Abstract: A system that incorporates the subject disclosure may include, for example, determining a mobility speed group assignment for a mobile communication device having a radio resource control connection with a wireless network where the mobility speed group assignment is selected from among a plurality of mobility speed groups according to a speed of the mobile communication device, determining a cell size for a serving cell of the wireless network that is providing the radio resource control connection, and selecting a handover policy based on the mobility speed group assignment and the cell size. Other embodiments are disclosed.

Abstract: Concepts and technologies are described herein for traffic steering across cell-types. According to one aspect disclosed herein, a mobile device enables radio access network (“RAN”) selection across multiple cell-types, including, but not limited to, macro cells, metro cells, femto cells, pico cells, and the like, based upon network conditions, local device information, and/or other information such as policies and user profiles. The local device information can include, but is not limited to, mobility state information, performance measurement information, battery utilization information, channel quality information, and user overrides.

Abstract: Concepts and technologies are described herein for traffic steering across cell-types. According to one aspect disclosed herein, a mobile device enables radio access network (“RAN”) selection across multiple cell-types, including, but not limited to, macro cells, metro cells, femto cells, pico cells, and the like, based upon network conditions, local device information, and/or other information such as policies and user profiles. The local device information can include, but is not limited to, mobility state information, performance measurement information, battery utilization information, channel quality information, and user overrides.

Abstract: Concepts and technologies are described herein for traffic steering across cell-types. According to one aspect disclosed herein, a mobile device enables radio access network (“RAN”) selection across multiple cell-types, including, but not limited to, macro cells, metro cells, femto cells, pico cells, and the like, based upon network conditions, local device information, and/or other information such as policies and user profiles. The local device information can include, but is not limited to, mobility state information, performance measurement information, battery utilization information, channel quality information, and user overrides.

Abstract: Adjusting RAN performance by adapting cell coverage area can help optimize a wireless communications network. RAN topology can be adapted based on analysis of real-time load conditions of RAN base stations. Analysis of the load conditions of RAN base stations can be performed in a core-network of a wireless carrier rather than distributing the analysis to RAN-side elements. Analysis can be based on receiving real-time load information relating to key performance indicators such as X2 load, S1 load, instant outbound handover count, instant inbound handover count, etc. Further, analysis can include the application of predetermined rules relating to preferential performance of the base stations. This can facilitate ranking neighboring base stations, adding new base stations, deleting base stations, black/white listing base stations, etc., in neighbor relations data structures, such as automatic neighbor relations structures for self-organizing networks, e.g., eNodeBs in LTE networks.

Abstract: Adjusting RAN performance by adapting cell coverage area can help optimize a wireless communications network. RAN topology can be adapted based on analysis of real-time load conditions of RAN base stations. Analysis of the load conditions of RAN base stations can be performed in a core-network of a wireless carrier rather than distributing the analysis to RAN-side elements. Analysis can be based on receiving real-time load information relating to key performance indicators such as X2 load, S1 load, instant outbound handover count, instant inbound handover count, etc. Further, analysis can include the application of predetermined rules relating to preferential performance of the base stations. This can facilitate ranking neighboring base stations, adding new base stations, deleting base stations, black/white listing base stations, etc., in neighbor relations data structures, such as automatic neighbor relations structures for self-organizing networks, e.g., eNodeBs in LTE networks.

Abstract: A system that incorporates the subject disclosure may include, for example, determining a mobility speed group assignment for a mobile communication device having a radio resource control connection with a wireless network where the mobility speed group assignment is selected from among a plurality of mobility speed groups according to a speed of the mobile communication device, determining a cell size for a serving cell of the wireless network that is providing the radio resource control connection, and selecting a handover policy based on the mobility speed group assignment and the cell size. Other embodiments are disclosed.

Abstract: A system that incorporates the subject disclosure may include, for example, monitoring a speed and an acceleration of a mobile communication device in a serving cell of a wireless network where the mobile communication device has a radio resource control connection with the wireless network, and selecting a first mobility speed group from among a plurality of mobility speed groups based on the speed and the acceleration of the mobile communication device, where handover parameter values are assigned to each speed group of the plurality of mobility speed groups, and where the handover parameters and their associated values are utilized for a handover by the wireless network from the serving cell to a target cell. Other embodiments are disclosed.

Abstract: Key performance indicators for self-organizing networks are dynamically updated and/or selected. A method includes evaluating a network condition, ranking key performance indicators (KPIs) based on the network condition resulting in ranking information, and determining weights of executing sets of instructions. The determining is based on the ranking and an association between the executing sets of instructions and the KPIs associated with the ranking information. Another method includes evaluating a network condition, and changing a value of a target of a KPI from a first value to a second value based on a change in the network condition from a first network condition to a second network condition. Another method includes evaluating a network condition, determining weighting factors for respective KPIs, wherein the weighting factors are based on evaluation of the network condition, and generating composite KPI information based, at least, on applying the weighting factors to the respective KPIs.

Abstract: Traffic associated with user equipment that are served by a first radio access network is steered to a second radio access network based on a cell user occupancy criterion. Cell user occupancy data that represents a maximum number of devices served by an access point is determined based on a type of the access point (e.g., macro access point, femto access point, WiFi access point, etc.). Further, based on the cell user occupancy data, a normalized index value is generated that is relative to different cell types/capacities. The cell user occupancy data is then transmitted to one or more neighboring access points that can utilize the cell user occupancy data to facilitate traffic steering, load balancing, and/or neighbor relationship management.

