Communication methods, environmental sensing methods, devices, equipment, media, and products

Abstract

Embodiments of the present application provide a communication method, a communication environment sensing method, a communication device, a network device, a computer-readable storage medium, and a computer program product. By analyzing wireless communication signals, communication environment sensing information is obtained, and furthermore, sensing of the wireless communication environment is relatively well realized.

Description

Technical Field

[0001] This application is proposed based on a Chinese patent application with an application number of 202210982215.2 and an application date of August 16, 2022, claims the priority of this Chinese patent application, and all the content of this Chinese patent application is incorporated herein by reference into this application.

[0002] The embodiments of this application relate to the field of communication technologies, and particularly to communication methods, communication environment sensing methods, communication devices, network devices, media, and products.

Background Art

[0003] The integration of communication and sensing (abbreviated as the integration of communication and sensing) is an important technology in future communication networks, such as 6G, etc. Communication is the transmission of information, and sensing is the detection and information acquisition of the environment, such as the communication environment. The integrated technology of communication and sensing has characteristics such as high spectral efficiency and relatively small interference between communication and sensing compared to the technology that separates communication and sensing. Therefore, in some technologies, it is desired to select a receiver that matches the communication environment based on the sensing result by sensing the communication environment.

[0004] In related technologies, communication physical layer receivers generally adopt a unified receiver processing framework and unified configuration parameters, and it is difficult for the receiver to match the sensing environment relatively well. How to match the receiver according to different communication environments has become an urgent problem to be studied and solved currently.

Summary of the Invention

Problems to be Solved by the Invention

[0005] The embodiments of this application provide a communication method, a communication environment sensing method, a communication device, a network device, a computer-readable storage medium, and a computer program product, which aim to sense a communication environment and match a receiver according to the sensed communication environment. [Means for solving the problem]

[0006] According to a first aspect, an embodiment of the present application provides a communication method which includes receiving an orthogonal frequency division multiplexing (OFDM) signal comprising at least a received pilot signal and a received data signal; performing channel estimation based on the received pilot signal to obtain channel features; obtaining first environmental information based on the channel features; obtaining interference feature data based on the OFDM signal to obtain second environmental information based on the interference feature data; obtaining communication environment sensing information based on the first environmental information and the second environmental information; and matching a receiver based on the communication environment sensing information.

[0007] According to a second aspect, an embodiment of the present application provides a method for sensing a communication environment, the method comprising: receiving an orthogonal frequency division multiplexed OFDM signal including at least a pilot signal and a data signal; performing channel estimation based on a pilot symbol included in the pilot signal to obtain channel features; obtaining first environmental information based on the channel features; obtaining corresponding interference feature data based on the OFDM signal to obtain second environmental information based on the interference feature data; and obtaining communication environment sensing information based on the first environmental information and the second environmental information.

[0008] According to a third aspect, an embodiment of the present application provides a communication device comprising: a receiving unit configured to receive an orthogonal frequency division multiplexed OFDM signal including at least a received pilot signal and a received data signal; a first analysis unit configured to perform channel estimation based on the received pilot signal to obtain channel features and to obtain first environmental information based on the channel features; a second analysis unit configured to obtain interference feature data based on the OFDM signal and to obtain second environmental information based on the interference feature data; and a processing unit configured to obtain communication environment sensing information based on the first environmental information and the second environmental information.

[0009] According to a fourth aspect, an embodiment of the present application provides a network device comprising a memory configured to store a computer program and a processor configured to execute the computer program and perform the communication method described in the first aspect or the communication environment sensing method described in the second aspect.

[0010] According to the fifth aspect, an embodiment of the present application provides a computer-readable storage medium in which computer-executable instructions are stored, and the computer-executable instructions are used to execute the communication method described in the first aspect or the communication environment sensing method described in the second aspect.

[0011] According to the sixth aspect, an embodiment of the present application provides a computer program product, the computer program product includes a computer program or computer instruction, the computer program or computer instruction is stored in a computer-readable storage medium, the processor of a computer device reads the computer program or computer instruction from the computer-readable storage medium, the processor executes the computer program or computer instruction, and causes the computer device to execute the communication method described in the first aspect or the communication environment sensing method described in the second aspect. [Brief explanation of the drawing]

