Node execution method and node equipment

Abstract

The present disclosure provides a method for executing target detection in a wireless communication system, comprising: grouping channel state information received by M antennas to obtain N groups of channel state information, M and N being natural numbers greater than 1; obtaining features based on the N groups of channel state information; and performing target detection based on the features.

Description

Technical Field

[0001] The present disclosure relates to a method executed by a node in a communication system and a node device. Background Art

[0002] Considering the development of wireless communication from generation to generation, these technologies are mainly developed for services targeting people, such as voice calls, multimedia services, and data services. With the commercialization of the fifth-generation (5G) communication system, the number of connected devices is expected to grow exponentially. These will be increasingly connected to the communication network. Examples of the Internet of Things can include vehicles, robots, drones, home appliances, displays, smart sensors connected to various infrastructures, construction machinery, and factory equipment. Mobile devices are expected to develop in various forms such as augmented reality glasses, virtual reality headsets, and holographic devices. Efforts have been made to develop improved 6G communication systems in order to provide various services by connecting hundreds of billions of devices and things in the sixth-generation (6G) era. For these reasons, 6G communication systems are called super 5G systems.

[0003] The 6G communication system, which is expected to be commercialized around 2030, will have a peak data rate of tera (1,000 gigabits) bps and a radio latency of less than 100 μsec, thus being 50 times the data rate of the 5G communication system and having 1 / 10 of its radio latency.

[0004] In order to achieve such high data rates and ultra-low latency, the implementation of 6G communication systems in terahertz (e.g., 95GHz to 3THz bands) has been considered. It is expected that since the path loss and atmospheric absorption in the terahertz band are more serious than those in the millimeter wave (mmWave) band introduced in 5G, the technology that can ensure the signal transmission distance (i.e., coverage) will become more critical. As the main technology to ensure coverage, it is necessary to develop radio frequency (RF) elements, antennas, new waveforms with better coverage than orthogonal frequency division multiplexing (OFDM), beamforming and massive multiple input multiple output (MIMO), full dimensional multiple input multiple output (FD-MIMO), array antennas, and multi-antenna transmission technologies such as massive antennas. In addition, new technologies have been discussed to improve signal coverage in the terahertz band, such as metamaterial-based lenses and antennas, orbital angular momentum (OAM), and reconfigurable intelligence surface (RIS).

[0005] In addition, in order to improve spectrum efficiency and overall network performance, the following technologies have been developed for 6G communication systems: full-duplex technology for enabling uplink and downlink transmissions to use the same frequency resources simultaneously; network technology that utilizes satellites, high-altitude platform stations (HAPS), etc. in an integrated manner; improved network structure to support mobile base stations, etc., and to enable network operation optimization and automation, etc.; dynamic spectrum sharing technology with conflict avoidance based on spectrum usage prediction: using artificial intelligence (AI) in wireless communications to improve overall network operations by utilizing AI from the design stage of developing 6G and internalizing end-to-end AI support functions; and next-generation distributed computing technology that overcomes the computing power limitations of user equipment (UE) through ultra-high performance communication and computing resources achievable on the network (such as mobile edge computing (MEC), cloud, etc.). In addition, attempts are continuing to strengthen connectivity between devices, optimize networks, promote software-based network entities, and increase openness of wireless communications by designing new protocols to be used in 6G communication systems, developing mechanisms for implementing hardware-based security environments and secure use of data, and developing technologies for maintaining privacy.

[0006] It is expected that the research and development of 6G communication systems including hyperconnectivity of person to machine (P2M) and machine to machine (M2M) will bring about the next hyperconnectivity experience. In particular, services such as truly immersive extended reality (XR), high-fidelity mobile holograms, and digital replicas are expected to be provided through 6G communication systems. In addition, services such as remote surgery, industrial automation, and emergency response for enhanced safety and reliability will be provided through 6G communication systems, making the technology applicable to various fields such as industry, healthcare, automobiles, and home appliances. Summary of the invention

[0007] In order to overcome the above technical problems or at least partially solve the above technical problems, the following technical solutions are proposed.

[0008] According to one aspect of the present invention, a method for performing target detection in a wireless communication system is provided. The method comprises: grouping channel state information received by M antennas to obtain N groups of channel state information, where M and N are natural numbers greater than 1; obtaining features based on the N groups of channel state information; and performing target detection based on the features.

[0009] According to an exemplary embodiment, grouping the channel state information received by the M antennas includes: grouping the channel state information received by the M antennas according to a corresponding relationship among the M antennas.

[0010] According to an exemplary embodiment, grouping the channel state information received by the M antennas according to the correspondence between the M antennas includes at least one of the following: combining the M antennas in pairs to obtain the correspondence between the M antennas, and grouping the channel state information received by the M antennas according to the correspondence between the M antennas; combining any one of the M antennas with any other antenna to obtain the correspondence between the M antennas, and grouping the channel state information received by the M antennas according to the correspondence between the M antennas.

[0011] According to an exemplary embodiment, obtaining the feature based on the N groups of channel state information includes: extracting frequency domain features for each group of channel state information, and obtaining the frequency domain features of the group of channel state information based on the frequency domain features and time information corresponding to the time window;

[0012] Performing target detection based on the features includes: performing target detection based on N groups of frequency domain features.

[0013] According to an exemplary embodiment, obtaining the frequency domain characteristics of the group of channel state information based on the frequency domain characteristics and the time information corresponding to the time window includes: obtaining the frequency domain characteristics corresponding to the time information in each time window based on the frequency domain characteristics and the time information corresponding to the time window; and performing feature extraction based on the frequency domain characteristics corresponding to the time information in each time window.

[0014] According to an exemplary embodiment, extracting frequency domain features for each set of channel state information includes: performing quotient and / or outlier processing on each set of channel state information, and extracting frequency domain features.

[0015] According to an exemplary embodiment, feature extraction based on frequency domain features corresponding to time information within each time window includes: performing outlier processing on the frequency domain features corresponding to the time information within each time window, and performing at least one of the following processing: statistical feature extraction, maximum eigenvalue extraction, and extraction of eigenvalues ​​other than the maximum eigenvalue.

[0016] According to an exemplary embodiment, obtaining features based on the N groups of channel state information includes: for each group of channel state information, obtaining channel state information in the time domain based on the channel state information; obtaining time domain features based on the time domain channel state information; and obtaining time domain features within each time window based on the time domain features and time information corresponding to the time window.

