Generating environmental information using wireless communication

Abstract

Generate an environmental information map from a wireless communication system with directional communication capabilities. Extract multiple features from communication link attempts in multiple beam directions and record the results, and train at least one machine learning model based on the multiple features extracted from the first beam direction and at least one additional beam direction to infer an environmental information map of the area between the first transmitter and the receiver.

Description

Technical Field

[0001] The present invention relates to cellular communications and, more particularly, to using directional communication capabilities to enable environmental sensing and mapping. Background Art

[0002] Current environmental sensing systems, such as those using radar, are not available in cellular communication systems because such radar systems do not have communication capabilities and do not have access to radio access network or radio network metadata. Additionally, such radar systems require separate infrastructure deployment and thus cannot utilize the cellular infrastructure. Summary of the Invention

[0003] One aspect of the present invention relates to a computer-implemented method for generating an environmental information map using a wireless communication system, the wireless communication system including at least one transmitter and at least one receiver, wherein at least one of the transmitter and receiver has a directional communication system. The method includes the steps of attempting a wireless communication link by transmitting a signal from a first transmitter to a receiver in a first beam direction, extracting a plurality of characteristic results from the link attempt for the first beam direction, and recording the plurality of extracted characteristics at the receiver, attempting at least one additional wireless communication link from the first transmitter to the receiver by transmitting signals in respective at least one additional beam directions, extracting a plurality of characteristic results from each of the at least one additional link attempts for each of the at least one additional beam directions, and recording the plurality of extracted characteristics at the receiver, and training at least one machine learning model based on the plurality of extracted characteristics from the first beam direction and the at least one additional beam direction to infer an environmental information map of the area between the first transmitter and the receiver.

[0004] A computer system may also be provided that includes one or more processors operable to execute one or more methods described herein.

[0005] A computer-readable storage medium may also be provided that stores an instruction program executable by a machine to execute one or more methods described herein.

[0006] Further features as well as the structure and operation of various embodiments are described in detail below with reference to the drawings. In the drawings, like reference numerals represent like or functionally similar elements. Brief Description of the Drawings

[0007] Figure 1 is a block diagram of an embodiment of the system disclosed in this specification.

[0008] Figure 2 is a block diagram of an embodiment of the system disclosed in this specification.

[0009] Figure 3A and 3B 3C is an example of a data graph showing the EVM link metric from beamforming.

[0010] Figure 4 is a flowchart of an embodiment of the method disclosed in this specification.

[0011] Figure 5 is a flowchart of an embodiment of the method disclosed in this specification.

[0012] Figure 6 is a flowchart of an embodiment of the method disclosed in this specification.

[0013] Figure 7 is a block diagram of an exemplary computing system suitable for implementing an embodiment of the invention disclosed in this specification. Detailed Description

[0014] In one embodiment, a wireless communication system utilizes directional communication capabilities to implement environmental sensing and mapping. The principles outlined in this disclosure apply to any wireless communication system in which at least one of the transmitter and receiver in the link has directional communication capabilities. The sensing and mapping can be 2D or 3D. The method can provide the relative positions of reflective surfaces (such as buildings) and obstacles that are not reflectors but provide attenuation (such as trees). One such wireless communication system is a cellular communication system that includes at least one base station communicating with a user equipment device. One such system utilizes millimeter wave (mmWave) 5G cellular communication technology. For example, beamforming algorithms are used to effectively create communication links in cellular communication systems with directional communication capabilities. The advantages of directional communication or millimeter wave frequencies are used to improve the granularity and resolution of environmental sensing and mapping. Another example is a 60 GHz WLAN communication system. These systems are developed for high-speed (~1 Gb / s) wireless communication indoors. In this case, the result is an indoor environment estimate.

[0015] In one embodiment, as Figure 1 shown, the cellular communication system 100 includes one or more base stations and one or more user equipments. In Figure 1 the example, two base stations 12 and 14 and one user equipment 16 are shown. Any one or both of the base stations 12 and 14 and the user equipment 16 may include directional communication capabilities. In Figure 1 the example, the base stations 12 and 14 have the ability to control the directions of multiple beams 18 and 20, and the user equipment 16 has the ability to control the directions of multiple beams 22. The directional capabilities can be provided through beamforming, beam control, beam shaping, or other directional beam techniques. In Figure 1In the example, base station 12 is a millimeter-wave 5G transmitter in the upper layer of building 24, and base station 14 is a millimeter-wave 5G transmitter in the upper layer of building 26. Objects in the environment between base stations 12, 14, and user equipment 16 include building 28, tree 30, and truck 32.

