Telefonaktiebolaget Lm Ericsson (publ)

Karthikeyan Premkumar

Subramanian Shivashankar

N Hari Kumar

Manoj Prasanna Kumar

Meenakshi Sundaram Govindassamy

Brindha Padmanaabhan

Ranjani Balakrishnan

Ruvaitha Banu

Senthamiz Selvi Arumugam

Saradindu Kar

The present disclosure relates to a method performed in an operator network providing data network connectivity to Machine-to-Machine (M2M) devices, for charging of a service session involving a plurality of the M2M devices. The method comprises, from each of the plurality of M2M devices involved in the service session, receiving a service message comprising a service identifier (SID) corresponding to the service session. The method also comprises, by means of the received SID from each of the plurality of M2M devices, determining the number of M2M devices involved in the service session. The method also comprises, based on the determined number of M2M devices, calculating a charging rate for the service session. The method also comprises using the calculated rate, charging the service session.

Methods and apparatuses for charging of a service session of M2M devices in a communication network

A method of allocating radio resources in a cellular network by a network node associated with a destination cell. Prior to a handover of a moving user equipment (UE) from an originating cell to the destination cell, receiving a moving user resource requirement and a predicted handover time for the moving UE, estimating a total resource requirement comprising a sum of the moving user resource requirement and an initial resource requirement comprising a sum of resource requirements of other UEs in the destination cell, determining whether a capacity of the destination cell is sufficient to provide for the total resource requirement. If the capacity is not sufficient, a decrement factor is determined for each UE in the destination cell by determining a required resource reduction using a difference between the capacity and the total resource requirement, determining a reduction factor for each UE in the destination cell by allocating a portion of the required resource reduction to each UE, determining a number of rescheduling events that will occur before the handover time and calculating the decrement factor for each existing UE by dividing the reduction factor by the number of rescheduling events and at each rescheduling event, and decrementing the resources available to each user equipment by the respective decrement factor. On handover, the moving UE is provided with an allocated radio resource equal to at least part of the moving user resource requirement.

Allocating radio resources in a cellular network

The present disclosure relates to a method related to an online charging system for a cellular radio communication system. The method includes obtaining a notification that a refill action of a user account for the communication system is pending. The notification includes an identifier (vid) of a refill resource for the refill action. The method also includes inspecting a refill database updated by an account management function of the online charging system to determine whether the refill resource identifier is included therein, indicating whether or not the user account has been refilled based on the refill resource.

Real-time co-ordinated fulfillment of refill transactions of a user account in a communication system

The present disclosure relates to a method performed by a network element in an operator network for assessing a quality of experience (QoE) of a service for a radio device. The method comprises receiving a first value of a key performance indicator (KPI) of the service from the radio device. The method also comprises receiving a second value of the KPI from the operator network serving the radio device. The method also comprises comparing the first and the second values of the KPI, and determining any deterioration of the QoE over the operator network, based on said comparing. The present disclosure also relates to a method of the radio device as well as to the network element and the radio device.

Assessing QoE of a service in a communication network

A stream processing handling arrangement provides a number of data handling blocks, selectable for forming a work flow. The blocks comprise data processing blocks. The arrangement receives selections of data handling blocks, block parameter settings and workflow property objectives, and compares the objectives with corresponding stream processing framework capabilities. The arrangement determines a stream processing framework for the work flow, provides a number software module groups, each corresponding to a different framework and comprising software modules corresponding to the data handling blocks, and chooses a group corresponding to the determined stream processing framework. The arrangement selects, in this group, software modules corresponding to the selected blocks and constructs an application using the selected software modules.

Simplified creation of an application in a selected stream processing platform

The present disclosure relates to a method performed by a network node configured for making automatic predictions in a telecommunication network. The method comprises obtaining a first value of a first key performance indicator (KPI) for a first network entity (NE) in the telecommunication network. The method also comprises obtaining a second value of a second KPI for a communication route between said first NE and a second NE. The method also comprises predicting, automatically and based on the obtained first and second values, that an alarm will be triggered at the second NE.

Alarm prediction in a telecommunication network

A method for propagating user preference information in a communications network, in which the user preference information may be available for a subset of users within the network. The method comprises generating individual user attribute vectors, based on user historical data and estimating user preference information. The method further comprises defining a community structure for the network and generating a stacked representation for users, the representation comprising the user attribute vector augmented with an aggregated vector of estimated user preferences of members of the user's community. The method further comprises learning a function relating the stacked representation to user preference, using the subset of users, and applying the learned function to users outside the subset.

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Chennai, India

Karthikeyan Premkumar