Abstract: An access point (AP) device for controlling spectrum usage of a hierarchical communication system, in which a spectrum reserved for an Incumbent is usable by at least one user equipment (UE) for transmission when the spectrum is not required by the Incumbent, is disclosed. The AP device includes a processor configured to receive a message from the Incumbent requesting vacating of a spectrum; generate a group of users affected by the message from the Incumbent requesting vacating of the spectrum; and perform a spectrum management operation on the group of users.

Abstract: An access point (AP) device for controlling spectrum usage of a hierarchical communication system, in which a spectrum reserved for an Incumbent is usable by at least one user equipment (UE) for transmission when the spectrum is not required by the Incumbent, is disclosed. The AP device includes a processor configured to receive a message from the Incumbent requesting vacating of a spectrum; generate a group of users affected by the message from the Incumbent requesting vacating of the spectrum; and perform a spectrum management operation on the group of users.

Abstract: The disclosure relates to an access point (AP) device for controlling spectrum usage of a hierarchical communication system, in which a spectrum reserved for an Incumbent is usable by at least one user equipment (UE) for transmission when the spectrum is not required by the Incumbent, the AP device comprising a processor configured to: enforce the at least one UE into a mode protecting use of the spectrum by the Incumbent based on a spectrum request indication from the Incumbent; and enable transmission of the at least one UE using the spectrum reserved for the Incumbent based on a spectrum availability indication from the Incumbent.

Abstract: The disclosure relates to an access point (AP) device for controlling spectrum usage of a hierarchical communication system, in which a spectrum reserved for an Incumbent is usable by at least one user equipment (UE) for transmission when the spectrum is not required by the Incumbent, the AP device comprising a processor configured to: enforce the at least one UE into a mode protecting use of the spectrum by the Incumbent based on a spectrum request indication from the Incumbent; and enable transmission of the at least one UE using the spectrum reserved for the Incumbent based on a spectrum availability indication from the Incumbent.

Abstract: The present disclosure relates to an early warning and recommendation system for proactive management of a wireless broadband network. Without human intervention, the system processes highly heterogeneous network and non-network data and applies unsupervised machine learning to the data to predict and understand the situations that lead to different network state conditions. More specifically, unsupervised clustering is applied to the data to understand “situations” that can lead to non-normal network state conditions. A deep neural network model of situations is then created to further understand the underlying data relationships between the elements of a situation and network states. The deep neural network model and Reinforcement Learning is used to provide recommendations as to changes in network configuration parameters to improve the state of a predicted situation associated with non-normal network conditions.

Abstract: The present disclosure relates to an early warning and recommendation system for proactive management of a wireless broadband network. Without human intervention, the system processes highly heterogeneous network and non-network data and applies unsupervised machine learning to the data to predict and understand the situations that lead to different network state conditions. More specifically, unsupervised clustering is applied to the data to understand “situations” that can lead to non-normal network state conditions. A deep neural network model of situations is then created to further understand the underlying data relationships between the elements of a situation and network states. The deep neural network model and Reinforcement Learning is used to provide recommendations as to changes in wireless/mobile broadband network configuration parameters that will improve the state of a predicted situation associated with non-normal network conditions.

Abstract: In embodiments, apparatuses, methods, and storage media may be described for presenting, by a user equipment (UE), a parameter of a network policy to a user, and receiving an indication of the user preference related to that parameter of the network policy. In response to receiving the indication, the UE may select one or more radio links with which the UE should communicatively couple based on the user preference and the network policy. Other embodiments may be described and/or claimed.

Abstract: In embodiments, apparatuses, methods, and storage media may be described for presenting, by a user equipment (UE), a parameter of a network policy to a user, and receiving an indication of the user preference related to that parameter of the network policy. In response to receiving the indication, the UE may select one or more radio links with which the UE should communicatively couple based on the user preference and the network policy. Other embodiments may be described and/or claimed.