Apparatus and method performed by node in wireless communication system

Abstract

The present disclosure relates to a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE). The present disclosure provides a method for performing target detection in a wireless communication system, including: grouping channel state information received by M antennas, to obtain N groups of channel state information, wherein the M and the N are natural numbers greater than 1; obtaining features based on the N groups of channel state information; performing target detection based on the features.

Description

APPARATUS AND METHOD PERFORMED BY NODE IN WIRELESS COMMUNICATION SYSTEM

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

[0002] Considering the development of wireless communication from generation to generation, the technologies have been developed mainly for services targeting humans, such as voice calls, multimedia services, and data services. With the commercialization of 5thgeneration (5G) communication systems, it is expected that the number of connected devices will exponentially grow. Increasingly, these will be connected to communication networks. Examples of connected things may include vehicles, robots, drones, home appliances, displays, smart sensors connected to various infrastructures, construction machines, and factory equipment. Mobile devices are expected to evolve in various form-factors, such as augmented reality glasses, virtual reality headsets, and hologram devices. In order to provide various services by connecting hundreds of billions of devices and things in the 6thgeneration (6G) era, there have been ongoing efforts to develop improved 6G communication systems. For these reasons, 6G communication systems are referred to as beyond-5G systems.

[0003] 6G communication systems, which are expected to be commercialized around 2030, will have a peak data rate of tera (1,000 giga)-level bps and a radio latency less than 100μsec, and thus will be 50 times as fast as 5G communication systems and have the 1 / 10 radio latency thereof.

[0004] In order to accomplish such a high data rate and an ultra-low latency, it has been considered to implement 6G communication systems in a terahertz band (for example, 95GHz to 3THz bands). It is expected that, due to more path loss and atmospheric absorption in the terahertz bands than those in millimeter wave (mmWave) bands introduced in 5G, technologies capable of securing the signal transmission distance (that is, coverage) will become more crucial. It is necessary to develop, as major technologies for securing the coverage, radio frequency (RF) elements, antennas, novel waveforms having a better coverage than orthogonal frequency division multiplexing (OFDM), beamforming and massive multiple input multiple output (MIMO), full dimensional MIMO (FD-MIMO), array antennas, and multiantenna transmission technologies such as large-scale antennas. In addition, there has been ongoing discussion on new technologies for improving the coverage of terahertz-band signals, such as metamaterial-based lenses and antennas, orbital angular momentum (OAM), and reconfigurable intelligent surface (RIS).

[0005] Moreover, in order to improve the spectral efficiency and the overall network performances, the following technologies have been developed for 6G communication systems: a full-duplex technology for enabling an uplink transmission and a downlink transmission to simultaneously use the same frequency resource at the same time, a network technology for utilizing satellites, high-altitude platform stations (HAPS), and the like in an integrated manner, an improved network structure for supporting mobile base stations and the like and enabling network operation optimization and automation and the like, a dynamic spectrum sharing technology via collision avoidance based on a prediction of spectrum usage, utilization of artificial intelligence (AI) in wireless communication for improvement of overall network operation by using AI at a designing phase for developing 6G and internalizing end-to-end AI support functions, and a next-generation distributed computing technology for overcoming the limit of user equipment (UE) computing ability through reachable super-high-performance communication and computing resources (such as mobile edge computing (MEC), clouds, and the like) over the network. In addition, efforts are made through designing new protocols to be used in 6G communication systems, developing mechanisms for implementing a hardware-based security environment and safe use of data, and developing technologies for maintaining privacy, to strengthen the connectivity between devices, optimize the network, promote softwarization of network entities, and increase the openness of wireless communications are continuing.

[0006] It is expected that research and development of 6G communication systems in hyper-connectivity, including person to machine (P2M) as well as machine to machine (M2M), will allow the next hyper-connected experience. More particularly, it is expected that services such as truly immersive extended reality (XR), high-fidelity mobile hologram, and digital replica could be provided through 6G communication systems. In addition, services such as remote surgery for security and reliability enhancement, industrial automation, and emergency response will be provided through the 6G communication system such that the technologies could be applied in various fields such as industry, medical care, automobiles, and home appliances.

[0007] The present disclosure provides apparatuses and methods performed by node and node device.

[0008] According to an aspect of an exemplary embodiment, there is provided a communication method in a wireless communication.

[0009] Aspects of the present disclosure provide efficient communication methods in a wireless communication system.