[0017] According to another aspect of the present invention, a first node in a wireless communication system is provided, wherein the first node comprises a transceiver and a controller, wherein the controller is configured to execute any one of the above methods.

[0018] According to another aspect of the present invention, a computer-readable storage medium in a wireless communication system is provided, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the processor executes any one of the above methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] For a more complete understanding of the present disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, wherein like reference numerals represent like parts:

[0020] Figure 1 An example wireless network according to an embodiment of the present disclosure is shown;

[0021] Figure 2 An example base station according to an embodiment of the present disclosure is shown;

[0022] Figure 3 An example user device according to an embodiment of the present disclosure is shown;

[0023] Figure 4 A schematic diagram showing a method executed by a node according to at least one embodiment of the present disclosure is shown;

[0024] Figure 5 A schematic diagram showing a method executed by a node according to at least one embodiment of the present disclosure is shown;

[0025] Figure 6 A schematic diagram showing a method executed by a node according to at least one embodiment of the present disclosure is shown;

[0026] Figure 7 A schematic diagram showing a method executed by a node according to at least one embodiment of the present disclosure is shown;

[0027] Figure 8 A schematic diagram showing a method executed by a node according to at least one embodiment of the present disclosure is shown;

[0028] Fig. 9 A schematic diagram showing a method executed by a node according to at least one embodiment of the present disclosure is shown;

[0029] Fig.10 A schematic diagram showing a method executed by a node according to at least one embodiment of the present disclosure is shown;

[0030] Fig.11 A schematic diagram showing a method executed by a node according to at least one embodiment of the present disclosure is shown;

[0031] Fig.12An exemplary structure of a first node device according to the present disclosure is shown. DETAILED DESCRIPTION

[0032] Before carrying out the following specific embodiments, it may be advantageous to set forth the definitions of certain words and phrases used throughout the patent document. The term "connection" and its derivatives refer to any direct or indirect communication between two or more elements, regardless of whether those elements are in physical contact with each other. The terms "send", "receive" and "communicate" and their derivatives include both direct and indirect communication. The terms "include" and "include" and their derivatives mean unrestricted inclusion. The term "or" is compatible, meaning and / or. The phrase "associated with" and its derivatives mean including, included, interconnected with it, including, included, connected to or connected with it, coupled to or coupled with it, can communicate with it, collaborate with it, interweave, juxtapose, approach, be bound to or bound with it, have, have the property of, have to ... or have a relationship with ... etc. The term "controller" means any device, system or part thereof that controls at least one operation. Such a controller can be implemented in a hardware manner or in a combination of hardware and software and / or firmware. The functionality associated with any particular controller, whether local or remote, may be centralized or distributed. The phrase "at least one of" when used with a list of items means that different combinations of one or more of the listed items may be used, and only one item in the list may be required. For example, "at least one of A, B, and C" includes any of the following combinations: A, B, C, A and B, A and C, B and C, and only A, only B, only C. Likewise, the term "set" means one or more. Thus, a set of items may be a single item or a set of two or more items.

[0033] Moreover, various functions as described below can be implemented or supported by one or more computer programs, each of which is formed by a computer-readable program code and embodied in a computer-readable medium. The terms "application" and "program" refer to one or more computer programs, software components, instruction sets, processes, functions, objects, categories, instances, related data, or a part thereof that are suitable for being implemented in a suitable computer-readable program code. The phrase "computer-readable program code" includes any type of computer code, including source code, object code, and executable code. The phrase "computer-readable medium" includes any type of medium that can be accessed by a computer, such as a read-only memory (ROM), a random access memory (RAM), a hard drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. "Non-transient" computer-readable media excludes wired, wireless, optical, or other communication links that convey instantaneous electrical signals or other signals. Non-transient computer-readable media include media in which data can be permanently stored and media in which data can be stored and rewritten later, such as rewritable optical discs or erasable memory devices.

[0034] Definitions for certain other words and phrases are provided throughout this patent document. Those of skill in the art should understand that in many, if not most instances, such definitions apply to prior, as well as future uses of such defined words and phrases.

[0035] The figures and various embodiments used to describe the principles of the present disclosure are included herein for illustration only and should not be construed in any way as limiting the scope of the present disclosure. In addition, those skilled in the art will appreciate that the principles of the present disclosure can be implemented in any appropriately arranged wireless communication system.

[0036] The following Figures 1 to 3 Various embodiments of the present disclosure are described as being implemented in a wireless communication system. Figures 1 to 3 The description is not meant to imply physical or architectural limitations to the manner in which different embodiments may be implemented. Different embodiments of the present disclosure may be implemented in any suitably arranged communications system.

[0037] Figure 1 An example wireless network according to an embodiment of the present disclosure is shown. Figure 1 The embodiment of the wireless network shown in FIG. 1 is for illustration only. Other embodiments of the wireless network 100 may be used without departing from the scope of the present disclosure.

[0038] like Figure 1As shown, the wireless network includes base station (gNB or gNodeB) 101, gNB 102, and gNB 103. gNB 101 communicates with gNB 102 and gNB 103. gNB 101 also communicates with at least one network 130 such as the Internet, a proprietary Internet Protocol (IP) network, or other data network.

[0039] The gNB 102 provides wireless broadband access to a network 130 for a plurality of first user equipments (UEs) within a coverage area 120 of the gNB 102. The plurality of first UEs include a UE 111 that may be located at a small business (SB); a UE 112 that may be located at an enterprise (E); a UE 113 that may be located at a WiFi hotspot (HS); a UE 114 that may be located at a first residence (R1); a UE 115 that may be located at a second residence (R2); and a UE 116 that may be a mobile device (M) such as a cellular phone, a wireless laptop, a wireless personal digital assistant (PDA), etc. The gNB 103 provides wireless broadband access to the network 130 for a plurality of second UEs within a coverage area 125 of the gNB 103. The plurality of second UEs include UE 115 and UE 116 and subscriber stations (SS, e.g., UEs) 117, 118, and 119. In some embodiments, one or more of gNBs 101 103 may communicate with each other and UE 111 116 using existing wireless communication technologies, and one or more of UE 111 119 may communicate directly with each other (e.g., UE 117 119) using other existing or proposed wireless communication technologies.

