Patent Description:
Conventional modeling of cellular and broadband network capabilities are often based on manual measurements of signals from telecommunications towers sampled infrequently. Typical cellular and broadband network infrastructure is ground based and unmoving, so conventional models of cellular and broadband networks assume a static network of stations that are fixed in place and have antennas pointed in fixed directions. Conventional telecommunications networks being fixed in place also means that the only adjustments that can be made to stations are tuning antennas in their fixed locations.

A network of stations carried by aerial vehicles allow for changes to locations of stations, as well as directing network signals according to need and desire (i.e., demand). Given the near constant movement of the aerial vehicle stations, particularly with transmitters on gimbals that can direct signals in a wide range of directions relative to a position of an aerial vehicle, it is not possible to use conventional modeling techniques to capture the time varying dynamics of a hybrid network composed of moving stations (i.e., on aerial vehicles) and ground stations.

Thus, improved techniques for simulating a dynamic hybrid network is desirable.

Internet of Drones: a Survey on Communications, Technologies, Protocols, Architectures and Services by Pietro Boccadoro et al. categorizes the multifaceted aspects of IoD, proposing a classification of the IoD environment. All the most relevant works belonging to each layer of the Internet protocol stack are classified according to the different issues peculiar of the layer.

In particular, the present invention is defined in the appended independent claims to which reference should be made.

The present disclosure provides techniques for simulating a dynamic hybrid network. A method for simulating a dynamic hybrid network may include modeling, at each time step in a time series, a transmission supply of a mesh network of a plurality of moving stations on a plurality of aerial vehicles in flight, including modeling point- to-point radio wave transmission capacity. The method may include modeling capacity demand as a function of user distribution data and user behavior data. The method may include computing, at each time step, an aggregate network metric for the dynamic hybrid network comprising at least a throughput. In some examples, the point may be a base station and another point may be a handset density at a location. In some examples, the dynamic hybrid network comprises the mesh network and one or more fixed stations. In some examples, the method may further comprise modeling the transmission supply of the mesh network relative to the transmission supply of the one or more fixed stations. In some examples, the plurality of aerial vehicles comprises
more than one type of aerial vehicle. In some examples, the mesh network comprises a homogeneous network of LTE stations. In some examples, the mesh network comprises a heterogeneous network of two or more types of stations. In some examples, the method further comprises modeling multi-station interference among the mesh network of the plurality of moving stations. In some examples, the method further comprises modeling multi-station interference between the mesh network and one or more fixed stations. In some examples, the aggregate network metric is based on a signal quality per handset output from modeling the transmission supply and a capacity demand per handset output from modeling the capacity demand. In some examples, the aggregate network metric is refreshed at each time step in the time series based on a refreshed transmission supply model based on updated current or most recent locations and antenna characteristics of moving stations in the mesh network. In some examples, modeling the capacity demand results in a cyclical representation of a capacity demand per handset. In some examples, modeling the capacity demand results in a static representation of a capacity demand per handset. In some examples, the method further comprises outputting a simulated dynamic hybrid network using the aggregate network metric. In some examples, the simulated dynamic hybrid network is represented using a heatmap. In some examples, the heatmap indicates strength of signal at each point on a map of a location or are of interest.

Various aspects and features of the present disclosure are described herein below with references to the drawings, wherein:.

The figures depict various example embodiments of the present disclosure for purposes of illustration only. One of ordinary skill in the art will readily recognize from the following discussion that other example embodiments based on alternative structures and methods may be implemented as long as such example embodiments fall within the scope of the claims.

The Figures and the following description describe certain embodiments by way of illustration only. One of ordinary skill in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described
herein.

The above and other needs are met by the disclosed methods, a non-transitory computer-readable storage medium storing executable code, and systems for dispatching fleets of aircraft by a fleet management and flight planning system. The terms "aerial vehicle" and "aircraft" are used interchangeably herein to refer to any type of vehicle capable of aerial movement, including, without limitation, High Altitude Platforms (HAPs), High Altitude Long Endurance (HALE) aircraft, unmanned aerial vehicles (UAVs), passive lighter than air vehicles (e.g., floating stratospheric balloons, other floating or wind-driven vehicles), powered lighter than air (LTA) vehicles (e.g., balloons and airships with some propulsion capabilities), fixed-wing vehicles (e.g., drones, rigid kites, gliders), various types of satellites, rockets, space stations, and other high altitude aerial vehicles.

The invention is directed to simulating a dynamic hybrid network by combining a precise model of a dynamic hybrid network (i.e., supply model) with a demand model. The dynamic hybrid network model comprises a model of a ground network and a model of an evolving mesh network of aerial vehicles (i.e., LTE-enabled or otherwise equipped to provide data connectivity) that are near constantly moving. The mesh network of aerial vehicles may represent a distribution and access layer of the network. Each aerial vehicle may carry one, two or more communications station(s), with transceivers generally facing out (e.g., from a central axis) of the aerial vehicle. The transceivers may comprise terminals that pivot on a gimbal, providing a wide range of directional capabilities. The mesh network of aerial vehicles comprises a moving network of stations with variable directional capabilities.