[0010] For a more complete understanding of the present disclosure and its advantages, reference is now made to the following description in conjunction with the accompanying drawings, wherein the same reference number indicates the same component:

[0011] FIG. 1 illustrates an example wireless network according to an embodiment of the present disclosure;

[0012] FIG. 2 illustrates an example base station according to an embodiment of the present disclosure;

[0013] FIG. 3 illustrates an example user device according to an embodiment of the present disclosure;

[0014] FIG. 4 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure;

[0015] FIG. 5 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure;

[0016] FIG. 6 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure;

[0017] FIG. 7 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure;

[0018] FIG. 8 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure;

[0019] FIG. 9 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure;

[0020] FIG. 10 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure;

[0021] FIG. 11 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure;

[0022] FIG. 12 illustrates an exemplary structure of a first node device according to the present disclosure.

[0023] FIG. 13 illustrates an example diagram of a user equipment, according to embodiments of the present disclosure.

[0024] FIG. 14 illustrates an example diagram of a base station, according to embodiments of the present disclosure.

[0025] Before undertaking the DETAILED DESCRIPTION below, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The term "connect" 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 one another. The terms "transmit," "receive," and "communicate," as well as derivatives thereof, contain both direct and indirect communication. The terms "include" and "comprise," as well as derivatives thereof, mean inclusion without limitation. The term "or" is inclusive, meaning and / or. The phrase "associated with," as well as derivatives thereof, means to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. The term "controller" means any device, system or part thereof that controls at least one operation. Such a controller may be implemented in hardware or a combination of hardware and software and / or firmware. The functionality associated with any particular controller may be centralized or distributed, whether locally or remotely. 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 needed. 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 A and B and C. Likewise, the term "set" means one or more. Accordingly, a set of items can be a single item or a collection of two or more items.

[0026] Moreover, various functions described below can be implemented or supported by one or more computer programs, each of which is formed from 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, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation 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 capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. A "non-transitory" computer readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable memory device.

[0027] Definitions for other certain words and phrases are provided throughout this patent document. Those of ordinary 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.

[0028] The figures included herein, and the various embodiments used to describe the principles of the present disclosure are by way of illustration only and should not be construed in any way to limit the scope of the disclosure. Further, those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged wireless communication system.

[0029] In order to overcome the above technical problems or at least partially solve them, the following technical solutions are proposed.

[0030] According to one aspect of the present invention, there is provided a method for performing target detection in a wireless communication system. The method includes: grouping channel state information received by M antennas, to obtain N groups of channel state information, wherein the M and the 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.

[0031] According to an exemplary implementation, wherein the grouping of the channel state information received by the M antennas includes: grouping the channel state information received by the M antennas according to the correspondence of the M antennas.

[0032] According to an exemplary implementation, wherein the grouping of the channel status information received by the M antennas according to the correspondence of the M antennas includes at least one of: combining the M antennas two by two to obtain the correspondence of the M antennas, and grouping the channel state information received by the M antennas according to the correspondence of the M antennas; combining any one of the M antennas with any other one of the M antennas to obtain the correspondence of the M antennas, and grouping the channel state information received by the M antennas according to the correspondence of the M antennas.

[0033] According to an exemplary implementation, wherein the obtaining of the 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 a time window.

[0034] The performing of the target detection based on the features includes: performing the target detection based on the N groups of frequency domain features.

[0035] According to an exemplary implementation, wherein the obtaining of the frequency domain features of the group of channel state information based on the frequency domain features and the time information corresponding to the time window includes: obtaining, based on the frequency domain features and the time information corresponding to the time window, the frequency domain features corresponding to the time information within each time window; and performing the feature extraction based on the frequency domain features corresponding to the time information within each time window.

[0036] According to an exemplary implementation, wherein the extracting of the frequency domain features for each group of channel state information includes: performing a quotient operation and / or performing an outlier processing for each group of channel state information, and extracting the frequency domain features.

[0037] According to an exemplary implementation, wherein the performing of the feature extraction based on the frequency domain features corresponding to the time information within each time window includes: performing the outlier processing on the frequency domain features corresponding to the time information within each time window, and performing at least one of statistical feature extraction, maximum eigenvalue extraction, and extraction of eigenvalues other than the maximum eigenvalue.

[0038] According to an exemplary implementation, wherein the obtaining of the features based on the N groups of channel state information includes: for each group of channel state information, obtaining the time domain channel state information based on the channel state information; obtaining time domain features based on the time domain channel state information; and obtaining the time domain features within each time window based on the time domain features and the time information corresponding to the time window.