[0012] [Figure 1] This is a schematic diagram of the receiver and related equipment in an OFDM system. [Figure 2] This is a flowchart of a communication environment sensing method according to one embodiment of this application. [Figure 3] This is a flowchart of a communication method according to one embodiment of this application. [Figure 4] This is a flowchart of a communication method according to one embodiment of this application. [Figure 5] This is a flowchart of a communication method according to one embodiment of this application. [Figure 6] This is a flowchart of a communication method according to one embodiment of this application. [Figure 7] This is a flowchart of a communication method according to one embodiment of this application. [Figure 8] This is a flowchart of a communication method according to one embodiment of this application. [Figure 9] This is a flowchart of a communication method according to one embodiment of this application. [Figure 10] This is a schematic diagram of a communication device according to one embodiment of the present invention. [Figure 11] This is a schematic diagram of a communication device according to one embodiment of the present invention. [Figure 12] This is a schematic diagram of an OFDM system according to one embodiment of this application. [Figure 13] This is a schematic diagram of a conventional receiver according to one embodiment of the present application. [Figure 14] This is a schematic diagram of an adaptive receiver according to one embodiment of the present application. [Figure 15] This is a schematic diagram of a neural network receiver according to one embodiment of the present application. [Figure 16] This is a schematic diagram of the structure of a neural network signal detector according to one embodiment of this application. [Figure 17] This is a schematic diagram showing a conventional receiver according to one embodiment of this application. [Figure 18]It is a schematic diagram of the conventional receiving mechanism of Scenario n according to an embodiment of the present application. [Figure 19] It is a schematic diagram of the structure of a communication device according to an embodiment of the present application.

Embodiments for Carrying Out the Invention

[0013] To make the object, technical solution and advantages of the present application clearer, the present application will be described in more detail below with reference to the drawings and embodiments. The specific embodiments described here are only used for interpreting the present application and are not intended to limit the present application.

[0014] In the schematic diagram of the device, the functional modules are divided, and the logical order is shown in the flowchart. However, in some cases, the module division in the device may be different, or the steps shown or described may be executed in an order different from that in the flowchart. Terms such as "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects and are not necessarily for describing a specific order or sequence.

[0015] In the description of the embodiments of the present application, unless otherwise clearly defined, terms such as installation, attachment, connection, etc. should be understood in a broad sense, and those skilled in the art may reasonably determine the specific meaning of the above terms in the embodiments of the present application by referring to the specific content of the technical solution. In the embodiments of the present application, terms such as "further", "exemplarily" or "optionally" are used for illustration, exemplification or explanation, and should not be construed as being more preferable or having more advantages than other embodiments or design solutions. The use of terms such as "further", "exemplarily" or "optionally" is intended to present related concepts in a specific manner.

[0016] The embodiments of this application may be used in various wireless communication systems, such as Global System of Mobile communication (GSM®), Code Division Multiple Access (CDMA) systems, Wideband Code Division Multiple Access (WCDMA®) systems, General Packet Radio Service (GPRS), Long Term Evolution (LTE) systems, Advanced Long Term Evolution (LIE-A) systems, Universal Mobile Telecommunication System (UMTS), 5G, Beyond Fifth Generation (B5G), and 6th Generation (6G) systems.

[0017] The embodiments of this application relate to a communication and sensing fusion matching receiver technology. This communication and sensing fusion technology achieves the fusion and symbiosis of communication and sensing functions using the same spectral resources through the joint design of air interfaces and protocols, and the sharing of software and hardware equipment. As a result, wireless networks can acquire sensing of target objects or environmental information by analyzing characteristic data of wireless communication signals while simultaneously performing data communication. Adopting communication and sensing fusion technology not only increases spectral utilization and equipment multiplexing rates but also expands the application scenarios of communication networks, for example, by being used in adaptive matching receivers or smart matching receivers. Receivers, such as link-level receivers, employ a unified processing framework and parameter configuration, making it difficult to perform adaptive processing according to different communication scenarios, such as different communication environments, different noise interference, different user characteristics, and ultimately different users, thus making it difficult to obtain excellent operational performance.

[0018] To describe this proposed technology in detail, the application scenarios of the embodiments of this application will be further explained using an application scenario related to a receiver in an OFDM system as an example.

[0019] Figure 1 is a schematic diagram of a receiver and its related equipment in an OFDM system. As shown in Figure 1, the signal undergoes CP rejection and FFT (Fast Fourier Transform) processing before being received by a symbol-level receiver (i.e., input to a symbol-level processing module), and after further processing by the symbol-level receiver, is input to a demodulation and decoding module. The implementation of this application may be used in various receivers, such as conventional receivers, new smart receivers, adaptive receivers, and receivers with neural network predictive models.