Abstract: Traffic associated with user equipment that are served by a first radio access network is steered to a second radio access network based on a rate of congestion criterion. Network load is monitored by an access point to determine rate of congestion data associated with the access point. As an example, the rate of congestion represents a change in network load of the access point over a defined time period. The rate of congestion data is then transmitted to one or more neighboring access points that can utilize the rate of congestion data to facilitate traffic steering, load balancing, and/or neighbor relationship management.

Abstract: Adjusting RAN performance by adapting cell coverage area can help optimize a wireless communications network. RAN topology can be adapted based on analysis of real-time load conditions of RAN base stations. Analysis of the load conditions of RAN base stations can be performed in a core-network of a wireless carrier rather than distributing the analysis to RAN-side elements. Analysis can be based on receiving real-time load information relating to key performance indicators such as X2 load, S1 load, instant outbound handover count, instant inbound handover count, etc. Further, analysis can include the application of predetermined rules relating to preferential performance of the base stations. This can facilitate ranking neighboring base stations, adding new base stations, deleting base stations, black/white listing base stations, etc., in neighbor relations data structures, such as automatic neighbor relations structures for self-organizing networks, e.g., eNodeBs in LTE networks.

Abstract: Traffic associated with user equipment that are coupled to a first radio access network is steered to a second radio access network based on an adaptable signal strength criterion. The signal strength criterion is related to real-time network load conditions of the first radio access network and can be broadcasted from a serving access point to the user equipment. Moreover, the signal strength criterion facilitates steering, to the second radio network, traffic associated with user equipment that are located closer to a cell edge of the first radio access network before steering traffic associated with user equipment are located further away from the cell edge. In addition, based on the network congestion within the first radio access network, the signal strength criterion is modified to adjust the number of user equipment that are steered to the second radio network.

Abstract: Concepts and technologies are described herein for traffic steering across cell-types. According to one aspect disclosed herein, a mobile device enables radio access network (“RAN”) selection across multiple cell-types, including, but not limited to, macro cells, metro cells, femto cells, pico cells, and the like, based upon network conditions, local device information, and/or other information such as policies and user profiles. The local device information can include, but is not limited to, mobility state information, performance measurement information, battery utilization information, channel quality information, and user overrides.

Abstract: Adjusting RAN performance by adapting cell coverage area can help optimize a wireless network. Base station coverage areas can be determined by distance analysis of detected neighbor base stations. This can be in response to determination of a physical cell identifier (PCI) conflict condition. When a PCI conflict is determined, a unique identifier for each base station can be employed to facilitate determining location information for the base stations. Location information can be employed to determine distance-based characteristics that can facilitate adaptation of wireless network neighbor relations to adapt the RAN coverage area topology. Distance-based characteristics can include nearest neighbor, proximity to a predetermined distance, etc. Additionally, PCI information can be updated to correct the PCI conflict condition.

Abstract: Adjusting RAN performance by adapting cell coverage area can help optimize a wireless communications network. RAN topology can be adapted based on analysis of historical performance of base stations. Analysis of the historical performance of base stations can be performed in the core-network of a wireless carrier rather than distributing the analysis to RAN elements. Analysis can be based on receiving historical information relating to key performance indicators such as call failure rate, call success rate, handover attempt count, handover attempt failure count, etc. Further, analysis can include the application of predetermined rules relating to preferential performance of the base stations. This can facilitate ranking neighboring base stations, adding new base stations, deleting base stations, black/white listing base stations, etc., in neighbor relations data structures, such as automatic neighbor relations structures for self-organizing networks, e.g., eNodeBs in LTE networks.

Abstract: Adjusting radio area network performance by adapting cell coverage area can help optimize the operating efficiency of a wireless network. Base station coverage areas can be determined by analysis of reported timing advance information. This can facilitate adjusting coverage areas. Further, integration with network topology planning components and management components can be employed. Moreover, prioritization of base stations in Automatic Neighbor Relations (ANR) neighbor lists can improve coverage balance by influencing complex heterogeneous coverage layout systems. Historical timing advance information can be employed to facilitate analysis of statistical coverage conditions for cells, analysis of coverage areas as they relate to performance metrics, analysis of coverage areas with regard to specific event such as handovers, etc. Moreover, coverage area analysis can be linked to alarm systems.

Abstract: Adjusting RAN performance by adapting cell coverage area can help optimize a wireless communications network. RAN topology can be adapted based on analysis of real-time load conditions of RAN base stations. Analysis of the load conditions of RAN base stations can be performed in a core-network of a wireless carrier rather than distributing the analysis to RAN-side elements. Analysis can be based on receiving real-time load information relating to key performance indicators such as X2 load, S1 load, instant outbound handover count, instant inbound handover count, etc. Further, analysis can include the application of predetermined rules relating to preferential performance of the base stations. This can facilitate ranking neighboring base stations, adding new base stations, deleting base stations, black/white listing base stations, etc., in neighbor relations data structures, such as automatic neighbor relations structures for self-organizing networks, e.g., eNodeBs in LTE networks.