[0039] According to another aspect of the present invention, there is provided a first node in a wireless communication system. The first node includes a transceiver and a processor, and the processor is configured to perform any of the above methods.

[0040] According to another aspect of the present invention, there is provided a computer-readable storage medium in a wireless communication system, storing computer executable instruction, when the computer executable instruction is executed by a processor, the processor performs any of the above methods.

[0041] FIGs. 1-3 below describe various embodiments of the present disclosure implemented in wireless communication systems. The descriptions of FIGs. 1-3 are 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 communication system.

[0042] FIG. 1 illustrates an example wireless network according to an embodiment of the present disclosure. The embodiment of the wireless network shown in FIG. 1 is for illustration only. Other embodiments of the wireless network 100 could be used without departing from the scope of this disclosure.

[0043] As shown in FIG. 1, the wireless network includes a base station (gNB or gNodeB) 101, a gNB 102, and a gNB 103. The gNB 101 communicates with the gNB 102 and the gNB 103. The gNB 101 also communicates with at least one network 130, such as the Internet, a proprietary Internet Protocol (IP) network, or other data network.

[0044] The gNB 102 provides wireless broadband access to the network 130 for a first plurality of user equipments (UE) within a coverage area 120 of the gNB 102. The first plurality of UEs include a UE 111, which may be located in a small business; a UE 112, which may be located in an enterprise (E); a UE 113, which may be located in a WiFi hotspot (HS); a UE 114, which may be located in a first residence (R1); a UE 115, which may be located in a second residence (R2); and a UE 116, which may be a mobile device (M), such as a cell phone, a wireless laptop, a wireless personal digital assistant (PDA), or the like. The gNB 103 provides wireless broadband access to the network 130 for a second plurality of UEs within a coverage area 125 of the gNB 103. The second plurality of UEs include the UE 115 and the UE 116, as well as subscriber stations (SS, for example, UEs) 117, 118 and 119. In some embodiments, one or more of the gNBs 101-103 may communicate with each other and with the UEs 111-116 using existing wireless communication techniques, and one or more of the UEs 111-119 may communicate directly with each other (e.g., UEs 117-119) using other existing or proposed wireless communication techniques.

[0045] 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 transmit point (TP), a transmit-receive point (TRP), an enhanced (or "evolved") base station (eNodeB or eNB), a 5G base station (gNB), a macrocell, a femtocell, a wireless fidelity (WiFi) access point (AP), or other wirelessly enabled devices. Base stations may provide wireless access in accordance with one or more wireless communication protocols, e.g., 5G 3GPP New Radio (NR) interface / access, Long Term Evolution (LTE), LTE Advanced (LTE-A), high speed packet access (HSPA), Wi-Fi 802.11a / b / g / n / ac, etc. For the sake of convenience, the various names for a base station-type apparatus and functionality are used interchangeably in this patent document to refer to network infrastructure components that provide wireless access to remote terminals. Also, 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 receive point, or a user device. For the sake of convenience, the various names for a user equipment-type device and functionality are used interchangeably in this patent document to refer to a remote wireless equipment that wirelessly accesses to a BS, regardless of the UE being a mobile device (such as a mobile telephone or smartphone) or normally considered as a stationary device (such as a desktop computer or a vending machine).

[0046] Dotted lines show the approximated ranges of the coverage areas 120 and 125, which are shown as approximately circles for the purposes of illustration and explanation only. It should be clearly understood that the coverage areas associated with gNBs, such as the coverage areas 120 and 125, may have other shapes, including irregular shapes, depending upon the configuration of the gNBs and variations in the radio environment associated with natural and man-made obstructions.

[0047] As described in more detail below, one or more of the UEs 111-119 include circuitry, programing, or a combination thereof. In certain embodiments, one or more of the gNBs 101-103 includes circuitry, programing, or a combination thereof.

[0048] Although FIG. 1 illustrates one example of a wireless network, various changes may be made to FIG. 1. For example, the wireless network could include any number of gNBs and any number of UEs in any suitable arrangement. Also, the gNB 101 could communicate directly with any number of UEs and provide those UEs with wireless broadband access to the network 130. Similarly, each gNB 102-103 could communicate directly with the network 130 and provide UEs with direct wireless broadband access to the network 130. Further, the gNBs 101, 102, and / or 103 could provide access to other or additional external networks, such as external telephone networks or other types of data networks.

