Use of imaging corresponding with RF beam pattern to facilitate configuring of wireless communication system

Abstract

A method and system to facilitate configuring of a wireless communication system. An example method includes capturing, by an imaging system at a first radio-frequency (RF) antenna structure of the wireless communication system, image data representing a field of view (FoV) corresponding with a beam pattern of the first RF antenna structure. Further, the example method includes detecting, through at least machine-based processing of the image data, an object within the FoV as being a serviceable platform at which to deploy a second RF antenna structure to engage in line-of-sight (LoS) RF communication with the first RF antenna structure, with the detecting of the object being based on the object being within the FoV and the FoV corresponding with the beam pattern of the first RF antenna structure. Still further, the example method includes triggering, based on the detecting, deployment of the second RF antenna structure at the detected object.

Description

Use of Imaging Corresponding with RF Beam Pattern to Facilitate Configuring of Wireless Communication SystemREFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 555,334, filed February 19, 2024, the entirety of which is hereby incorporated by reference.BACKGROUND

[0002] A typical wireless communication system includes one or more base stations (also referred to as access nodes, access points, or the like) configured to serve user equipment devices (UEs) such as cell phones, tracking devices, wirelessly equipped personal computers, gaming devices, Internet of Things (loT) devices, and other wirelessly-equipped devices, whether or not technically “user” operated.

[0003] Each such base station may include an antenna structure and associated circuitry to facilitate engaging in radio frequency (RF) communications in accordance with an applicable wireless technology. Example wireless technologies include, without limitation, cellular or other wireless wide area network (WWAN) technologies such as 4G Long Term Evolution (4GLTE), 5G New Radio (5GNR), 6G (International Mobile Telecommunications- 2030 (IMT-2030)), and beyond, as well as wireless local area network (WLAN) technologies such as Wi-Fi, and wireless personal area network (WPAN) technologies such as Bluetooth, Zigbee, and ultra-wideband.BRIEF DESCRIPTION OF THE DRAWINGS

[0004] Figure l is a simplified illustration of an dense urban area with obstructions occluding some coverage of an example high-frequency serving node.

[0005] Figure 2 is variation of the arrangement of Figure 1, showing inclusion of an imaging device collocated with an RF antenna structure.

[0006] Figure 3 is variation of the arrangement of Figure 2, showing an added mmWave repeater at a location deemed to be within line of sight.

[0007] Figure 4 is a flow chart illustrating an example method.

[0008] Figure 5 is a simplified block diagram of an example system.

[0009] Figure 6 is a simplified illustration of an example RF -communi cation unit with a collocated imaging device.

[0010] Figures 7a-c and Figures 8a-c are simplified illustrations of examples of multiple RF-communication units being aggregated together.

[0011] Figure 9 is a simplified illustration of an assembly that may contain potentially multiple RF-communication units along with a collocated imaging device.

[0012] Figures 10 and 11 are simplified block diagrams of example network arrangements.DETAILED DESCRIPTION

[0013] In accordance with a wireless technology, each base station could be configured to provide coverage and service on a number of radio-frequency (RF) carriers. Each such carrier could be frequency division duplex (FDD), with separate frequency channels for downlink and uplink communication, or time division duplex (TDD), with a single frequency channel multiplexed over time between downlink and uplink use. And each such frequency channel could be defined as a specific range of frequency (e.g., in RF spectrum) having a bandwidth (width in frequency) and a center frequency and thus extending from a low-end frequency to a high-end frequency.

[0014] On the downlink and uplink, the coverage provided by a base station on each such carrier could define an air interface configured in a specific manner to provide physical resources (air interface resources) for carrying information wirelessly between the base station and UEs.

[0015] Without limitation, for instance, the air interface could be divided over time into a continuum of frames, subframes, and symbol time segments, and over frequency into subcarriers that could be modulated to carry data. The example air interface could thus define an array of time-frequency resource elements each being at a respective symbol time segment and subcarrier, and the subcarrier of each resource element could be modulated to carry data. Further, in each subframe or other transmission time interval, the resource elements on the downlink and uplink could be grouped to define physical resource blocks (PRBs) that the base station could allocate as needed to carry data between the base station and served UEs. In addition, certain resource elements on the example air interface could be reserved for special purposes.

[0016] Each such carrier could be defined within an industry standard frequency band, by its frequency channel(s) being defined within the frequency band. For 5GNR service, for instance, there are generally two ranges of frequency bands, Frequency Range 1 (FR1) andFrequency Range 2 (FR2). FR1 encompasses sub-6 Gigahertz (GHz) frequency bands, such as bands between 410 MHz and 7.125 GHz. And FR2 encompasses much higher frequency bands between 24.25 GHz to 71 GHz.

[0017] Because of their high frequency and consequent short wavelength, FR2 bands are also known as millimeter-wave (mmWave) bands. (Technically, mmWave frequencies extend from 30 GHz to 300 GHz. But in practice, the term “mmWave” is used to refer to frequencies starting at 24 GHz, such as those of the FR2 bands. Further, other high- frequency bands exist outside of the FR2 range. For instance, the microwave spectrum encompasses frequencies from 300 MHz to 30 GHz.)

[0018] MmWave (like other high frequency bands) offers many advantages.

[0019] One of the most significant advantages is that the frequency bandwidth of most mmWave carriers is far greater than that of FR1 carriers. For instance, the typical bandwidth of FR1 carriers ranges from 5 MHz to 100 MHz. Whereas, the typical bandwidth of mmWave carriers ranges from 50 MHz to 400 MHz (and possibly as high as 2000 MHz (i.e., 2 GHz) for frequencies above 52 GHz. The potentially far greater bandwidth of a mmWave carrier translates to far more air interface resource elements per unit time, which means that the mmWave carrier can carry far more data per unit time, thus supporting ultra-fast data transfer rates, theoretically reaching tens of gigabits per second (Gbps), with ultra-low latency. This makes mmWave potentially ideal for applications like high-definition video streaming, real-time gaming, virtual / augmented reality, and distributed artificial intelligence (Al) processing, autonomous-vehicle control, and remote surgery, among other examples.

[0020] Another significant advantage of mmWave is that it supports greater network capacity, allowing more devices to be connected and communicate simultaneously without compromising speed or quality. In particular, because the frequency bandwidth of mmWave carrier is typically very wide, the air interface resources per unit time on that mmWave carrier could be allocated among potentially many devices, allowing the devices to communicate on the carrier concurrently. This makes mmWave potentially ideal for use in densely populated areas such as urban areas and / or at large events such as concerts and conventions.