[0020] In the OFDM system shown in 1, if we represent the received frequency domain signal as Y,

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[0021] In some technologies, the communication physical layer receiver employs a unified receiver processing framework and unified configuration parameters, making it difficult to perceive complex communication environments, such as wireless communication environments, and furthermore, it cannot dynamically control the optimal receiving algorithm according to different wireless communication environments. In other technologies, there are physical layer OFDM receivers based on artificial intelligence (AI), such as model-driven OFDM receivers, data-driven OFDM receivers, or data-model dual-driven OFDM receivers. However, these technologies lack the ability to perceive complex communication environments, such as wireless communication, making it difficult for OFDM receivers to achieve excellent performance that matches wireless communication environments.

[0022] The following describes an embodiment of this application using a wireless communication environment as an example.

[0023] Embodiments of this application provide a wireless communication method, a wireless communication environment sensing method, a wireless communication device, a network device, a computer-readable storage medium, and a computer program product, which obtain communication environment sensing information by analyzing wireless communication signals and further achieve relatively good sensing of the wireless communication environment. Another embodiment of this application further enables the selection of different receivers under different wireless communication environments by sensing the wireless communication environment, thereby achieving smart and dynamic control of receivers, such as link-level receivers, and improving system performance.

[0024] The embodiments of this application will be further described below with reference to the drawings.

[0025] Figure 2 is a flowchart of a communication environment sensing method according to one embodiment of the present application. As shown in Figure 2, this communication environment sensing method may be applied to an OFDM communication system. In the embodiment of Figure 2, this communication environment sensing method may include, but is not limited to, steps S100, S200, S300, S400, and S500.

[0026] Step S100: Receive the OFDM signal.

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[0032] In one embodiment, channel estimation may employ an estimation algorithm based on a training sequence or a blind estimation algorithm. The estimation algorithm based on a training sequence may include methods such as least squares channel estimation (LS channel estimation), least mean squares error channel estimation (MMSE channel estimation), and low-order least mean squares error channel estimation (LMMSE channel estimation). The embodiments of this application are not limited. To facilitate the description of this technical invention, LS channel estimation will be described below as an example.

[0033] In one embodiment, the channel features may include one or more of the frequency domain fading coefficient, the time relevance coefficient, the energy distribution of the time domain channel estimate, and the Rice coefficient, and the channel features may further include other parameters, data, or content available to characterize the channel characteristics.

[0034] Step S300: Based on the channel characteristics, obtain the first environmental information.

[0035] In one embodiment, the first environmental information may include one or more of the following: communication environment speed information, communication line-of-sight information, and communication environment multipath delay information, and the first environmental information may further include other information that can be used to characterize the communication environment conditions.

[0036] In another embodiment, the first environmental information may include one or more of terminal feature information and user feature information. Here, terminal feature information is parameter information for characterizing terminal features, and user feature information is parameter information for characterizing user features.

[0037] In another embodiment, the communication environment speed information may include one or more of high speed, medium speed, and low speed. Line-of-sight information may include one or more of line-of-sight and line-of-sight information. Communication multipath delay information may include one or more of high multipath delays and low multipath delays.

[0038] In another embodiment, obtaining first environmental information based on channel features may be done by comparing channel features with a preset threshold, or by inputting the channel features into a neural network.

[0039] Step S400: Interference feature data is obtained based on the OFDM signal, and second environmental information is obtained based on the interference feature data.

[0040] In one embodiment, the interference feature data may include one or more of noise interference, received signal strength indicators, and signal covariance matrices, and the interference feature data may further include other data available to characterize the communication interference situation characteristics.

[0041] In another embodiment, the noise interference may include one or more of the following: full-bandwidth noise interference power, spatial frequency domain dimensional noise interference, and resource block RB granularity noise interference power, and the noise interference may further include other parameters, data, or content available to characterize the communication noise interference characteristics.

[0042] In one embodiment, the second environmental information may include communication interference information.

[0043] In another embodiment, the communication interference information may include one or more of the following: interference present, no interference, interference intensity information, and interference location information. The communication interference information may further include other information available to characterize the communication interference characteristics.

[0044] In another embodiment, interference feature data is obtained based on an OFDM signal, and second environmental information is obtained based on the interference feature data. This can be done by comparing the interference feature data with a communication interference threshold, by inputting the interference feature data into a second neural network, or by using a clustering algorithm, such as a K-means algorithm, to obtain the interference feature data.

[0045] Step S500: Communication environment sensing information is obtained based on the first environmental information and the second environmental information.