[0049] FIG. 2 illustrates an example of the base station according to an embodiment of the present disclosure. The embodiment of the gNB 102 illustrated in FIG. 2 is for illustration only, and the gNBs 101 and 103 of FIG. 1 could have the same or similar configuration. However, gNBs come in a wide variety of configurations, and FIG. 2 does not limit the scope of the present disclosure to any particular implementation of the gNB.

[0050] As shown in FIG 2, the gNB 102 includes multiple antennas 200a-200n, multiple radio frequency (RF) transceivers 201a-201n, a transmit (TX) processing circuitry 203, and a receive (RX) processing circuitry 204. The gNB 102 further includes a controller / processor 205, a memory 206, and a backhaul or network interface 207.

[0051] The RF transceivers 201a201n receive, from the antennas 200a-200n, incoming RF signals, such as signals transmitted by UEs in the network 100. The RF transceivers 201a-201n down-convert the incoming RF signals to generate intermediate frequency (IF) or baseband signals. The IF or baseband signals are sent to the RX processing circuitry 204, which generates processed baseband signals by filtering, decoding, and / or digitizing the baseband or IF signals. The RX processing circuitry 204 transmits the processed baseband signals to the controller / processor 205 for further processing.

[0052] The TX processing circuitry 203 receives analog or digital data (such as voice data, web data, electronic mail, or interactive video game data) from the controller / processor 205. The TX processing circuitry 203 encodes, multiplexes, and / or digitizes the outgoing baseband data to generate processed baseband or IF signals. The RF transceivers 201a-201n receive the outgoing processed baseband or IF signals from the TX processing circuitry 203 and up-converts the baseband or IF signals to RF signals that are transmitted via the antennas 201a-201n.

[0053] The controller / processor 205 can include one or more processors or other processing devices that control the overall operation of the gNB 102. For example, the controller / processor 205 could control the reception of forward channel signals and the transmission of reverse channel signals by the RF transceivers 201a-201n, the RX processing circuitry 204, and the TX processing circuitry 203 in accordance with well-known principles. The controller / processor 205 could support additional functions as well, such as more advanced wireless communication functions.

[0054] For instance, the controller / processor 205 could support beam forming 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 could be supported in the gNB 102 by the controller / processor 205.

[0055] The controller / processor 205 is also capable of executing programs and other processes resident in the memory 206, such as an operating system (OS). The controller / processor 205 can move data into or out of the memory 206 as required by an executing process.

[0056] The controller / processor 205 is also coupled to the backhaul or network interface 207. The backhaul or network interface 207 allows the gNB 102 to communicate with other devices or systems over a backhaul connection or over a network. The interface 207 could support communications over any suitable wired or wireless connections. For example, when the gNB 102 is implemented as part of a cellular communication system (such as one supporting 5G, LTE, or LTE-A), the interface 207 could allow the gNB 102 to communicate with other gNBs over a wired or wireless backhaul connection. When the gNB 102 is implemented as an access point, the interface 207 could 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 suitable structure supporting communications over a wired or wireless connection, such as an Ethernet or RF transceiver.

[0057] The memory 206 is coupled to the controller / processor 205. A part of the memory 206 could include a random access memory (RAM), and another part of the memory 206 could include a Flash memory or other read only memory (ROM).

[0058] Although FIG. 2 illustrates one example of gNB 102, various changes may be made to FIG. 2. For example, the gNB 102 could include any number of the components shown in FIG. 2. As a particular example, an access point could include a plurality of interfaces 207, and the controller / processor 205 could support routing functions to route data between different network addresses. As another particular example, while shown as including a single instance of TX processing circuitry 203 and a single instance of RX processing circuitry 204, the gNB 102 could include multiple instances of each (such as one per RF transceiver). Also, various components in FIG. 2 could be combined, further subdivided, or omitted and additional components could be added according to particular needs.

[0059] FIG. 3 illustrates an example user equipment according to an embodiment of the present disclosure. The embodiment of the UE 116 illustrated in FIG. 3 is for illustration only, and the UEs 111-115 and 117-119 of FIG. 1 could have the same or similar configuration. However, UEs come in a wide variety of configurations, and FIG. 3 does not limit the scope of the present disclosure to any particular implementation of a UE.

[0060] As shown in FIG. 3, the UE 116 includes an antenna 301, a radio frequency (RF) transceiver 302, a TX processing circuitry 303, a microphone 304, and an RX processing circuitry 305. The UE 116 further includes a speaker 306, a controller or processor 307, an input / output (I / O) interface (IF) 308, a touchscreen display 310, and a memory 311. The memory 311 includes an OS 312 and one or more applications 313.