[0021] Still further, another significant advantage of mmWave is that its small wavelength size allows for use of small antennas, and so mmWave devices can be more compact and easier to integrate into various environments.

[0022] On the other hand, a downside of mmWave (like other high frequency bands) is that such high frequencies tend to suffer from high path loss. In particular, mmWaves tendto have a relatively short propagation distance, on the order of up to 1 kilometer for lower mmWave frequencies and only a few meters for higher mmWave frequencies. Further, mmWaves are easily blocked or degraded by physical objects such as trees, walls, and buildings. For instance, a 70 GHz signal may suffer from two to five times greater penetration loss through a brick wall than a 1 GHz signal would.

[0023] As a result, mmWave communications tend to work best when they are line of sight (LoS). Namely, mmWave communications are usually best suited for use where there are minimal or no significant RF obstructions between the communicating endpoints, such as between a base station antenna and a served UE, providing a relatively clear path from one endpoint to the other. For instance, mmWave may work best when there are no trees or buildings between the endpoints.

[0024] Given the desire for mmWave communications to be LoS and / or given the relatively short propagation distance of mmWaves, it may be necessary in some situations to implement mmWave repeaters. A mmWave repeater works to amplify and possibly redirect mmWave signals, which may help to improve coverage, capacity, and performance, and particularly to fill in LoS coverage gaps. Namely, a mmWave repeater may usefully amplify weak signals and redirect those signals to extend the range of a mmWave network, thus enabling the network to support reliable communication in potentially complex environments such as in dense urban areas. Further, if a mmWave repeater is a passive repeater that receives, amplifies, and transmits signals without full base station functions, the mmWave repeater may consume far less power and cost far less than a mmWave base station.

[0025] One technical challenge in deploying a mmWave (or other high frequency) communication system is determining where to situate repeaters or other mmWave communication nodes. For instance, if a wireless service provider operates a first mmWave node, such as a mmWave base station or repeater, that has an antenna structure (e.g., one or more mmWave antennas, perhaps a mmWave antenna array)) at a given physical position, an issue may be where the service provider should position a second mmWave node such as a repeater or fixed-position UE, to facilitate good communication between the second mmWave node and the first mmWave node. In particular, at issue may be where to situate the second mmWave node such that an antenna structure of the second mmWave node would be within LoS of the antenna structure of the first mmWave node.

[0026] As a practical example of this technical issue, consider the arrangement illustrated by Figure 1. This example arrangement is an isometric view of a portion of arelatively dense urban area in which a service provider has deployed a mmWave base station 100 that has a fixed position mmWave antenna structure 102 (e.g., mounted on an antenna tower, utility pole, or building), and where the mmWave base station has a defined (e.g., fixed or dynamically variable) RF beam pattern (i.e., antenna pattern) 104 defining a scope of mmWave coverage of the base station through its mmWave antenna structure 102.

[0027] While it would desirable for this mmWave base station 100 to serve numerous locations throughout this area, the dense nature of the area may result in some potentially serviceable locations being occluded from the mmWave base station 100. As shown, for instance, within the RF beam pattern 104 of the base station 100, there are at least two buildings 106, 108, but the base station antenna structure 102 has LoS 110 of just a portion 112 of the rooftop 114 of building 106 and does not have LoS of the other building 108. Namely, building 106 in this scenario unfortunately shadows or occludes building 108.

[0028] In this scenario, the base station 100 may be able to provide quality LoS mmWave service to a mmWave node positioned at the portion 112 of the rooftop 114 but not to a mmWave node positioned on building 108. However, if the service provider were to deploy a mmWave repeater at the portion 112 of the rooftop 114 of building 106, there is a possibility that that repeater could facilitate extending mmWave coverage of the system to a mmWave node situated on top of building 108. Furthermore, in a more complex arrangement, there may be many more candidate serviceable locations within the area, and this repeating might be extended iteratively to accommodate serving more of those locations.

[0029] The present disclosure provides a technical mechanism that can help to identify serviceable locations, namely, locations where mmWave nodes could be deployed to facilitate configuring coverage of a wireless communication system.

[0030] The disclosed principles are not necessarily limited to mmWave, cellular, or other wireless technology or to any particular air-interface structure, but could extend more generally to any high-frequency wireless technology now known or later developed that may benefit from LoS communication.

[0031] In accordance with example embodiments, an imaging device will be deployed at, and possibly integrated with, a mmWave antenna structure and will have an imaging direction and field of view (FoV) that at least generally corresponds with the RF beam pattern of the mmWave antenna structure. This imaging device will be configured to capture image data representing that FoV, and thus the image data would correspond with the RF beam pattern of the mmWave antenna structure. A computing system will then engage in machine-based processing of the image data and will thereby detect within the FoV an object as being a serviceable platform on which to deploy another mmWave node such as a repeater or fixed- position UE, among other possibilities.

[0032] As a specific example of this process, consider a scenario where the imaging device is a stereoscopic optical camera, having two camera sensors or lenses separated along a horizontal axis, with the sensors or lenses being configured to each concurrently capture a respective digital image of the same scene as each other from largely the same origin but at slightly different imaging directions, thus mimicking human binocular vision and depth perception. Through photogrammetry or other processing, the camera or other entity could then stitch these images together to establish a digital three-dimensional (3D) model of the scene, and a computing system could consider the 3D model along with associated geospatial data (e.g., satellite imagery and / or street-address data) as a basis to identify one or more objects as serviceable locations.

[0033] As shown in Figure 2, as a variation of Figure 1, such a stereoscopic camera 200 could be mounted at the mmWave base station antenna structure 102, possibly as an integral or adjunct component of the antenna structure. In particular, the stereoscopic camera 200 could be physically positioned and configured in relation to the antenna structure 102 such that the FoV 202 of the camera 200 generally corresponds with the RF beam pattern 104 of the antenna structure 102.

[0034] This general correspondence does not need to be exact. However, it would help to have the FoV 202 of the camera 200 be aligned as closely as possible with the RF beam pattern 104. This can include an average optical axis of the camera 200 being aligned as closely as possible with the beam direction of the RF beam pattern 104 and the angular width of the FoV being aligned as closely as possible with the beamwidth of the RF beam pattern 104. In practice, a goal could be for the camera 200 to be able to capture an image having a FoV that is largely representative of the a scene encompassed by the RF beam pattern 104 of the mmWave antenna structure 102, even if not coterminous.

[0035] As shown further in Figure 2, an example implementation could include a computing system 204, which may be separate from and / or integrated with one or more of the other illustrated components. This computing system 204 may be configured to use the 3D model along with geospatial data, as noted above, to detect one or more objects, such as buildings, utility poles, or radio towers, as serviceable locations.