[0016] In one embodiment of the method of the present disclosure, the first base station 12 transmits beam 18 in a given beam direction to instruct the user equipment 16 to attempt to form a cellular communication link. The user equipment 16 attempts to establish a communication link in the given beam direction using beam 22. The user equipment 16 extracts and records multiple features of this direction generated by the link attempt. These features may include communication link metrics such as error vector magnitude (EVM), bit error rate (BER), received signal strength indicator (RSSI), signal-to-noise ratio (SNR), waveform quality factor, and signal-to-interference ratio (SIR). For example, a uniform degradation of link quality (measured by EVM) or uniform attenuation (measured by received signal strength indicator RSSI) in several link directions in one polarization may indicate the presence of rain. Other features and metrics generated by the link attempt, such as channel frequency response and delay spread, may also be extracted and recorded.

[0017] For additional beam directions in which the user equipment 16 is capable of forming a beam, the communication link attempt between the first base station 12 and the user equipment 16 is repeated. In one embodiment, the link may be attempted for all directions in which the user equipment 16 is capable of forming a beam. The user equipment 16 extracts and records multiple features of the additional beam directions generated by the link attempt.

[0018] In one embodiment, for different output powers from the user equipment 16 and / or different output powers from the first base station 12, all communication link attempts between the first base station 12 and the user equipment 16 for all directions are repeated. The user equipment 16 extracts and records multiple features obtained from the link attempts at different output powers.

[0019] In one embodiment, for different gradually decreasing amplitudes from the user equipment 16 and / or different gradually decreasing amplitudes from the first base station 12, all communication link attempts between the first base station 12 and the user equipment 16 for all directions are repeated. The user equipment 16 extracts and records multiple features obtained from the link attempts at different output powers. Beamforming control may be performed using independent phase and gain control at each antenna element.

[0020] In one embodiment, for one or more additional antenna polarizations, all communication link attempts for all directions between the first base station 12 and the user equipment 16 can be repeated. For example, communication links can be attempted where both the first base station 12 and the user equipment 16 have vertical polarization, then where both the first base station 12 and the user equipment 16 have horizontal polarization, then where the first base station 12 has horizontal polarization and the user equipment 16 has vertical polarization, and vice versa. The user equipment 34 extracts and records multiple features obtained from link attempts with different polarizations.

[0021] In one embodiment, for a given beam direction of the beam 20, the second base station 14 accessing the same user equipment 16 attempts a communication link. In one embodiment, for some or all additional directions in which the user equipment 16 can form a beam, the communication link attempts between the second base station 14 and the same user equipment 16 can be repeated. In one embodiment, for all directions, all communication link attempts between the second base station 14 and the same user equipment 16 can be repeated for one or more of different output powers and additional antenna polarizations. The user equipment 16 extracts and records multiple features obtained from the link attempts of the second base station 14. In this embodiment, features are collected from different regions of the relevant environmental space. Thus, a rich set of feature information is formed for multiple beam directions, power levels, polarizations, and base station positions.

[0022] The algorithm converts this set of feature information into inferred 3D environmental information, such as the presence of walls, buildings, and / or foliage. In one embodiment, changes in weather conditions can be inferred. Different from prior art methods based on communication with detected entities, this method is capable of extracting information about non - communicating entities in the environment. The inferred environment can include an inferred object 34 of the building 28, an inferred object 36 of the tree 30, and an inferred object 38 of the truck 32. The method provides the relative positions of two reflective surfaces of the building 38 and the truck 42, as well as obstacles that are not reflectors but provide attenuation, such as the tree 40.

[0023] Figure 2 is an example of one embodiment of a system 40 for environmental sensing and mapping according to the present disclosure. The system 40 includes a millimeter - wave 5G transmitter (TX) 42 and a millimeter - wave 5G receiver (RX) 44. Both the TX 42 and the RX 44 can be base stations, user equipment, or any other device capable of performing directional communication. A non - communicating entity such as a tree 46 is in the environment.

[0024] In one embodiment, the system 40 includes a machine - learning - based intelligent sensing and mapping system that utilizes the directional communication capabilities of the TX 42 and the RX 44. Various machine - learning algorithms can be used, such as regression, structured and unstructured, supervised and unsupervised, reinforcement learning, and Bayesian learning.

