Viavi Solutions UK Limited

Jdsu UK Limited

Arieso Limited

Mohsen Zadeh-Koochak

Michael Joseph Flanagan

Howard John Thomas

David Charles Padfield

Mihai Stanciuc

Nicholas James Randell

Stefan Ulrich Thiel

Peter Randall

Walter Featherstone

Christopher Michael Murphy

Krassimir Stoyanov Vassilev

Nicolas G Cotanis

Sthiven Pinilla

Li-Peng Wang

Stefan Thiel

A device may detect a service condition associated with a network. The device may determine a prioritization of cells of the network based on characteristics of one or more subscribers to the cells of the network. The characteristics may include at least one of: a service type characteristic, an application type characteristic, a mobility characteristic, a vector characteristic, an altitude characteristic, a position characteristic, a location type characteristic, a device type characteristic, a device capability characteristic, a connection characteristic, a subscription characteristic, a call type characteristic, a user profile characteristic, a terrain characteristic, or an operator relationship characteristic. The device may perform an alteration to one or more network parameters based on the prioritization of cells of the network to reduce an impact of the service condition.

Network optimization based on characteristic information regarding a network

A device obtains uplink information associated with a base station and obtains downlink information associated with a mobile device in communication with the base station. The device determines observed network performance information based on the uplink information and the downlink information and determines a predictive model, based on the uplink information and the downlink information, to predict network performance information. The device also changes network configuration data, associated with the base station, to generate changed network configuration data and determines predicted network performance information for the changed network configuration data based on the predictive model. The device further selectively transmits the changed network configuration data, to the base station, based on comparing the predicted network performance information and the observed network performance information.

Modeling mobile network performance

Techniques for dynamic network optimization using geolocation and network modeling are disclosed. In one particular exemplary embodiment, the techniques may be realized as a system for network optimization. The system may comprise a data collection system configured to collect geolocated subscriber records from a plurality of mobile devices. The system may also comprise a SON optimization system communicatively coupled to the data collection system via a network. The SON optimization system may comprise at least one pre-processor configured to receive and process geolocated subscriber records from the data collection system. The SON optimization system may further comprise a network simulator configured to perform network simulation analysis based on the processed geolocated subscriber records, and provide a new network configuration based on the network simulation analysis, wherein the new network configuration is estimated to improve network performance.

Techniques for dynamic network optimization using geolocation and network modeling

A device may include one or more processors configured to receive information associated with one or more performance goals to be achieved within a radio access network (RAN). The device may receive performance information for mobile devices associated with the RAN. The device may determine that at least one performance goal, of the one or more performance goals, is not being achieved within the RAN. The determining may be based on the information associated with the one or more performance goals and the performance information. The device may generate a small cell plan for improving performance within the RAN toward achievement of the at least one performance goal. The small cell plan may identify a set of locations at which a set of small cells are to be activated, deactivated, or deployed. The device may output information associated with the small cell plan.

Small cell planning

Techniques for dynamic network optimization using geolocation and network modeling are disclosed. In one particular exemplary embodiment, the techniques may be realized as a system for improving performance of a mobile communications network. The system may comprise one or more processors communicatively coupled to a mobile communications network. The one or more processors may be configured to determine allowable changes within a network and rank a cell or parameter based on a priority rule. The one or more processors may be further be configured to generate a plurality of network configurations and evaluate strength of the plurality of network configurations. The one or more processors may also be configured to output a set of network configurations that is strongest among the plurality of network configurations.

A method and system for a cellular mobile communication system () are provided. The location of a mobile communication unit () in the cellular wireless communications system () can be estimated from: a first estimate of position, based on received signal power difference for signals received by the mobile communication unit () from at least two base stations (); and a second estimate of the position of the mobile communication unit (), from further information. A likelihood function for each estimate is weighted. The weighted first and second likelihood functions are combined, and one or more locations are selected as the estimated location. Propagation model parameters may be iteratively updated and/or refined, using location estimates obtained for large numbers of calls in the cellular mobile communication system ().

System and methods of mobile geolocation

A method for deriving pathloss estimation values within a cellular communication network. The method comprises receiving pathloss data obtained from a plurality of wireless communication units located within the cellular communication network, receiving location information corresponding to the plurality of wireless communications units, associating received pathloss data with individual sectors within a coverage area of the cellular communication network based at least partly on the location information for the respective wireless communications unit, and deriving pathloss estimation values for individual sectors within the cellular communication network based at least partly on the received pathloss data.

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Woolton Hill, United Kingdom

Mohsen Zadeh-Koochak