A method of modeling the dynamic hybrid network comprises, at each time step in a time series, modeling a transmission supply of the mesh network of aerial vehicles, modeling capacity demand, and computing aggregate network metrics, including throughput (e.g., in megabytes per second or other transmission volume per time scale) and number of users served. Some or all of the aggregate network metrics may indicate how well supply matches with demand and may be provided as feedback to a fleet management system to alter (e.g., by a controller) a station location, direction, and transmission strength, to improve the aggregate network metrics (e.g., to tune throughput to equal or smaller than demand).

Modeling the transmission supply of the mesh network may include modeling point-to-point radio wave transmission capacity, a point being a base station and another point being a handset density at a location (e.g., abstract representation of handset density). Modeling the transmission supply of the mesh network also may include modeling multi-station interference (i.e., interference between fixed stations and current and/or forecasted locations of moving stations), including one or both downlink (i.e., station to handset) and uplink (i.e., handset to station). In some examples, modeling the transmission supply further may include modeling individual radio frequency transmissions from each station to each handset. Modeling the transmission supply may be based on a plurality of antenna characteristics (e.g., direction, location, frequency, signal transmission pattern (i.e., lobes), signal transmission strength).

An output of a transmission supply model may comprise a signal quality per handset metric (e.g., signal to interference plus noise ratio (SINR), received signal strength indicator (RSSI), reference signal received power (RSRP), reference signal received quality (RSRQ)) for each time step in a time series. Another output of a transmission supply model may comprise a heat map indicating strength of signal at each point on a map of a location or area of interest.

The capacity demand may be modeled as a function of user distribution data and user behavior data (e.g., anonymized usage data), the output of which may comprise a capacity demand per handset at each point in a plurality of points representing a geographical location or area of interest. User behavior data may be geographically distinct (e.g., differ significantly from one area or country to another). Demand may be static or may vary in time (e.g. having a day-night cycle).

Resulting aggregated network metrics are calculated for each time step, as the transmission supply model may change with each time step, as described above, due to changes in positions and orientations of base stations (e.g., LTE base stations) in the network.

In some examples, the network may be an LTE network or other single standard network operating on a same frequency band, wherein adjacent LTE stations will interfere with each other. In some examples, each aerial vehicle may house multiple LTE stations.

<FIG> are diagrams of exemplary aerial vehicle systems, which may carry communications stations in a dynamic hybrid network, in accordance with one or more embodiments. In <FIG>, there is shown a diagram of system <NUM> for control and navigation of aerial vehicle 120a, which may carry communication stations (e.g., communications units 111a, terminals 112a, and other components that serve to enable wireless communication with other parts of the network) of a dynamic hybrid network. In some examples, aerial vehicle 120a may be a passive vehicle, such as a balloon or satellite, wherein most of its directional movement is a result of environmental forces, such as wind and gravity. In other examples, aerial vehicles 120a may be actively propelled. In an embodiment, system <NUM> may include aerial vehicle 120a and ground station <NUM>. In this embodiment, aerial vehicle 120a may include balloon 101a, plate <NUM>, altitude control system (ACS) 103a, connection 104a, joint 105a, actuation module 106a, and payload 108a. In some examples, plate <NUM> may provide structural and electrical connections and infrastructure. Plate <NUM> may be positioned at the apex of balloon 101a and may serve to couple together various parts of balloon 101a. In other examples, plate <NUM> also may include a flight termination unit, such as one or more blades and an actuator to selectively cut a portion and/or a layer of balloon 101a. In other examples, plate <NUM> further may include other electronic components (e.g., a sensor, a part of a sensor, power source, communications unit). ACS 103a may include structural and electrical connections and infrastructure, including components (e.g., fans, valves, actuators, etc.) used to, for example, add and remove air from balloon 101a (i.e., in some examples, balloon 101a may include an interior ballonet within its outer, more rigid shell that may be inflated and deflated), causing balloon 101a to ascend or descend, for example, to catch stratospheric winds to move in a desired direction. Balloon 101a may comprise a balloon envelope comprised of lightweight and/or flexible latex or rubber materials (e.g., polyethylene, polyethylene terephthalate, chloroprene), tendons (e.g., attached at one end to plate <NUM> and at another end to ACS 103a) to provide strength and stability to the balloon structure, and a ballonet, along with other structural components. In various embodiments, balloon 101a may be non-rigid, semi-rigid, or rigid.

