Method and system for providing a wireless coverage area

Abstract

A method for providing an area of wireless coverage on cash-in-transit vehicle routes includes collecting data about established base stations, the building density and the terrain height in an area of radio frequency (RF) signal coverage of a local environment, training a machine learning model on the collected local environment coverage data to determine points in the local environment at which RF signal repeaters will be positioned, obtaining data about the route of a cash-in-transit vehicle, determining a target point in the local environment along the cash-in-transit vehicle route where the RF signal level is absent or below a set threshold value, determining a positioning point in the local environment for a radio repeater for providing an area of RF signal coverage encompassing said target point, generating a flight plan for an unmanned aerial vehicle (UAV) equipped with an RF signal repeater, and sending the UAV to the repeater positioning point. The technical result consists in increasing the accuracy of determining positioning points for RF signal repeaters.

Description

METHOD AND SYSTEM FOR PROVIDING RADIO COMMUNICATION COVERAGE AREA OF TECHNOLOGY

[0001] This solution relates to the field of computer technology, in particular to the field of automated data processing to ensure radio coverage along the routes of cash-in-transit vehicles using unmanned aerial vehicles (UAVs) with installed repeaters. LEVEL OF TECHNOLOGY

[0002] To provide cash collection services to clients, cash collection vehicles (CCVs) are used. They travel along specific routes to serve clients located in various locations within a given territory. A common problem is that given certain infrastructure conditions (development, interference, etc.), CCT routes can include areas without radio signal coverage, particularly cellular coverage, which can lead to loss of operational control over the CCT's movements.

[0003] To ensure radio signal coverage along ITS routes, UAVs equipped with radio signal repeaters can be used to compensate for the absence or weakness of a signal at specific points. An example of this approach for using UAVs is disclosed in patent application US 20180293897 A1 (T Mobile USA Inc, October 11, 2018). This known solution proposes, based on calculations of radio signal coverage, to generate signal transmission points to the UAV at algorithmically determined points.

[0004] A drawback of this approach is that it relies primarily on signal strength data within the coverage area and does not take into account infrastructure parameters to identify weak signal points and deploy UAVs there to improve coverage. This approach also cannot be used to ensure communication while cash-in-transit vehicles are moving. ESSENCE OF THE INVENTION

[0005] The technical problem that the claimed invention is aimed at solving is the use of UAVs in areas where ITS routes pass to provide radio signal coverage.

[0006] The technical result is to provide a radio signal coverage area along the ITS routes.

[0007] Another technical result is an increase in the efficiency of determining points for placing radio signal repeaters to ensure coverage along ITS routes.

[0008] In a preferred embodiment, a method is claimed for providing a radio coverage area along the routes of cash-in-transit vehicles (CIT), performed using at least one computing device and comprising the steps of: collecting data on the radio signal coverage area of ​​the terrain, including at least information on the installed base stations (BS), as well as the building density and the height of the relief; training a machine learning model based on the collected data on the coverage area of ​​the terrain, during which the model is trained to determine points on the terrain for the placement of radio signal repeaters; receiving data on the route of movement of at least one CIT; determining at least one target point on the terrain along the route of the CIT, at which the radio signal level is absent or below a specified threshold value;using the said machine learning model, a point on the terrain is determined for the placement of a radio signal repeater that provides a radio signal coverage area covering the said target point; a flight mission is generated for an unmanned aerial vehicle (UAV) with an installed radio signal repeater, containing at least the route of the UAV and the coordinates of the repeater placement point determined using the machine learning model; the UAV is directed to the repeater placement point.

[0009] In one of the particular examples of the method implementation, additional data on interference at radio frequencies in the coverage area is obtained.

[0010] In another particular example of the method implementation, the time of arrival of the ITS at the target point is calculated.

[0011] In another particular example of the method implementation, the flight mission additionally includes the time of arrival of the ITS at the target point.

[0012] In another particular example of the method implementation, the flight task is transmitted to the UAV, which ensures arrival at the repeater placement point by the time of the ITS arrival.

[0013] In another particular example of the implementation of the method, when forming a flight mission, the proximity to the base stations installed in a given area is determined.

[0014] In another particular example of the implementation of the method, the UAV communicates wirelessly with the base station in the area where the repeater is located.

[0015] In another particular example of the implementation of the method, the UAV communicates via an atmospheric optical communication line.

[0016] In another particular example of the implementation of the method, the repeater on the UAV contains a rotating antenna.

