRL) based packet scheduling. In one embodiment, a method performed by a network node for DRB-based scheduling comprises performing a DRL-based scheduling procedure using a preference vector for a plurality of network performance metrics correlated to one of a plurality of desired network performance behaviors, the preference vector defining weights for the plurality of network performance metrics correlated to the one of the plurality of desired network performance behaviors. In this manner, DRL-based scheduling is provided in a manner in which multiple performance metrics are jointly optimized.

Abstract: A framework for transfer learning for radio resource management-related tasks in radio access networks is defined in terms of functional entities of the network operator and associated signaling. A source learner (110) provides parameters or policies that incorporate knowledge related to another context than the actual context in which the RRM-related task is to be executed. A target learner (120) converts the source policies and parameters into models or target policies relevant to the actual context of the RRM-related task. An actor (130) receiving target learner's output is enabled to determine an RRM-related action for accomplishing the RRM-related task.

Abstract: A framework for inductive tasks in RANs includes a source learner, a target learner and an inference entity that can be merged into existing or future RAN functions. The signaling mechanism between different framework entities may also be merged into existing or future network interfaces. The framework can be reused by multiple inductive tasks to perform a variety of tasks such as prediction of some network phenomena or to improve certain KPIs in the network.

Abstract: A framework for transfer learning for radio resource management-related tasks in radio access networks is defined in terms of functional entities of the network operator and associated signaling. A source learner (110) provides parameters or policies that incorporate knowledge related to another context than the actual context in which the RRM-related task is to be executed. A target learner (120) converts the source policies and parameters into models or target policies relevant to the actual context of the RRM-related task. An actor (130) receiving target learner's output is enabled to determine an RRM-related action for accomplishing the RRM-related task.

Abstract: Agent node and method therein, for configuring a radio resource parameter of a radio cell of a communication system. The agent node is configured to obtain a control policy; determine a feature representing a state of at least a part of the communication system, at a first time period; determine a control action to be performed for control in the radio cell at the first time period, out of a set of available control actions associated with the radio cell, based on the obtained control policy and the determined at least one feature; and configure an of the radio cell based on the determined control action.

Abstract: Agent node (210, 500) and method (600) therein, for configuring a radio resource parameter of a radio cell (215) of a communication system (200). The agent node (210, 500) is configured to obtain a control policy (430); determine a feature representing a state of at least a part of the communication system (200), at a first time period; determine a control action to be performed for control in the radio cell (215) at the first time period, out of a set of available control actions associated with the radio cell (215), based on the obtained control policy (430) and the determined at least one feature; and configure an instruction of the radio cell (215) based on the determined control action. Also, a corresponding trainer node (400) and method (800) for providing the agent node (210, 500) with the control policy (430), based on received parameters is disclosed.