[0046] The embodiments of this application provide a method for sensing a communication environment, which can obtain communication environment sensing information through the extraction and analysis of communication environment features, and further enable relatively good sensing of the wireless communication environment. In communication scenarios, it is possible not only to enhance the ability to sense the environment, but also to better utilize wireless signals to acquire surrounding physical environment information, uncover communication capabilities, and improve the user experience.

[0047] Figure 3 is a flowchart of a communication method according to one embodiment of the present application. As shown in Figure 3, this communication method may include, but is not limited to, steps S100, S200, S300, S400, S500, and S600.

[0048] Step S100: Receive the OFDM signal.

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[0050] Step S300: Based on the channel characteristics, obtain the first environmental information.

[0051] Step S400: Interference feature data is obtained based on the OFDM signal, and second environmental information is obtained based on the interference feature data.

[0052] Step S500: Communication environment sensing information is obtained based on the first environmental information and the second environmental information.

[0053] Step S600: Match the receiver based on the communication environment sensing information.

[0054] The embodiment of this application provides a communication method that obtains communication environment sensing information by extracting and analyzing communication environment characteristics, and by utilizing the communication environment sensing information to select different receivers under different wireless communication environments, thereby enabling smart and dynamic control of receivers, such as link level receivers, and improving system performance. The contents described in the communication environment sensing method above also apply to this embodiment.

[0055] Figure 4 is a flowchart of a communication method according to one embodiment of the present application. As shown in Figure 4, this communication method may further include step S700.

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[0057] The embodiments of this application provide a communication method that senses the communication environment, obtains communication environment sensing information, and matches different receivers, thereby not only achieving smart and dynamic control of receivers but also improving system performance.

[0058] Figure 5 is a flowchart of a communication method according to one embodiment of the present application. As shown in Figure 5, this communication method may further include step S800.

[0059] Step S800: Demodulate the transmitted signal and obtain demodulated data.

[0060] Figure 6 is a flowchart of a communication method according to one embodiment of the present application, and also provides a further explanation of step S600. As shown in Figure 6, step S600 may include, but is not limited to, steps S610 and S620.

[0061] Step S610: Obtain the communication scenario type based on the communication environment sensing information.

[0062] In one embodiment, the communication scenario type may be determined by comprehensively considering one or more of the following: communication environment speed information, communication line-of-sight information, communication multipath delay information, terminal characteristic information, user characteristic information, and communication interference information. Different communication scenario types may be constructed based on different combinations, and the embodiments of this application are not limited thereto.

[0063] To facilitate further description of the communication scenario types of the embodiments of this application, the following exemplifies the communication scenario types in terms of combinations of communication environment speed information, line-of-sight information, communication multipath delay information, and communication interference information.

[0064] Table 1 is a list of communication types according to one embodiment of this application, and includes nine different communication scenario types.

[0065] [Table 1]

[0066] Table 2 is a list of communication types according to another embodiment of this application, and includes 18 different communication scenario types.

[0067] [Table 2]

[0068] Step S620: Match the receiver based on the communication scenario type.

[0069] In one embodiment, the receiver may be a conventional receiver, a smart receiver, a neural network receiver, or any other type of receiver.

[0070] In another embodiment, the neural network receiver may be a receiver having a neural network detector, which may be pre-trained.

[0071] Figure 7 is a flowchart of a communication method according to one embodiment of the present application, and also provides a further explanation of step S700. As shown in Figure 7, step S700 may include, but is not limited to, steps S710 and S720.

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[0075] Figure 8 is a flowchart of a communication method according to one embodiment of the present application, and also provides a further explanation of step S710. As shown in Figure 8, step S710 may include, but is not limited to, steps S711 and S712.

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[0078] Figure 9 is a flowchart of a communication method according to one embodiment of the present application, and also provides a further explanation of step S800. As shown in Figure 9, step S800 may include, but is not limited to, steps S810 and S820.

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[0081] Figure 10 is a schematic diagram of a communication device according to one embodiment of the present application. As shown in Figure 10, the communication device includes a receiving unit, a first analysis unit, a second analysis unit, and a processing unit.

[0082] The receiving unit is configured to receive an orthogonal frequency division multiplexed OFDM signal that includes at least a received pilot signal and a received data signal. The first analysis unit is configured to perform channel estimation based on the received pilot signal to obtain channel characteristics, and to obtain first environmental information based on the channel characteristics, wherein the first environmental information includes at least one of communication environment speed information, communication line-of-sight information, and communication multipath delay information. The second analysis unit is configured to obtain interference feature data based on the OFDM signal and to obtain second environmental information, which includes at least communication interference information, based on the interference feature data. The processing unit is configured to obtain communication environment sensing information based on the first environmental information and the second environmental information.