[0017] In a preferred embodiment, a system for providing a radio coverage area along the routes of cash-in-transit vehicles (CIT) is also claimed, comprising: a computing device configured to collect data on the radio signal coverage area of ​​the terrain, including at least information on the installed base stations (BS), as well as the building density and the height of the relief; to train a machine learning model based on the collected data on the coverage area of ​​the terrain, during which the model is trained to determine points on the terrain for the placement of radio signal repeaters; to obtain data on the route of movement of at least one CIT; to determine at least one target point on the terrain along the route of the CIT, in which the radio signal level is absent or below a specified threshold value; determining, using said machine learning model, a point on the terrain for placing a radio signal repeater that provides a radio signal coverage area covering said target point; forming a flight mission for a UAV with an installed radio signal repeater, wherein the flight mission contains at least the route of the UAV and the coordinates of the repeater placement point determined using the machine learning model; transmitting the flight mission to the UAV; An unmanned aerial vehicle (UAV) designed with the capability of receiving a flight mission from a computing device and moving to a specified repeater location. DESCRIPTION OF DRAWINGS

[0018] Fig. 1 illustrates the general scheme of the claimed solution.

[0019] Fig. 2 illustrates a block diagram of the method.

[0020] Fig. 3 illustrates an example of calculating the signal power of base stations.

[0021] Fig. 4... IMPLEMENTATION OF THE INVENTION

[0022] Fig. 1 shows the general implementation principle of the proposed solution. Solving many technical and applied problems requires optimizing the transmission infrastructure (i.e., selecting / modifying the configuration / location of transmitters and antennas, etc.) to effectively achieve target indicators. These indicators typically include signal coverage of a given area under certain conditions (terrain, presence of buildings and structures, transport network topology, etc.). However, target indicators in addition to coverage may also include other characteristics—for example, capacity or maximum network throughput in a given area, etc. Technically, the problem is formulated as follows: under given conditions and taking into account constraints, select a transmitter (repeater) configuration that most effectively ensures the achievement of target indicators.Such problems arise both during the creation of the transmission infrastructure (for calculating / designing the initial configuration) and during operation due to changes in conditions and / or signal coverage requirements.

[0023] As shown in Fig. 1, information (10) about the radio signal coverage area of ​​the area includes such data as: information about the installed base stations (BS) (11 - 13), building density and terrain height. Additionally, the BS type, power, equipment installed on the BS, BS antenna directivity, etc. can be taken into account. Based on these parameters, the coverage area of ​​the BS (11 - 13) by the signal can be determined.

[0024] The resulting information (10) is used on a selected computing device (20), such as a PC or server, to train a machine learning model (ML model). The ML model evaluates target indicators (coverage quality, etc.) for a given configuration (transmitter locations, transmitter types, etc.) and also allows for the determination of optimal locations for generating stable radio signal coverage (implementation of an algorithm for finding optimal coverage from the given configurations).

[0025] Example input data for the ML model: init_signal_dbm = 40 dBm : outgoing signal power height = 20 m : antenna height buildings_rate_phil_phi2_rl_r2 = 0.7 : percentage of area covered by buildings for the ring sector at a distance from rl to r2 from the transmitter between angles phi 1 and phi2 from north ground_rate_phil_phi2_rl_r2 = 0.7 : percentage of terrain above the transmitter for the ring sector at a distance from rl to r2 from the transmitter between angles phi 1 and phi2 from north } Parameters phi 1, phi2, rl, r2 are usually configured by the system.

[0026] To improve the accuracy and speed of implementation of the proposed method, various heuristics and / or built-in pre-training mechanisms for the ML model and optimization module can be used. For example, the following heuristics can be used when selecting configuration options to exclude configurations with transmitter locations that: - points located too close to each other, and there are no obstacles (mountains, buildings) between these points. The distance threshold at which points are excluded is calculated using heuristics. - reached the top of the ranking, but have too little coverage within the task. The coverage threshold, starting from which points are excluded, is calculated using heuristics.

[0027] The result of the ML model is the determination of a point (32) to which a UAV (30) with an installed repeater is directed, in which it is necessary to organize a coverage zone (31) of a radio signal for the ITS (40) located at this point.

[0028] Let's consider in more detail the method (200) for providing radio signal coverage. The first stage (201) involves collecting data (10) on the radio signal coverage area. This data includes information such as: information on installed base stations (11-13), building density, and terrain elevation. Additionally, the base station type, power, equipment installed on the base station, directionality, radio frequency interference data in the coverage area, etc., can be taken into account.

[0029] Next, at step (202), the ML model is trained based on the data obtained at step (201). As shown in Fig. 3, to calculate the signal level at point Bi, with the transmitter located at point A and with predetermined conditions (height of surrounding buildings, terrain, etc.), one of the known physical-mathematical models (hereinafter referred to as the FM model) for calculating radio signal propagation is used. To calculate the signal level in the entire target coverage area, a radial grid with the origin at the transmitter installation point is used. At each i-th node of the grid Bi, the signal level is calculated, and it is assumed that the signal around node Bi within a radius R is equal to the signal at node Bi. The signal around a node within a radius R is the same as at the node itself.

