Abstract: The present disclosure describes various embodiments of systems and methods of detecting, classifying, and making a threat assessment of an unmanned aerial vehicle (UAV). One such method comprises detecting a radio frequency (RF) signal; determining that the RF signal is generated from an unmanned aerial vehicle (UAV) based on the detected RF signal; classifying at least a make and model of the UAV based on the detected RF signal; sensing for a remote identification field data broadcasted by the UAV; receiving remote identification information of the UAV from a network database, if the network database is available; assessing a threat likelihood of the UAV based on joint processing of at least the RF signal based classification of the UAV and the received remote identification information of the UAV; and signaling an alert containing a description of the UAV and the threat if the UAV is assessed as a harmful threat.

Abstract: The present disclosure describes various embodiments of systems and methods of detecting, classifying, and making a threat assessment of an unmanned aerial vehicle (UAV). One such method comprises detecting a radio frequency (RF) signal; determining that the RF signal is generated from an unmanned aerial vehicle (UAV) based on the detected RF signal; classifying at least a make and model of the UAV based on the detected RF signal; sensing for a remote identification field data broadcasted by the UAV; receiving remote identification information of the UAV from a network database, if the network database is available; assessing a threat likelihood of the UAV based on joint processing of at least the RF signal based classification of the UAV and the received remote identification information of the UAV; and signaling an alert containing a description of the UAV and the threat if the UAV is assessed as a harmful threat.

Abstract: Systems, devices, and methods are described for controlling, and communicating with, unmanned aerial vehicles (UAVs) over a millimeter wave (mmWave) communication network established between a base station and the UAVs. A communication system may include one or more unmanned aerial vehicles (UAVs). The communication system may further include one or more base stations including millimeter wave (mmWave) antennas configured to generate control signals to the one or more UAVs over an mmWave communication network.

Abstract: Methods, apparatuses, and systems for organizing data delivering unmanned aerial vehicles (UAVs) are provided. Inter-cluster coordinators can organize data delivering unmanned aerial vehicle base stations (UAV-BSs). Various beamforming techniques (e.g., LZFBF and ZFBF) can be incorporated, and the inter-cluster coordinator can operate on a base station that serves as a controlling network node.

Abstract: Apparatuses, systems, and methods of communication using visible light communications (VLC) are provided. A system can include a VLC transmitter and a VLC receiver suitable for implementing a diversity combining technique. The diversity combining technique can include equal gain combining, selective best combining, and maximal ratio combining. The VLC transmitter can be a multi-element diversity transmitter and can provide illumination as well as data transfer.

Abstract: Techniques and systems are disclosed for addressing the challenges in interference and mobility management in broadband, UAV-assisted heterogeneous network (BAHN) scenarios. Implementations include BAHN control components, for example, at a controlling network node of a BAHN. Generally, a component implementing techniques for managing interference and handover in a BAHN gathers state data from network nodes or devices in the BAHN, determines a candidate BAHN model that optimizes interference and handover metrics, and determines and performs model adjustments to the network parameters, BS parameters, and UAV-assisted base station (UABS) device locations and velocities to conform to the optimized candidate BAHN model. Also described is a UABS apparatus having a UAV, communications interface for communicating with a HetNet in accordance with wireless air interface standards, and a computing device suitable for implementing BAHN control or reinforcement learning components.

Abstract: Techniques are disclosed for determining a specific velocity of a device attached to a wireless network. The specific velocity can be determined using a handover count of the base station boundary transitions over a time window and/or using a set of sojourn time samples that each denote the duration the device remains in the zone of a particular base station. Techniques operate effectively in cellular networks having high base station densities. The specific velocity estimates may be inputs to components on the device or network to adjust a local device function or performance behavior.

Abstract: Apparatuses, systems, and methods of communication using visible light communications (VLC) are provided. A system can include a VLC transmitter and a VLC receiver suitable for implementing a diversity combining technique. The diversity combining technique can include equal gain combining, selective best combining, and maximal ratio combining. The VLC transmitter can be a multi-element diversity transmitter and can provide illumination as well as data transfer.

Abstract: Techniques and systems are disclosed for addressing the challenges in interference and mobility management in broadband, UAV-assisted heterogeneous network (BAHN) scenarios. Implementations include BAHN control components, for example, at a controlling network node of a BAHN. Generally, a component implementing techniques for managing interference and handover in a BAHN gathers state data from network nodes or devices in the BAHN, determines a candidate BAHN model that optimizes interference and handover metrics, and determines and performs model adjustments to the network parameters, BS parameters, and UAV-assisted base station (UABS) device locations and velocities to conform to the optimized candidate BAHN model. Also described is a UABS apparatus having a UAV, communications interface for communicating with a HetNet in accordance with wireless air interface standards, and a computing device suitable for implementing BAHN control or reinforcement learning components.

Abstract: Methods, apparatuses, and systems for organizing data delivering unmanned aerial vehicles (UAVs) are provided. Inter-cluster coordinators can organize data delivering unmanned aerial vehicle base stations (UAV-BSs). Various beamforming techniques (e.g., LZFBF and ZFBF) can be incorporated, and the inter-cluster coordinator can operate on a base station that serves as a controlling network node.