[0025] In one embodiment, the RX 44 in the system 40 includes a front-end (FE) radio device 48 and a computer processing system 50. The computer processing system 50 includes a demodulation and feature extraction module 52 and a machine learning model and inference algorithm module 54. The system 40 also includes a radio access network (RAN) 56. The FE 48 communicates with the computer processing system 50 and the RAN 56. The TX 42 and the RX 44 perform directional communication using one or more beams 58 and 60, respectively. The RX 44 in the FE 48 receives the signal transmitted from the TX 42, and the module 52 demodulates and performs feature extraction. Typical receiver demodulation hardware is used to obtain the raw data from the input waveform. The signal processing hardware analyzes the raw data and extracts features. The features can be in the form of various communication link metrics. The extracted features can also include Fourier transform coefficients, coefficients of other mathematical transforms, or other features that can result from the specific environment under consideration. Neural networks can also be used for feature extraction. Before demodulation, relevant features can also be extracted, for example, the amplitude of the received signal estimated by the received signal strength indicator (RSSI).

[0026] These features are locally used by the machine learning model and inference algorithm module 54 within the RX 44 to perform 2D or 3D model scoring, thereby creating a 2D or 3D inference map. In one embodiment, the raw waveform is fed into the machine learning model and inference algorithm 54 in the RX 44, and the machine learning model and inference algorithm module 54 extracts features.

[0027] The demodulated data, the extracted features, and the 2D or 3D inference are passed to the RAN 56. The RAN 56 uses the inference to generate control signals for the TX 42 based on the 2D or 3D environment model. In one embodiment, the RAN 56 uses the inference information from the machine learning model and inference algorithm module 54 to perform application-specific threshold processing for generating the control signals.

[0028] In one embodiment, the RAN 56 has memory and computing resources to include a machine learning model and an inference algorithm module 62. In one embodiment, module 62 performs active learning. The RAN 56 consumes a temporary occupancy of machine learning resources and TX 42 resources to modify beamforming based on an initial score from the FE 88 machine learning model 54. The modification may include finer-grained beamforming or scanning at a higher frequency. Module 62 may use sequential learning, which uses temporal information extracted at the RAN 56 for more advanced inference that results in more detailed environmental sensing. In one embodiment, the RAN 56 also uses the information obtained to improve its own machine learning model 62. In one embodiment, the RAN 56 periodically updates the machine learning model 54 on the FE 48. In another embodiment, the computer processing system 50 in the RX 44 does not have computing resources to perform feature extraction and inference tasks. In such a case, the demodulated data is directly transmitted to the RAN where feature extraction and learning-based algorithms can be performed.

[0029] As described above, one type of feature that can be extracted from an attempted cellular communication link from a base station to a user equipment device is a communication link metric. One such communication link metric that can be extracted is the error vector magnitude (EVM). For example, Figure 3A FIG. 70 shows EVM data that can be extracted by the demodulation and feature extraction module 52 for a transmitter with an equivalent isotropic radiated power (EIRP) of 14 dBm in a building transmitting to a receiver sixty meters away in the line of site path. The link formed when the main lobe is pointed at the transmitter or any one of the four side lobes is pointed at the transmitter 72 is visible in the data. In another example, for a transmitter of one unit with 10 dBm EIRP, transmitting through a large tree to a receiver of sixteen units may result in Figure 3B the error vector magnitude EVM data graph 74 shown in FIG. The data results provide some sense of the environment, possibly including the type of tree shown in FIG. 76. By increasing the power using a two-element transmitter with 16 dBm EIRP, transmitting through the large tree to the same sixteen-element receiver may result in Figure 3C the error vector magnitude EVM data graph 78 shown in FIG. At 82, three lobes 80 can be seen and more details of the tree can be inferred. The ML model and inference algorithm 54 can use this EVM data to generate a 2D or 3D environmental map.

[0030] A variety of beamforming algorithms can be used, which will result in a rich set of features extracted from communication link attempts. In one example of a beamforming algorithm that can be used as a tree search algorithm, it includes an initialization phase where a coarse sector codebook and a fine beam codebook are maintained at the transmitter, a coarse sector codebook and a fine beam codebook are maintained at the receiver, the transmitter-side codebook information is sent to the receiver, and the receiver feeds back the receiver-side codebook information to the transmitter. The coarse sector training in the second phase includes, for each possible pair of a transmit sector i and a receive sector j, transmitting a training sequence with sector i, receiving with sector j, and recording the SNR as 1 / 2(i; j). The receiver selects the best pair of a transmit sector and a receive sector such that the corresponding SNR is the maximum, and the receiver feeds back the transmitter-side sector index The fine beam training in the third phase includes, for each possible pair of a transmit beam p within the coverage of a sector and a receive beam q within the coverage of a sector , transmitting a training sequence with beam p and receiving with beam q, and recording the SNR as The receiver selects the best pair of a transmit beam and a receive beam such that the corresponding SNR is the maximum, and the receiver feeds back the transmitter-side beam index The transmit beam and the receive beam are used for data transmission.

