Abstract: A service group based dynamic optimization of WWAN and WLAN aggregation in user equipment (UE) is described. The UE has a first radio access technology (RAT) interface associated with a Wireless Wide Area Network (WWAN) based interface that is served by a corresponding base station (BS), and a second RAT interface associated with a Wireless Local Area Network (WLAN) based interface that is served by an Access Point (AP), wherein, for downlink communications, the first and second RAT interfaces are aggregated. Each UE belongs to at least one service group, wherein the UE performs service group based dynamic optimization of WWAN and WLAN aggregation, with flow split ratios and resource allocation between RAT interfaces being jointly optimized for each service group and each RAT interface.

Abstract: User equipments (UEs) that have multiple radio access technologies (RATs) are grouped together so that each group may be subjected to its own set of medium access (MAC) protocols for each RAT, while a flow destined for a UE may be communicated via one or multiple aggregated RAT interfaces on the said UE. The network operator or service provider that serves a plurality of subscribers defines a service group for said plurality of subscribers and controls the corresponding virtualized MACs for the said service group.

Abstract: Self-Organizing Network (SON) technology has been developed to make the planning, configuration, management, and self-healing of mobile networks easier and faster, where such planning, configuration and management of the mobile networks are targeted to optimize the value offered to a group of subscribers using criteria such as (a) subscriber type (consumer, corporate, IOT, etc.), (b) service type (mission critical, public safety, VoIP, etc.), (b) subscriber usage volume (high, medium, low), (c) subscriber's requested/subscribed QoS, and (d) subscriber's paying value (i.e., revenue). The same teachings can be extended to also optimize a single subscriber's service or a service type.

Abstract: Any one of the following dual connectivity attributes is assigned to a group profile of each service group: (1) macro cell only (serving both data/control planes via a macro cell base station.), (2) small cell only (serving data/control planes via a small cell base station), (3) dual connectivity enabled-A (serving data plane of UEs via a small cell base station, and serving control plane via a macro cell base station), and (4) dual connectivity enabled-B (serving data plane via both a macro cell base station and a small cell base station, and serving control plane via a macro cell base station), wherein the RAN controller calculates and sets the optimal splitting between the macro cell base station and the small cell base station dynamically, such that desired load balancing is achieved.

Abstract: Self-Organizing Network (SON) technology has been developed to make the planning, configuration, management, and self-healing of mobile networks easier and faster, where such planning, configuration and management of the mobile networks are targeted to optimize the value offered to a group of subscribers using criteria such as (a) subscriber type (consumer, corporate, IOT, etc.), (b) service type (mission critical, public safety, VoIP, etc.), (b) subscriber usage volume (high, medium, low), (c) subscriber's requested/subscribed QoS, and (d) subscriber's paying value (i.e., revenue). The same teachings can be extended to also optimize a single subscriber's service or a service type.

Abstract: Any one of the following dual connectivity attributes is assigned to a group profile of each service group: (1) macro cell only (serving both data/control planes via a macro cell base station.), (2) small cell only (serving data/control planes via a small cell base station), (3) dual connectivity enabled-A (serving data plane of UEs via a small cell base station, and serving control plane via a macro cell base station), and (4) dual connectivity enabled-B (serving data plane via both a macro cell base station and a small cell base station, and serving control plane via a macro cell base station).

Abstract: In Vehicle-to-Infrastructure (V2I) communications, the broadcast data towards the vehicles must use the network resources efficiently while the unicast data must arrive reliably with ultra-low-latency. Furthermore, the connections must remain network-attached while vehicles move rapidly. A system/method are described through which a vehicle with a special communications module and internal computer can connect to one or more 5G vehicular network slices (VNS) with multiple Radio Access Technologies (multi-RAT) in order to efficiently communicate with local or remote transportation information databases and applications, road safety and emergency infrastructures. The infrastructure of this disclosure uses the network-slicing feature of a 5G mobile network to carve out a vehicular data and control planes specialized to offer the vehicular service only.

Abstract: User equipments (UEs) that have multiple radio access technologies (RATs) are grouped together so that each group may be subjected to its own set of medium access (MAC) protocols for each RAT, while a flow destined for a UE may be communicated via one or multiple aggregated RAT interfaces on the said UE. The network operator or service provider that serves a plurality of subscribers defines a service group for said plurality of subscribers and controls the corresponding virtualized MACs for the said service group.

