Abstract: 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.

Abstract: A method performed by a first communication device operating in a wireless communications network. The first communication device obtains a first set of one or more values indicating an observed speed of a boat relative to a power of an engine of the boat. The speed and the first indication are obtained by from sensors in the boat. The first communication device then obtains a second indication of an external factor on the hull of the boat causing friction against water. The obtaining of the second indication is based at least on: the obtained first set, and a reference. The reference is based on one of: a) a threshold, and b) a mathematical model. The first communication device also initiates providing a third indication of the external factor to a device, based on the obtained second indication.

Abstract: 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.

Abstract: Computer implemented methods for training a Student Neural Network, SNN, and for managing an environment of a communication network using a trained SNN are disclosed. The SNN is for generating an action prediction matrix for an environment in a communication network, the action prediction matrix comprising action predictions for a plurality of nodes or resources in the environment. The training method comprises using a Reinforcement Learning process to train a Teacher Neural Network, TNN, to generate an action prediction for a resource or node in the environment, and using the trained TNN to generate a first training data set including action predictions for individual nodes or resources. The training method further comprises generating a second training data set from the first training data set such that the second training data set includes action prediction matrices, and using the second training data set to update values of the parameters of the SNN.

Abstract: A method performed by a first communication device operating in a wireless communications network. The first communication device obtains a first set of one or more values indicating an observed speed of a boat relative to a power of an engine of the boat. The speed and the first indication are obtained by from sensors in the boat. The first communication device then obtains a second indication of an external factor on the hull of the boat causing friction against water. The obtaining of the second indication is based at least on: the obtained first set, and a reference. The reference is based on one of: a) a threshold, and b) a mathematical model. The first communication device also initiates providing a third indication of the external factor to a device, based on the obtained second indication.

Abstract: There is provided a method performed by an entity for managing connectivity of a device to a network. The method comprises selecting, from a plurality of connectivity service providers in the network, a connectivity service provider (CSP) to connect the device to the network. The selection is based on information about the device.

Abstract: Method performed by a first communication device (101) operating in a wireless communications network (100). The first communication device (101) obtains (201) a first set of one or more values indicating an observed speed of a boat (151) relative to a power of an engine (161) of the boat (151). The speed and the first indication are obtained by from sensors (171, 172) in the boat (151). The first communication device (101) then obtains (205) a second indication of an external factor on the hull of the boat (151) causing friction against water. The obtaining (205) of the second indication is based at least on: the obtained first set, and a reference. The reference is based on one of: a) a threshold, and b) a mathematical model. The first communication device (101) also initiates providing (206) a third indication of the external factor to a device (103), based on the obtained second indication.

Abstract: A method for dynamically assigning communication resources between two or more radio access technologies (RAT) in a wireless access network. The method includes obtaining a network observation ot indicating a current state of the wireless access network, predicting a sequence of future states of the wireless access network by iteratively simulating hypothetical communication resource assignments a1, a2, a3 over a time window w starting from the current state, evaluating a reward function for each hypothetical communication resource assignment a1, a2, a3 over the time window w, and dynamically assigning the communication resources based on the simulated hypothetical communication resource assignment a1 associated with maximized reward function over the time window w when the wireless access network is in the current state.

Abstract: Communications from a content server to an end device via a proxy device are controlled. The content server and the proxy device are connected by a first link using a streaming control protocol. The proxy device also has a connection to the end device over a second link. The method includes obtaining information about a context of the end device. The method further includes determining where a decision should be made about controlling transmissions from the content server over the first link, based on the information about the context of the end device. The method further includes controlling transmissions from the content server over the first link, based on information relating to a Quality of Experience at the end device.

Abstract: A method for managing allocation of radio resources to users in a cell of a communication network during an allocation episode is disclosed. The method comprises generating a representation of a scheduling state of the cell for the allocation episode and generating a radio resource allocation decision for the allocation episode by performing a series of steps sequentially for each radio resource or for each user in the representation. The steps comprise selecting a radio resource or a user and using a trained neural network to update a partial radio resource allocation decision for the allocation episode on the basis of a current version of the scheduling state representation and updating the scheduling state representation to include the updated partial radio resource allocation decision. The method further comprises initiating allocation of cell radio resources to users during the allocation episode in accordance with the generated radio resource allocation decision.