[0036] The computing system 204 may include or have access to configuration data 206 that defines the location of the antenna structure 102 and the relevant geometric scope of the FoV extending from that location. For instance, the configuration data 206 may indicate the geolocation (e.g., latitude, longitude, and altitude coordinates) of the antenna structure 102 and the geometric (e.g., geographic) scope of the RF beam pattern 104 originating at that location. The geometric scope of the RF beam pattern 104 may effectively establish the relevant geometric scope of the FoV for present purposes. Namely, the relevant geometric scope in which to search for one or more serviceable locations in the 3D model captured by the camera 200 can be reasonably limited to the geometric scope covered by the RF beam pattern 104.

[0037] The configuration data 206 may also include information about the existence and position of one or more other mmWave nodes, such as base stations, repeaters, and fixed- position UEs. For instance, the configuration data 206 may specify the geographic coordinates and other position information establishing where one or more such other mmWave nodes are positioned.

[0038] The computing system 204 may further include or have access to geospatial data 208, such as satellite images of and street-address information for a region encompassing the RF beam pattern 104. This geospatial data 208 may establish the presence, configuration, and geographic location of objects such as those noted above. For instance, this geospatial data 208 may establish the geographic coordinates and street addresses of buildings 106, 108, as well as the heights and other features of those buildings. Further, this geospatial data 208 may establish similar information for other objects such as utility poles, radio towers, and the like.

[0039] In practice, the camera 200 may capture stereoscopic images representing the FoV 202 that generally corresponds with the RF beam pattern 104, and the camera 200 may apply photogrammetry to translate those images into image data in the form of a 3D model of the space to the extent visible in the FoV. In various implementations, this 3D model may include representations of physical objects within the space, with representations of shapes, sizes, and relative positions of elements, as well as surface characteristics such as textures, colors, and materials, lighting conditions, terrain, and topography, among other possibilities. The 3D model may thus create a virtual representation of the space, mimicking the real -world environment.

[0040] The camera 200 may then provide this 3D model to the computing system204 for processing. Alternatively, the camera 200 may provide its raw captured images to thecomputing system 204, and the computing system 204 may generate the 3D model based on those images.

[0041] The computing system 204 may then engage in processing to identify obj ects within the 3D model. In particular, the computing system 204 could engage in machineprocessing, applying an advanced computer vision technique such as 3D object detection to identify objects such as buildings, utility poles, and the like within the 3D model of the area encompassed by the RF beam pattern 104.

[0042] To begin with, the computing system 204 could filter the 3D model to limit the spatial scope of the 3D model to be approximately coincident with the RF beam pattern 104 of the mmWave antenna structure 102, to help limit the analysis to objects that are within RF range of the antenna structure 102. For instance, to the extent the 3D model extends beyond the scope of the RF beam pattern 104, the computing system 204 could truncate the 3D model to extend approximately no further than the scope of the RF beam pattern 104.

[0043] Further, the computing system 204 could apply 3D object detection to identify objects in the 3D model. For instance, the computing system 204 could apply one or more artificial intelligence (Al) models analyze the 3D data to extract relevant features that characterize different objects, such as buildings, utility poles, trees, and other structures. This may include information about shapes, sizes, and spatial relationships. The computing system 204 could then classify objects in the 3D space, by applying one or more machine-learning (ML) algorithms. These ML algorithms may comprise deep-learning models such as convolutional neural networks (CNNs), trained on large datasets of labeled 3D objects, and thus configured to recognize patterns and features associated with different types of buildings and other structures. The computing system 204 could then localize the classified objects, determining their precise location, orientation, and dimensions within the 3D model. And the computing system 204 may thus output results, such as bounding boxes or segmented regions that indicate the locations and extents of the identified objects.

[0044] Through this process, the computing system 204 may thereby identify, locate, and classify a number of physical objects within the 3D model that represents the space visible to the mmWave antenna structure 102. Further, given knowledge of the geographic scope of the 3D model, the computing system 204 could translate the location and dimensions of each such classified object in the 3D model into real -world geographic location and dimensions of the object.

[0045] Thus, for instance, the computing system 204 may thereby identify, geolocate, and classify the portion 112 of the rooftop 114 of building 106, with the portion 112, rooftop 114, and / or building 106 being a representative object. However, the computing system 204 may not be able to identify, geo-locate, and classify building 108, since building 108 is shadowed by building 106.

[0046] The computing system 204 may further make use of the geospatial data 208 a basis to enhance the computing system’s object detection. For instance, by referring to the geospatial data 208, the computing system 204 may match the portion 112 of rooftop 114 of building 106 with a specific building that is at a specific street address, to determine a full scope of the building 106 even if only part of the building was in LoS of the antenna structure 102. In addition, the computing system 204 may determine from the geospatial data 208 what type of building the identified building 106 is, such as that it is a residential building or office building that may benefit from mmWave service for instance.

[0047] In addition, the computing system 204 may seek to identify one or more such objects based specifically on the each of the one or more objects being a serviceable location for mmWave service.

[0048] For instance, the computing system’s object classification could work to identify objects that could serve as physical platforms at which to deploy a mmWave node such as a mmWave repeater or a fixed-position mmWave UE, among other possibilities. Examples of such objects could include rooftops of buildings, utility poles, radio towers, and other structures.

[0049] Further, the computing system’s object classification could also work to identify objects where mmWave service could be useful, such as multi -unit residential buildings, single-family homes, offices, or other places where people may benefit from being within mmWave coverage, even if such objects may not be suitable platforms at which to deploy mmWave nodes such as repeaters or fixed-position UEs for instance.

[0050] As to each of one or more such objects that the computing system 204 may thus identify as a potentially serviceable platform at which to deploy a mmWave node, the computing system 204 may further determine whether a mmWave node is already deployed at the object. For instance, the computing system 204 may refer to the configuration data 206 to determine whether a mmWave node is already deployed at the object. The computing system 204 may thereby limit any such identified objects to those where a mmWave node is not already deployed.

[0051] As to each of one or more objects thus identified as a potentially serviceable platform at which to deploy a mmWave node, the computing system 204 could then trigger deployment of a mmWave node at the object. More particularly, the computing system 204 could trigger deployment of an antenna structure of such a mmWave node at the identified object.

[0052] Because this process establishes that this identified object is visible in the FoV that corresponds with the RF beam pattern of the antenna structure 102 of the mmWave base station 100, this process thereby establishes that this identified object is within LoS of the antenna structure 102 of the mmWave base station. Based on this fact and perhaps further based on finding that the object is a suitable platform at which to deploy a mmWave node such as repeater or a fixed-position UE, the process thus usefully establishes a reasonable position at which to deploy the antenna structure of a mmWave node that would be within LoS of the antenna structure 102 of the mmWave base station 100.