[0083] The embodiments of this application provide a communication device that can obtain communication environment sensing information by extracting and analyzing communication environment features, and can also achieve relatively good sensing of the wireless communication environment. In communication scenarios, it is possible to not only enhance the ability to sense the environment, but also to better utilize wireless signals to acquire surrounding physical environment information, uncover communication capabilities, and improve the user experience.

[0084] As shown in Figure 11, the communication device may further include a matching unit.

[0085] The matching unit is configured to match receivers based on communication environment sensing information.

[0086] The embodiments of this application provide another communication device that obtains communication environment sensing information by extracting and analyzing communication environment characteristics, and uses this information to select different receivers under different wireless communication environments. This enables smart and dynamic control of receivers, such as link-level receivers, thereby improving system performance.

[0087] The communication and sensing fusion adaptive receiver of the embodiment of this application can better detect the transmitted signal X from the received signal Y in different communication environments, such as a wireless transmission environment, and includes, but is not limited to, the following steps.

[0088] Step 1: Smart sensing and recognition are performed, i.e., OFDM signals, such as OFDM frequency domain signals, are used to sense and recognize the wireless transmission environment and interference present in the environment. An example of such a method is as follows:

[0089] ● It employs OFDM frequency domain signals to sense the wireless transmission environment and recognize the wireless channel scenario in which the current terminal or user is located. It uses OFDM pilot signals to recognize the wireless channel scenario, and the sensing and recognition algorithm includes, but is not limited to, algorithms that extract channel features and perform threshold determination based on the experience of conventional experts, or algorithms that extract channel features based on the experience of experts and combine them with a neural network.

[0090] ● OFDM frequency domain signals are used to sense whether interference exists in the environment, or the intensity of the interference, and to recognize whether interference exists in the current three-dimensional resources of time, space, and frequency, what type of interference exists, or the intensity of the interference that exists. Here, methods such as full-bandwidth noise interference power, received signal strength indicator (RSSI), and covariance matrices of noise interference or signals in the spatial frequency domain may be used to sense and recognize interference. The sensing and recognition algorithms include, but are not limited to, algorithms that extract interference features and perform thresholding based on the experience of conventional experts, or algorithms that extract interference features based on the experience of experts and combine them with neural networks.

[0091] ● Overall Assessment: Based on the wireless transmission environment and the results of sensing and recognizing interference in the environment, an overall assessment is performed to determine which scenario the current user is in within the scenario set. The scenario type set is presented exemplarily as wireless channel scenarios 1, 2, ..., N without interference, and scenarios 1, 2, ..., N with interference.

[0092] Step 2: The receiver is matched, that is, an OFDM signal is detected by adaptively selecting a receiver that matches the wireless transmission environment based on the recognition result.

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[0095] Here, the receiver in different scenarios may be a conventional receiver, and there may be different algorithmic flow processing depending on the scenario. In different multipath delay extensions, the channel estimation module may employ filters of different window lengths to remove noise and interference, and under different speeds, it may employ different pilot configurations and different frequency offset estimation and compensation algorithms. Alternatively, the receiver may be a smart adaptive receiver, and each scenario corresponds to one neural network signal detector, these neural network signal detectors are pre-trained offline to facilitate online detection and constitute a set of models. Here, the structure of the neural network includes, but is not limited to, deep neural networks, convolutional neural networks, residual neural networks, or residual neural networks with attention mechanisms.

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[0097] To further describe the communication environment sensing method, communication device, network device, computer-readable storage medium, and computer program product according to the embodiments of this application, different examples are described below in combination.

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[0099] Step 1: Perform smart sensing and recognition, that is, use OFDM frequency domain signals to sense and recognize the wireless transmission environment and interference present in the environment.

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[0101] This system uses OFDM frequency-domain resource block (RB) granularity noise interference power to detect interference present in the environment and recognize whether interference exists in the current frequency-domain resource, as well as the interference source and intensity. It collects RB-level noise interference power across the entire frequency-domain bandwidth and uses a K-means clustering algorithm to identify which RBs across the bandwidth are affected and their intensity.

[0102] Overall Judgment: Based on the wireless transmission environment and the detection and recognition results of interference in the environment, an overall judgment is made to determine which scenario in the scenario set all of the current terminal's scheduling RBs belong to. Here, the scenario set may consist of six types: low speed with no interference, medium speed and high speed, and low speed with interference, medium speed and high speed. Recognition of the wireless channel scenario results in all RB results being the same, but the interference recognition result is the RB level. In this example, a judgment is made for each RB of the UE, and the final recognition result of the RB level is obtained.