[0061] The RF transceiver 302 receives, from the antenna 301, an incoming RF signal transmitted by a gNB of the network 100. The RF transceiver 302 down-converts the incoming RF signal to generate an IF or baseband signal. The IF or baseband signal is sent to the RX processing circuitry 305, which generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or IF signal. The RX processing circuitry 305 transmits the processed baseband signal to the speaker 306 (such as for voice data) or to the processor 307 for further processing (such as for web browsing data).

[0062] The TX processing circuitry 303 receives analog or digital voice data from the microphone 304 or other outgoing baseband data (such as web data, e-mail, or interactive video game data) from the processor 307. The TX processing circuitry 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 circuitry 303 and up-converts the baseband or IF signal to an RF signal that is transmitted via the antenna 301.

[0063] The processor 307 may include one or more processors or other processing devices and execute the OS 312 stored in the memory 311 in order to control the overall operation of the UE 116. For example, the processor 307 could control the reception of forward channel signals and the transmission of reverse channel signals by the RF transceiver 302, the RX processing circuitry 305, and the TX processing circuitry 303 in accordance with well-known principles. In some embodiments, the processor 307 includes at least one microprocessor or microcontroller.

[0064] The processor 307 is also capable of executing other processes and programs resident in the memory 311, such as processes for CSI reporting on uplink channel. The processor 307 may move data into or out of the memory 311 as required by an executing process. In some embodiments, the processor 307 is configured to execute the applications 313 based on the OS 312 or in response to signals received from gNBs or an 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 laptop computers and handheld computers. The I / O interface 309 is the communication path between these accessories and the processor 307.

[0065] The processor 307 is also coupled to the touchscreen display 310. The user of the UE 116 can use the touchscreen display 310 to enter data into the UE 116. The touchscreen 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 web sites.

[0066] The memory 311 is coupled to the processor 307. A part of the memory 311 could include RAM, and another part of the memory 311 could include a Flash memory or other ROM.

[0067] Although FIG. 3 illustrates one example of UE 116, various changes may be made to FIG. 3. For example, various components in FIG. 3 could be combined, further subdivided, or omitted and additional components could be added according to particular needs. As a particular example, the processor 307 could be divided into multiple processors, such as one or more central processing units (CPU) and one or more graphic processing units (GPU). Also, while FIG. 3 illustrates the UE 116 configured as a mobile telephone or smartphone, UEs could be configured to operate as other types of mobile or stationary devices.

[0068] The core concept of communication-aware integration is to use the same set of hardware equipment to realize the sensing function of the surrounding environment at the cost of as little resource overhead as possible on the basis of ensuring the basic communication function. That is, the communication-aware integration node also has the function of sensing, the content of the sensing includes the distance, orientation, speed and even the type and so on of objects in the surrounding environment. Unlike technology for positioning the access terminal in traditional communication system, communication perception integration technology can further implement the perception of non-access objects of a variety of information, which greatly increases the ability of communication system to dynamically adjust the work state according to the surrounding environment (scheduling, beam management, early warning of the access terminal, etc.).

[0069] Using the channel state information (CSI) of the received sensing signals to perform the presence detection of people in the ambient space with high detection rate (Pd) and low false alarm rate (Pfa) is a scenario of great commercial value. Presence detection is divided into motion presence detection and stationary presence detection, the stationary presence detection is a scenario that needs to be focused on in the commercialization process. However, due to more variations in the actual commercial environment, the existing communication-aware integrated node cannot achieve more accurate detection.

[0070] In this term, the present disclosure mainly relates to a sensing function and / or a communication function of a communication-aware integrated node, and proposes a communication-integrated node as well as a method performed by the communication-aware integrated node. The communication-integrated node and the method performed by the communication-integrated node according to the embodiments of the present disclosure, is capable of implementing a more accurate detection, while guaranteeing the generalization and stability of the scheme in different environments.

[0071] The communication-aware integration node is hereinafter referred to as a first node, and the first node may specifically be any wireless communication device, for example, a base station, a terminal device, a bypass device, and the like.

