Telcordia Technologies, Inc.

Toshiba America Research, Inc.

Kabushiki Kaisha Toshiba

Vikram Kaul

Stephanie Demers

Gregory P Pollini

David Famolari

Abhrajit Ghosh

Sanjai Narain

Rajesh Rasik Talpade

Gary Levin

Gregory P Polini

A method of automatic calibration in which the simulation adapts itself to more closely resemble the actual network is provided. For a given network architecture and a probabilistic customer usage profile a simulation provides an estimate of key performance metrics. These simulated metrics are compared against actual measurements from the network. To the extent that they do not match within a prescribed tolerance, an iterative adaptive calibration procedure is used to perturb slightly the probabilistic model of network usage.

Method and procedures for automatic calibration of a wireless communications system simulation

A MOAF algorithm is used to resize cells for balancing capacity. The MOAF algorithm bases its decision on a cell and all other cells which the algorithm decides are sufficiently close in a propagation sense to affect the results. The MOAF algorithm also automatically determines those cells in an area which are most heavily loaded and those cells which are lightly loaded. The MOAF algorithm will only decrease the size of a cell if it determines specific adjacent cells that are willing and able to accept the load, and the MOAF algorithm will increase the size of a cell only if there is a nearby heavily loaded cell that requires the removal of load. Moreover, the MOAF algorithm can be tuned (via the threshold parameter T) to shift the focus of the optimization from avoidance of coverage holes to the avoidance of creation of excessive handover legs. Because the changes are electrical rather than physical they can be deployed instantaneously via the network management system thus saving the cost of sending a field crew to the site. Moreover, it is possible to make these changes much more frequently than has been possible in the past thereby permitting the optimization of the network to be done in near real time to meet the temporal changes in the geographic distribution of customers.

Modified overhead adjustment function

A system and method provides a solution to the problem of applying end-to-end requirements of connectivity, security, reliability and performance to configure a network and ultimately assign network components to the network. All requirements are modeled as constraints and a constraint solver does the resolution. Not every constraint to be solved is solved by the model-finder. Instead, we 'factor away' subsets of a constraint that can be efficiently solved via a special-purpose constraint solver, such as an SQL/Prolog engine, linear programming system, or even an algorithm, leaving behind a constraint that truly requires the power of model-finding, and that is often efficiently solvable by existing model-finders. Such constraints are compiled into quantifier-free constraints that are Boolean combinations of constraints of two forms x=y and x=c where x, y are variables and c is a constant. Such constraints can be efficiently solved by modern SAT-based model-finders. The system intelligently removes the quantifiers instead of having the model-finder remove the quantifiers. An 'unsatisfiability-core' analysis efficiently guides the search for configurations to change to restore consistency.

Scalable and interactive method of generating and modifying network configurations to enforce compliance with high-level requirements

Embodiments of the present invention disclose a secure localization infrastructure using transmitters that can transmit messages at multiple distinct power levels throughout a community of reference points. Transmitters send messages at different power levels in a manner that every location in the system corresponds to a unique set of messages. Received messages are reported back to the localization infrastructure, which then determines location by comparing the messages reported.

Approach to secure localization in wireless networks

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Ocean, NJ, United States of America

Vikram Kaul