Telefonaktiebolaget Lm Ericsson (publ)

Raimundas Gaigalas

Jonas Lennart Wiorek

Mats Zachrison

Christian Skärby

Tor Kvernvik

Stephen G Craig

Henrik Nyberg

Nils Brännström

Sara Khosravi

Olof Enander

Jonas Wiorek

Nils BRÄNNSTRÖM

Methods and systems for dynamically selecting a link adaptation policy, LAP. In some embodiments, the method includes generating a machine learning, ML, model, wherein generating the ML model comprises providing training data to an ML algorithm. The method further includes using channel quality information, additional information, and the ML model to select a LAP from a set of predefined LAPs. In some embodiments, the additional information comprises: neighbor cell information about a second cell served by a second TRP, distance information indicating a distance between a UE and a first TRP, and/or gain information indicating radio propagation gain between the UE and the serving node. The method further includes the first TRP transmitting second data to the UE using the selected LAP.

Link adaptation optimized with machine learning

Methods and systems for dynamically selecting a link adaptation policy, LAP. In some embodiments, the method includes using channel quality information, additional information, and a machine learning, ML, model to select a LAP from a set of predefined LAPs, the set of predefined LAPs comprising a first LAP and a second LAP. In some embodiments, the additional information comprises: neighbor cell information about a second cell served by a second TRP, distance information indicating a distance between a UE and a first TRP, and/or gain information indicating a radio propagation gain between the UE and the serving node. The method further includes the first TRP transmitting data to the UE using the selected LAP.

Link adaptation optimization with contextual bandits

A method performed by a method performed by a network node for performing admission control in a wireless communications network is provided. The network node serves a first and one or more second UEs. The network node receives () from the first UE, an access request for a radio resource for communication between the first UE and the network node. The network node further estimates () a first prediction of a requirement of the radio resource related to the access request, based on a measured initial data traffic between the network node and the first UE. The network node determines () a first threshold related to the first prediction, as a function of a measured data traffic load between the network node and the one or more second UEs. The network node then performs () admission control by deciding whether or not to admit the radio resource to the first UE, based on whether or not the first prediction exceeds the first threshold.

Network node and method in a wireless communications network

A method performed by a network node for performing Congestion Control in a wireless communications network is provided. The network node serves a number of User Equipments, UEs, comprising a first and one or more second UEs. The first UE has a radio resource allocated for communication between the first UE and the network node. The network node estimates () a first prediction of a forthcoming usage of the allocated radio resource, based on a measured data traffic between the network node and the first UE. The network node further determines () a first threshold related to the first prediction, as a function of a measured data traffic load between the network node and the number of UEs. The network node then performs () congestion control by deciding whether or not to initiate a removal of the radio resource allocated to the first UE, based on whether or not the first prediction exceeds the first threshold.

A method performed by a network node for managing a radio resource between the network node and a first User Equipment, in a wireless communications network is provided. The network node serves a number of UEs, comprising the first UE and one or more second UEs. The first UE has a radio resource allocated for communication between the first UE and the network node. The network node estimates () a prediction of a forthcoming empty data flow related to the allocated radio resource, based on a measured data flow between the network node and the first UE. The network node further determines () a threshold related to the prediction, based on a measured data flow between the network node and the number of UEs. The network node then decides () whether or not to initiate a removal of the radio resource allocated to the first UE based on whether or not the prediction exceeds the threshold.

Network node and method for deciding removal of a radio resource allocated to a UE

A scheduler for ranking-based scheduling of multiple users on a shared communication channel of a serving base station in a wireless communication system comprises a determiner (), a priority ranker (), a user selector () and a resource allocator (). The determiner () is configured to determine, for each user of a given traffic class, measured bandwidth share ratios with respect to each of a number of other traffic classes. The priority ranker () is configured to determine, for each user, a priority rank based on i) achievable bit rate of the user according to reported channel quality, and ii) a quality of service component representing compliance of the measured bandwidth share ratios to corresponding target bandwidth share ratios. The user selector () is configured to select a subset of the users among those users having the highest priority ranks, and the resource allocator () is configured to allocate transmission resources of the base station to the selected subset of the users for transmission of user data on the shared communication channel.

Dynamic bandwidth allocation control in a multi-access radio communication system

A device associated with a network receives feedback information associated with a data channel, and determines whether a condition in the data channel is changing based on the feedback information associated with the data channel. The device also calculates an implicit channel quality estimate based on the feedback information. The device further transmits a trigger requesting a channel quality report when the condition in the data channel is changing, and receives the requested channel quality report.

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Raimundas Gaigalas