[0072] The most widely used communication systems are those based on 3GPP protocols, e.g., 4G communication systems such as LTE, LTE-A, and 5G communication systems such as NR, and systems based on IEEE 802's wireless local area network (Wi-Fi) protocols, e.g., 802.11a / b / g / n / ac / ax / bf communication systems. The signal waveforms used in these communication systems are OFDM modulation based waveforms. Considering forward compatibility, the OFDM communication signal may be used as a sense signal, for example. Specifically, the sensing signal may be a physical signal and / or a physical channel that may be used for sensing purposes, e.g., when the first node is a base station, the sensing signal may be, for example, a downlink reference signal or a downlink physical channel, and so on, and when the first node is a terminal, the sensing signal may be, for example, an uplink reference signal or an uplink physical channel. Alternatively, when the first node is a wireless network access point (AP) device, the sensed signal may be a WiFi signal.

[0073] The sensing signal sent by the sensing communication node is received again by the first node in the form of an echo after being reflected by the target reflector, and through signal processing on the echo signal, the presence detection can be performed on the target object, or sensing information such as distance, speed, orientation, and so on, of the sensed target object can be obtained.

[0074] FIG. 4 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure.

[0075] The present invention proposes a method for performing target detection in a wireless communication system, as shown in FIG. 4, including:

[0076] Step 401: grouping channel state information received by M antennas, to obtain N groups of channel state information, wherein the M and the N are natural numbers greater than 1;

[0077] Step 402: obtaining features based on the N groups of channel state information;

[0078] Step 403: performing target detection based on the features.

[0079] The target detection method proposed in the present invention may acquire channel state information based on the received signal, and then perform preprocessing and feature extraction based on the channel information, and perform target detection or target state / action recognition based on the features, e.g., based on the features, to determine whether there are people in a certain area or to determine the number of people in the area as well as the change of the number of people, the type of action of the people in the area, or the speed of the people in the area. Alternatively, the target may be a movable robot or an animal, e.g., a vacuum cleaner robot, or a pet such as a dog or a cat. As mentioned above, the channel state information can also be replaced with RSSI, antenna gain, MCS, etc. The method of the present invention can perform target detection more accurately, and obtain target-related information.

[0080] FIG. 5 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure.

[0081] Taking the extraction of frequency domain features as an example, a possible target detection method, as shown in FIG. 5, includes:

[0082] Step 501: grouping channel state information received by M antennas, to obtain N groups of channel state information, wherein the M and the N are natural numbers greater than 1;

[0083] Step 502: performing an optional pre-processing flow, e.g., performing a quotient operation and / or performing an outlier processing for each group of channel state information;

[0084] Step 503: extracting frequency domain features from the preprocessed channel state information;

[0085] Step 504: dividing one or more time windows, obtaining frequency domain features of the group of channel state information based on the frequency domain features and time information corresponding to a time window;

[0086] Step 505: performing at least one of statistical feature extraction, maximum eigenvalue extraction, and extraction of eigenvalues other than the maximum eigenvalue;

[0087] Step 506: performing the target detection based on the N groups of frequency domain features.

[0088] Optionally, before obtaining the channel state information received by the M antennas, the node may receive a signal, perform channel estimation on the received signal, thereby obtaining the original channel state information. Alternatively, after obtaining the original channel state information, the node may extract one or more subcarriers in the original channel state information based on a frequency domain feature of the signal, to compose new channel state information by, for example, removing guard bands, removing an intermediate frequency, etc. An example of removing guard bands may be that when the received original channel state information contains 64 subcarriers and the transmitted signal occupies intermediate 60 subcarriers, the node may extract the channels on the intermediate 60 subcarriers to compose the new channel state information.

[0089] [The First embodiment]

[0090] FIG. 6 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure.

[0091] For the above steps 501 and 502, a possible way of grouping and performing a quotient operation on the channel state information is shown in FIG. 6 comprises performing channel estimation on the received signals corresponding to the M antennas to obtain channel state information, grouping the channel state information. The grouping may be based on the correspondence of the M antennas, e.g., the grouping is based on the relative relationship / relative position relationship between the antennas. Taking M=4 as an example, one possible way of grouping is to group antenna 1 and antenna 2 into one group and antenna 3 and antenna 4 into another group. Taking M=4 as another example, an alternative grouping method is to group antenna 1 and antenna 2 into one group, antenna 1 and antenna 3 into one group, antenna 1 and antenna 4 into one group, antenna 2 and antenna 3 into one group, and antenna 3 and antenna 4 into one group.