Connection 104a may structurally, electrically, and communicatively, connect balloon 101a and/or ACS 103a to various components comprising payload 108a. In some examples, connection 104a may provide two-way communication and electrical connections, and even two-way power connections. Connection 104a may include a joint 105a, configured to allow the portion above joint 105a to pivot about one or more axes (e.g., allowing either balloon 101a or payload 108a to tilt and turn). Actuation module 106a may provide a means to actively turn payload 108a for various purposes, such as improved aerodynamics, facing or tilting solar panel(s) 109a advantageously, directing payload 108a and propulsion units (e.g., propellers <NUM> in <FIG>) for propelled flight, or directing components of payload 108a advantageously.

Payload 108a may include solar panel(s) 109a, avionics chassis 110a, broadband communications unit(s) 111a, and terminal(s) 112a, as well as other components to enable a communications station in the dynamic hybrid network. Solar panel(s) 109a may be configured to capture solar energy to be provided to a battery or other energy storage unit, for example, housed within avionics chassis 110a. Avionics chassis 110a also may house a flight computer (e.g., computing device <NUM>, as described herein), a transponder, along with other control and communications infrastructure (e.g., a controller comprising another computing device and/or logic circuit configured to control aerial vehicle 120a). Communications unit(s) 111a may include hardware to provide wireless network access (e.g., LTE, fixed wireless broadband via <NUM>, Internet of Things (IoT) network, free space optical network or other broadband networks). Terminal(s) 112a may comprise one or more parabolic reflectors (e.g., dishes) coupled to an antenna and a gimbal or pivot mechanism (e.g., including an actuator comprising a motor). Terminal(s) <NUM>(a) may be configured to receive or transmit radio waves to beam data long distances (e.g., using the millimeter wave spectrum or higher frequency radio signals). In some examples, terminal(s) 112a may have very high bandwidth capabilities. Terminal(s) 112a also may be configured to have a large range of pivot motion for precise pointing performance. Terminal(s) 112a also may be made of lightweight materials.

In other examples, payload 108a may include fewer or more components, including propellers <NUM> as shown in <FIG>, which may be configured to propel aerial vehicles 120a-b in a given direction. In still other examples, payload 108a may include still other components well known in the art to be beneficial to flight capabilities of an aerial vehicle. For example, payload 108a also may include energy capturing units apart from solar panel(s) 109a (e.g., rotors or other blades (not shown) configured to be spun, or otherwise actuated, by wind to generate energy). In another example, payload 108a may further include or be coupled to an imaging device, such as a downward-facing camera and/or a star tracker. In yet another example, payload 108a also may include various sensors (not shown), for example, housed within avionics chassis 110a or otherwise coupled to connection 104a or balloon 101a. Such sensors may include Global Positioning System (GPS) sensors, wind speed and direction sensors such as wind vanes and anemometers, temperature sensors such as thermometers and resistance temperature detectors (i.e., RTDs), speed of sound sensors, acoustic sensors, pressure sensors such as barometers and differential pressure sensors, accelerometers, gyroscopes, combination sensor devices such as inertial measurement units (IMUs), light detectors, light detection and ranging (LIDAR) units, radar units, cameras, other image sensors, and more. These examples of sensors are not intended to be limiting, and those skilled in the art will appreciate that other sensors or combinations of sensors in addition to these described may be included without departing from the scope of the present disclosure.

Ground station <NUM> may include one or more server computing devices 115a-n, which in turn may comprise one or more computing devices (e.g., computing device <NUM> in <FIG>). In some examples, ground station <NUM> also may include one or more storage systems, either housed within server computing devices 115a-n, or separately (see, e.g., computing device <NUM> and repositories <NUM>). Ground station <NUM> may be a datacenter servicing various nodes of one or more networks (e.g., aerial vehicle network <NUM> in <FIG>).

<FIG> shows a diagram of system <NUM> for control and navigation of aerial vehicle 120b. All like-numbered elements in <FIG> are the same or similar to their corresponding elements in <FIG>, as described above (e.g., balloon 101a and balloon 101b may serve the same function, and may operate the same as, or similar to, each other). In some examples, balloon 101b may comprise an airship hull or dirigible balloon. In this embodiment, aerial vehicle 120b further includes, as part of payload 108b, propellers <NUM>, which may be configured to actively propel aerial vehicle 120b in a desired direction, either with or against a wind force to speed up, slow down, or redirect, aerial vehicle 120b. In this embodiment, balloon 101b also may be shaped differently from balloon 101a, to provide different aerodynamic properties. In some examples, balloon 101b may include one or more fins (not shown) coupled to one or more of a rear, upper, lower, or side, surface (i.e., relative to a direction in which balloon 101b is heading).

As shown in <FIG>, aerial vehicles 120a-b may be largely wind-influenced aerial vehicles, for example, balloons carrying a payload (with or without propulsion capabilities) as shown, or fixed wing high altitude drones (e.g., aerial vehicle 211c in <FIG>) with gliding and/or full propulsion capabilities. However, those skilled in the art will recognize that the systems and methods disclosed herein may similarly apply and be usable by various other types of aerial vehicles.