[0030] Since the calculations of the FM model even for one transmitter (repeater) take a lot of time and computational resources, a pre-trained ML model can be used to approximate the assessment of target indicators for given configurations.

[0031] The ML model is trained on synthetic data obtained by calculating target indicators (coverage) for different transmitter locations: Ci = f(propsi, 1), where Ci is coverage, propsi is the conditions of the corresponding transmitter location, and li is the coordinates of the transmitter location (see definitions and calculation procedure below).

[0032] Calculation of features (terrain characteristics props;) The input features of the ML model are terrain statistics: the number of landscape areas above the transmitter's location (with coordinates li) in the first (2nd, 3rd, 4th) coordinate quadrant within a radius of rl to r2 relative to the location, and the number of buildings per unit area in these zones. Here, rl and r2 can have different values ​​(configurable parameters).

[0033] Calculation of the target (target variable) The target variable of the ML model is calculated as the ratio of the area of ​​the covered territory (with the transmitter located at coordinates li, based on the FM model calculations) to the maximum possible area under the given parameters—what the coverage from this point would be if there were no obstacles in the signal propagation path. Data for training the ML model is synthesized by simulating the installation of a transmitter (repeater) at a given point and calculating the coverage area in a radial grid based on the corresponding FM model of radio signal propagation.

[0034] Method for finding optimal coverage The input of the ML model is: - Points Ai with coordinates li that need to be ranked (which can be used to install the transmitter); - Bi points / areas that need to be covered by the signal; - Propsi conditions (relief, buildings, vegetation) for the location of Ai transmitter points.

[0035] At step (203), information about the ITS routes is collected, where zones and points are identified (step 204) where the radio signal level is absent or weak enough, i.e., below a set threshold, to enable the required level of communication with the ITS. Such a zone is typically delineated by geographic coordinates, which allows, using the ML model at step (205), to determine points for subsequent ranking in terms of selecting the optimal location for the UAV repeater (30). For the best repeater locations, coverage zones (31) are calculated using the aforementioned ML model, which calculates the coverage quality from possible UAV placement points.

[0036] When determining the point (32) for directing the UAV (30) to it, the proximity of the BS (11 - 13) is also taken into account, which is necessary to ensure communication between the BS and the UAV repeater (30) to ensure the required signal power and the formation of a coverage zone (31) of the radio signal. Communication between the UAV (30) and the BS can be organized according to wireless data transmission channel, for example, using an atmospheric optical communication line (FSO - free-space optics).

[0037] Additionally, the time of arrival of the UAV (30) at a certain point (32) for placing a repeater can be calculated, which can be calculated based on the speed of movement of the ITS (40), the time of its expected arrival in an area with an absent or weak radio signal, road traffic and other information.

[0038] At step (206), a flight mission for the UAV (30) is generated and transmitted via the selected data transmission channel. The flight mission contains the geographic coordinates of the destination point (32) where the UAV (30) and its relay are to be positioned, as well as the UAV's route, including its flight altitude and trajectory. Additionally, the UAV's (30) speed may be adjusted depending on the required arrival time at the point (32).

[0039] Multiple UAVs can also be used, each positioned at a designated point to provide radio signal coverage. UAVs can alternate at the same point based on their battery charge levels. In this case, a backup flight mission is created with a set time step required for the first UAV currently at the point to return to the charging station, and the second UAV is directed to the point to deploy a repeater, ensuring the required radio signal coverage.

[0040] Fig. 4 shows a general view of a computing device (400) with which the claimed solution can be implemented. In general, the computing device (400) comprises one or more processors (401) connected by a common information exchange bus, memory means such as RAM (402) and ROM (403), input / output interfaces (404), input / output devices (405), and a device for network interaction (406).

[0041] The processor (401) (or several processors, a multi-core processor) may be selected from a range of devices that are widely used today, such as those from Intel™, AMD™, Apple™, Samsung Exynos™, MediaTEK™, Qualcomm Snapdragon™, etc. The processor also includes a graphics processor, such as an NVIDIA or ATI GPU, which is also suitable for the full or partial implementation of the method (200, 300). The memory may be the available memory capacity of the graphics card or graphics processor.

[0042] RAM (402) is a random access memory and is intended for storing machine-readable instructions executed by the processor (401) for execution necessary operations for logical data processing. RAM (402), as a rule, contains executable instructions of the operating system and corresponding software components (applications, software modules, etc.).