[0031] Other beamforming algorithms can be used in this disclosure. Another such example is the agile link algorithm, which includes using subarrays to create multi-arm beams in different orthogonal directions, the overlapping directions between different multi-arm beams provide information about the direction of arrival, and soft voting is used to estimate the direction of arrival to eliminate sidelobe effects. The system can pick multi-arm beams to create a random hash function and use voting to estimate the true direction. Another example algorithm is the gradient descent-based algorithm.

[0032] In one embodiment, the method of this disclosure can be used for micrometeorological mapping. Millimeter-wave channels are highly dependent on weather. The amount and type of precipitation can be detected using differential attenuation measurements, such as real-time attenuation compared to the attenuation during clear weather. The measurements are real-time and highly localized.

[0033] In one embodiment, the method can be used for air pollution mapping. Millimeter-wave channels can depend on air pollutants. Air quality can be detected using differential attenuation measurements, for example, real-time attenuation compared to the attenuation during clear and low-pollutant weather. The measurements are real-time and highly localized.

[0034] Figure 4 is a flowchart of an embodiment of a method according to the present disclosure. The method includes the following steps: S10 attempt a cellular communication link from a first BS to a UE in a beam direction, S12 extract features obtained from the link attempt, S14 record the extracted features, S16 attempt an additional cellular communication link from the first BS to the UE in an additional beam direction, S18 extract features obtained from the additional link attempt, S20 record the extracted additional features, S22 train a machine learning model based on the extracted features, and S24 infer an environmental information map.

[0035] Figure 5 is a flowchart of an embodiment of a method according to the present disclosure. The method includes the following steps: S26 demodulate a link attempt signal, S28 extract features from the demodulated signal, S30 provide a first machine learning model with an inference algorithm on a front-end radio device, S32 provide a second machine learning model with an inference algorithm located on a radio access network, S34 transfer the extracted features from the front-end radio device to the radio access network, S36 generate a control signal for a beam direction of a first base station, and S38 transfer the control signal from the radio access network to the first base station.

[0036] Figure 6 is a flowchart of an embodiment of a method according to the present disclosure. The method includes the following steps, S38 attempt cellular communication links in multiple beam directions, S38 attempt cellular communication links at multiple power levels, S40 attempt cellular communication links at multiple antenna polarizations, S42 attempt cellular communication links from multiple base stations, S44 extract features obtained from the multiple link attempts, S46 record the extracted features, S48 train a machine learning model based on the extracted features, and S50 infer an environmental information map.

[0037] Figure 7 shows a schematic diagram of an example computer or processing system that can implement a method for environmental sensing and generating an environmental information map in an embodiment of the present disclosure. The computer system is only one example of a suitable processing system for implementing modules 52, 54, and 62 and is not intended to impose any limitation on the scope of use or functionality of the embodiments of the methods described herein. The illustrated processing system can operate with many other general-purpose or special-purpose computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations suitable for use with Figure 7 the illustrated processing system may include, but are not limited to, personal computer systems, server computer systems, thin clients, fat clients, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe systems, and distributed cloud computing environments including any of the above systems or devices.

[0038] A computer system can be described in the general context of computer system-executable instructions, such as program modules executed by a computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system can be practiced in a distributed cloud computing environment where tasks are performed by remote processing devices linked through a communications network. In a distributed cloud computing environment, program modules can be located in both local and remote computer system storage media including memory storage devices.

[0039] Components of a computer system can include, but are not limited to, one or more processors or processing units 100, a system memory 106, and a bus 104 that couples various system components including the system memory 106 to the processor 100. The processor 100 can include a program module 102 that executes the methods described herein. The module 102 can be programmed into the integrated circuit of the processor 100 or loaded from the memory 106, a storage device 108, or a network 114, or a combination thereof.

[0040] The bus 104 can represent one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example and not limitation, these architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0041] A computer system can include various computer system-readable media. Such media can be any available media accessible by the computer system and it can include both volatile and non-volatile media, removable and non-removable media.