[0053] Triggering the deployment of a mmWave node at the identified object could take various forms. By way of example, the triggering could involve the computing system 204 providing an output signal such as an engineering ticket to which an engineer or automated system would respond by installing a mmWave node such as a repeater or fixed-position UE with its antenna structure at the identified object and oriented to receive service from the mmWave base station 100.

[0054] As to each of the one or more objects thus identified as potentially serviceable locations for receiving mmWave service, whether or not as a platform on which to deploy a mmWave node, the computing system 204 may trigger a process to potentially establish mmWave service at the object. For instance, if the computing system 204 identifies a home or office that could benefit from receiving mmWave service, possibly checking to see that the home or office does not already receive mmWave service, the computing system 204 may trigger marketing to sell mmWave service for use at that home or office.

[0055] As a specific example of this process, a mmWave service provider may have data listing various street addresses throughout a market area and indicating for each street address whether there is a subscription to receive mmWave service possibly through use of a fixed-position mmWave UE that extends communication service into a home or office at the street address. Through the present process, the computing system 204 could identify each of various street addresses of homes or offices within LoS of the mmWave base station antenna structure 102 and, for any street address that does not currently have a mmWave servicesubscription, could flag the street address as a candidate to receive a mmWave service subscription.

[0056] In an example implementation, each of various mmWave nodes deployed in a market area could be configured with an imaging device as discussed above, to facilitate machine-based identification of serviceable locations within LoS of the mmWave node as discussed above. As a result, the computing system 204 could then iteratively engage in the above process.

[0057] For example, the computing system 204 could engage in the above process with respect to the mmWave base station 100, to identify one or more serviceable locations within LoS of the antenna structure 102 of the base station, and the computing system 204 could consequently trigger deployment of a mmWave repeater on the portion 112 of the rooftop 114 of building 106, perhaps doing so further in response to the computing system 204 determining from the geospatial data 208 that building 108 is shadowed by building 106 and could benefit from implementation of a mmWave repeater at that position on building 106.

[0058] In some implementations, for instance, the computing system’ s identification of an object that may be a serviceable platform at which to deploy a mmWave repeater or other mmWave node could further involve determining, based on RF planning data, that a geographic location of the object would provide at least a potential view of a serviceable area that is not within LoS of the antenna structure 102, and the computing system 204 could then trigger deployment of a mmWave node antenna structure at the position of that identified object.

[0059] This deployment of a mmWave repeater at that position may be in addition to deploying a fixed-position mmWave UE at that position to serve building 106; alternatively, a combination repeater / fixed-position UE could be deployed.

[0060] Based on this deployment trigger, a wireless service provider my thus install a mmWave repeater with a mmWave antenna structure on the portion 112 of the rooftop 114 of building 106. Figure 3 illustrates an example of this arrangement.

[0061] As shown in Figure 3, a mmWave repeater 300 has a mmWave antenna structure 302 situated on the portion 112 of the rooftop 114 of building 106. This example repeater antenna structure 302 may include one or more mmWave antennas oriented to facilitate LoS mmWave communication with the antenna structure 102 of the mmWave base station 100. Further, this repeater antenna structure 302 may also include one or more mmWave antennas to generally facing another direction to possibly facilitate extending service of the mmWave base station 100 to other locations.

[0062] Like the antenna structure 102 of the mmWave base station 100, this mmWave repeater 300 may be equipped with an imaging device 304 such as a stereoscopic camera that has an imaging direction and FoV generally corresponding with an RF beam pattern of the repeater antenna structure 302. Thus, this imaging device 304 could similarly be configured to capture image data representing that FoV, which would correspond with the RF beam pattern of the repeater antenna structure 302. And the computing system 204 could then repeat the above analysis now with respect to the repeater 300, so as to identify one or more serviceable locations within LoS of the repeater antenna structure 302. Through this process, the computing system 204 may thereby identify one or more serviceable locations at which to deploy another mmWave node such as a repeater and / or fixed-position UE, among other possibilities. For instance, the computing system 204 may thereby identify the rooftop of building 108 at which to deploy such a mmWave node, and the computing system 204 may responsively trigger deployment of a mmWave node at that location.

[0063] In applying this process to find serviceable locations within LoS of the repeater 300, it may be useful to consider various potential directions of coverage of the repeater antenna structure 302. To facilitate this, the repeater 300 could be equipped with a stepper motor to rotate to various incremental directions one or more coverage antennas of the repeater 300 together with the imaging device 304. For each direction, the imaging device 304 could capture images and generate and provide to the computing system 204 a respective 3D model, and the computing system 204 could apply the processing discussed above to find one or more serviceable locations within LoS of the repeater antenna structure 302.

[0064] Through this process, the computing system 204 may thereby determine a preferred direction of coverage for the repeater antenna structure 302, such as a direction that provides LoS coverage of the greatest number of serviceable locations, one or more most desirable serviceable locations, or the like. And the computing system 204 could then signal to the repeater 300 to cause the repeater 300 to set the coverage direction of its antenna structure 302 at that preferred direction.

[0065] This process could the continue to repeat iteratively, enabling a wireless service provider to identify more and more serviceable locations within the market area and to deploy more and more mmWave nodes at those serviceable locations and / or market wireless service to more and more such locations.

[0066] Note that numerous variations from and enhancements of the processes discussed above are possible as well.

[0067] As one example, other forms of imaging devices could be used to establish a 3D model of the space that generally corresponds with the RF beam pattern of a mmWave antenna structure. For instance, instead of using a stereoscopic optical camera having a FoV generally aligned with the RF beam pattern, another implementation could use a lidar imaging system having a FoV generally aligned with the RF beam pattern. A lidar imaging system could work to establish a point cloud 3D model, which the computing system 204 could then evaluate in the same manner described above.

[0068] As another example, as noted above, the wireless technology at issue could take a form other than mmWave. For instance, the wireless technology could be microwave communication instead and / or some other type of wireless communication that may benefit from LoS communication.

[0069] As yet another example, the computing system 204 could be distributed among various entities. For instance, the computing system could be distributed among various mmWave nodes and / or imaging devices throughout a market area.

[0070] As still another example, one or more 3D models established through the process above could also involve application of ray tracing to generate more realistic images of areas covered by one or more mmWave nodes within a market area, which may be useful for service marketing or other purposes.