<FIG> is a diagram of an exemplary hybrid network, in accordance with one or more embodiments. Hybrid network <NUM> may include aerial vehicles 201a-b, user devices <NUM>, and ground infrastructure <NUM>, in Country A. Hybrid network <NUM> also may include aerial vehicles 211a-c, user devices <NUM>, and ground infrastructure <NUM> in Country B. Hybrid network <NUM> also may include offshore facilities 214a-c and aerial vehicles 216a-b servicing at least said offshore facilities 214a-c. Hybrid network <NUM> may further include satellite <NUM> and Internet <NUM>. Aerial vehicles 201a-b, 211a-c, and 216a-b may comprise balloon, other floating (i.e., lighter than air), propelled or partially propelled (i.e., propelled for a limited amount of time or under certain circumstances, and not propelled at other times or under other circumstances), fixed-wing, or other types of high altitude aerial vehicles, as described herein. For example, aerial vehicles 201a-b, 211a-c, and 216a-b may be the same or similar to aerial vehicles 120a-b described above. User devices <NUM> and <NUM> may include a cellular phone, tablet computer, smart phone, desktop computer, laptop computer, and/or any other computing device known to those skilled in the art. Ground infrastructure <NUM> and <NUM> may include always-on or fixed location computing device (i.e., capable of receiving fixed broadband transmissions), ground terminal (e.g., ground station <NUM>), tower (e.g., a cellular tower), and/or any other fixed or portable ground infrastructure for receiving and transmitting various modes of connectivity described herein known to those skilled in the art. User devices <NUM> and <NUM>, ground infrastructure <NUM> and <NUM>, and offshore facilities 214a-c, may be capable of receiving and transmitting signals to and from aerial vehicles 201a-b, 211a-c, and 216a-b, and in some cases, to and from each other. Offshore facilities 214a-c may include industrial facilities (e.g., wind farms, oil rigs and wells), commercial transport (e.g., container ships, other cargo ships, tankers, other merchant ships, ferries, cruise ships, other passenger ships), and other offshore applications. In some examples, offshore facilities 214a-c may include offshores stations that are part of a hybrid network.

Hybrid network <NUM> may support ground-to-vehicle communication and connectivity, as shown between ground infrastructure <NUM> and aerial vehicle 201b, as well as aerial vehicles 211b-c and ground infrastructure <NUM>. In these examples, aerial vehicles 201b and 211b-c each may exchange data with either or both a ground station (e.g., ground station <NUM>), a tower, or other ground structures configured to connect with a grid, Internet, broadband, and the like. Hybrid network <NUM> also may support vehicle-to-vehicle (e.g., between aerial vehicles 201a and 201b, between aerial vehicles 211a-c, between aerial vehicles 216a-b, between aerial vehicles 201b and 216b, between aerial vehicles 211b and 216b), satellite-to-vehicle (e.g., between satellite <NUM> and aerial vehicles 201a-b, between satellite <NUM> and aerial vehicle 216b), vehicle-to-user device (e.g., between aerial vehicle 201a and user devices <NUM>, between aerial vehicle 211a and user devices <NUM>), and vehicle-to-offshore facility (e.g., between one or both of aerial vehicles 216a-b and one or more of offshore facilities 214a-c) communication and connectivity. In some examples, ground stations within ground infrastructures <NUM> and <NUM> may provide core network functions, such as connecting to the Internet and core cellular data network (e.g., connecting to LTE EPC or other communications platforms, and a software defined network system) and performing mission control functions. In some examples, ground infrastructures <NUM> and <NUM> also may include towers that may serve as stations in a hybrid network. In some examples, the ground-to-vehicle, vehicle-to-vehicle, and satellite-to-vehicle communication and connectivity enables distribution of connectivity with minimal ground infrastructure. For example, aerial vehicles 201a-b, 211a-c, and 216a-b may serve as base stations (e.g., LTE eNodeB base stations), capable of both connecting to the core network (e.g., Internet and core cellular data network), as well as delivering connectivity to each other, to user devices <NUM> and <NUM>, and to offshore facilities 214a-c. As such, aerial vehicles 201a-b and 211a-c represent a distribution layer of hybrid network <NUM>. User devices <NUM> and <NUM> each may access cellular data and Internet connections directly from aerial vehicles 201a-b and 211a-c.

<FIG> is a simplified block diagram of an exemplary computing system forming part of the systems of <FIG>, in accordance with one or more embodiments. In one embodiment, computing system <NUM> may include computing device <NUM> and storage system <NUM>. Storage system <NUM> may comprise a plurality of repositories and/ or other forms of data storage, and it also may be in communication with computing device <NUM>. In another embodiment, storage system <NUM>, which may comprise a plurality of repositories, may be housed in one or more of computing device <NUM> (not shown). In some examples, storage system <NUM> may store state data, commands, flight policies, flight commands, communication stations commands, and other various types of information as described herein. This information may be retrieved or otherwise accessed by one or more computing devices, such as computing device <NUM> or server computing devices 115a-n in <FIG>, in order to perform some or all of the features described herein. Storage system <NUM> may comprise any type of computer storage, such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, write-capable, and read-only memories. In addition, storage system <NUM> may include a distributed storage system where data is stored on a plurality of different storage devices, which may be physically located at the same or different geographic locations (e.g., in a distributed computing system such as system <NUM> in <FIG>). Storage system <NUM> may be networked to computing device <NUM> directly using wired connections and/or wireless connections. Such network may include various configurations and protocols, including short range communication protocols such as Bluetooth™, Bluetooth™ LE, the Internet, World Wide Web, intranets, virtual private networks, wide area networks, local networks, private networks using communication protocols proprietary to one or more companies, Ethernet, WiFi and HTTP, and various combinations of the foregoing.