[0042] System memory 106 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory or others. The computer system may also include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 108 may be provided for reading from and writing to an immovable, non-volatile magnetic medium (e.g., "hard disk drive"). Although not shown, a disk drive for reading from and writing to a removable, non-volatile disk (e.g., "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media may be provided. In such cases, each may be connected to bus 104 through one or more data media interfaces.

[0043] The computer system may also communicate with one or more external devices 116, such as a keyboard, a pointing device, a display 118, etc.; one or more devices that enable a user to interact with the computer system; and / or any device that enables the computer system to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may occur via an input / output (I / O) interface 110.

[0044] In addition, the computer system may communicate with one or more networks 114 via a network adapter 112, such as a local area network (LAN), a general wide area network (WAN), and / or a public network (e.g., the Internet). As described, network adapter 112 communicates with other components of the computer system via bus 104. It should be understood that although not shown, other hardware and / or software components may be used in conjunction with the computer system. Examples include but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0045] The present invention may be a system, method, and / or computer program product at any possible level of integration of technical details. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to execute aspects of the present invention.

[0046] A computer-readable storage medium can be a tangible device that is capable of retaining and storing instructions for use by an instruction execution device. The computer-readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer-readable storage medium includes the following: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanical encoding device such as a punched card or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium should not be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., an optical pulse through an optical fiber cable), or an electrical signal transmitted through a wire.

[0047] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a corresponding computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include a copper transmission cable, an optical transmission fiber, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the corresponding computing / processing device.

[0048] The computer-readable program instructions for performing the operations of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuits, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and procedural programming languages such as the "C" programming language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer through any type of network connection, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, to perform aspects of the present invention, an electronic circuit, including, for example, a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), may execute the computer-readable program instructions by utilizing the state information of the computer-readable program instructions to personalize the electronic circuit.

[0049] Aspects of the present invention are described herein with reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0050] These computer-readable program instructions may be provided to a processor of a computer or other programmable data processing apparatus to produce a machine, such that the instructions executed via the processor of the computer or other programmable data processing apparatus create a means for implementing the functions / acts specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, and / or other devices to operate in a particular manner, such that the computer-readable storage medium in which the instructions are stored comprises an article of manufacture including instructions for implementing aspects of the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0051] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device to produce a computer-implemented process, such that the instructions executed on the computer, other programmable apparatus, or other device implement the functions / acts specified in one or more blocks of the flowchart and / or block diagram.

[0052] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of instructions, which includes one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions noted in the block may not occur in the order noted in the figures. For example, two blocks shown in succession may in fact be implemented as one step, executed simultaneously, substantially simultaneously, partially or wholly in time-overlapped manner, or these blocks may sometimes be executed in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0053] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the terms "comprises" and / or "comprising", when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0054] In all apparatus or step plus function elements in the following claims, the corresponding structures, materials, acts, and equivalents (if any) are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or to limit the invention to the disclosed form. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the invention. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.

[0055] In addition, although the preferred embodiments of the invention have been described using specific terms, such description is for illustrative purposes only and it should be understood that changes and variations may be made without departing from the scope of the appended claims.

Claims

1. A computer-implemented method for generating an environmental information map using a wireless communication system, the wireless communication system including at least one transmitter and at least one receiver, at least one of the transmitter and receiver having a directional communication system, the method comprising: Attempting a wireless communication link by transmitting a signal from a first transmitter to a receiver in a first beam direction; Extracting a plurality of features resulting from the link attempt for the first beam direction and recording the plurality of extracted features at the receiver; Attempting at least one additional wireless communication link from the first transmitter to the receiver by transmitting a signal in a respective at least one additional beam direction; Extracting a plurality of features resulting from each of the at least one additional link attempts for each of the at least one additional beam directions and recording the plurality of extracted features at the receiver; Attempting a plurality of wireless communication links from the first transmitter to the receiver at a first antenna polarization of the first transmitter and the receiver; Extracting a plurality of features resulting from the plurality of link attempts at the first antenna polarization and recording the plurality of features at the receiver; And Using the plurality of extracted features from the first beam direction and the at least one additional beam direction and using the plurality of features from the first antenna polarization to perform an inference algorithm to infer an environmental information map of the area between the first transmitter and the receiver.

2. The method according to claim 1, further comprising generating a control signal for setting a beam direction of the directional communication system of the first transmitter based on the environmental information map to establish a communication link from the first transmitter to the receiver.

3. The method according to claim 1, further comprising demodulating the signal received by the receiver as a result of each of the attempted wireless communication links, and extracting the plurality of features from the demodulated signal.