[0071] Yet further, as another example, as a service provider deploys more and more mmWave nodes throughout a market area through this process, the computing system 204 could also work to provision the mmWave nodes to work on non-overlapping mmWave carriers, to help avoid inter-channel interference between nodes. For instance, the computing system 204 could determine the carrier(s) on which a given mmWave node operates and, with the benefit of knowledge gained through the present process, could provision any other mmWave nodes within LoS of that node to operate on one or more different mmWave carriers.

[0072] Still further, as another example, the image data and other data established through the present process could be used in other ways to enhance wireless network planning. For instance, the data could be provided as input to a separate network mapping or network planning tool, to facilitate further determination of where to deploy wireless service.

[0073] Yet further, the present process could run repeatedly, continuously, periodically, and / or in response to one or more other trigger events, to account for changes in topography and other geospatial information over time, helping to facilitate and enhance wireless coverage as appropriate.

[0074] Figure 4 is a flow chart illustrating an example method to facilitate configuring a wireless communication system. As shown in Figure 4, at block 400, the example method includes capturing, by an imaging system at a RF antenna structure of the wireless communication system, image data representing a field of view (FoV) corresponding with a beam pattern of the first RF antenna structure. At block 402, the example method includes detecting, through at least machine-based processing of the image data, an object within the FoV as being a serviceable platform at which to deploy a second RF antenna to engage in LoS RF communication with the first RF antenna, with the detecting of the object being based on the object being within the FoV and the FoV corresponding with the beam pattern of the first RF antenna. Further, at block 404, the example method includes, based on the detecting, triggering deployment of the second RF antenna at the detected object.

[0075] The RF antenna structures in this method could be RF antenna structures of respective RF communication nodes such as base stations, repeaters, or fixed-position UE devices, among other possibilities.

[0076] Further, in other implementations, the method could occur from the perspective of a computing system or other entity. For instance, the method could involve a computing system receiving the image data captured and / or otherwise established by the imaging system, detecting a serviceable location through machine-based processing of that image data, and triggering deployment of a second RF antenna structure at the detected serviceable location.

[0077] Still further, in some implementations, the imaging system could comprise an optical imaging system, and the image data could comprise optical-image data. For instance, the imaging system could comprise a stereo-optical imaging system. Further, the image data could comprise a 3D model of the FoV.

[0078] Yet further, in some implementations, the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure could involve using machine-learning as a basis to identify within the FoV the object as a predefined type indicative of the object being a serviceable platform for the second RF antenna structure. Examples of predefined types include a building, a rooftop, a utility pole, and a radio tower, among others.

[0079] In addition, in some implementations, the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure could involvedetermining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV, and determining, based on secondary geolocation data (e.g., geospatial data such as satellite image data and / or street address data), that the object shadows another serviceable location.

[0080] Further, in some implementations, the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure could involve determining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV, and determining, based on RF planning data, that the determined geolocation of the object provides at least a potential view of an serviceable area that is not within LoS of the first RF antenna structure.

[0081] Still further, in some implementations, the object may be a portion of an encompassing object, and the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure could involve determining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV and determining, based on secondary geolocation data (e.g., geospatial data, such as satellite image data), that the object is the portion of the encompassing object.

[0082] In some such implementations, the encompassing object may be a building rooftop that is partially within LoS of the first RF antenna structure and partially not within LoS of the first RF antenna structure, where the object is a portion of the rooftop that is within LoS of the first RF antenna structure. Alternatively, the encompassing object may be a tower or pole that partially within LoS of the first RF antenna structure and partially not within LoS of the first RF antenna structure, and wherein the object is a portion of the tower or pole that is within LoS of the first RF antenna structure.

[0083] Yet further, in some implementations, the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure could involve determining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV and determining that no second RF antenna structure is currently deployed at the determined geolocation.

[0084] As additionally discussed above, in some implementations, the first RF antenna structure and second RF antenna structure could each be a mmWave antenna structure.Further, in some implementations, the first RF antenna structure could be an antenna structure of a wireless base station or a wireless repeater, and the second RF antenna structure could be an antenna structure of a wireless repeater or a fixed wireless access (FWA) network, such as a fixed-position UE that feeds communication service into a building or other facility.

[0085] Further, in some implementations, the second RF antenna structure could be an antenna structure of a wireless repeater, the image data could be first image data, the FoV could be a first FoV, the object could be a first object, the serviceable platform could be a first serviceable platform, and the method could additionally involve iteratively repeating the process after deployment of the second RF antenna structure at the detected object. For instance, this could involve capturing, by an imaging system at the second RF antenna structure, second image data representing a second FoV corresponding with a beam pattern of the second RF antenna structure. Further, this could involve detecting, detecting, through at least machine-based processing of the second image data, a second object within the FoV as being a second serviceable platform at which to deploy a third RF antenna structure to engage in LoS RF communication with the second RF antenna structure, with the detecting of the second object being based on the second object being within the second FoV and the second FoV corresponding with the beam pattern of the second RF antenna structure. And this could additionally involve triggering, based on the detecting, deployment of the third RF antenna structure at the detected second object.

[0086] Yet further, in some implementations, the act of triggering deployment of an RF antenna structure (such as the second or third RF antenna structure) at a detected object could involve signaling to invoke engineering installation of the RF antenna structure at the detected object.

[0087] Figure 5 is a simplified block diagram of an example system in line with the present disclosure. As shown in Figure 5, the example system includes a first RF antenna structure 500 configured to provide a beam pattern, an imaging system 502 at the first RF antenna structure, the imaging system being configured to capture image data representing a FoV corresponding with the beam pattern of the first RF antenna structure, and a computing system 504 including at least one processor 506, non-transitory data storage 508, and program instructions 510 stored in the non-transitory data storage and executable by the at least one processor to carry out various operations.

[0088] In line with the discussion above, the operations could include detecting, through at least machine-based processing of the image data, an object within the FoV as beinga serviceable platform at which to deploy a second RF antenna structure to engage in LoS RF communication with the first RF antenna structure, with the detecting of the object being based on the object being within the FoV and the FoV corresponding with the beam pattern of the first RF antenna structure. Further, the operations could include triggering, based on the detecting, deployment of the second RF antenna structure at the detected object.

[0089] In some example implementations, the at least one processor 506 of the computing system 504 could include one or more general purpose processing units (e.g., microprocessors) and / or one or more specialized processing units (e.g., digital signal processors (DSPs), graphics processing units (GPUs), neural processing units (NPUs), etc.) Further, the non-transitory data storage 508 could comprise one or more volatile and / or nonvolatile storage components (e.g., optical, magnetic, or flash storage, random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically EPROM (EEPROM), cache memory, and / or other computer-readable media, etc.), possibly integrated in whole or in part with the at least one processor 506. Still further, the computing system could include a network communication interface to facilitate communicating with other entities, such as with various mmWave nodes, among other possibilities.