[0103] Step 2: Determine the adaptive receiver, that is, adaptively select a receiver that matches the wireless transmission environment based on the RB-level scenario recognition results from Step 1, and perform OFDM signal detection. As shown in Figure 12, the process in the figure is the processing flow for each RB. Based on the channel scenario recognition results and interference recognition results obtained in Step 1, select the receiver that best matches the current propagation environment from a set of receivers for different scenarios.

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[0106] Example 2: Figure 14 is a schematic diagram of an adaptive receiver according to one embodiment of the present invention. The adaptive receiver in step 2 of Embodiment 1 may be the smart adaptive receiver shown in Figure 14. The smart adaptive receiver includes a least-squares (LS) channel estimation submodule and a scenario-adaptive OFDM signal detector submodule.

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[0109] 2. Scenario-Adaptive OFDM Signal Detectors: The set of neural network signal detectors in this example may consist of six types: slow, medium, and fast neural network signal detectors without interference, and slow, medium, and fast neural network signal detectors with interference. These six types of neural network signal detectors are pre-trained offline to facilitate online detection. Based on the channel scenario recognition and interference recognition results obtained in Step 1, a neural network model that best matches the current propagation environment is selected from the set of neural network signal detector models.

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[0111] Here, the neural network signal detector consists of two subnetworks: an extended channel estimation subnetwork and a channel equalization subnetwork. The extended channel estimation subnetwork removes noise and interference, improves the quality of channel estimates, performs channel interpolation, and obtains channel estimates of data symbols. The channel equalization subnetwork is used for detecting OFDM signals as an alternative to the conventional channel equalization function. Both subnetworks employ residual neural networks with channel attention mechanisms. The two subnetworks are trained together, and the mean squared error (MSE) of the transmitted data and its estimates is used as the loss function, as shown, for example, in equation (3) below.

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[0114] Step 1: Perform smart sensing and recognition, that is, use OFDM frequency domain signals to sense and recognize the wireless transmission environment and interference present in the environment.

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[0117] Overall Judgment: Based on the wireless transmission environment and the results of interference detection and recognition in that environment, an overall judgment is made to determine which scenario the current user is in within the scenario set. There are a total of 18 scenario sets, with 9 scenarios with interference and 9 scenarios without interference, as shown in Table 2. Recognition of wireless channel scenarios is the same for all RB results, but interference recognition results are at the RB level. Here, all RBs of the UE are grouped, and if 4 RBs make up one RB group (if there are fewer than 4, one RB group is processed), then if there is one RB judgment result indicating interference within an RB group, interference is recognized as existing in that RB group, and the final recognition result is at the RB group level.

[0118] Step 2: Determine the smart adaptive receiver, i.e., as shown in Figure 14, adaptively select a smart receiver that matches the wireless transmission environment based on the RB group level scenario recognition results from Step 1 and perform OFDM signal detection. The process in Figure 14 is the processing flow for each RB group, and the smart adaptive receiver includes an LS channel estimation submodule and a scenario-adaptive OFDM signal detector submodule.

[0119] 1. Select an appropriate neural network OFDM signal detector. Based on the channel scenario recognition and interference recognition results obtained in Step 1, select the neural network model that best matches the current propagation environment from the set of neural network signal detector models (a total of 18 types, shown in Table 2). All models in this set of neural network signal detectors are pre-trained offline to facilitate online detection.

[0120] 2. Perform OFDM signal detection.

[0121] Figure 16 is a schematic diagram of the structure of a neural network signal detector according to one embodiment of this application. In each scenario of this example, the structure of the neural network signal detector detects multiple OFDM signals according to a streaming structure, as shown in Figure 16.

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[0125] The processing flow for subsequent pilots and data symbols is the same as above until the demodulation result of the last data symbol is obtained.

[0126] Here, the neural network signal detector in each scenario employs a convolutional neural network with residuals. During offline training, the mean squared error (MSE) of the transmitted data and its estimate is used as the loss function, and training is performed using the Adam optimizer, referring to equation (3). Once the loss function converges to a certain extent, network training is complete, the model parameters are saved, and they are used for subsequent online signal detection.

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[0128] Example 4: Figure 17 is a schematic diagram showing a conventional receiver according to one embodiment of the present application. As shown in Figure 17, the difference between this example and Example 3 is that the conventional receiver in scenario n replaces the neural network signal detector in scenario n.