[0092] After the grouping of the channel state information, within each group, the quotient operation is performed on the channel state information. For example, the channel state information of antenna p is noted as , wherein hmn(p)represents the channel state information of the mth subcarrier of the pth antenna at moment n. m=1,...,M, wherein M is a positive integer. The channel state information is grouped based on the location between the antennas, e.g., antenna q can be any antenna of the receiving device different from antenna p. Then and can be divided into one group. A quotient between the channel state information is obtained by performing the quotient operation on any two of the channel state information within the group, e.g., . Alternatively, when represents the channel between the lth antenna of the transmitter and the pth antenna of the receiver, one possible implementation of performing the quotient operation on the channel state information is ,i.e., for the same receiver antenna, performing the quotient operation based on the channel state information corresponding to the different antennas of the transmitter.

[0093] Optionally, after the grouping of the channel state information and before performing the quotient operation for each group of channel state information, the channel state information within or between the groups may also be normalized. Note that the normalization may also be a 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. This applies to data that is otherwise distributed over a finite range. Alternativley, mean-variance normalization generally normalizes the mean to 0 and the variance to 1. This applies to situations where the distribution is not clearly bounded. The benefit of normalization is that it can facilitate subsequent signal processing, such as when using machine learning to process the signal, using the normalized data for training can accelerate the speed of convergence of machine learning.

[0094] Optionally, 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 realization method of obtaining the time domain channel state information based on the frequency domain channel state information is to perform a column-by-column Fourier inverse transform or discrete Fourier inverse transform on the frequency domain channel state information of the antenna p to obtain the time domain channel state information of the antenna p.

[0095] [The Second embodiment]

[0096] For S504 of the above step, a possible preprocessing / feature extraction method includes performing an modulo operation on the channel state information, or obtaining the phase of the channel state information, or preprocessing the phase, or calibrating the phase. In addition, the features of the channel state information on a plurality of subcarriers are averaged at the same moment, or, the features of the channel state information on the same subcarriers are averaged at different moments.

[0097] FIG. 7 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure.

[0098] A possible preprocessing / feature extraction method of performing phase calibration process on the phase information of the channel state, as shown in FIG. 7, the method of performing includes: performing a phase fetch operation on the channel state information , wherein, represents the channel state information of the mth subcarrier of the pth antenna at the moment n, is the amplitude of the channel state information, is the phase of the channel state information, and the phase of the channel state information can be obtained, wherein is the phase information of the channel state of the mth subcarrier of the pth antenna at the moment n. Based on the phase of the channel state information of all M subcarriers, m=1,...,M, wherein M is a positive integer, can be obtained, wherein a is the slope of the phase of m subcarriers of the pth antenna at the moment n, and b is the phase offset. The calibrated phase is , wherein m=1,...,M, n=1,...,N, wherein M and N are positive integers.

[0099] Alternativley, a phase calibration process may also be performed on the result after performing the quotient operation for the channel state information in the first embodiment.

[0100] It should be noted that a beneficial effect of performing the above phase calibration process on the phase information is that phase errors introduced by unknown carrier frequency offsets (CFO) and / or sampling frequency offsets (SFO) can be removed, the accuracy of the phase information can be improved, and the validity of the eigenvalues extracted based on the phase information can be enhanced.

[0101] [The Third embodiment]

[0102] FIG. 8 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure.

[0103] For the step S504, a possible preprocessing / feature extraction method, as shown in FIG. 8, 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 is that the channel state information of the above antenna p may be segmented as , wherein represents the values of all subcarriers of the channel state information of the antenna p within the time window. The obtaining of the first information based on the segmented channel state information includes obtaining a mean or a variance or a standard deviation for the segmented channel state information in the time dimension, or optionally, obtaining the amplitude / the phase / the preprocessed phase based on the segmented channel state information, and obtaining the first information based on the amplitude / the phase / the preprocessed phase. For example, the mean or the variance or the standard deviation of the amplitude / the phase / the preprocessed phase is obtained in the time dimension to obtain the first information. The individual time windows may be overlapping or non-overlapping, and the length of the individual time windows may be equal or unequal. Optionally, the corresponding channel first information within each time window may be averaged in the subcarrier dimension, or averaged over a portion of the subcarriers.

