Predictive scheduling for uplink transmission in a cellular network

For controlling radio transmission in a cellular network, a network node detects data traffic between the cellular network and a user equipment. The detected data traffic is based on a transport protocol which involves transmission of an acknowledgement to acknowledge successful reception of data by the user equipment. The network node estimates a time at which sending of an future acknowledgement by the user equipment is expected. On the basis of the estimated time, the network node allocates UL radio resources to the user equipment. By sending an uplink grant, the network node may indicate the allocated uplink radio resources to the user equipment.

Rate shaping for wireless communication using token bucket that allows token debt

A modified token bucket algorithm (100) in a rate shaping function (20) of a wireless communication network allows for the "borrowing'1 of tokens, creating the possibility of a token debt, or a token bucket with a negative Token Bucket Counter (TBC) value. In this modified algorithm, an incoming packet is passed along (112) so long as the TBC is positive (108), even if the packet must "borrow" some tokens, driving the TBC negative. Subsequent incoming packets are stalled (110) until the TBC reaches a positive value (108). In one embodiment, the modified token bucket algorithm (100) is not applied to a separate rate shaper, but rather to a queue size limiter (26) that operates with a scheduler (24) on a single queue (22). The inventive scheduler (24) and queue size limiter (26) deliver fewer, larger packets for transmission, allowing for more efficient packing within transmission frames (reducing or eliminating required padding), and allowing other traffic to be scheduled, thus increasing ...

PREDICTION OF A PERFORMANCE INDICATOR

The present invention relates to a method for predicting a performance indicator for a service in a network. The method is performed by a network node of the network, and the method comprises obtaining measurement data of a metric affecting a service communicating via a radio access network, RAN, node, wherein the metric is independent of the service communicating via the RAN node, inputting the obtained measurement data into a prediction model for performance of the service communicating via the RAN node, wherein the prediction model has been trained with measurement data from the RAN node and measurement data from an end node, and predicting the performance indicator for performance of the service in the network. A network node, a computer program and a computer program product are also presented. 1. A method for predicting a performance indicator for a service in a network , the method being performed by a network node of the network , the method comprising:obtaining measurement data of a metric affecting a service communicating via a radio access network, RAN, node, wherein the metric is independent of the service communicating via the RAN node;inputting the obtained measurement data into a prediction model for performance of the service communicating via the RAN node, wherein the prediction model has been trained with measurement data from the RAN node and measurement data from an end node; andpredicting the performance indicator for performance of the service in the network.2. The method according to claim 1 , wherein the obtained measurement data comprises one or more of the following metrics of the RAN node: number of users claim 1 , radio connection quality claim 1 , round trip time claim 1 , bandwidth claim 1 , latency claim 1 , queueing delay claim 1 , and scheduling parameters.3. The method according to claim 1 , wherein the end node measurement data comprises one or more of the following metrics: radio connection quality claim 1 , bandwidth claim 1 , ...

PREDICTIVE SCHEDULING FOR UPLINK TRANSMISSION IN A CELLULAR NETWORK

For controlling radio transmission in a cellular network, a network node detects data traffic between the cellular network and a user equipment. The detected data traffic is based on a transport protocol which involves transmission of an acknowledgement to acknowledge successful reception of data by the user equipment. The network node estimates a time at which sending of an future acknowledgement by the user equipment is expected. On the basis of the estimated time, the network node allocates UL radio resources to the user equipment. By sending an uplink grant, the network node may indicate the allocated uplink radio resources to the user equipment. 1. A method of controlling radio transmission in a cellular network , the method comprising:{'b': '100', 'detecting by a network node () detecting data traffic between the cellular network and a user equipment, the data traffic being based on a transport protocol which involves transmission of an acknowledgement to acknowledge successful reception of data by the user equipment;'}estimating by the network node a time at which sending of an future acknowledgement by the user equipment is expected; andon the basis of the estimated time, allocating by the network node uplink radio resources to the user equipment; andindicating by the network node the allocated uplink radio resources to the user equipment.2. The method according to claim 1 , further comprising:estimating by the network node a size of the future acknowledgement; andallocating by the network node the uplink radio resources on the basis of the estimated size.3. The method according to claim 1 ,wherein said estimating comprises determining an estimated time interval between sending of data to the user equipment and reception of the acknowledgment for this data.4. The method according to claim 3 ,wherein said estimating comprises determining a transmission delay between the network node and a sender of the data.5. The method according to claim 1 ,wherein said ...