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[0130] Figure 19 is a schematic diagram of the structure of a communication device according to one embodiment of the present application. As shown in Figure 19, this communication device 1000 includes a memory 1100 and a processor 1200. The number of memory 1100 and processor 1200 may be one or more, and in Figure 19, one memory 1101 and one processor 1201 are used as an example. The memory 1101 and processor 1201 in the communication device may be connected via a bus or other method, with connection via a bus being used as an example in Figure 19.

[0131] The memory 1101 may be used as a computer-readable storage medium to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the method according to any one embodiment of this application. The processor 1201 implements the above method by executing the software programs, instructions and modules stored in the memory 1101.

[0132] The memory 1101 may mainly include a program storage area and a data storage area. Here, the program storage area may store an operating system and application programs necessary for at least one function. The memory 1101 may also include high-speed random access memory and non-volatile memory, such as at least one magnetic disk memory device, flash memory device, or other non-volatile solid-state memory device. In some examples, the memory 1101 further includes memory installed remotely from the processor 1201, and these remote memories may be connected to equipment via a network. Examples of the above network include, but are not limited to, the Internet, intranet, local area network, mobile communication network, and combinations thereof.

[0133] One embodiment of this application further provides a computer-readable storage medium on which computer-executable instructions are stored, and these computer-executable instructions are used to execute a communication method or a communication environment sensing method according to any one embodiment of this application.

[0134] One embodiment of this application further provides a computer program product, which includes a computer program or computer instruction, the computer program or computer instruction being stored in a computer-readable storage medium, and the processor of a computer device reads the computer program or computer instruction, the processor-executed computer program or computer instruction from the computer-readable storage medium, and causes the computer device to execute a communication method or communication environment sensing method according to any one embodiment of this application.

[0135] All or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0136] In hardware embodiments, the distinctions between functional modules / units mentioned above do not necessarily correspond to distinctions between physical assemblies. For example, a single physical assembly may have multiple functions, or a single function or step may be performed in conjunction with several physical assemblies. Some or all physical assemblies may be implemented as software executed by a processor, such as a central processor, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as a dedicated integrated circuit. Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-temporary media) and communication media (or temporary media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information (e.g., computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other media that can be used to store desired information and can be accessed by a computer. As is well known to those skilled in the art, communication media generally include computer-readable instructions, data structures, program modules or other data in modulated data signals such as carriers or other transmission mechanisms, and may include any information transmission medium.

[0137] The terms "component," "module," and "system" used herein are used to represent computer-related entities, hardware, firmware, hardware-software combinations, software, or software in execution. For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, or a computer. As illustrated, both an application running on a computing device and a computing device may be components. One or more components may reside in a process or execution thread, and components may be located in one computer or distributed between two or more computers. These components may be executed from various computer-readable media in which various data structures are stored. Components may communicate via local or remote processes based on signals, for example, having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, or a network, e.g., the Internet interacting with other systems via signals).

[0138] Although several embodiments of this application have been described above with reference to the drawings, this does not limit the scope of the rights of this application. Any modifications, equivalent substitutions, and improvements made by a person skilled in the art that do not depart from the scope and substance of the present invention should all be within the scope of the rights of this application.

Claims

1. A method of communication, Receiving an orthogonal frequency division multiplexed OFDM signal that includes at least a received pilot signal and a received data signal, Based on the received pilot signal, channel estimation is performed to obtain channel characteristics, Based on the channel characteristics, obtaining first environmental information, wherein the first environmental information includes at least communication environment speed information, or at least communication line-of-sight information, or at least communication environment speed information and communication line-of-sight information, or at least communication environment speed information and communication multipath delay information, or at least communication line-of-sight information and communication multipath delay information, or at least communication environment speed information, communication line-of-sight information, and communication multipath delay information. Based on the OFDM signal, interference feature data is obtained, and based on the interference feature data, second environmental information is obtained which includes at least communication interference information. Based on the first environmental information and the second environmental information, communication environment sensing information is obtained. A method comprising matching a receiver based on the aforementioned communication environment sensing information.

2. Matching receivers based on the aforementioned communication environment sensing information is The method according to claim 1, comprising obtaining a communication scenario type based on the communication environment sensing information, and matching a receiver based on the communication scenario type.

3. The method according to claim 1, further comprising performing signal detection based on the received pilot signal, received data signal, and preset local pilot signal to obtain a transmission signal.

4. The above-mentioned process of detecting a signal based on the received pilot signal, received data signal, and preset local pilot signal to obtain a transmission signal is as follows: Based on the received pilot signal and a preset local pilot signal, channel estimation is performed to obtain a channel estimate. The method according to claim 3, comprising performing channel equalization based on the channel estimate and the received data signal to obtain a transmission signal.

