Abstract: Improved techniques are provided for managing and optimizing network resources and spectrum access in a Self-Organizing Network (SON). A Spectrum Access System (SAS) collects network-related information from a plurality of network sources, such as base stations and user equipments (UEs), to perform optimization and organization across different networks, network operators, and network technologies. In some embodiments, the SAS may use the network information and a Radio Environment Map to optimize TDD synchronization in the SON. In other embodiments, the SAS may use the network information to populate a global Neighbor Relation Table. The SAS also may use the network information to optimize one or more network parameters, such as Physical Cell Identities or Root Sequence Indexes, antenna parameters, transmit power levels, handover thresholds, channel assignments, and so on, for use in the SON.

Abstract: Improved techniques are provided for managing and optimizing network resources and spectrum access in a Self-Organizing Network (SON). A Spectrum Access System (SAS) collects network-related information from a plurality of network sources, such as base stations and user equipments (UEs), to perform optimization and organization across different networks, network operators, and network technologies. In some embodiments, the SAS may use the network information and a Radio Environment Map to optimize TDD synchronization in the SON. In other embodiments, the SAS may use the network information to populate a global Neighbor Relation Table. The SAS also may use the network information to optimize one or more network parameters, such as Physical Cell Identities or Root Sequence Indexes, antenna parameters, transmit power levels, handover thresholds, channel assignments, and so on, for use in the SON.

Abstract: Improved techniques are provided for managing and optimizing network resources and spectrum access in a Self-Organizing Network (SON). A Spectrum Access System (SAS) collects network-related information from a plurality of network sources, such as base stations and user equipments (UEs), to perform optimization and organization across different networks, network operators, and network technologies. In some embodiments, the SAS may use the network information and a Radio Environment Map to optimize TDD synchronization in the SON. In other embodiments, the SAS may use the network information to populate a global Neighbor Relation Table. The SAS also may use the network information to optimize one or more network parameters, such as Physical Cell Identities or Root Sequence Indexes, antenna parameters, transmit power levels, handover thresholds, channel assignments, and so on, for use in the SON.

Abstract: A cloud-based system for maintaining a distributed cloud spectrum access system (SAS) is disclosed. The system may include one or more processors; and a memory storing instructions that cause the one or more processors to perform the a method including: syncing one or more edge databases at a frontend-SAS with corresponding cloud databases at a backend-SAS; monitoring a connection between the frontend-SAS and the backend-SAS; determining the connection between the frontend-SAS and the backend-SAS has been severed; continuing to operate within an interference headroom; attempting one or more robustness mechanisms to ensure impact to the frontend-SAS is minimized and to reestablish the connection between the frontend-SAS and the backend-SAS; and upon determining the connection is reestablished, reconciling the one or more edge databases at the frontend-SAS with the corresponding cloud databases at the backend-SAS.

Abstract: The disclosed embodiments provide improved methods and systems for predicting spectrum allocation in a wireless access network. The method may comprise receiving data associated with an access point (AP), creating, based on the received data and a machine learning algorithm, a decision model for predicting whether to allocate spectrum to the AP, generating a prediction, using the decision model, whether to allocate spectrum to the AP, and sending the prediction to a spectrum access system (SAS) in the wireless access network. The SAS may be configured to allocate spectrum to the AP based on the prediction.

Abstract: The disclosed embodiments provide improved methods and systems for predicting spectrum allocation in a wireless access network. The method may comprise receiving data associated with an access point (AP), creating, based on the received data and a machine learning algorithm, a decision model for predicting whether to allocate spectrum to the AP, generating a prediction, using the decision model, whether to allocate spectrum to the AP, and sending the prediction to a spectrum access system (SAS) in the wireless access network. The SAS may be configured to allocate spectrum to the AP based on the prediction.

Abstract: The disclosed embodiments provide a software-defined spectrum controller that may be used to dynamically and adaptively change rules and/or policies in a wireless network. The SDSC may dynamically change the rules or policies based on usage of spectrum and/or network resources or any other criterion, e.g., established by a service provider, to allow the service provider to use the spectrum and network resources more efficiently for delivering content and services to customers. In accordance with the disclosed embodiments, the SDSC may use at least one optimization algorithm to determine which policies or rules should be enforced at each of the network nodes in the wireless network. Based on the optimization algorithm, the SDSC may interact with other network nodes to dynamically change one or more of the rules or policies that are enforced in the service provider's network to provide content and services to customers.