METHODS, APPARATUS AND MACHINE-READABLE MEDIA RELATING TO MACHINE-LEARNING IN A COMMUNICATION NETWORK

A method performed by a first network entity in a communications network includes training a model to obtain a local model update including an update to values of one or more parameters of the model, in which training the model includes inputting training data into a machine learning algorithm. The method further includes applying a serialisation function to the local model update to construct a serial representation of the local model update, thereby removing information indicative of a structure of the model, and transmitting the serial representation of the local model update to an aggregator entity in the communications network. 1. A method performed by a first network entity in a communications network , the method comprising:training a model to obtain a local model update comprising an update to values of one or more parameters of the model, wherein training the model comprises inputting training data into a machine learning algorithm;applying a serialisation function to the local model update to construct a serial representation of the local model update, thereby removing information indicative of a structure of the model; andtransmitting the serial representation of the local model update to an aggregator entity in the communications network.215-. (canceled)16. A first network entity for a communications network , the first network entity comprising processing circuitry and a machine-readable medium storing instructions which , when executed by the processing circuitry , cause the first network entity to:train a model to obtain a local model update comprising an update to values of one or more parameters of the model, wherein training the model comprises inputting training data into a machine learning algorithm;apply a serialisation function to the local model update to construct a serial representation of the local model update, thereby removing information indicative of a structure of the model; andtransmit the serial representation of the local model ...

METHODS, APPARATUS AND MACHINE-READABLE MEDIA RELATING TO MACHINE-LEARNING IN A COMMUNICATION NETWORK

A method performed by a first entity in a communications network is provided. The first entity belongs to a plurality of entities configured to perform federated learning to develop a model. In the method, the first entity trains a model using a machine-learning algorithm, generating a model update. The first entity generates a first mask, receives an indication of one or more respective second masks from a subset of the remaining entities of the plurality of entities, and combines the first mask and the respective second masks to generate a combined mask. The first entity transmits an indication of the first mask to one or more third entities of the plurality of entities. The first entity applies the combined mask to the model update to generate a masked model update and transmits the masked model update to an aggregating entity of the communications network. 1. A method performed by a first entity in a communications network , the first entity belonging to a plurality of entities configured to perform federated learning to develop a model , each entity of the plurality of entities storing a version of the model , training the version of the model , and transmitting an update for the model to an aggregating entity for aggregation with other updates for the model , the method comprising:training a model using a machine-learning algorithm, and generating a model update comprising updates to values of one or more parameters of the model;generating a first mask;receiving an indication of one or more respective second masks from only a subset of the remaining entities of the plurality of entities, the subset consisting of one or more second entities of the plurality of entities;transmitting an indication of the first mask to one or more third entities of the plurality of entities;combining the first mask and the respective second masks to generate a combined mask;applying the combined mask to the model update to generate a masked model update; andtransmitting the masked ...

Methods, apparatus and machine-readable media relating to machine-learning in a communication network

A method performed by a co-ordination network entity in a communications network includes transmitting a request message to a network registration entity in the communications network for identification information for a plurality of candidate network entities in the communications network capable of performing collaborative learning, and receiving identification information for the plurality of candidate network entities from the network registration entity. The method further includes initiating, at one or more network entities of the plurality of candidate network entities, training of a model using a machine-learning algorithm as part of a collaborative learning process.

PREDICTION OF A PERFORMANCE INDICATOR

The present invention relates to a method for predicting a performance indicator for a service in a network. The method is performed by a network node of the network, and the method comprises obtaining measurement data of a metric affecting a service communicating via a radio access network, RAN, node, wherein the metric is independent of the service communicating via the RAN node, inputting the obtained measurement data into a prediction model for performance of the service communicating via the RAN node, wherein the prediction model has been trained with measurement data from the RAN node and measurement data from an end node, and predicting the performance indicator for performance of the service in the network. A network node, a computer program and a computer program product are also presented. 1. A method for estimating a performance indicator for a service in a network , the method being performed by a network node of the network , the method comprising:obtaining current measurement data of a metric affecting a service communicating via a network node associated with a radio access network (RAN);inputting the obtained current measurement data into a prediction model for performance of the service communicating via the network node associated with the RAN, wherein the prediction model has been trained with prior measurement data from the network node associated with the RAN and prior measurement data from an end node; andestimating the performance indicator for performance of the service in the network.2. The method according to claim 1 , wherein the obtained current measurement data comprises one or more of the following metrics of the network node associated with the RAN: number of users claim 1 , radio connection quality claim 1 , round trip time claim 1 , bandwidth claim 1 , latency claim 1 , queueing delay claim 1 , and scheduling parameters.3. The method according to claim 1 , wherein the prior measurement data from the end node comprises one or more of ...

Predictive Adaptive Queue Management

The present disclosure generally relates to the field of queue management. More specifically, the present disclosure relates to a technique of adjusting adaptive queue management operation in a wireless communication network. A method embodiment comprises determining (S), by an access network node () of the wireless communication network, whether an increase in capacity for data transmission between the access network node () and a wireless communication device () is expected, and adjusting (S), by the access network node (), AQM operation associated with the access network node (), if it is determined that an increase in capacity for data transmission between the access network node () and the wireless communication device () is expected.

RADIO COVERAGE MAP GENERATION

Embodiments of the disclosure provide methods, apparatus and computer programs for generating a radio coverage map. A method comprises: obtaining image data of a geographical area, the image data comprising: a representation of the environment in the geographical area; and an indication of one or more transmission point locations corresponding to the locations of one or more transmission points in a wireless communications network; and applying a generative model to the image data, to generate a radio coverage map of the geographical area. 1. A method of generating a radio coverage map , the method comprising: a representation of an environment in the geographical area; and', 'an indication of one or more transmission point locations corresponding to locations of one or more transmission points in a wireless communications network; and, 'obtaining image data of a geographical area, the image data comprisingapplying a generative model to the image data, to generate a radio coverage map of the geographical area, wherein the generative model comprises generative adversarial networks.2. The method according to claim 1 , wherein the image data comprises a plurality of pixels and a plurality of layers claim 1 , each layer comprising claim 1 , for each of the plurality of pixels claim 1 , respective values for one or more parameters.3. The method according to claim 2 , wherein one or more first layers of the plurality of layers comprise the representation of the environment claim 2 , and wherein one or more second layers of the plurality of layers comprise the indication of the one or more transmission point locations.4. The method according to claim 3 , wherein the one or more first layers comprise respective predefined values for predefined types of object belonging to the environment in the geographical area.5. The method according to claim 3 , wherein the one or more first layers comprise values for a height of an object belonging to the environment in the geographical ...

METHODS AND APPARATUS FOR ROAMING BETWEEN WIRELESS COMMUNICATIONS NETWORKS

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 () 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 () from the first wireless access point to the second wireless access point, based on the determination. 1. A method performed by a first wireless device , the first wireless device being 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 , the method comprising:acquiring 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; androaming from the first wireless access point to the second wireless access point, based on the determination.2. The method as in claim 1 , wherein the first reinforcement learning agent shares a reward function with a second reinforcement learning agent claim 1 , the second reinforcement learning agent being associated with a second wireless device.3. The method as in claim 2 , wherein the first wireless device and the second wireless device form part of a group of wireless devices and the shared reward function is shared between the wireless devices in the group of wireless devices.4. The method as in claim 3 , wherein the devices in the group of wireless devices have at least one common connection parameter.5. (canceled)6. (canceled)7. The method as in ...

Managing communication in a wireless communications network

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.

Predictive scheduling for uplink transmission in a cellular network

For controlling radio transmission in a cellular network, a network node (100) detects data traffic between the cellular network and a user equipment (10). The detected data traffic is based on a transport protocol which involves transmission of an acknowledgement to acknowledge successful reception of data by the user equipment (10). The network node (100) estimates a time at which sending of an future acknowledgement (715) by the user equipment (10) is expected. On the basis of the estimated time, the network node allocates UL radio resources to the user equipment (10). By sending an uplink grant (714), the network node (100) may indicate the allocated uplink radio resources to the user equipment (10).

Managing communication in a wireless communications network

Queue management system and methods

A system and method are provided for managing a queue of packets transmitted from a sender to a receiver across a communications network. The sender has a plurality of sender states and a queue manager situated in between the sender and receiver may have a corresponding plurality of queue manager states. The queue manager has one or more queue management parameters which may have distinct predetermined values for each of the queue manager states. When the queue manager detects an event that is indicative of a change in the sender's state, the queue manager may change its state correspondingly.

Radio coverage map generation

Embodiments of the disclosure provide methods, apparatus and computer programs for generating a radio coverage map. A method comprises: obtaining image data of a geographical area, the image data comprising: a representation of the environment in the geographical area; and an indication of one or more transmission point locations corresponding to the locations of one or more transmission points in a wireless communications network; and applying a generative model to the image data, to generate a radio coverage map of the geographical area.

Performing a handover procedure

Methods, apparatus and machine-readable media relating to machine-learning in a communication network

The disclosure provides a method performed by a first network entity in a communications network. The method comprises: transmitting one or more query messages, the one or more query messages comprising an indication of one or more attributes to be fulfilled by second network entities for participation in a collaborative machine-learning process to update a global copy of a machine-learning model, wherein the one or more attributes comprises an attribute relating to underfitting or overfitting of respective local copies of the machine-learning model maintained by the second network entities; receiving information identifying a first plurality of second network entities fulfilling the one or more attributes; and selecting, from the first plurality of second network entities, one or more second network entities for participation in the collaborative machine-learning process.

Rate shaping triggered discontinuous transmission in wireless communications

Reinforcement learning systems for controlling wireless communication networks

A computer implemented method of training a reinforcement learning model for controlling a dynamic system includes generating a trajectory sample of a simulated system that corresponds to the dynamic system, the trajectory sample including a current state s t of the simulated system at time t, an action a t taken on the simulated system at time t according to a policy π, a subsequent state s t+1 of the simulated system following the action a t , and a reward r associated with the action at, and estimating a robust target value V π (s t ) for the trajectory sample, wherein the robust target value V π (s t ) includes an expected value of a sum of the reward r and a minimum estimated value V π (s t+1 ) of the simulated system at the subsequent state s t+1 based on a plurality of transition possibilities p from the current state st in response to the action a t . The method updates a value function estimator based on the robust target value, and updates the policy based on the trajectory and the value function estimator.

Selection of global machine learning models for collaborative machine learning in a communication network

A computer-implemented method performed by a local computing device for collaborative machine learning in a communication network is provided. The method comprises receiving from a global computing device, a plurality of global ML models. The method further comprises evaluating a metric on a set of data of the local computing device for each respective global ML model from the plurality of global ML models. The evaluating comprises (i) generating a random number, and (ii) comparing the random number to a predetermined value. The method further comprises selecting a global ML model from the plurality of global ML models, wherein the selecting is (i) a random global ML model from the plurality of global ML models when the random number is less than the predetermined value, or (ii) a global ML model from the plurality of global ML models having a greatest performance on the set of data of the local computing device when the random number is greater than the predetermined value. The method further comprises transmitting the selected global ML model, or a gradient of the local computing device from the selected global ML model to the global computing device.

Methods, apparatus and machine-readable media relating to machine-learning in a communication network

A method performed by a first entity in a communications network is provided. The first entity belongs to a plurality of entities configured to perform federated learning to develop a model. In the method, the first entity trains a model using a machine-learning algorithm, generating a model update. The first entity generates a first mask, receives an indication of one or more respective second masks from a subset of the remaining entities of the plurality of entities, and combines the first mask and the respective second masks to generate a combined mask. The first entity transmits an indication of the first mask to one or more third entities of the plurality of entities. The first entity applies the combined mask to the model update to generate a masked model update and transmits the masked model update to an aggregating entity of the communications network.