5. The method according to claim 3, further comprising demodulating based on the transmitted signal to obtain demodulated data.

6. The received pilot signal includes at least the first received pilot signal, The received data signal includes at least a first received data signal and a second received data signal. The above-mentioned process of detecting a signal based on the received pilot signal, received data signal, and preset local pilot signal to obtain a transmission signal is as follows: Based on the first received pilot signal and the local pilot signal, channel estimation is performed to obtain the first channel estimate. Channel equalization is performed based on the first channel estimate and the first received data signal to obtain the first transmitted data estimate. Based on the estimated value of the first transmitted data, a hard determination is made to obtain hard determination data. Based on the hard judgment data and the first received data signal, channel estimation is performed to obtain a second channel estimate. The method according to claim 3, further comprising performing channel equalization based on the second channel estimate and the second received data signal to obtain a second transmitted data estimate.

7. Based on the estimated value of the first transmitted data, demodulation is performed to obtain the first demodulated data. The method according to claim 6, further comprising demodulating based on the second estimated transmitted data to obtain second demodulated data.

8. The aforementioned channel features are, The method according to claim 1, characterized in that it includes at least one of the frequency domain fading coefficient, the time relevance coefficient, the energy distribution of the time domain channel estimate, and the Rice coefficient.

9. The aforementioned communication environment speed information includes at least one of high speed, medium speed, and low speed. The aforementioned line-of-sight information includes at least one of the following: within line of sight and outside line of sight. The method according to claim 1, wherein the communication multipath delay information includes at least one of high multipath delay and low multipath delay.

10. Based on the channel characteristics described above, obtaining the first environmental information is possible. The channel characteristics are compared with a preset threshold to obtain the first environmental information. The method according to claim 1, comprising at least one of inputting the channel features into a first neural network to obtain the first environmental information.

11. The aforementioned interference feature data is The method according to claim 1, comprising at least one of noise interference, a received signal strength indicator, and a signal covariance matrix.

12. The aforementioned noise interference is The method according to claim 11, comprising at least one of full-bandwidth noise interference power, spatial frequency domain dimensional noise interference, and resource block RB granularity noise interference power.

13. The aforementioned communication interference information is, It includes at least one of the following: interference present, no interference, interference intensity, and interference location information. The method according to claim 1, characterized in that the interference intensity includes at least one of high interference, medium interference, and low interference.

14. Obtaining second environmental information based on interference feature data corresponding to the OFDM signal is possible. By comparing the aforementioned interference characteristic data with the communication interference threshold, a second set of environmental information is obtained. The interference feature data is input into a second neural network to obtain the second environmental information, The method according to claim 1, comprising at least one of obtaining the second environmental information by a clustering algorithm based on the interference feature data.

15. The aforementioned first environmental information is, The method according to claim 1, further comprising terminal feature information, which is parameter information for characterizing terminal features.

16. The above-mentioned channel estimation is performed based on the received pilot signal and a preset local pilot signal, and the channel estimate is obtained. Based on the received pilot signal and a preset local pilot signal, channel estimation is performed to obtain a first channel estimate. The method according to claim 4, further comprising performing time-frequency offset estimation and compensation, channel estimation noise reduction, and channel interpolation processing on the first channel estimate to obtain a second channel estimate.

17. A method for sensing the communication environment, Receiving an orthogonal frequency division multiplexed OFDM signal that includes at least a pilot signal and a data signal, Channel estimation is performed based on the pilot symbols included in the aforementioned pilot signal to obtain channel characteristics. Based on the channel characteristics, a first environmental information is obtained, wherein the first environmental information includes at least communication environment speed information, communication line-of-sight information, communication multipath delay information, terminal characteristic information, and user characteristic information. Based on the OFDM signal, interference feature data is obtained, and based on the interference feature data, second environmental information is obtained which includes at least communication interference information. A method comprising obtaining communication environment sensing information based on the first environmental information and the second environmental information.

18. The method according to claim 17, further comprising obtaining a communication scenario type based on the aforementioned communication environment sensing information, wherein the communication scenario type is obtained by comprehensively determining based on at least one of the following: communication environment speed information, communication line-of-sight information, communication multipath delay information, terminal characteristic information, and communication interference information.

19. It is a communication device, Memory configured to store computer programs, A communication device comprising a processor configured to execute the computer program described above and to perform the communication method described in any one of claims 1 to 16, or the communication environment sensing method described in claim 17 or 18.

20. A computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are used to execute the communication method described in any one of claims 1 to 16, or the communication environment sensing method described in claim 17 or 18.

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