Abstract: A method performed by a first wireless device served by a first wireless access point in a first wireless communications network, the first wireless communications network being operated by a first network operator, comprises acquiring (202) a determination from a first reinforcement learning agent of whether to roam from the first wireless access point to a second wireless access point in a second wireless communications network, the second wireless communications network being operated by a second network operator. The method further includes roaming (204) from the first wireless access point to the second wireless access point, based on the determination.

Abstract: Some embodiments herein relate to a method performed by a wireless communication device for managing communication in a wireless communications network. The wireless communication device obtains an indicator indicating a model and one or more trained model parameters for the model, wherein the model is related to an event being one of the following events: a handover procedure, a cell reselection procedure, and a beam reselection procedure. The wireless communication device further selects the model based on the obtained indicator. The wireless communication device executes the selected model using the obtained one or more trained model parameters; and triggers a process, being associated with the event, based on an output of the executed model.

Abstract: 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.

Abstract: A method and system of filtering abnormal network parameter values are described. A network node receives first values of first network parameters that are not trusted by the network node. The network node determines, based on an expected correlation between the first values of the first network parameters and second values of second network parameters, whether the first values of the first network parameters are anomalies. Responsive to determining that the first values of the first network parameters are not anomalies, the network node causes the first values to be used as input to train a network prediction model that is used for optimization of traffic in the network, and responsive to determining that the first values of the first network parameters are anomalies, the network node causes the first values to not be used to train the network prediction model.

Abstract: 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.

Abstract: A method for enabling forecasting of key performance indicators, KPIs, in a communication network is presented. The method is performed by a network node and comprises collecting (S100) time series data of KPIs in the communication network, extracting (S110) k-principal components of the collected time series data, determining (S120) a principal component score, PCS, from the collected time series data and the extracted k-principal components, and building (S130) a PCS forecast model from the determined PCS and period of time-attributes. Network nodes and computer program products thereof are also presented.

Abstract: It is presented a method for managing communication data. The method is performed by a connection manager server (20) and comprising the step of: causing store (41) of historical communication data, for a communication session between a user and one or more contacts, into a database (13), wherein the historical communication data comprises communication data, a session identifier, a communication type, a communication context, and a user identifier for each contact having participated in the communication session. Methods for indexing communication data, and for recommending a communication type, are also presented. Corresponding connection manager servers (20), computer programs (64, 65) and a computer program product (62, 63) are also presented.

Abstract: Method performed by a first communication device (101) operating in a wireless communications network (100). The first communication device (101) obtains (201) a first set of one or more values indicating an observed speed of a boat (151) relative to a power of an engine (161) of the boat (151). The speed and the first indication are obtained by from sensors (171, 172) in the boat (151). The first communication device (101) then obtains (205) a second indication of an external factor on the hull of the boat (151) causing friction against water. The obtaining (205) of the second indication is based at least on: the obtained first set, and a reference. The reference is based on one of: a) a threshold, and b) a mathematical model. The first communication device (101) also initiates providing (206) a third indication of the external factor to a device (103), based on the obtained second indication.

Abstract: A method for enabling forecasting of key performance indicators, KPIs, in a communication network is presented. The method is performed by a network node and comprises collecting (S100) time series data of KPIs in the communication network, extracting (S110) k-principal components of the collected time series data, determining (S120) a principal component score, PCS, from the collected time series data and the extracted k-principal components, and building (S130) a PCS forecast model from the determined PCS and period of time-attributes. Network nodes and computer program products thereof are also presented.

Abstract: Some embodiments herein relate to a method performed by a wireless communication device for managing communication in a wireless communications network. The wireless communication device obtains an indicator indicating a model and one or more trained model parameters for the model, wherein the model is related to an event being one of the following events: a handover procedure, a cell reselection procedure, and a beam reselection procedure. The wireless communication device further selects the model based on the obtained indicator. The wireless communication device executes the selected model using the obtained one or more trained model parameters; and triggers a process, being associated with the event, based on an output of the executed model.