Computing device <NUM> also may include a memory <NUM>. Memory <NUM> may comprise a storage system configured to store a database <NUM> and an application <NUM>. Application <NUM> may include instructions which, when executed by a processor <NUM>, cause computing device <NUM> to perform various steps and/or functions, as described herein. Application <NUM> further includes instructions for generating a user interface <NUM> (e.g., graphical user interface (GUI)). Database <NUM> may store various algorithms and/ or data, including neural networks (e.g., encoding flight policies, as described herein) and data regarding wind patterns, weather forecasts, past and present locations of aerial vehicles (e.g., aerial vehicles 120a-b, 201a-b, 211a-c), sensor data, map information, air traffic information, among other types of data. Memory <NUM> may include any non-transitory computer-readable storage medium for storing data and/or software that is executable by processor <NUM>, and/or any other medium which may be used to store information that may be accessed by processor <NUM> to control the operation of computing device <NUM>.

Computing device <NUM> may further include a display <NUM>, a network interface <NUM>, an input device <NUM>, and/or an output module <NUM>. Display <NUM> may be any display device by means of which computing device <NUM> may output and/or display data. Network interface <NUM> may be configured to connect to a network using any of the wired and wireless short range communication protocols described above, as well as a cellular data network, a satellite network, free space optical network and/or the Internet. Input device <NUM> may be a mouse, keyboard, touch screen, voice interface, and/or any or other hand-held controller or device or interface by means of which a user may interact with computing device <NUM>. Output module <NUM> may be a bus, port, and/or other interface by means of which computing device <NUM> may connect to and/or output data to other devices and/or peripherals.

In some examples computing device <NUM> may be located remote from an aerial vehicle (e.g., aerial vehicles 120a-b, 201a-b, 211a-c) and may communicate with and/ or control the operations of an aerial vehicle, or its control infrastructure as may be housed in avionics chassis 110a-b, via a network. In one embodiment, computing device <NUM> is a data center or other control facility (e.g., configured to run a distributed computing system as described herein), and may communicate with a controller and/or flight computer housed in avionics chassis 110a-b via a network. As described herein, system <NUM>, and particularly computing device <NUM>, may be used for planning a flight path or course for an aerial vehicle based on wind and weather forecasts to move said aerial vehicle along a desired heading or within a desired radius of a target location. Various configurations of system <NUM> are envisioned, and various steps and/or functions of the processes described below may be shared among the various devices of system <NUM>, or may be assigned to specific devices.

<FIG> is a simplified block diagram of an exemplary distributed computing system, in accordance with one or more embodiments. System <NUM> may comprise two or more computing devices 301a-n. In some examples, each of 301a-n may comprise one or more of processors 404a-n, respectively, and one or more of memory 402a-n, respectively. Processors 404a-n may function similarly to processor <NUM> in <FIG>, as described above. Memory 402a-n may function similarly to memory <NUM> in <FIG>, as described above.

<FIG> is a diagram showing an exemplary modeling flow for simulating a dynamic hybrid network, in accordance with one or more embodiments. Diagram <NUM> includes a network service simulator <NUM> configured to simulate a dynamic hybrid network, including providing aggregate network metric <NUM> (e.g., throughput, number of users served) to indicate how well network supply (e.g., service coverage) matches with demand. Network service simulator <NUM> may be implemented using any of the computing systems described herein (e.g., distributed or otherwise) and configured to model network supply at various levels of abstraction. For example, transmission model <NUM> may be configured to model signal transmission from a station <NUM> to a handset <NUM>. In some examples, station <NUM> may include every station in a dynamic hybrid network. In other examples, station <NUM> may include a subset of the stations in a dynamic hybrid network (e.g., stations located in or near a geographical area of interest (e.g., a country or set of countries, a region, an island chain or group, a body of water), stations providing service to a geographical area of interest or a group of users (e.g., within a country or set of countries, a region, an island chain or group, a body of water), stations communicating by a given communications standard or protocol, stations with a given set of characteristics and/or capabilities). A model of station <NUM> also may account for a plurality of antenna characteristics, including direction of transmission, transmission frequency, signal transmission coverage (i.e., strength and pattern), location (e.g., latitude-longitude, GPS location), and other characteristics.

In some examples, handset <NUM> may include all existing handsets in areas served or potentially served by the hybrid network. In other examples, handset <NUM> may include a subset of existing handsets in areas served or potentially served by the hybrid network (e.g., selected based on one, or a combination, of location (e.g., GPS location, triangulated by a plurality of stations in a network, proximity to or being within a service region of a subset of the stations in a dynamic hybrid network), user information, hardware or software characteristics of a handset (e.g., enabled for a given standard or protocol, installed with a given application, installed with a given hardware component and/or configuration, installed with a given operating system). Based on transmission model <NUM>, a point-to-point transmission model <NUM> may be derived based on a station (i.e., one point) to handset density (i.e., another point) transmission rate, volume, or other measurement or extrapolation. The handset density may comprise an abstract representation of handset density at a location (e.g., including or in proximity to a station). The point-to-point transmission model <NUM> may represent a global scale or a subset thereof (e.g., country, continent, region, group of islands), for example, depending on limits that may have been placed on transmission model <NUM>. Multi-station interference <NUM> may be modeled based on point-to-point transmission model <NUM>, and may provide a model of interference between fixed stations (e.g., ground infrastructure <NUM> and <NUM> in <FIG>), semi-fixed stations (e.g., offshore stations on relatively fixed offshore facilities 214a-214b in <FIG>) and current and/or forecasted locations of moving stations (e.g., as may be carried on aerial vehicles 120a-b in <FIG> and 201a-b, 211a-c, and 216a-b in <FIG>, offshore facilities 214c in <FIG>, and the like). Multi-station interference <NUM> may be used to simulate a transmission supply model <NUM> comprising a signal quality per handset metric <NUM> (e.g., SINR, RSSI, RSRP, RSRQ, or other metric). In some examples, simulating transmission supply model <NUM> may include performing a regression analysis based on one or both of point-to-point transmission model <NUM> and multi-station interference <NUM> over actual and simulated moving station locations over time (e.g., based on actual and simulated vehicle trajectories). In other examples, simulating transmission supply model <NUM> may include providing one or both of point-to-point transmission model <NUM> and multi-station interference <NUM> to a machine learning model trained to determine one or more transmission supply metrics (e.g., signal quality per handset metric <NUM>). In some examples, multi-station interference <NUM> may incorporate or account for actual antenna characteristics of stations (e.g., station <NUM>) in the hybrid network being modeled. In other examples, multi-station interference <NUM> may be based on average, estimated or assumed antenna characteristics of stations (e.g., station <NUM>), and actual antenna characteristics may be considered or reconsidered in simulating transmission supply model <NUM> to produce signal quality per handset metric <NUM>. Transmission supply model <NUM> may be simulated at each time step in a time series providing a depiction of transmission supply across a dynamic hybrid network comprising a plurality of moving stations (e.g., a mesh network being carried by aerial vehicles).

Demand model <NUM> may be generated based on user distribution <NUM> data and user behavior <NUM> data (e.g., anonymized usage data). Demand model <NUM> may be configured to output a capacity demand per handset <NUM> (e.g., at each point in a plurality of points representing a geographical location or area of interest). User behavior <NUM> may be geographically distinct (e.g., differ significantly from one area or country to another). Demand model <NUM> may represent a static snapshot of demand (e.g., at a given time, either globally or for a given location) or may vary in time (e.g. a day-night, weekday-weekend, or other periodic cycle, updated based on periodic or ad hoc updates to user distribution <NUM> and/or user behavior <NUM> data).

Demand model <NUM> may output capacity demand per handset <NUM>, which may be combined with a transmission supply metric (e.g., signal quality per handset <NUM>) output from transmission supply model <NUM> to produce an aggregate network metric <NUM>. In some examples, aggregate network metric <NUM> may include a one or more of a throughput value, a number of users served, and other indication of how well network supply (e.g., broadband and/or cellular service coverage) matches with demand. In some examples, aggregate network metric <NUM> may be refreshed at each time step in a time series based on a refreshed transmission supply model <NUM> based on updated current or most recent locations and antenna characteristics of moving stations in a mesh network, even when demand model <NUM> represents a static or cyclical representation of capacity demand per handset <NUM> (i.e., that is not updated in real time or using current or recent user distribution <NUM> data and/or user behavior <NUM> data). In some examples, a self-consistent loop adjusting transmission model <NUM> to actual or modeled demand may be implemented by providing capacity demand per handset <NUM> back to transmission model <NUM>, such that a station <NUM> load factor may be computed as a ratio between station <NUM>'s actual capacity supply based on capacity demand per handset <NUM> and station <NUM>'s absolute maximum capacity. In some examples, supplied capacity may be capped either by an absolute maximum capacity or by demand requested by demand model <NUM> (e.g., user demand model). Where station <NUM>'s supplied capacity (i.e., output power) is dynamic, adjusted according to updated outputs from demand model <NUM>, transmission model <NUM> may change accordingly leading to adjustments in point-to-point transmission model <NUM> and multi-station interference <NUM>. This feedback from demand model <NUM> may be provided directly or indirectly (e.g., as part of aggregate network metric <NUM>).

In some examples, aggregate network metric <NUM> may be provided to and/or impact flights <NUM> (i.e., of aerial vehicles in a fleet of aerial vehicles in flight carrying one or more moving stations in the network of station(s) <NUM>). For example, aggregate network metric <NUM> may be used by a flight controller to cause changes to one or more of flights <NUM> to improve throughput (e.g., change a trajectory of a vehicle carrying a station, change a direction of transmission, change a transmission strength). Updated positioning, directionality, transmission strength, among other station characteristics, may also be fed back to transmission model <NUM> (e.g., providing changes to station <NUM>).

<FIG> are diagrams of exemplary dynamic hybrid networks with coordinated service coverage, in accordance with one or more embodiments. In <FIG>, network <NUM> depicts a plurality of exemplary aerial vehicles <NUM>-<NUM>, each carrying a plurality of moving stations (e.g., stations 514a-d on aerial vehicle <NUM>) comprising a mesh network, providing service to an area adjacent to, and unserved by, a group of fixed stations <NUM>-<NUM>, which may comprise towers on the ground or other types of ground stations. In some examples, fixed stations <NUM>-<NUM> may be configured to transmit and receive radio waves in two directions, as shown. In other examples, fixed stations <NUM>-<NUM> may be configured to transmit and receive radio waves in three or more directions.

Dotted lobes show transmission patterns of each of the moving stations on each of aerial vehicles <NUM>-<NUM>. In some examples, aerial vehicles <NUM>-<NUM> may receive commands from a control and navigation system configured to cause aerial vehicles <NUM>-<NUM> to travel (e.g., to move up, down, or stay at a given altitude to be carried by winds) in patterns or on trajectories optimized based on metrics (e.g., aggregate network metric <NUM>) and/or maps of transmission supply and demand models of a dynamic hybrid network comprising a mesh network of moving stations on aerial vehicles <NUM>-<NUM> and fixed stations <NUM>-<NUM>. Thus, in some examples, one or more of aerial vehicles <NUM>-<NUM> may be at varying altitudes.

As shown, one or more stations on aerial vehicles <NUM>-<NUM> may have directional variability (e.g., stations on aerial vehicles <NUM>-<NUM> are largely facing directly out from each respective vehicle approximately <NUM> degrees from each adjacent station, whereas stations on aerial vehicles <NUM>-<NUM> show gimbals facing in a direction that is more or less than <NUM> degrees from each adjacent station) in order to optimize coverage in a desired area.

The moving stations in <FIG> are shown to have largely uniform transmission capacities (e.g., all fully powered, or all powered to a same or similar threshold). In some examples, as shown in <FIG>, there may be variability not just in transmission direction of the moving stations, but also in transmission capacity (i.e., strength). Network <NUM> comprises a mesh network of moving stations (e.g., stations 568a-b, 570a-f) on aerial vehicles <NUM>-<NUM>, providing service to complement sparsely located fixed stations <NUM>-<NUM> (e.g., located in an area with terrain not conducive to building dense ground infrastructure). The dynamic hybrid network model described herein may include modeling coverage of fixed stations <NUM>-<NUM> to avoid interference by the moving stations. In some examples, each station's transmission strength may be adjusted (e.g., controlled remotely or self-adjusting based on demand input) higher, lower, on and off, due to various factors (e.g., minimize interference, pockets of high or low demand in a service region as indicated by a demand model (e.g., demand model <NUM>), proximity to a border (e.g., between an in-service area (e.g., region, country, state, sovereignty) and out-of-service area, edge or buffer zone of a restricted area). Each of the moving stations in network <NUM> may be selectively turned off, adjusted in transmission strength, and altered in direction.

In some examples, the stations in networks <NUM> and <NUM> may form a self-organizing network (SON), wherein they may communicate their location, transmission strength, outputs from supply and demand models, and other information to each other, and in response, adjust their own antenna characteristics to maximize the number of users to serve.

<FIG> is a flow diagram illustrating a method for simulating a dynamic hybrid network, in accordance with one or more embodiments. Method <NUM> begins with modeling, at each time step in a time series, a transmission supply of a mesh network of a plurality of moving stations on a plurality of aerial vehicles in flight, including modeling point-to-point radio wave transmission capacity at step <NUM>. In some examples, the dynamic hybrid network may comprise the mesh network and one or more fixed stations. In some examples, the transmission supply of the mesh network may be modeled relative to the transmission supply of one or more fixed stations. The plurality of aerial vehicle may comprise more than one type of aerial vehicle (i.e., a heterogeneous fleet) or one type of aerial vehicle (i.e., homogeneous fleet). In some examples, the mesh network may comprise a homogeneous network of LTE stations. In other examples, the mesh network may comprise a heterogeneous network of two or more types of stations. In some examples, the transmission supply may be modeled based on a model of multi-station interference among the mesh network of the plurality of moving stations, which may be based on the point-to-point radio wave transmission capacity. In some examples, the model of multi-station interference also may account for interference between the mesh network and one or more fixed stations.

A capacity demand may be modeled as a function of user distribution data and user behavior data at step <NUM>. In some examples, the capacity demand model may represent a static or cyclical, and abstracted, representation of capacity demand per handset (i.e., not updated in real time or using current or recent user distribution data and/or user behavior data). At each time step, an aggregate network metric may be computed at step <NUM>, the aggregate network metric comprising at least a throughput. The aggregate network metric also may be expressed in terms of a number of users being served, or other indication of how network demand is being met by network supply. In some examples, the aggregate network metric may be based on a signal quality per handset output from modeling the transmission supply and a capacity demand per handset output from modeling the capacity demand. The aggregate network metric may be refreshed at each time step in a time series based on a refreshed transmission supply model based on updated current or most recent locations and antenna characteristics of moving stations in a mesh network.

In some examples, a simulated dynamic hybrid network may be output using the aggregate network metric, the simulated dynamic hybrid network being represented using a heatmap, chart, spreadsheet, table, list, or other visual representation. In some examples, the simulated dynamic hybrid network may be represented using a series of static outputs. In other examples, the simulated dynamic hybrid network may be represented in a dynamic (i.e., changing with time) and/or interactive map or other visual interface.

<FIG> is a flow diagram illustrating a method for optimizing throughput of a dynamic hybrid network, in accordance with one or more embodiments. Method <NUM> begins with modeling a dynamic hybrid network at step <NUM>, which may include modeling a transmission supply of a mesh network of moving stations at each time step in a time series, modeling capacity demand as a function of user distribution data and user behavior data, and computing an aggregate network metric comprising at least a throughput. In some examples, modeling the dynamic hybrid network may include performing a regression analysis over a series of locations from an actual vehicle trajectory. In other examples, modeling the dynamic hybrid network may include performing a regression analysis over a series of locations from a simulated vehicle trajectory. As described above, modeling the transmission supply of the mesh network may include modeling a point-to-point transmission between a plurality of moving stations in the mesh network and a handset density. Such moving stations may be carried by an aerial vehicle, which may be part of a fleet of aerial vehicles.

Feedback may be provided to a controller at step <NUM>, the controller being configured to provide commands to one or both of an aerial vehicle and a moving station, or one or more components thereof. The controller may cause a change to one, or a combination of, a location, direction, and transmission strength of a station in the network at step <NUM>, the change configured to increase the throughput. In some examples, the controller also may cause a change to other antenna characteristics. In still other examples, another controller may be configured to navigate an aerial vehicle in response to the feedback as well. In some examples, one or more of the moving stations in the mesh network may form a self-organizing network (SON), each station configured to adjust an antenna characteristic in response to information received from another station. In some examples, the controller may be configured to shut-off, reduce transmission strength, or otherwise configure or alter the characteristics of a station based on the feedback in view of other rules or parameters (e.g., overriding considerations such as traveling towards or in proximity to restricted zones (i.e., no-fly zones), out-of-network regions or countries, and the like).

While specific examples have been provided above, it is understood that the present invention can be applied with a wide variety of inputs, thresholds, ranges, and other factors, depending on the application. For example, the time frames and ranges provided above are illustrative, but one of ordinary skill in the art would understand that these time frames and ranges may be varied or even be dynamic and variable, depending on the implementation.

The methods or flow charts provided may be implemented in a computer program, software, or firmware tangibly embodied in a computer-readable storage medium for execution by a general-purpose computer or
processor.

Examples of computer-readable storage mediums include a read only memory (ROM), random-access memory (RAM), a register, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks.

Claim 1:
A method for simulating a dynamic hybrid network comprising one or more fixed stations (<NUM>-<NUM>) and a mesh network of a plurality of moving stations (<NUM>-<NUM>) on a plurality of aerial vehicles in flight, the method comprising:
modeling (<NUM>), at each time step in a time series, a transmission supply of the mesh network, including modeling point-to-point radio wave transmission capacity;
modeling (<NUM>) capacity demand as a function of user distribution data and user behavior data, wherein modeling the capacity demand results in a cyclical representation or a static representation of a capacity demand per handset; and
computing (<NUM>), at each time step and based on combining the transmission supply and the cyclical or static representation of the capacity demand per handset, an aggregate network metric for the dynamic hybrid network comprising at least a throughput, wherein the aggregate network metric is refreshed at each time step in the time series based on the transmission supply refreshed based on (<NUM>) at least one of an updated current location or most recent location and (<NUM>) antenna characteristics of the plurality of moving stations in the mesh network.
providing (<NUM>) feedback to a controller of the dynamic hybrid network, the feedback comprising at least the throughput.