[0040] Depending on the network type, the term "base station" or "BS" may refer to any component (or collection of components) configured to provide wireless access to a network, such as a transmission point (TP), a transceiver point (TRP), an enhanced (or "evolved") base station (eNodeB or eNB), a 5G base station (gNB), a macro cell, a femto cell, a wireless fidelity (WiFi) access point (AP), or other wireless-capable device. A base station may provide wireless access according to one or more wireless communication protocols, such as 3GPP 5G New Radio (NR), Long Term Evolution (LTE), Advanced LTE (LTE A), High Speed ​​Packet Access (HSPA), WiFi 802.11a / b / g / n / ac, etc. For convenience, various names of base station type devices and functions are used interchangeably in this patent document to refer to network infrastructure components that provide wireless access to remote terminals. Moreover, depending on the network type, the term "user equipment" (UE) may refer to any component such as a mobile station (MS), a subscriber station (SS), a remote terminal, a wireless terminal, a receiving point, or a user device. For convenience, various names of user equipment type devices and functions are used interchangeably in this patent document to refer to a remote wireless device that wirelessly accesses a BS, regardless of whether the UE is a mobile device (such as a mobile phone or smart phone) or a device generally considered to be a fixed device (such as a desktop computer or a vending machine).

[0041] Dashed lines illustrate the approximate extents of coverage areas 120 and 125, which are shown as generally circular for purposes of illustration and explanation only. It should be clearly understood that coverage areas associated with a gNB, such as coverage areas 120 and 125, may have other shapes, including irregular shapes, depending on the configuration of the gNB and variations in the wireless environment associated with natural and artificial obstacles.

[0042] As described in more detail below, one or more of the UEs 111 119 include circuitry, programming, or a combination thereof. In certain embodiments, one or more of the gNBs 101 103 include circuitry, programming, or a combination thereof.

[0043] although Figure 1 An example of a wireless network is shown, but Figure 1Various changes may be made. For example, wireless network 100 may include any number of gNBs and any number of UEs in any suitable arrangement. Moreover, gNB 101 may communicate directly with any number of UEs and provide those UEs with wireless broadband access to network 130. Similarly, each gNB 102 103 may communicate directly with network 130 and provide UEs with direct wireless broadband access to network 130. In addition, gNBs 101, 102, and / or 103 may provide access to other or additional external networks such as an external telephone network or other types of data networks.

[0044] Figure 2 An example base station according to an embodiment of the present disclosure is shown. Figure 2 The embodiment of the gNB 102 shown in FIG. 1 is for illustration only, and Figure 1 gNBs 101 and 103 may have the same or similar configurations. However, gNBs appear in a variety of configurations, and Figure 2 The scope of the present disclosure is not limited to any particular implementation of the gNB.

[0045] like Figure 2 As shown in FIG. 1 , gNB 102 includes multiple antennas 200a 200n, multiple radio frequency (RF) transceivers 201a 201n, transmit (TX) processing circuitry 203, and receive (RX) processing circuitry 204. gNB 102 also includes a controller / processor 205, a memory 206, and a backhaul or network interface 207.

[0046] The RF transceiver 201a 201n receives incoming RF signals from the antenna 200a 200n, such as signals sent by a UE in the network 100. The RF transceiver 201a 201n down-converts the incoming RF signals to generate an intermediate frequency (IF) or baseband signal. The IF or baseband signal is sent to the RX processing circuit 204, which generates a processed baseband signal by filtering, decoding and / or digitizing the baseband or IF signal. The RX processing circuit 204 sends the processed baseband signal to the controller / processor 205 for further processing.

[0047] The TX processing circuit 203 receives analog or digital data (such as voice data, web data, email, or interactive video game data) from the controller / processor 205. The TX processing circuit 203 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The RF transceiver 201a 201n receives the outgoing processed baseband or IF signal from the TX processing circuit 203 and up-converts the baseband or IF signal to an RF signal that is transmitted via the antenna 201a 201n.

[0048] Controller / processor 205 may include one or more processors or other processing devices that control the overall operation of gNB 102. For example, controller / processor 205 may control the reception of forward channel signals and the transmission of reverse channel signals by RF transceivers 201a 201n, RX processing circuitry 204, and TX processing circuitry 203 in accordance with well-known principles. Controller / processor 205 may also support additional functionality, such as more advanced wireless communication functionality.

[0049] For example, the controller / processor 205 may support beamforming or directional routing operations, in which outgoing signals from multiple antennas 200a-200n are weighted differently to effectively steer the outgoing signals in a desired direction. Any of a variety of other functions may be supported in the gNB 102 by the controller / processor 205.

[0050] Controller / processor 205 is also capable of executing programs and other processes, such as an operating system (OS), located in memory 206. Controller / processor 205 may move data into or out of memory 206 as required by the executing processes.

[0051] The controller / processor 205 is also connected to a backhaul or network interface 207. The backhaul or network interface 207 allows the gNB 102 to communicate with other devices or systems via a backhaul connection or via a network. The interface 207 may support communication over any appropriate (one or more) wired or wireless connections. For example, when the gNB 102 is implemented as part of a cellular communication system (such as a cellular communication system supporting 5G, LTE, or LTE A), the interface 207 may allow the gNB 102 to communicate with other gNBs via a wired or wireless backhaul connection. When the gNB 102 is implemented as an access point, the interface 207 may allow the gNB 102 to communicate over a wired or wireless local area network or over a wired or wireless connection to a larger network (such as the Internet). The interface 207 includes any appropriate structure that supports communication over a wired or wireless connection such as an Ethernet or RF transceiver.

[0052] Memory 206 is connected to controller / processor 205. A portion of memory 206 may include random access memory (RAM), and another portion of memory 206 may include flash memory or other read-only memory (ROM).

[0053] although Figure 2 An example of gNB 102 is shown, but the Figure 2 For example, gNB 102 may include any number of Figure 2As a specific example, the access point may include multiple interfaces 207, and the controller / processor 205 may support routing functions to route data between different network addresses. As another specific example, although shown as including a single instance of TX processing circuitry 203 and a single instance of RX processing circuitry 204, the gNB 102 may include multiple instances of each (such as one for each RF transceiver). For example, Figure 2 The various components in may be combined, further subdivided, or omitted, and additional components may be added according to specific needs.

[0054] Figure 3 An example user device according to an embodiment of the present disclosure is shown. Figure 3 The embodiment of UE 116 shown in FIG. 1 is for illustration only, and Figure 1 UEs 111 115 and 117 119 may have the same or similar configurations. However, UEs may appear in a variety of configurations, and Figure 3 The scope of the present disclosure is not limited to any particular implementation of the UE.