Abstract: Improved techniques are provided for managing and optimizing network resources and spectrum access in a Self-Organizing Network (SON). A Spectrum Access System (SAS) collects network-related information from a plurality of network sources, such as base stations and user equipments (UEs), to perform optimization and organization across different networks, network operators, and network technologies. In some embodiments, the SAS may use the network information and a Radio Environment Map to optimize TDD synchronization in the SON. In other embodiments, the SAS may use the network information to populate a global Neighbor Relation Table. The SAS also may use the network information to optimize one or more network parameters, such as Physical Cell Identities or Root Sequence Indexes, antenna parameters, transmit power levels, handover thresholds, channel assignments, and so on, for use in the SON.

Abstract: The disclosed embodiments provide a software-defined spectrum controller that may be used to dynamically and adaptively change rules and/or policies in a wireless network. The SDSC may dynamically change the rules or policies based on usage of spectrum and/or network resources or any other criterion, e.g., established by a service provider, to allow the service provider to use the spectrum and network resources more efficiently for delivering content and services to customers. In accordance with the disclosed embodiments, the SDSC may use at least one optimization algorithm to determine which policies or rules should be enforced at each of the network nodes in the wireless network. Based on the optimization algorithm, the SDSC may interact with other network nodes to dynamically change one or more of the rules or policies that are enforced in the service provider's network to provide content and services to customers.

Abstract: A system and method is provided for a predictive connectivity layer. In the disclosed embodiments, resources, such as bandwidth, processing, and memory, at a network node are dynamically allocated based on one or more predicted user behaviors. A predicted user behavior may be determined based on one or more previous actions of a user or a group of users at the network node. For example, if a user accesses the network node to download a particular web site at the same time every morning, the predictive technique may determine that the user will attempt to download the same web site the next morning, and therefore cache a copy of the web site before the user's next attempt to access the network through the network node, Similarly, the network node may predict an amount of bandwidth or other resources to allocate based on previous behavior of one or more users.

Abstract: A system and method is provided for a predictive connectivity layer. In the disclosed embodiments, resources, such as bandwidth, processing, and memory, at a network node are dynamically allocated based on one or more predicted user behaviors. A predicted user behavior may be determined based on one or more previous actions of a user or a group of users at the network node. For example, if a user accesses the network node to download a particular web site at the same time every morning, the predictive technique may determine that the user will attempt to download the same web site the next morning, and therefore cache a copy of the web site before the user's next attempt to access the network through the network node. Similarly, the network node may predict an amount of bandwidth or other resources to allocate based on previous behavior of one or more users.

Abstract: The disclosed embodiments provide a software-defined spectrum controller that may be used to dynamically and adaptively change rules and/or policies in a wireless network. The SDSC may dynamically change the rules or policies based on usage of spectrum and/or network resources or any other criterion, e.g., established by a service provider, to allow the service provider to use the spectrum and network resources more efficiently for delivering content and services to customers. In accordance with the disclosed embodiments, the SDSC may use at least one optimization algorithm to determine which policies or rules should be enforced at each of the network nodes in the wireless network. Based on the optimization algorithm, the SDSC may interact with other network nodes to dynamically change one or more of the rules or policies that are enforced in the service provider's network to provide content and services to customers.

Abstract: The disclosed embodiments include methods, systems, articles of manufacture, and access devices configured to switch frequencies in a wireless access network. The techniques described in the disclosed embodiments may be used to facilitate efficient switching of frequencies in a dynamic spectrum environment. In one aspect, the disclosed embodiments may be implemented in a Long Term Evolution access network. The disclosed embodiments receive, by an access device, information allocating at least one frequency channel to be used by the access device. The disclosed embodiments also transmit, to one or more user equipment connected to the access device, an instruction for directing the one or more user equipment to look a first frequency channel. As a result, the disclosed embodiments allow efficient switching of frequencies even if the spectrum may be allocated dynamically.

Abstract: The disclosed embodiments provide a software-defined spectrum controller that may be used to dynamically and adaptively change rules and/or policies in a wireless network. The SDSC may dynamically change the rules or policies based on usage of spectrum and/or network resources or any other criterion, e.g., established by a service provider, to allow the service provider to use the spectrum and network resources more efficiently for delivering content and services to customers. In accordance with the disclosed embodiments, the SDSC may use at least one optimization algorithm to determine which policies or rules should be enforced at each of the network nodes in the wireless network. Based on the optimization algorithm, the SDSC may interact with other network nodes to dynamically change one or more of the rules or policies that are enforced in the service provider's network to provide content and services to customers.

Abstract: The disclosed embodiments provide improved methods and systems for predicting spectrum allocation in a wireless access network. The method may comprise receiving data associated with an access point (AP), creating, based on the received data and a machine learning algorithm, a decision model for predicting whether to allocate spectrum to the AP, generating a prediction, using the decision model, whether to allocate spectrum to the AP, and sending the prediction to a spectrum access system (SAS) in the wireless access network. The SAS may be configured to allocate spectrum to the AP based on the prediction.

Abstract: The disclosed embodiments provide a method for interconnecting a plurality of networks, including a first network, with a second network via an interworking gateway (IWG). The method may comprise receiving a request from the first network for a user in the first network to access at least one service or application available in the second network, determining at least one criteria associated with the second network to enforce for the user in the first network, and enforcing the at least one criteria for communications associated with the user between the first and second networks. The IWG may be a cloud-based service that is configured to perform the aforementioned steps. The IWG may also be configured to be dynamically scalable to support multiple networks in the plurality of networks requesting interconnection with the second network.

Abstract: The disclosed embodiments include methods, systems, articles of manufacture, and access devices configured to switch frequencies in a wireless access network. The techniques described in the disclosed embodiments may be used to facilitate efficient switching of frequencies in a dynamic spectrum environment. In one aspect, the disclosed embodiments may be implemented in a Long Term Evolution access network. The disclosed embodiments receive, by an access device, information allocating at least one frequency channel to be used by the access device. The disclosed embodiments also transmit, to one or more user equipment connected to the access device, an instruction for directing the one or more user equipment to look a first frequency channel. As a result, the disclosed embodiments allow efficient switching of frequencies even if the spectrum may be allocated dynamically.

Abstract: Systems and methods for providing mobile services are disclosed. In one implementation, an access point (AP) is provided, which may include a set of one or more base-station functions for use by a user equipment (UE) connected to the AP over a wireless communication interface. The one or more base-station functions may be configured to receive information from the UE. The AP may further include a set of one or more core-network functions configured to receive the information from the set of one or more base-station functions and a distributed portion of a service. The distributed portion of the service may be configured to receive the information from the one or more core-network functions and communicate the information to a corresponding cloud portion of the service running on a cloud platform. The service may be provided by a combination of the distributed portion and the cloud portion of the service.

Abstract: Embodiments assign shared spectrum resources to access points. Embodiments group the access points into coexistence groups of access points, and generate a vertex graph including vertices that each represent one of the coexistence groups, where an edge connecting a pair of vertices in the vertex graph represents an overlap between coverage contours of a pair of coexistence groups represented by the pair of vertices, and the edge is assigned a weight that is proportional to the overlap as compared to other overlaps between coverage contours of other pairs of coexistence groups. Embodiments generate at least one vertex-colored graph by assigning a color to each vertex in the vertex graph, group the coexistence groups into groups of coexistence groups based on their corresponding assigned colors in the at least one vertex-colored graph, and map shared spectrum transmit frequencies to the groups of coexistence groups.

Abstract: Embodiments assign shared spectrum resources to access points. Embodiments group the access points into coexistence groups of access points, and generate a vertex graph including vertices that each represent one of the coexistence groups, where an edge connecting a pair of vertices in the vertex graph represents an overlap between coverage contours of a pair of coexistence groups represented by the pair of vertices, and the edge is assigned a weight that is proportional to the overlap as compared to other overlaps between coverage contours of other pairs of coexistence groups. Embodiments generate at least one vertex-colored graph by assigning a color to each vertex in the vertex graph, group the coexistence groups into groups of coexistence groups based on their corresponding assigned colors in the at least one vertex-colored graph, and map shared spectrum transmit frequencies to the groups of coexistence groups.