Abstract: Techniques and systems are disclosed for addressing the challenges in interference and mobility management in broadband, UAV-assisted heterogeneous network (BAHN) scenarios. Implementations include BAHN control components, for example, at a controlling network node of a BAHN. Generally, a component implementing techniques for managing interference and handover in a BAHN gathers state data from network nodes or devices in the BAHN, determines a candidate BAHN model that optimizes interference and handover metrics, and determines and performs model adjustments to the network parameters, BS parameters, and UAV-assisted base station (UABS) device locations and velocities to conform to the optimized candidate BAHN model. Also described is a UABS apparatus having a UAV, communications interface for communicating with a HetNet in accordance with wireless air interface standards, and a computing device suitable for implementing BAHN control or reinforcement learning components.

Abstract: Techniques and systems are disclosed for addressing the challenges in interference and mobility management in broadband, UAV-assisted heterogeneous network (BAHN) scenarios. Implementations include BAHN control components, for example, at a controlling network node of a BAHN. Generally, a component implementing techniques for managing interference and handover in a BAHN gathers state data from network nodes or devices in the BAHN, determines a candidate BAHN model that optimizes interference and handover metrics, and determines and performs model adjustments to the network parameters, BS parameters, and UAV-assisted base station (UABS) device locations and velocities to conform to the optimized candidate BAHN model. Also described is a UABS apparatus having a UAV, communications interface for communicating with a HetNet in accordance with wireless air interface standards, and a computing device suitable for implementing BAHN control or reinforcement learning components.

Abstract: Techniques are disclosed for determining a specific velocity of a device attached to a wireless network. The specific velocity can be determined using a handover count of the base station boundary transitions over a time window and/or using a set of sojourn time samples that each denote the duration the device remains in the zone of a particular base station. Techniques operate effectively in cellular networks having high base station densities. The specific velocity estimates may be inputs to components on the device or network to adjust a local device function or performance behavior.

Abstract: Techniques are disclosed for determining a specific velocity of a device attached to a wireless network. The specific velocity can be determined using a handover count of the base station boundary transitions over a time window and/or using a set of sojourn time samples that each denote the duration the device remains in the zone of a particular base station. Techniques operate effectively in cellular networks having high base station densities. The specific velocity estimates may be inputs to components on the device or network to adjust a local device function or performance behavior.

Abstract: Techniques are disclosed for determining a specific velocity of a device attached to a wireless network. The specific velocity can be determined using a handover count of the base station boundary transitions over a time window and/or using a set of sojourn time samples that each denote the duration the device remains in the zone of a particular base station. Techniques operate effectively in cellular networks having high base station densities. The specific velocity estimates may be inputs to components on the device or network to adjust a local device function or performance behavior.

Abstract: Methods and apparatuses are disclosed herein for population tracking, counting and/or movement estimation. In one embodiment, the method comprises receiving mobile phone operational data indicative of user equipment location, where the event data includes location area update messages and periodic registration messages; and performing travel estimation based on the mobile phone operation data, including performing interpolation on data associated with one or more individuals in a population to estimate intermediate positions of a trajectory of each of the one or more individuals for a specified time period based on a shortest path mesh sequence estimation algorithm.

Abstract: In a system including one or more femtocells within a service area of a macrocell, a method includes: (a) receiving from a base station information regarding available resources at each femtocell; (b) measuring a signal-interference-to-noise ratio (SINR) at a mobile station relative to the macrocell and each femtocell; and (c) selecting for the mobile station one of the femtocells for a hand-off, wherein the femtostation is selected based on the combined capacity of the macrocell and the femtocells after the hand-off.

Abstract: Systems and methods are provided for a distributed inter-cell interference avoidance (ICIA) technique for avoiding co-channel interference between femtocell networks and macrocell networks. At the macrocell, user equipments sense the downlink (DL) spectrum and detects whether there are any nearby interfering femtocells. If there is any interference, a macrocell base station appropriately re-schedules the DL resources and also uses a mapping function to re-schedule uplink (UL) resources based on the re-scheduled DL resources. At the femtocell, a femtocell base station senses the UL spectrum to detect for interference from nearby macrocell users. If there is interference, femtocell gives priority for use of the resources to the macrocell by releasing UL resources. Femtocell may also use the same mapping function to obtain the DL resources used by the macrocell and to re-schedule DL and UL resources of the femtocell to avoid using the DL and UL resources of the macrocell.

Abstract: In a system including one or more femtocells within a service area of a macrocell, a method includes: (a) receiving from a base station of each femtocell information regarding available resources at the femtocell; (b) measuring an signal-interference-to-noise ratio (SINR) at a mobile station relative to the macrocell and each femtocell; and (c) selecting for the mobile station one of the femtocells for a hand-off, wherein the femtostation is selected based on the combined capacity of the macrocell and the femtocells after the hand-off.

Abstract: In one embodiment, a method of mitigating uplink inter-carrier interference (ICI) from macrocell mobile stations at a dedicated channel femtocell base station is provided that includes: determining a timing offset for a femtocell uplink symbol timing that reduces the ICI based upon an expected spatial distribution for the macrocell mobile stations with respect to the femtocell base station; communicating the timing offset to at least one femtocell mobile station; and at the femtocell base station, receiving an uplink symbol transmission from the at least one femtocell mobile station according to the timing offset.