[0090] Various features discussed above could be applied in this context as well, and vice versa.

[0091] The RF antenna structures and associated imaging devices mentioned in this disclosure could take any of various forms. Without limitation, Figures 6-10 illustrate some example arrangements, where the antenna structures comprise mmWave antennas with collocated imaging devices.

[0092] In these example arrangements, an antenna structure could comprise one or more RF-communication units, possibly multiple such units adjacent to each other, perhaps ganged (e.g., along an axis defined by an access node tower, among other possibilities), each configured to provide one or more highly focused RF antenna beams, so as to cooperatively provide an RF beam pattern.

[0093] Each RF-communication unit could have a housing containing at least one mmWave system-on-a-chip (SoC) operable to provide at least one steerable RF antenna beam, with the housing having an outer wall that shields RF signaling and has an RF lens through which the at least one steerable RF antenna beam can pass. The RF shielding of the outer wall of each of these RF-communication units could facilitate minimizing or avoiding RF interference between the units when the units are ganged or otherwise in close physicalproximity to each other. Further, passing the at least one steerable RF antenna beam provided by the at least one mmWave SoC of each unit through the RF lens of the unit could provide at least one focused RF antenna beam. Therefore, with the multiple units ganged together, the resulting RF antenna structure could provide multiple focused RF antenna beams, which could cooperatively provide a desired scope of wireless coverage.

[0094] Each example RF-communication unit could further include a Global Navigation Satellite System (GNSS) (e.g., Global Positioning System (GPS)) receiver and / or other module configured to determine the geolocation of the RF-communication unit, as well as processing logic to report that geolocation and perhaps additional configuration data computing system 204 to facilitate the processing discussed above. Alternatively, in some implementations, the geolocation of the RF-communication unit and thus generally of the antenna structure could be manually entered.

[0095] Figure 6 shows an example of how one such RF-communication unit 600 (with outer wall 602 and lens 604) could have a collocated imaging device 606, such as stereoscopic optical camera for instance. As shown in Figure 7, the RF-communication unit provides a respective RF beam pattern 608. Further, the imaging device operates to establish or facilitate generation of a 3D model having a respective FoV 610 generally corresponding with the RF beam pattern 608. In this arrangement, RF-communication unit 600 and imaging device 606 may be mounted or housed together or integrated or provided together in another manner.

[0096] Figures 7a-7c and 8a-8c show examples of how multiple such RF- communication units could be ganged together to provide improved wireless coverage with an composite RF beam pattern. Further, Figure 9 illustrates how a group of such units may be housed in within a radome or other structure 900, with an integrated imaging device 902 having an FoV 904 likewise generally aligned with the composite RF beam pattern 906.

[0097] The arrangements and processes discussed above could usefully provide a self-organizing distributed mmWave network configuration, using Al object analysis to selfoptimize for both the build and maintenance processes by taking data inputs from a host of geographically distributed smart network elements, dynamically as they are added and removed from the network. Further, the disclosed principles could be applied with respect to other more traditional network topologies as well.

[0098] In some implementations, each network element of such a system could include one or more sensors, such as but not limited to stereo optical sensors, RF sensors orintegrated radios, GNSS receivers, temperature sensors, and the like, configured to gather information about surroundings of the network element. Further each network element could be configured to gather data regarding its network traffic, signal statistics, and the like. And any or all of this data could be continually fed into an Al model.

[0099] With such an arrangement, individual network elements may also be able to connect to each other in an ad-hoc manner using any and all technologies and methods available. The network elements connected to each other may then be able to pool their available resources, such as but not limited to CPU / GPU / TPU, and / or other processing resources, memory, storage, and the like and to use various wireless and / or wired communication protocols and technologies, with their pooled resources allowing remote access and serving as a distributed general compute server, storage, and / or cloud platform.

[0100] An Al model in this arrangement may continually gather new information about the network-element deployment geography as more and more network elements get deployed, with the Al model operating to predict and inform preferred service locations with respect to each added network element having its own array of sensors and real-time data feeds.

[0101] With examples discussed above, for instance, stereo optical sensors at, within, or otherwise with network elements (possibly in a detachable dongle that could be implemented at the time of install), could capture up to a 360 degree view of the surroundings, either by mechanically rotating stereo optical elements or using a larger 360-degree array, among other possibilities.

[0102] As the network elements get distributed and deployed in a given geography together with optical sensors or other imaging devices, techniques such as photogrammetry and / or Al processing may stitch image data together to create an up to date (e.g., real-time updatable) 3D photorealistic model of a given deployment area, with increasing accuracy as the network-element density increases. Further, an Al model could be trained to predict material types (e.g., trees, brick, glass, etc.), aiding in RF-channel prediction, and could integrate and overlay various types of geospatial data within the 3D model, from external databases including but not limited to satellite images and address data.

[0103] The Al model may continually receive data from RF sensors within the network elements and have a dynamic understanding the RF environment allowing it to dynamically predict interference, and to suggest and perform various interference mitigation actions, such as channel allocation as noted above for instance. In example implementations, the Al model may use optical sensors along with 3D models to predict the radio channel qualityat the various frequencies and locations, possibly referenced against existing and third party data, and to suggest serviceable locations versus non-serviceable locations, including specific placement positions for serviceable locations.

[0104] By continually ingesting new data from distributed network elements, the Al model may thus make predictions and provide suggestions, alerts, and the like, and / or may more directly trigger network configuration changes or take other actions to attain a desired outcome that may help to increase network capacity and coverage and improve user experience. For instance, the Al model may facilitate expanding capacity, managing redundancy, optimizing channel reuse, selecting sites for deploying new equipment, resolving outage issues, and responding to network degradation events.

[0105] In line with the discussion above, the Al model may facilitate predicting serviceable locations based on predicted and measured RF coverage patterns, matching to geospatial data such as satellite images and address data. Further, the Al model may facilitate predicting RF coverage patterns from new not-yet-acquired sites and makes suggestions, based on GPS coordinates or address data, of locations at which to provide added coverage or other service.

[0106] Referring to Figures 10 and 11, a user node 1107 may be a network element that creates an underlying and fundamental fabric for the presently disclosed architecture. The user node 1107 may contain a CPU, GPU, NPU, TPU, memristor, and / or other type of processors, as well as memory, storage (e.g., HHD, SSD, flash, etc.) Additionally, the node 1107 may have an out-of-band network connection from a third party Internet Service Provider (ISP), using technologies such as cable (e.g., DOCSIS), fiber optic (e.g., GPON), 5G NR, satellite, DSL, etc. Node 1107may use these or other wired and / or wireless network connection mechanisms to form ad-hoc connections with other network elements. Further, node 1107 may be indoor or outdoor.