[0043] ROM (403) is one or more permanent data storage devices, such as a hard disk drive (HDD), a solid-state drive (SSD), flash memory (EEPROM, NAND, etc.), optical storage media (CD-R / RW, DVD-R / RW, BlueRay Disc, MD), etc.

[0044] To organize the operation of the device components (400) and to organize the operation of external connected devices, various types of I / O interfaces (404) are used. The choice of the appropriate interfaces depends on the specific design of the computing device, which may include, but are not limited to: PCI, AGP, PS / 2, IrDa, FireWire, LPT, COM, SATA, IDE, Lightning, USB (2.0, 3.0, 3.1, micro, mini, type C), TRS / Audio jack (2.5, 3.5, 6.35), HDMI, DVI, VGA, Display Port, RJ45, RS232, etc.

[0045] To ensure user interaction with the computing device (400), various I / O information means (405) are used, for example, a keyboard, display (monitor), touch display, touchpad, joystick, mouse, light pen, stylus, touch panel, trackball, speakers, microphone, augmented reality means, optical sensors, tablet, light indicators, projector, camera, biometric identification means (retina scanner, fingerprint scanner, voice recognition module), etc.

[0046] The network interaction means (406) ensures the transmission of data by the device (400) via an internal or external computer network, for example, an Intranet, the Internet, a LAN, etc. One or more means (406) may be, but are not limited to: an Ethernet card, a GSM modem, a GPRS modem, an LTE modem, a 5G modem, a satellite communication module, an NFC module, a Bluetooth and / or BLE module, a Wi-Fi module, etc.

[0047] Additionally, satellite navigation tools included in the device (300) can also be used, for example, GPS, GLONASS, BeiDou, Galileo.

[0048] The submitted application materials disclose preferred examples of the implementation of the technical solution and should not be interpreted as limiting other, particular examples of its implementation that do not go beyond the scope of the requested legal protection, which are obvious to specialists in the relevant field of technology.

Claims

FORMULA 1. A method for providing a radio coverage area along the routes of cash-in-transit vehicles (CIT), performed using at least one computing device and comprising the steps of: collecting data on the radio signal coverage area of ​​the area, including at least information on the installed base stations (BS), as well as the building density and the height of the terrain; training a machine learning model based on the collected data on the coverage area of ​​the area, during which the model is trained to determine points on the area for the placement of radio signal repeaters; receiving data on the route of movement of at least one CIT; determining at least one target point on the area along the route of the CIT, at which the radio signal level is absent or below a specified threshold value;using the said machine learning model, a point on the terrain is determined for the placement of a radio signal repeater that provides a radio signal coverage area covering the said target point; a flight mission is formed for an unmanned aerial vehicle (UAV) with an installed radio signal repeater, containing at least the route of movement of the UAV and the coordinates of the repeater placement point determined using the machine learning model; the UAV is directed to the repeater placement point.

2. The method according to paragraph 1, characterized in that additionally data on interference at radio frequencies in the coverage area is obtained.

3. The method according to paragraph 1, characterized in that the time of arrival of the ITS at the target point is calculated.

4. The method according to paragraph 3, characterized in that the flight mission additionally includes the time of arrival of the ITS at the target point.

5. The method according to paragraph 4, characterized in that the flight task is transmitted to the UAV, which ensures arrival at the location of the repeater by the time of arrival of the ITS.

6. The method according to paragraph 1, characterized in that when forming a flight mission, the proximity to the base stations installed in a given area is determined.

7. The method according to paragraph 1, characterized in that the UAV communicates wirelessly with the base station in the area where the repeater is located.

8. The method according to claim 8, characterized in that the UAV communicates via an atmospheric optical communication line.

9. The method according to claim 1, characterized in that the repeater on the UAV contains a rotating antenna.

10. A system for providing radio coverage along routes of cash-in-transit vehicles (CIT), comprising: a computing device configured to collect data on the radio signal coverage area of ​​the terrain, including at least information on installed base stations (BS), as well as building density and terrain height; to train a machine learning model based on the collected data on the coverage area of ​​the terrain, during which the model is trained to determine points on the terrain for the placement of radio signal repeaters; to obtain data on the route of movement of at least one CIT; to determine at least one target point on the terrain along the route of the CIT, at which the radio signal level is absent or below a specified threshold value;determining, using said machine learning model, a point on the terrain for placing a radio signal repeater that provides a radio signal coverage area covering said target point; forming a flight mission for a UAV with an installed radio signal repeater, wherein the flight mission contains at least the route of the UAV and the coordinates of the repeater placement point determined using the machine learning model; transmitting the flight mission to the UAV; An unmanned aerial vehicle (UAV) designed with the capability of receiving a flight mission from a computing device and moving to a specified repeater location.

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