[0055] like Figure 3 As shown in FIG. 1 , UE 116 includes an antenna 301, a radio frequency (RF) transceiver 302, a TX processing circuit 303, a microphone 304, and a receive (RX) processing circuit 305. UE 116 also includes a speaker 306, a controller or processor 307, an input / output (I / O) interface (IF) 308, a touch screen display 310, and a memory 311. The memory 311 includes an OS 312 and one or more applications 313.

[0056] RF transceiver 302 receives incoming RF signals transmitted by a gNB of network 100 from antenna 301. RF transceiver 302 downconverts the incoming RF signals to generate an IF or baseband signal. The IF or baseband signal is sent to RX processing circuitry 305, which generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. RX processing circuitry 305 sends the processed baseband signal to speaker 306 (such as for voice data) or processor 307 for further processing (such as for web browsing data).

[0057] The TX processing circuit 303 receives analog or digital voice data from the microphone 304 or other outgoing baseband data (such as web data, email, or interactive video game data) from the processor 307. The TX processing circuit 303 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate a processed baseband or IF signal. The RF transceiver 302 receives the outgoing processed baseband or IF signal from the TX processing circuit 303 and up-converts the baseband or IF signal to an RF signal that is transmitted via the antenna 301.

[0058] The processor 307 may include one or more processors or other processing devices and executes the OS 312 stored in the memory 311 to control the overall operation of the UE 116. For example, the processor 307 may control the reception of forward channel signals and the transmission of reverse channel signals by the RF transceiver 302, the RX processing circuit 305, and the TX processing circuit 303 according to well-known principles. In some embodiments, the processor 307 includes at least one microprocessor or microcontroller.

[0059] The processor 307 is also capable of executing other processes and programs located in the memory 311, such as a process for CSI reporting on the uplink channel. The processor 307 can move data into or out of the memory 311 as needed to execute the process. In some embodiments, the processor 307 is configured to execute the application 313 based on the OS 312 or in response to a signal received from the gNB or operator. The processor 307 is also coupled to the I / O interface 309, which provides the UE 116 with the ability to connect to other devices such as laptops and portable computers. The I / O interface 309 is the communication path between these accessories and the processor 307.

[0060] Processor 307 is also connected to touch screen display 310. A user of UE 116 may use touch screen display 310 to enter data into UE 116. Touch screen display 310 may be a liquid crystal display, light emitting diode display, or other display capable of rendering text and / or at least limited graphics, such as from a website.

[0061] The memory 311 is connected to the processor 307. A portion of the memory 311 may include RAM, and another portion of the memory 311 may include flash memory or other ROM.

[0062] although Figure 3 An example of UE 116 is shown, but the Figure 3 Make various changes. For example, Figure 3The various components in the embodiment may be combined, further subdivided, or omitted, and additional components may be added as required. As a specific example, processor 307 may be divided into multiple processors, such as one or more central processing units (CPUs) and one or more graphics processing units (GPUs). Furthermore, although Figure 3 The UE 116 is shown configured as a mobile phone or smart phone, but the UE may be configured to operate as other types of mobile or stationary devices.

[0063] The core concept of communication perception integration is to use the same set of hardware equipment to achieve the perception function of the surrounding environment at the cost of as little resource expenditure as possible while ensuring basic communication functions. That is, the communication perception integrated node also has the function of perception, and the perceived content includes the distance, direction, speed and even type of objects in the surrounding environment. Different from the technology of locating access terminals in traditional communication systems, communication perception integration technology can also realize the perception of various information of non-access objects, which greatly increases the ability of the communication system to dynamically adjust the working status (scheduling, beam management, early warning of access terminals, etc.) according to the surrounding environment.

[0064] Using the channel state information (CSI) of the received sensing signal to detect the presence of people in the environment with a high detection rate (Pd) and a low false alarm rate (Pfa) is a scenario with great commercial value. Presence detection is divided into motion presence detection and static presence detection, among which static detection is the scenario that needs to be focused on during commercialization. However, due to the many changes in the actual commercial environment, the existing communication perception integrated node cannot achieve more accurate detection.

[0065] In this regard, the present disclosure mainly relates to the perception function and / or communication function of a communication perception integrated node, and proposes a communication integrated node and a method executed by the communication perception integrated node. The communication integrated node and the method executed by the communication perception integrated node according to the embodiment of the present disclosure can achieve more accurate detection while ensuring the generalization and stability of the solution in different environments.

[0066] The communication perception integrated node is referred to as the first node below. The first node can specifically be any wireless communication device, such as a base station, a terminal device, a bypass device, etc.

[0067] The most widely used communication systems at present include systems based on 3GPP protocols, such as 4G communication systems such as LTE and LTE-A, and 5G communication systems such as NR, and systems based on IEEE 802 wireless local area network (Wi-Fi) protocols, such as 802.11a / b / g / n / ac / ax / bf and other communication systems. The signal waveforms used by these communication systems are all waveforms based on OFDM modulation. Considering forward compatibility, for example, OFDM communication signals can be used as perception signals. Specifically, the perception signal can be a physical signal and / or physical channel that can be used for perception purposes. For example, when the first node is a base station, the perception signal can be a downlink reference signal or a downlink physical channel, etc.; when the first node is a terminal, the perception signal can be an uplink reference signal or an uplink physical channel, etc. Alternatively, when the first node is a wireless network access point device (AP), the perception signal can be a WiFi signal.

[0068] The perception signal sent by the perception communication node is reflected by the target reflector and then received by the first node in the form of an echo. By processing the echo signal, the presence of the target object can be detected, or perception information such as the distance, speed, and direction of the target object can be obtained.

[0069] The present invention provides a method for performing target detection in a wireless communication system, comprising:

[0070] Step 401: grouping channel state information received by M antennas to obtain N groups of channel state information, where M and N are natural numbers greater than 1;

[0071] Step 402: Obtaining features based on the N groups of channel state information;

[0072] Step 403: Perform target detection based on the features.

[0073] The target detection method proposed in the present invention can obtain channel state information based on the received signal, then perform preprocessing and feature extraction based on the channel information, and then perform target detection or target state / action recognition based on the features. For example, based on the features, it can be determined whether there are people in a certain area, or the number of people in the area and the change in the number of people, or the action type of the people in the area, or the speed of the people in the area. Alternatively, the target can also be a movable robot or an animal, such as a sweeping robot, or pets such as cats and dogs. As mentioned above, the channel state information can also be replaced by RSSI, antenna gain, MCS, etc. The method of the present invention can perform target detection more accurately, and obtain target-related information.

[0074] Taking the extraction of frequency domain features as an example, a possible target detection method is Figure 5 As shown, including:

[0075] Step 501: grouping channel state information received by M antennas to obtain N groups of channel state information, where M and N are natural numbers greater than 1;

[0076] Step 502: for each set of channel state information, perform an optional pre-processing process, for example, quotient and / or outlier processing;

[0077] Step 503: extracting frequency domain features from the preprocessed channel state information;

[0078] Step 503: Divide one or more time windows, and obtain frequency domain features of the group of channel state information based on the frequency domain features and time information corresponding to the time windows;

[0079] Step 504: performing at least one of the following processing on the frequency domain features: statistical feature extraction, maximum eigenvalue extraction, and extraction of eigenvalues ​​other than the maximum eigenvalue;

[0080] Step 505: Perform target detection based on N groups of frequency domain features.

[0081] Optionally, before obtaining the channel state information received by M antennas, the node can: receive a signal, perform channel estimation on the received signal to obtain the original channel state information; Optionally, after obtaining the original channel state information, the node can extract one or more subcarriers in the original channel state information according to the frequency domain characteristics of the signal to form new channel state information. For example, remove the guard band, remove the intermediate frequency, etc. An example of removing the guard band can be that the received original channel state information contains 64 subcarriers, and when the transmitted signal occupies the middle 60 subcarriers, the node can extract the channels on the middle 60 subcarriers to form new channel state information.

[0082] Embodiment 1

[0083] With respect to the above steps 501 and 502, a possible channel state information grouping and quotient method is as follows: Figure 6 As shown, it includes: performing channel estimation on the received signals corresponding to the M antennas to obtain channel state information, and grouping the channel state information. The grouping may be based on the corresponding relationship among the M antennas, for example, grouping based on the relative relationship / relative position relationship between the antennas. Taking M=4 as an example, one possible grouping method is to group antenna 1 and antenna 2 into a group, and to group antenna 3 and antenna 4 into a group. Taking M=4 as an example, another possible grouping method is to group antenna 1 and antenna 2 into a group, to group antenna 1 and antenna 3 into a group, to group antenna 1 and antenna 4 into a group, to group antenna 2 and antenna 3 into a group, and to group antenna 3 and antenna 4 into a group.

[0084] After the channel state information is grouped, the channel state information is divided into quotients within each group. For example, the channel state information of antenna p is recorded as Among them, h mn (p) represents the channel state information of the mth subcarrier of the pth antenna at time n. m = 1, ..., M, where M is a positive integer. The channel state information is grouped based on the position between antennas. For example, antenna q can be any antenna of the receiving device that is different from antenna p. Then H (p) and H (q) Can be divided into a group. Take the quotient of any two of the channel state information in the group to obtain the quotient between the channel state information, for example, Or, when H (p,l) When representing the channel between the lth antenna of the transmitter and the pth antenna of the receiver, a possible implementation method of performing quotient on the channel state information is: (p,l) =H (p,l) / H (p,m) That is, for the same receiver antenna, the quotient is performed based on the channel state information corresponding to different transmitter antennas.

[0085] Optionally, after the channel state information is grouped, before taking the quotient of each group of channel state information, the channel state information within the group or between groups can be normalized. Note that the normalization method can also be maximum value normalization. For example, the maximum value is normalized to 1 and the minimum value is normalized to -1; or the maximum value is normalized to 1 and the minimum value is normalized to 0. It is applicable to data that is originally distributed in a limited range; or, mean variance normalization, generally normalizes the mean to 0 and the variance to 1. It is applicable to situations where the distribution has no obvious boundaries. The advantage of normalization is that it can facilitate subsequent signal processing. For example, when processing signals using machine learning, training with normalized data can speed up the convergence of machine learning.

[0086] Optionally, the time domain channel state information is obtained based on the frequency domain channel state information, and then feature extraction is performed based on the time domain channel state information. A possible implementation method for obtaining the time domain channel state information based on the frequency domain channel state information is to obtain the frequency domain channel state information H of the antenna p. (p) Perform inverse Fourier transform or discrete inverse Fourier transform column by column to obtain the time domain channel state information of antenna p

[0087] Embodiment 2

[0088] For the above step S504, a possible preprocessing / feature extraction method includes: taking the modulus of the channel state information, or taking the phase, or preprocessing the phase, or calibrating the phase. And averaging the features of the channel state information on multiple subcarriers at the same time, or averaging the features of the channel state information of the same subcarrier at different times.

[0089] A possible preprocessing / feature extraction method is to perform phase calibration on the phase information of the channel state, such as Figure 7 As shown, the processing method includes: Perform phase operation, where represents the channel state information of the mth subcarrier of the pth antenna at time n, ||h mn (p) || is the amplitude of the channel state information, The phase of the channel state information can be obtained by in is the phase information of the channel state of the mth subcarrier of the pth antenna at time n. Based on the phase of the channel state information of all M subcarriers Where M is a positive integer, we can get Where a is the slope of the phase of the M subcarriers of the pth antenna at the nth time, and b is the phase offset. The calibrated phase is, Wherein m=1,…,M, n=1,…,N, where M and N are positive integers.

[0090] Alternatively, phase calibration processing may be performed on the result of taking the quotient of the channel state information in the first embodiment.

[0091] It should be noted that for phase information The beneficial effect of performing the above phase calibration process is that it can remove the phase error introduced by the unknown carrier frequency offset (CFO) and / or sampling frequency offset (SFO), improve the accuracy of the phase information, and thus enhance the effectiveness of the eigenvalues ​​extracted based on the phase information.

[0092] Embodiment 3

[0093] For step S504, a possible preprocessing / feature extraction method is as follows: Figure 8 As shown, it includes: performing channel estimation on the received signal to obtain channel state information, setting a time window to segment the channel state information in the time dimension, and obtaining the first information based on the segmented channel state information. A possible implementation method is that the channel state information of the above antenna p can be segmented as in, The values ​​of all subcarriers representing the channel state information of antenna p within the time window t1. The first information is obtained based on the segmented channel state information, including taking the mean or variance or standard deviation of the segmented channel state information in the time dimension, or, optionally, obtaining the amplitude / phase / preprocessed phase based on the segmented channel state information, and obtaining the first information based on the amplitude / phase / preprocessed phase, for example, taking the mean or variance or standard deviation of the amplitude / phase / preprocessed phase in the time dimension to obtain the first information. Wherein, each time window may be overlapping or non-overlapping, and the length of each time window may be equal or unequal. Optionally, the first information of the corresponding channel in each time window may be averaged in the subcarrier dimension, or some subcarriers may be averaged.

[0094] With respect to step 503 and step 504, a possible feature extraction method includes: setting multiple time windows; obtaining features corresponding to the time information in each time window based on the features of the channel state information and the time information corresponding to the time window; and extracting features based on the frequency domain features or time domain features corresponding to the time information in each time window. Among them, the features in a certain time window can be extracted based on the frequency domain features or time domain features corresponding to the time information in the time window, or can be extracted based on the frequency domain features or time domain features corresponding to the time information in K time windows before or after the time window, or can be extracted based on the frequency domain features or time domain features corresponding to the time information in the time window and the K time windows before and / or after the time window. Taking setting 20 time windows as an example, time windows 1, 2, ... 20 correspond to 20 or 20 groups of frequency domain features or time domain features respectively. The features corresponding to the 5th time window can be extracted based on the frequency domain features or time domain features of the 5th time window, or can be extracted based on the frequency domain features or time domain features of the 1st to 5th time windows, or can be extracted based on the frequency domain features or time domain features of the 4th to 6th time windows.

[0095] Embodiment 4

[0096] For step S505, a possible preprocessing / feature extraction method is as follows: Fig. 9 As shown, it includes: performing channel estimation on the received signal to obtain channel state information, setting a time window to segment the channel state information in the time dimension, obtaining multiple first information based on the segmented channel state information, and obtaining second information based on the multiple first information. For example, the channel state information of the above antenna p can be segmented into in, The values ​​of all subcarriers representing the channel state information of antenna p in time window t1. (p) Find the autocorrelation of each segmented element in Take 2 as an example, the autocorrelation operation is Again Calculate the eigenvalue decomposition (EVD) or singular value decomposition (SVD) to obtain the eigenvalue or singular value λ 1 ,λ 2 ,...,λ M , based on one or more of the eigenvalues ​​or singular values, obtain the channel eigenvalue in the time window. Taking the eigenvalue as an example, the first channel information in the time window is obtained based on one or more eigenvalues, including: 1 ,λ 2 ,...,λ M Normalize it, for example, Extract the maximum eigenvalue or the normalized maximum eigenvalue, or find the inverse of the maximum eigenvalue or the normalized eigenvalue, so as to obtain the first information of the channel in the time window. Optionally, obtaining multiple first information based on the segmented channel state information may also include obtaining the amplitude / phase / preprocessed phase of the channel state information based on the segmented channel state information, and performing the above-mentioned autocorrelation and eigenvalue decomposition operations on the amplitude / phase / preprocessed phase to obtain multiple first information related to the amplitude / phase / preprocessed phase. Taking time window t1 as an example, a possible implementation method of obtaining the second information based on multiple first information is to use two different first information, for example, the first information value M1 related to the amplitude and the first information value M2 related to the phase as the horizontal and vertical coordinates of a point in a plane coordinate system, then the point uniquely determined by the eigenvalue M1 and the eigenvalue M2 in the plane coordinate system is the second information corresponding to time window t1. Optionally, target detection is performed based on the second information, including clustering or classifying the second information values ​​in multiple time windows, for example, clustering or classifying points uniquely determined by eigenvalue M1 and eigenvalue M2 in the plane coordinate system, wherein the clustering algorithm may include K-MEANS clustering algorithm, DBSCAN smiley face clustering algorithm, mean shift clustering algorithm, etc.

[0097] Embodiment 5

[0098] For step S505, a possible preprocessing / feature extraction method is as follows: Fig.10 As shown, including: channel state information Perform phase operation, where represents the channel state information of the mth subcarrier of the pth antenna at time n, ||h mn (p) || is the amplitude of the channel state information, The phase of the channel state information can be obtained by in is the phase information of the channel state of the mth subcarrier of the pth antenna at time n, where m = 1, ..., M, n = 1, ..., N, where M and N are positive integers. Next, based on the phase information Φ of the channel state at different time n in the time dimension (p) For feature extraction, for example, we can extract k The phase information of the channel state is segmented in the time dimension, a plurality of first information is obtained based on the segmented phase information of the channel state, and the second information is obtained based on the plurality of first information. Specifically, the channel state information of the antenna p can be divided into K segments: in, Represents the time window w k The phase information of the channel state of all subcarriers corresponding to the inner antenna p, where the length of the K time windows is l k Can be equal or unequal, satisfying Where N is the time length of the channel state information. Find the autocorrelation and obtain the autocorrelation matrix with dimension M×M Where M is the number of subcarriers. Then the autocorrelation matrix Perform eigenvalue decomposition (EVD) or singular value decomposition (SVD) to obtain eigenvalues ​​or singular values ​​λ 1 ,λ 2 ,...,λ K , where λ 1 >λ 2 >...>λ K , based on one or more of the obtained eigenvalues ​​or singular values, the time window w is obtained k Corresponding second information, after the above processing is performed on the phase information of the channel state in all K time windows, K second information can be obtained, which is called the second information set. Specifically, the time window w is obtained based on one or more eigenvalues ​​or singular values k The corresponding second information method may be to select any eigenvalue or singular value λ except the largest eigenvalue or singular value i ,i=2,...,K as the time window w k Corresponding second information; or, selecting to remove the largest eigenvalue or singular value, and for the remaining eigenvalues ​​or singular values ​​λ i , i=2,...,K to sum As the time window w k The corresponding second information, where 1<c 1 <c 2 ≤K,c 1 and c 2 is a positive integer. Or, for eigenvalues ​​or singular values ​​λ 1 ,λ2 ,...,λ K Normalize it, for example, Perform the above processing on the normalized eigenvalues ​​to obtain the time window w k The corresponding second information.

[0099] Optionally, in addition to the phase information based on the channel state, it may also be based on the following data: amplitude information of the channel state, received signal strength indication (RSSI), antenna gain information, etc. Alternatively, the second information set is obtained by performing the signal processing process described in this embodiment after preprocessing the phase information of the channel state and the above data as described in Embodiments 1, 2, and 3.

[0100] Embodiment 6

[0101] A method for processing data outliers is proposed below. It should be noted that the outlier processing method proposed in the present invention can perform outlier processing on the entire data at the sampling point level for channel state information and / or features extracted based on channel state information (for example, amplitude / phase / processed phase) and / or other parameters of the received signal (for example, RSSI, antenna gain, MCS, etc.); it can also perform inter-segment outlier processing, that is, after segmenting from the time dimension, extract a feature from each segment of data, and perform outlier processing on the features corresponding to different segments; it can also perform intra-segment outlier processing, that is, after segmenting from the time dimension, perform sampling point level outlier processing on each segment of data.

[0102] For step S502 or the optional items in step S505, a possible method for identifying and processing abnormal values ​​of channel state information is as follows: Fig.11As shown, it includes: performing channel estimation on the received signal to obtain channel state information; obtaining first information based on the channel state information; performing outlier processing on the first information to obtain third information. Among them, the first information can be obtained based on the segmented channel state information. Performing outlier processing on the first information to obtain the third information includes, obtaining the first information difference based on the first information, making a threshold judgment on the elements in the first information, and when a certain element exceeds the threshold, it is judged as an outlier and performs outlier processing. Among them, obtaining the first information difference based on the first information includes, performing a sliding average on the elements contained in the first information to obtain the smoothed first information, and performing a difference and modulus value on the first information and the smoothed first information to obtain the first information difference. Performing a threshold judgment on the elements in the first information includes, obtaining a threshold based on the first information difference, for example, taking the mean of the first information difference as the threshold; when an element in the first information is greater than the threshold, the element is identified as an outlier. It should be noted that the method of outlier identification is not limited to the above-mentioned threshold judgment method, and other methods may also be used in the actual data processing process, such as the median absolute deviation (MAD) method. Outlier processing includes replacing the outlier in the first information, and the replacement content may be the non-outlier value closest to the outlier in time or sampling time, and the replaced sequence is the third information.

[0103] The data outlier processing method proposed in this embodiment can be used in combination with one or more other embodiments of the present invention. A specific example is that according to the method of embodiment six, the third information is obtained based on the first information at the sampling point level, and the third information is segmented according to time, and the amplitude / phase / mean / variance of the pre-processed phase is calculated based on the segmented third information as the second information.

[0104] Embodiment 7

[0105] In some instances, the device performs target detection or target state / action recognition through a neural network, for example, identifying the presence of a person / machine / animal, drawing a moving path, detecting the number of people, etc. Among them, the method for detecting the number of people is to select the number of people corresponding to the output unit with the largest value among all output units of the neural network.

[0106] Specifically, the structure of the "neural network" used for personnel quantity detection includes but is not limited to Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Restricted Boltzmann machine (RBM), Graph Neural Network (GNN), Deep Belief Network (DBN), Bi-directional Recurrent Deep Neural Network (BRDNN), Transformer network, etc.

[0107] Specifically, the input of the neural network is a second information set, which includes the second information of the 1st to Tth continuous time windows.

[0108] Specifically, the output of the neural network is the detection result of the time corresponding to the Tth time window. When the detection target is the number of people in the space, each output of the neural network corresponds to all possible numbers of people in the specific space to be detected, and the output of the detector can be the probability or value of each number of people.

[0109] Specifically, after the neural network completes the detection of the Tth time window, the second information set will be updated in a sliding manner in chronological order with a time window as a step. For example, when the T+1 moment is detected, the second information set will include the second information of the 2nd to T+1th time windows. By inputting the information included in the second information set into the neural network, the neural network obtains the detection result of the T+1th moment.

[0110] Possible implementation methods corresponding to some of the above embodiments are given below. The features below may exist in the form of vectors or matrices.

[0111] An implementation method corresponding to the first and second embodiments includes: dividing the channel state information received by M antennas, for example, M=4, into N groups, for example, taking the data received by the nth and n+1th antennas as one group, and then taking the channel state information received by the nth antenna as a quotient of the channel state information corresponding to the n+1th antenna, and then performing an angle or phase operation on the result of the quotient to obtain the processed frequency domain feature information. Optionally, the frequency domain feature information can be subjected to an operation of discarding abnormal values, such as discarding the corresponding data of the subcarriers in the guard intervals on both sides and / or the middle DC subcarriers because they do not carry useful information.

[0112] An implementation method corresponding to the sixth embodiment includes: inputting a feature related to the channel state information, taking the median of the feature, subtracting the feature from the median, and squaring the difference to obtain a first intermediate result, taking the median of the first intermediate result to obtain a second intermediate result, taking the feature as a quotient of the second intermediate result to obtain a third intermediate result, finding all numbers less than the first threshold in the third intermediate result and taking the average as the fourth intermediate result, finding the index corresponding to all numbers greater than or equal to the second threshold in the third intermediate result, selecting the number corresponding to the index from the third intermediate result, replacing it with the fourth intermediate result, and obtaining the feature after removing the outlier. The second threshold and the first threshold may be equal or different.

[0113] An implementation method corresponding to the third embodiment includes: determining an index according to a certain time range, and obtaining channel state information in a time window associated with the time range according to the index and the channel state information. For example, the channel state information of antenna p is H (p) , time window t1 corresponds to H (p) From the kth column to the k+nth column, the channel state information of all subcarriers corresponding to antenna p in time window t1 can be expressed as The physical meaning is to extract H (p) The kth column to the k+nth column form a new matrix as the channel state information of all subcarriers corresponding to the antenna p in the time window t1.

[0114] An implementation method corresponding to the sixth embodiment includes: inputting a feature related to the channel state information, calculating the mean of the feature, calculating the modulus of the difference between the feature and its mean, and sorting the modulus, filtering the sorted sequence according to a condition, and returning the index of the data satisfying the condition, and calculating the variance of the feature in each time window based on the index and the feature. A total of T features of continuous time windows are obtained.

[0115] An implementation method corresponding to Example 7 includes: inputting the features of all T time windows obtained into the neural network to obtain the detection result of the Tth time window. Repeat the above feature extraction process to obtain the features of the 2nd to T+1th time windows and input them into the neural network to obtain the detection result of the T+1th time window. Repeat the above process to obtain the detection result of any T+x time window. Among them, when the detection target is the number of people in the space, each output of the neural network corresponds one-to-one to all possible numbers of people in the specific space to be detected, and the output of the detector can be the probability or value of each number of people. Among them, the method for obtaining the detection result of the Tth time window is to select the number of people corresponding to the maximum output unit of the neural network.

[0116] The above method described in the present disclosure may be executed by a first node device including a transceiver and a processor. The first node device, for example, may be a receiver, such as a base station, a UE, a relay node, and the like. Fig.11 FIG. 2 shows an exemplary structure of a first node device according to the present disclosure. Fig.11 As shown, the first node device includes a transceiver 1110 and a processor 1120 coupled to the transceiver 1110. The transceiver 1110 is configured to send and receive signals. The processor 1120 is configured to execute the user selection method described in the present disclosure. The present disclosure can also be implemented as a computer storage medium. The computer storage medium stores computer executable instructions. When the stored computer executable instructions are executed by the processor, the processor executes the aforementioned method of the present disclosure.

[0117] It can be understood that “at least one of / at least one” described in the present disclosure includes any and / or all possible combinations of the listed items, the various embodiments described in the present disclosure and the various examples in the embodiments can be changed and combined in any appropriate form, and the “ / ” described in the present disclosure means “and / or”.

[0118] The various illustrative logical blocks, modules, and circuits described in the present disclosure may be implemented or executed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0119] The steps of the method or algorithm described in the present disclosure can be directly embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software module can reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium can be integrated into a processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in a user equipment terminal. In an alternative, the processor and the storage medium can reside in a user equipment terminal as discrete components.

[0120] In one or more exemplary designs, the functions may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include both computer storage media and communication media, the latter including any media that facilitates the transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a general or special purpose computer.

[0121] The description set forth herein, in conjunction with the accompanying drawings, describes example methods and apparatus and does not represent all examples that may be implemented or within the scope of the claims. The term "example" as used herein means "used as an example, instance, or illustration," rather than "preferred" or "superior to other examples." The detailed description includes specific details for the purpose of providing an understanding of the described techniques. However, these techniques may be practiced without these specific details. In some cases, well-known structures and devices are shown in block diagram form to avoid obscuring the concepts of the described examples.

[0122] Although this specification contains a number of specific implementation details, these should not be interpreted as limitations on any invention or the scope of the claimed protection, but rather as descriptions of specific features of specific embodiments of specific inventions. Certain features described in this specification in the context of separate embodiments may also be implemented in combination in a single embodiment. On the contrary, the various features described in the context of a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although features may be described above as working in certain combinations, and even initially claimed as such, in some cases, one or more features from the claimed combination may be deleted from the combination, and the claimed combination may be directed to a sub-combination or a variation of the sub-combination.

[0123] It should be understood that the specific order or hierarchy of steps in the method of the present invention is an illustration of an exemplary process. Based on design preferences, it is understood that the specific order or hierarchy of steps in the method can be rearranged to achieve the functions and effects disclosed in the present invention. The attached method claims present the elements of various steps in an example order and are not meant to be limited to the specific order or hierarchy presented unless otherwise specifically stated. In addition, although elements can be described or claimed in the singular, the plural number is also contemplated unless a limitation to the singular is explicitly stated. Therefore, the present disclosure is not limited to the examples shown, and any device for performing the functions described herein is included in the various aspects of the present disclosure.

[0124] The text and drawings are provided as examples only to help readers understand the present disclosure. They are not intended and should not be interpreted as limiting the scope of the present disclosure in any way. Although certain embodiments and examples have been provided, it is obvious to those skilled in the art based on what is disclosed herein that the embodiments and examples shown may be changed without departing from the scope of the present disclosure.

Claims

1. A method for performing target detection in a wireless communication system, the method comprising: Grouping the channel state information received by M antennas to obtain N groups of channel state information, where M and N are natural numbers greater than 1; Obtaining features based on the N sets of channel state information; Target detection is performed based on the features.

2. The method according to claim 1, wherein: Grouping the channel state information received by M antennas includes: According to the corresponding relationship between the M antennas, the channel state information received by the M antennas is grouped.

3. The method according to claim 2, wherein grouping the channel state information received by the M antennas according to the correspondence between the M antennas comprises at least one of the following: The M antennas are combined in pairs to obtain a correspondence relationship between the M antennas, and the channel state information received by the M antennas is grouped according to the correspondence relationship between the M antennas; Any one of the M antennas is combined with any other antenna to obtain a corresponding relationship among the M antennas, and channel state information received by the M antennas is grouped according to the corresponding relationship among the M antennas.

4. The method according to claim 1, wherein: Obtaining features based on the N groups of channel state information includes: extracting frequency domain features for each group of channel state information, and obtaining frequency domain features of the group of channel state information based on the frequency domain features and time information corresponding to the time window; Performing target detection based on the features includes: performing target detection based on N groups of frequency domain features.

5. The method according to claim 4, wherein: Based on the frequency domain features and the time information corresponding to the time window, obtaining the frequency domain features of the group of channel state information includes: Based on the frequency domain features and the time information corresponding to the time window, obtaining the frequency domain features corresponding to the time information in each time window; Feature extraction is performed based on the frequency domain features corresponding to the time information within each time window.

6. The method according to claim 4, wherein: For each set of channel state information, extracting frequency domain features includes: Each group of channel state information is subjected to quotient analysis and / or outlier processing, and frequency domain features are extracted.

7. The method according to claim 5, wherein: Based on the frequency domain features corresponding to the time information in each time window, feature extraction includes: Outlier processing is performed on the frequency domain features corresponding to the time information in each time window, and at least one of the following processing is performed: statistical feature extraction, maximum eigenvalue extraction, and extraction of eigenvalues ​​other than the maximum eigenvalue.

8. The method according to claim 1, wherein: Obtaining features based on the N groups of channel state information includes: for each group of channel state information, obtaining channel state information in the time domain based on the channel state information; obtaining time domain features based on the time domain channel state information; and obtaining time domain features within each time window based on the time domain features and time information corresponding to the time window.

9. The method according to claim 8, wherein: The obtaining of the time domain feature based on the channel state information in the time domain includes: The channel state information in the time domain is subjected to quotient calculation and / or outlier processing, and time domain features are extracted.

10. The method according to claim 8, wherein: Based on the time domain features and the time information corresponding to the time window, the time domain features in each time window are obtained, including: Based on the time domain features and the time information corresponding to the time window, obtaining the time domain features corresponding to the time information in each time window; Feature extraction is performed on the time domain features corresponding to the time information in each time window.

11. The method according to claim 10, wherein: Feature extraction of the time domain features corresponding to the time information in each time window includes: performing outlier processing on the time domain features corresponding to the time information in each time window, and performing at least one of the following processing: statistical feature extraction, maximum eigenvalue extraction, and extraction of eigenvalues ​​other than the maximum eigenvalue.

12. A first node, comprising: a transceiver configured to transmit and receive signals; and A processor is coupled to the transceiver and configured to execute the method according to any one of claims 1 to 11.

13. A computer-readable storage medium storing computer-executable instructions, wherein when the computer-executable instructions are executed by a processor, the processor executes the method according to any one of claims 1 to 11.

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