4. The method according to claim 3, wherein the plurality of features are communication link metrics extracted from the demodulated signal waveform.

5. The method according to claim 1, wherein The plurality of features are directly extracted from the signal.

6. The method according to claim 1, further comprising training at least one machine learning model based on the plurality of extracted features from the first beam direction and the at least one additional beam direction to infer the environmental information map of the area between the first transmitter and the receiver, the at least one machine learning model including a first machine learning model for performing machine learning model scoring for inference of the environment.

7. The method according to claim 6, wherein, The at least one machine learning model includes a second machine learning model located on a radio access network, the second machine learning model having an inference algorithm for performing machine learning model scoring for inference of the environment.

8. The method according to claim 7, wherein, The first machine learning model is located on a front-end radio device or a second transmitter that communicates between the receiver and the radio access network.

9. The method according to claim 8 further comprises transmitting the plurality of extracted features and the scored inference from the front-end radio device or the second transmitter to the radio access network.

10. The method according to claim 9 further comprises generating a control signal for setting the beam direction of the directional communication system of the first transmitter based on the environmental information map inferred using the second machine learning model, and transmitting the control signal from the radio access network to the first transmitter.

11. The method according to claim 9 further comprises training the second machine learning model using sequence learning and temporal information obtained from the plurality of extracted features.

12. The method according to claim 9 further comprises updating the first machine learning model based on the inference learned by the second machine learning model.

13. The method according to claim 1, wherein the directional communication system comprises at least one of beamforming, beam steering, and beam shaping.

14. The method according to claim 1, wherein the environmental information map comprises at least one of local inference of objects, weather conditions, and air pollutants.

15. The method according to claim 1 further comprises: attempting a wireless communication link by transmitting a signal from the first transmitter to the receiver at a first power output level of the first transmitter and the receiver; at the receiver, extracting a plurality of features obtained from the link attempt at the first power output level and recording the plurality of features; attempting at least one additional wireless communication link from the first transmitter to the receiver at at least one respective different output power level different from the first power output level of at least one of the first transmitter and the receiver; extracting a plurality of features obtained from each of the at least one additional link attempt for each of the at least one different output power levels and recording the plurality of features at the receiver; and performing the inference algorithm using the plurality of features extracted from at least one beam direction and at least one output power level to infer an environmental information map of the area between the first transmitter and the receiver.

16. The method according to claim 14 further comprises: attempting at least one additional wireless communication link from the first transmitter to the receiver at at least one respective different antenna polarization different from the first antenna polarization of at least one of the first transmitter and the receiver; extracting a plurality of features obtained from each of the at least one additional link attempt at each of the at least one different antenna polarizations and recording the plurality of features at the receiver; and performing the inference algorithm using features at at least one beam direction and at least one of at least one output power level and at least one antenna polarization to infer an environmental information map of the area between the first transmitter and the receiver.

17. The method according to claim 1 further comprises: Attempt a wireless communication link by sending a signal from the second transmitter to the receiver at least one beam direction and at one or more of at least one antenna polarization of the second transmitter and receiver and at least one output power level of the second transmitter and the receiver; Extract a plurality of features obtained from the link attempt at at least one of the at least one beam direction, at least one of the at least one antenna polarization of the second transmitter and receiver, and at least one output power level of the second transmitter and the receiver, and record the plurality of features at the receiver; Extract a plurality of features obtained from each of the at least one additional link attempt for each of the at least one beam direction, at least one of the at least one antenna polarization of the second transmitter and receiver, and at least one output power level of the second transmitter and the receiver, and record a plurality of communication link metrics at the receiver; And Use the plurality of features at at least one of the at least one beam direction and at least one of the at least one output power level and at least one antenna polarization to perform the inference algorithm to infer an environmental information map of the area between the first transmitter and the receiver.

18. A computer system for generating an environmental information map using a wireless communication system, the wireless communication system including at least one transmitter and at least one receiver, at least one of the transmitter and receiver having a directional communication system, the method including: One or more computer processors; One or more non-transitory computer-readable storage media; Program instructions stored on the one or more non-transitory computer-readable storage media, the program instructions when implemented by the one or more processors cause the computer system to perform the steps according to any one of the preceding claims.

19. A computer program product, including: Program instructions on a computer-readable storage medium, wherein causing a computer to execute the program instructions causes the computer to perform the method for generating an environmental information map using a wireless communication system according to any one of claims 1 to 17.

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