[0107] Node 1107 may also be able to repeat and share its out-of-band third party connection with other network elements including but not limited to UEs. Additionally, node 1107 may collect a variety of data including but not limited to network statistics (e.g., traffic, signal levels, packet loss, etc.), radio statistics (e.g., channel occupancy, noise, interference etc.), stereo optical images, location and time data. And any or all of this data could be stored and processed locally and accessed remotely.

[0108] Instances of node 1107 could be deployed ad hoc and at random through a given deployment, with each instance of node 1107 collecting data about its surroundingenvironment and feeding this data into a central Al algorithm that is then able to make predictions, provide suggestions, and take actions accordingly.

[0109] Element 1101 may be a wireless customer premises equipment (CPE) device (which may operate according to WiGig, WiFi, 4G, 5G, 6G, or mmWave FWA, among other possibilities), which may connect with a wireless AP 1102, 1201. In some instances, element 1101 may use a high gain antenna to create a narrow beam that increases link budget and reduces interference. Further, element 1101 may contain a CPU, GPU, NPU, TPU, memristor, and / or one or more other types of processing units, memory, and storage (eg. HDD, SSD, Flash), wired networking (e.g., Ethernet, fiber, etc.)

[0110] Element 1120 may be a wireless access point (which may operate according to WiGig, WiFi, 4G, 5 G, 6G, or mmWave FWA, among other possibilities) that in many but not all cases is collocated with element 1101. Element 1102 may act as a repeater for signals communicated to / from element 1101. In some implementations, element 1102 may have a broad main beam and a broad coverage area. Further element 1102 may likewise contain a CPU, GPU, NPU, TPU, memristor, and / or one or more other types of processing units, memory, and storage (eg. HDD, SSD, Flash), wired networking (e.g., Ethernet, fiber, etc.) In addition, element

[0111] Element 1201 may be a wireless access point (which may operate according to WiGig, WiFi, 4G, 5G, 6G, or mmWave FWA, among other possibilities) that may serve as an aggregation site for pairs of elements 1101, 1201 distributed throughout deployment areas. In example implementations, each instance of element 1201 may comprise a large arrays of access points collocated at the same site as each other. Further, element 1201 may likewise contain a CPU, GPU, NPU, TPU, memristor, and / or one or more other types of processing units, memory, and storage (eg. HDD, SSD, Flash), wired networking (e.g., Ethernet, fiber, etc.)

[0112] Element 1110 may then be a mobile computing device, and element 1106 may be a stereo optical sensor, among other possibilities.

[0113] In various implementations, the various elements illustrated in Figures 10 and 11 may form ad-hoc connections with each other using any supported networking technologies.

[0114] The present disclosure also contemplates non-transitory data storage (e.g., one or more non-transitory computer-readable medium components (e.g., optical, magnetic, or flash storage, RAM, ROM, EPROM, EEPROM, cache memory, and / or other computer-readable media, etc.)) holding program instructions executable by at least one processor of a device to cause the device to carry out various operations described herein.

[0115] Further, the disclosure also contemplates a computer program including a set of program instructions executable by at least one processor to carry out various operations described herein, such as detecting of one or more serviceable locations and triggering deployment of mmWave nodes or other RF communication infrastructure.

[0116] Example embodiments have been described above. Those skilled in the art will understand, however, that changes and modifications may be made to these embodiments without departing from the true scope and spirit of the invention.

Claims

CLAIMSWhat is claimed is:

1. A method to facilitate configuring a wireless communication system, the method comprising: capturing, by an imaging system at a first radio-frequency (RF) antenna structure of the wireless communication system, image data representing a field of view (FoV) corresponding with a beam pattern of the first RF antenna structure; detecting, through at least machine-based processing of the image data, an object within the FoV as being a serviceable platform at which to deploy a second RF antenna structure to engage in line-of-sight (LoS) RF communication with the first RF antenna structure, wherein the detecting of the object is based on the object being within the FoV and the FoV corresponding with the beam pattern of the first RF antenna structure; and triggering, based on the detecting, deployment of the second RF antenna structure at the detected object.

2. The method of claim 1, wherein the imaging system comprises an optical imaging system, and wherein the image data comprises optical-image data.

3. The method of claim 1, wherein the imaging system comprises a stereo-optical imaging system.

4. The method of claim 1, wherein the image data comprises a three-dimensional model of the FoV.

5. The method of claim 1, wherein the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure comprises: using machine-learning as a basis to identify within the FoV the object as a predefined type indicative of the object being a serviceable platform for the second RF antenna structure.

6. The method of claim 6, wherein the predefined type is selected from the group consisting of a building, a rooftop, a utility pole, and a radio tower.

7. The method of claim 1, wherein the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure comprises: determining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV; and determining, based on secondary geolocation data, that the object shadows another serviceable location.

8. The method of claim 7, wherein the secondary geolocation data comprises satellite image data.

9. The method of claim 1, wherein the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure comprises: determining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV; and determining, based on RF planning data, that the determined geolocation of the object provides at least a potential view of an serviceable area that is not within LoS of the first RF antenna structure.

10. The method of claim 1, wherein the object is a portion of an encompassing object, and wherein the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure comprises: determining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV; and determining, based on secondary geolocation data, that the object is the portion of the encompassing object.

11. The method of claim 10, wherein the secondary geolocation data comprises satellite image data.

12. The method of claim 10, wherein the encompassing object is a rooftop that is partially within LoS of the first RF antenna structure and partially not within LoS of the first RF antenna structure, and wherein the object is a portion of the rooftop that is within LoS of the first RF antenna structure.

13. The method of claim 10, wherein the encompassing object is a tower or pole that partially within LoS of the first RF antenna structure and partially not within LoS of the first RF antenna structure, and wherein the object is a portion of the tower or pole that is within LoS of the first RF antenna structure.

14. The method of claim 1, wherein the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure comprises: determining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV; and determining that no second RF antenna structure is currently deployed at the determined geolocation.

15. The method of claim 1, wherein the first RF antenna structure and second RF antenna structure are each a millimeter wave (mmWave) antenna structure.

16. The method of claim 1, wherein the first RF antenna structure is an antenna structure of a wireless base station or a wireless repeater, and wherein the second RF antenna structure is an antenna structure of a wireless repeater or a fixed wireless access (FWA) network.

17. The method of claim 1, wherein the second RF antenna structure is an antenna structure of a wireless repeater, wherein the image data is first image data, wherein the FoV is a first FoV, wherein the object is a first object, wherein the serviceable platform is a firstserviceable platform, and wherein the method further comprises, after deployment of the second RF antenna structure at the detected object: capturing, by an imaging system at the second RF antenna structure, second image data representing a second FoV corresponding with a beam pattern of the second RF antenna structure; detecting, through at least machine-based processing of the second image data, a second object within the FoV as being a second serviceable platform at which to deploy a third RF antenna structure to engage in LoS RF communication with the second RF antenna structure, wherein the detecting of the second object is based on the second object being within the second FoV and the second FoV corresponding with the beam pattern of the second RF antenna structure; and triggering, based on the detecting, deployment of the third RF antenna structure at the detected second object.

18. The method of claim 1, wherein triggering deployment of the second RF antenna structure at the detected object comprises signaling to invoke engineering installation of the second RF antenna structure at the detected object.

19. A system comprising: a first radio frequency (RF) antenna structure configured to provide a beam pattern; an imaging system at the first RF antenna structure, the imaging system being configured to capture image data representing a field of view (FoV) corresponding with the beam pattern of the first RF antenna structure; a computing system including at least one processor, non-transitory data storage, and program instructions stored in the non-transitory data storage and executable by the at least one processor to carry out operations including: detecting, through at least machine-based processing of the image data, an object within the FoV as being a serviceable platform at which to deploy a second RF antenna structure to engage in line-of-sight (LoS) RF communication with the first RF antenna structure, wherein the detecting of the object is based on the object being within the FoV and the FoV corresponding with the beam pattern of the first RF antenna structure, andtriggering, based on the detecting, deployment of the second RF antenna structure at the detected object.

20. The system of claim 19, wherein the imaging system comprises an optical imaging system, and wherein the image data comprises optical-image data.

21. The system of claim 19, wherein the imaging system comprises a stereo-optical imaging system.

22. The system of claim 19, wherein the image data comprises a three-dimensional model of the FoV.

23. The system of claim 19, wherein the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure comprises: using machine-learning as a basis to identify within the FoV the object as a predefined type indicative of the object being a serviceable platform for the second RF antenna structure.

24. The system of claim 23, wherein the predefined type is selected from the group consisting of a building, a rooftop, a utility pole, and a radio tower.

25. The system of claim 19, wherein the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure comprises: determining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV; and determining, based on secondary geolocation data, that the object shadows another serviceable location.

26. The system of claim 25, wherein the secondary geolocation data comprises satellite image data.

27. The system of claim 19, wherein the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure comprises: determining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV; and determining, based on RF planning data, that the determined geolocation of the object provides at least a potential view of an serviceable area that is not within LoS of the first RF antenna structure.

28. The system of claim 19, wherein the object is a portion of an encompassing object, and wherein the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure comprises: determining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV; and determining, based on secondary geolocation data, that the object is the portion of the encompassing object.

29. The system of claim 28, wherein the secondary geolocation data comprises satellite image data.

30. The system of claim 28, wherein the encompassing object is a rooftop that is partially within LoS of the first RF antenna structure and partially not within LoS of the first RF antenna structure, and wherein the object is a portion of the rooftop that is within LoS of the first RF antenna structure.

31. The system of claim 28, wherein the encompassing obj ect is a tower or pole that partially within LoS of the first RF antenna structure and partially not within LoS of the first RF antenna structure, and wherein the object is a portion of the tower or pole that is within LoS of the first RF antenna structure.

32. The system of claim 19, wherein the detecting of the object within the FoV as being the serviceable platform at which to deploy the second RF antenna structure to engage in LoS RF communication with the first RF antenna structure comprises: determining a geolocation of the object based on position of the object within the FoV and based on origin location and direction of the FoV; and determining that no second RF antenna structure is currently deployed at the determined geolocation.

33. The system of claim 19, wherein the first RF antenna structure and second RF antenna structure are each a millimeter wave (mmWave) antenna structure.

34. The system of claim 19, wherein the first RF antenna structure is an antenna structure of a wireless base station or a wireless repeater, and wherein the second RF antenna structure is an antenna structure of a wireless repeater or a fixed wireless access (FWA) network.

35. The system of claim 19, wherein the second RF antenna structure is an antenna structure of a wireless repeater, wherein the image data is first image data, wherein the FoV is a first FoV, wherein the object is a first object, wherein the serviceable platform is a first serviceable platform, and wherein the operations additionally include, after deployment of the second RF antenna structure at the detected object: capturing, by an imaging system at the second RF antenna structure, second image data representing a second FoV corresponding with a beam pattern of the second RF antenna structure; detecting, through at least machine-based processing of the second image data, a second object within the FoV as being a second serviceable platform at which to deploy a third RF antenna structure to engage in LoS RF communication with the second RF antenna structure, wherein the detecting of the second object is based on the second object being within the second FoV and the second FoV corresponding with the beam pattern of the second RF antenna structure; and triggering, based on the detecting, deployment of the third RF antenna structure at the detected second object.

36. The system of claim 19, wherein triggering deployment of the second RF antenna structure at the detected object comprises signaling to invoke engineering installation of the second RF antenna structure at the detected object.

37. At least one non-transitory computer-readable medium having stored thereon program instructions executable by at least one processor to carry out operations comprising: detecting, through at least machine-based processing of image data captured by an imaging system at a first radio frequency (RF) antenna structure and representing a field of view (FoV) corresponding with a beam pattern of the RF antenna structure, an object within the FoV as being a serviceable platform at which to deploy a second RF antenna structure to engage in line-of-sight (LoS) RF communication with the first RF antenna structure, wherein the detecting of the object is based on the object being within the FoV and the FoV corresponding with the beam pattern of the first RF antenna structure; and triggering, based on the detecting, deployment of the second RF antenna structure at the detected object.

38. A computer program comprising program instructions executable by at least one processor to carry out operations comprising: detecting, through at least machine-based processing of image data captured by an imaging system at a first radio frequency (RF) antenna structure and representing a field of view (FoV) corresponding with a beam pattern of the RF antenna structure, an object within the FoV as being a serviceable platform at which to deploy a second RF antenna structure to engage in line-of-sight (LoS) RF communication with the first RF antenna structure, wherein the detecting of the object is based on the object being within the FoV and the FoV corresponding with the beam pattern of the first RF antenna structure; and triggering, based on the detecting, deployment of the second RF antenna structure at the detected object.

Images