Abstract: Network device selection or handover schemes enable higher network capacity based on partially-observable Markov decision processes. Unavailable cell loading information is observed and/or predicted from non-serving base stations and actions are taken to maintain an active base station set or network device candidate data for selection in routing communications of a mobile device in a mobile device cell selection or handover procedure. A reward function is considered in the selection based on various parameters comprising system capacity, handover times, and mobility of a mobile device or mobile station.

Abstract: Network device selection or handover schemes enable higher network capacity based on partially-observable Markov decision processes. Unavailable cell loading information is observed and/or predicted from non-serving base stations and actions are taken to maintain an active base station set or network device candidate data for selection in routing communications of a mobile device in a mobile device cell selection or handover procedure. A reward function is considered in the selection based on various parameters comprising system capacity, handover times, and mobility of a mobile device or mobile station.

Abstract: Network device selection or handover schemes enable higher network capacity based on partially-observable Markov decision processes. Unavailable cell loading information is observed and/or predicted from non-serving base stations and actions are taken to maintain an active base station set or network device candidate data for selection in routing communications of a mobile device in a mobile device cell selection or handover procedure. A reward function is considered in the selection based on various parameters comprising system capacity, handover times, and mobility of a mobile device or mobile station.

Abstract: Network device selection or handover schemes enable higher network capacity based on partially-observable Markov decision processes. Unavailable cell loading information is observed and/or predicted from non-serving base stations and actions are taken to maintain an active base station set or network device candidate data for selection in routing communications of a mobile device in a mobile device cell selection or handover procedure. A reward function is considered in the selection based on various parameters comprising system capacity, handover times, and mobility of a mobile device or mobile station.

Abstract: The disclosed subject matter relates to a wireless communications environment. Femto-assisted location estimation schemes can determine UE location information based on information from either macro base stations or femto base stations in a LTE-A heterogeneous network. Positions information for a femto base station can be employed even though the exact positions of fBS may not be available. The femto base station position information can be depicted as a probabilistic distribution. Bayesian estimation based on TDOA and utilization of a particle filter can facilitate determining UE location information. Furthermore, a simplified scheme can reduce computational complexity.

Abstract: The disclosed subject matter relates to a wireless communications environment. Femto-assisted location estimation schemes can determine UE location information based on information from either macro base stations or femto base stations in a LTE-A heterogeneous network. Positions information for a femto base station can be employed even though the exact positions of fBS may not be available. The femto base station position information can be depicted as a probabilistic distribution. Bayesian estimation based on TDOA and utilization of a particle filter can facilitate determining UE location information. Furthermore, a simplified scheme can reduce computational complexity.