[0104] For steps 503 and 504, a possible feature extraction method includes: setting a plurality of time windows; obtaining, based on the features of the channel state information and the time information corresponding to the time window, features corresponding to the time information within each of the time windows; and performing the feature extraction based on the frequency domain features or time domain features corresponding to the time information within each of the time windows. The features within a certain time window may be extracted based on the frequency domain feature or the time domain feature corresponding to the time information within the time window, or may be extracted based on the frequency domain feature or the time domain feature corresponding to the time information within the K time windows before or after the time window, or may be extracted based on the frequency domain features or time domain features time window corresponding to the time information within the time window and the K time windows before and / or after the time window. For an example in which 20 time windows are set up, and time windows 1, 2, ......20 correspond to 20 or 20 groups of frequency domain features or time domain features, the features corresponding to the 5th time window may be extracted based on the frequency domain features or the time domain features of the 5th time window, or may be extracted based on the frequency domain features or the time domain features of the 1st to 5th time windows, or, may be extracted based on the frequency domain features or the time domain features of the 4th to 6th time windows.

[0105] [The Fourth embodiment]

[0106] FIG. 9 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure.

[0107] For step S505, a possible preprocessing / feature extraction method, as shown in FIG. 9, 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 a plurality of first information based on the segmented channel state information, and obtaining second information based on the plurality of first information. For example, the above channel state information of the antenna p may be segmented as , wherein represents the values of all subcarriers of the channel state information of the antenna p within the time window t1. The autocorrelation is sought for the elements after each segment in . Taking as an example, the autocorrelation operation is , and eigenvalue decomposition (EVD) or singular value decomposition (SVD) is performed on to obtain eigenvalues or singular values , and the channel eigenvalues within the time window are obtained based on one or more of the eigenvalues or singular values. Taking the eigenvalues as an example, the obtaining of the channel first information within the time window based on the one or more eigenvalues includes, normalizing the eigenvalues , for example, , extracting the largest eigenvalue or the largest eigenvalue after normalization, or inverting the largest eigenvalue or the largest eigenvalue after normalization, so as to obtain the channel first information within the time window. Optionally, the obtaining of the plurality of first information based on the segmented channel state information may also include, obtaining the amplitude / the phase / the preprocessed phase of the channel state information based on the segmented channel state information, and performing the above autocorrelation and eigenvalue decomposition operation on the amplitude / the phase / the preprocessed phase to obtain the plurality of first information related to the amplitude / phase / preprocessed phase. Taking the time window t1 as an example, in a possible embodiment of obtaining the second information based on the plurality of first information, two different first information, e.g., a first information value M1 related to the amplitude and a first information value M2 related to the phase, are used as the horizontal and vertical coordinates of a point in a certain planar coordinate system, and then the point which is uniquely determined by the eigenvalue M1 and the eigenvalue M2 in the planar coordinate system is the second information corresponding to the time window t1. Optionally, the performing of the target detection based on the second information includes, for example, performing a clustering processing or a classification processing on the second information values within the plurality of time windows, for example, performing the clustering process or the classification process on the points which is uniquely determined by the eigenvalue M1 and the eigenvalue M2 in the planar coordinate system, wherein the clustering algorithm may include a K-MEANS clustering algorithm, a DBSCAN smiley-face clustering algorithm, a mean shift clustering algorithm, and so on.

[0108] [The Fifth embodiment]

[0109] FIG. 10 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure.

[0110] For step S505, a possible preprocessing / feature extraction method, as shown in FIG. 10, includes: performing an operation of obtaining phase on the channel state information represents the channel state information of the mth subcarrier of the pth antenna at the moment n, is the amplitude of the channel state information, is the phase of the channel state information, and the phase of the channel state information can be obtained , wherein is the mth subcarrier of the pth antenna at the moment n, wherein m=1,...,M, n=1,...,N, wherein the M and N are positive integers. Next, feature extraction is performed on the phase information of the channel state based on different moments n in the time dimension, for example, the phase information of the channel state may be segmented in the time dimension based on the time window Wk,a plurality of first information are obtained based on the segmented phase information of the channel state, and a second information is obtained based on the plurality of first information. Specifically, the above channel state information of the antenna p can be divided into K segments as , wherein , k=1,...,K represents the phase information of the channel states of all subcarriers corresponding to the antenna p within the time window Wk,wherein the lengths lkof the K time windows may be equal or unequal, in which a condition of is met, wherein N is the time length of the channel state information. Next, the autocorrelation for is solved to obtain the autocorrelation matrix of M X M dimensions, where M is the number of subcarriers. Eigenvalue decomposition (EVD) or singular value decomposition (SVD) is performed on the autocorrelation matrix to obtain the eigenvalues or singular values , wherein . The second information corresponding to the time window Wkis obtained based on one or more of the obtained eigenvalues or singular values, and K second informations, which are called the second information set, can be obtained by performing the above processing on the phase information of the channel state in all K time windows. