Telefonaktiebolaget Lm Ericsson (publ)

Javier Baliosian

Francoise Sailhan

Ann Devitt

Rolf Stadler

A method of discovering overlapping cells in a telecommunications network comprising a plurality of base stations, wherein steps carried out by an individual base station comprise determining sets of terminals in ranges of its associated cells and sending a message with information about said sets of terminals to other base stations. The method also includes receiving messages from said other base stations with information about sets of terminals in ranges of their associated cells. The individual base station calculates intersection of its own set of terminals with those received from said other base stations and classifies cells as overlapping if the corresponding intersection is equal or above a predefined level.

Method of discovering overlapping cells

A method of processing event notifications and event subscriptions in a telecommunications or data network comprising a plurality of nodes, wherein an individual node performs the steps of maintaining a first repository for storing a set of event subscriptions encoded in a first summary and maintaining a second repository for storing the set of event subscriptions. The node encodes a received event notification into a second summary and then checks if the received event notification is a member of the set by comparing the first summary and the second summary. If the event notification is not a member of the set said notification is discarded.

Method of processing event notifications and event subscriptions

Method for forecasting instable policy enforcement, is described, wherein a behavior dynamic Bayesian network (DBN) model and a policy finite state transducers extended with tautness functions and identities (TFFST) model is analytically composed to derive predictions of the consequences of enforcing a given policy, in particular to detect flip-flop configuration changes in a system. The method comprises the steps of&#x2014;translating () the Bayesian network that holds the Behavior Model (BM) into a finite state transducers extended with tautness functions and identities (TFFST); &#x2014;computing () the union of the Bayesian network (BM) and Policy Model (PM) finite state transducers extended with tautness functions and identities (TFFSTs); &#x2014;composing () the finite state transducers extended with tautness functions and identities (TFFST) produced in the previous step with itself; and&#x2014;detecting () repetitions of events in the input and the output of every possible path; &#x2014;if at least one repetition is found, detecting a possible instability ().

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Athlone, Ireland

Javier Baliosian