Rate shaping for wireless communication using token bucket that allows token debt

Rate shaping triggered discontinuous transmission in wireless communications

A modified token bucket algorithm (100) in a rate shaper in a wireless communication network allows for the "borrowing" of tokens, creating the possibility of a token debt, or a token bucket with a negative Token Bucket Counter (TBC) value (108). In this modified token bucket algorithm (100), an incoming packet is passed along (114) so long as the TBC is positive (108), even if the packet must "borrow" some tokens, driving the TBC negative. Subsequent incoming packets are stalled until the TBC reaches a positive value. The token bucket refills at a known rate (104); accordingly, the duration of traffic stalling, when the TBC is negative, is known. During this time, the UE is forced into DRX mode (110), saving battery power by not monitoring DPCCH for traffic that has been halted. The DRX, or sleep, mode (110) may be invoked in several ways.

Rate shaping triggered discontinuous transmission in wireless communications

A modified token bucket algorithm (100) in a rate shaper in a wireless communication network allows for the "borrowing" of tokens, creating the possibility of a token debt, or a token bucket with a negative Token Bucket Counter (TBC) value (108). In this modified token bucket algorithm (100), an incoming packet is passed along (114) so long as the TBC is positive (108), even if the packet must "borrow" some tokens, driving the TBC negative. Subsequent incoming packets are stalled until the TBC reaches a positive value. The token bucket refills at a known rate (104); accordingly, the duration of traffic stalling, when the TBC is negative, is known. During this time, the UE is forced into DRX mode (110), saving battery power by not monitoring DPCCH for traffic that has been halted. The DRX, or sleep, mode (110) may be invoked in several ways.

Prediction of a performance indicator

The present invention relates to a method for predicting a performance indicator for a service in a network. The method is performed by a network node of the network, and the method comprises obtaining measurement data of a metric affecting a service communicating via a radio access network, RAN, node, wherein the metric is independent of the service communicating via the RAN node, inputting the obtained measurement data into a prediction model for performance of the service communicating via the RAN node, wherein the prediction model has been trained with measurement data from the RAN node and measurement data from an end node, and predicting the performance indicator for performance of the service in the network. A network node, a computer program and a computer program product are also presented.

Selection of Global Machine Learning Models for Collaborative Machine Learning in a Communication Network

A computer-implemented method performed by a local computing device for collaborative machine learning in a communication network is provided. The method comprises receiving from a global computing device, a plurality of global ML models. The method further comprises evaluating a metric on a set of data of the local computing device for each respective global ML model from the plurality of global ML models. The evaluating comprises (i) generating a random number, and (ii) comparing the random number to a predetermined value. The method further comprises selecting a global ML model from the plurality of global ML models, wherein the selecting is (i) a random global ML model from the plurality of global ML models when the random number is less than the predetermined value, or (ii) a global ML model from the plurality of global ML models having a greatest performance on the set of data of the local computing device when the random number is greater than the predetermined value. The method further comprises transmitting the selected global ML model, or a gradient of the local computing device from the selected global ML model to the global computing device.

Methods, apparatus and machine-readable media relating to machine-learning in a communication network

A method performed by a first network entity in a communications network is provided. The method comprises receiving a request from a second network entity, the request comprising one or more selection criteria for selecting network entities to participate in a collaborative learning process to train a model using a machine learning algorithm. The method further comprises transmitting a response message comprising an indication of whether or not the first network entity satisfies the one or more selection criteria.

Managing a wireless device which has available a machine learning model that is operable to connect to a communication network

A method (100) is disclosed for managing a wireless device that is operable to connect to a communication network, wherein the communication network comprises a Radio Access Network (RAN), and wherein the wireless device has available for execution a Machine Learning (ML) model that is operable to provide an output, on the basis of which a RAN operation performed by the wireless device may be configured. The method, performed by a RAN node of the communication network, comprises, on fulfilment of a trigger condition, causing an ML model Assurance Information, MAI, Request to be sent to the wireless device (110), the MAI Request comprising an indication of the ML model to which the MAI Request relates. The method further corpses receiving, from the wireless device, an MAI Response, wherein the MAI Response comprises ML model characteristic information generated by the wireless device using the ML model (120), and configuring the RAN operation performed by the wireless device according to the received MAI Response (130).