Abstract: A service group based dynamic optimization of WWAN and WLAN aggregation in user equipment (UE) is described. The UE has a first radio access technology (RAT) interface associated with a Wireless Wide Area Network (WWAN) based interface that is served by a corresponding base station (BS), and a second RAT interface associated with a Wireless Local Area Network (WLAN) based interface that is served by an Access Point (AP), wherein, for downlink communications, the first and second RAT interfaces are aggregated. Each UE belongs to at least one service group, wherein the UE performs service group based dynamic optimization of WWAN and WLAN aggregation, with flow split ratios and resource allocation between RAT interfaces being jointly optimized for each service group and each RAT interface.

Abstract: Any one of the following dual connectivity attributes is assigned to a group profile of each service group: (1) macro cell only (serving both data/control planes via a macro cell base station.), (2) small cell only (serving data/control planes via a small cell base station), (3) dual connectivity enabled-A (serving data plane of UEs via a small cell base station, and serving control plane via a macro cell base station), and (4) dual connectivity enabled-B (serving data plane via both a macro cell base station and a small cell base station, and serving control plane via a macro cell base station), wherein the RAN controller calculates and sets the optimal splitting between the macro cell base station and the small cell base station dynamically, such that desired load balancing is achieved.

Abstract: Any one of the following dual connectivity attributes is assigned to a group profile of each service group: (1) macro cell only (serving both data/control planes via a macro cell base station.), (2) small cell only (serving data/control planes via a small cell base station), (3) dual connectivity enabled-A (serving data plane of UEs via a small cell base station, and serving control plane via a macro cell base station), and (4) dual connectivity enabled-B (serving data plane via both a macro cell base station and a small cell base station, and serving control plane via a macro cell base station).

Abstract: A joint optimization module (jointly optimizing MRO and MLB) includes: (a) an interpolator receiving a measurement report containing: signal strength/quality values, other performance indicator values, and load values reported by each base station; identifying one or more time instances that lack measurements (of signal strength/quality, performance indicator, or load values), and interpolating values for such time instances; (b) a storage unit storing the received measurement report and interpolated measurement values; (c) a MLB unit receiving the measurement report and outputting optimized HO parameters from a load balancing perspective to an optimizer; (d) a MRO unit receiving the measurement report and outputting optimized HO parameters based on reducing HO related link failures; and (e) an optimizer optimizing, jointly for both MLB and MRO, one or more HO parameters per user group based on the stored measurement reports and interpolated measurement values, the MLB module output, and the MRO module output

Abstract: A hand over (HO) parameter optimization module includes: (a) an interpolator that receives a measurement report comprising: (1) signal strength or signal quality values as measured by an user equipment (UE), (2) performance indicator values as measured by the UE, and (3) load values associated with at least one base station that serves the UE, identifying one or more time instances that lack signal strength, signal quality, performance indicator, or load values, and interpolating values for the identified one or more time instances; (b) storage that stores the received measurement report and interpolated values; and (c) an optimizer that optimizes one or more HO parameters per group based on stored measurement reports and interpolated values.

Abstract: A joint optimization module (jointly optimizing MRO and MLB) includes: (a) an interpolator receiving a measurement report containing: signal strength/ quality values, other performance indicator values, and load values reported by each base station; identifying one or more time instances that lack measurements (of signal strength/quality, performance indicator, or load values), and interpolating values for such time instances; (b) a storage unit storing the received measurement report and interpolated measurement values; (c) a MLB unit receiving the measurement report and outputting optimized HO parameters from a load balancing perspective to an optimizer; (d) a MRO unit receiving the measurement report and outputting optimized HO parameters based on reducing HO related link failures; and (e) an optimizer optimizing, jointly for both MLB and MRO, one or more HO parameters per user group based on the stored measurement reports and interpolated measurement values, the MLB module output, and the MRO module outpu

Abstract: A hand over (HO) parameter optimization module includes: (a) an interpolator that receives a measurement report comprising: (1) signal strength or signal quality values as measured by an user equipment (UE), (2) performance indicator values as measured by the UE, and (3) load values associated with at least one base station that serves the UE, identifying one or more time instances that lack signal strength, signal quality, performance indicator, or load values, and interpolating values for the identified one or more time instances; (b) storage that stores the received measurement report and interpolated values; and (c) an optimizer that optimizes one or more HO parameters per group based on stored measurement reports and interpolated values.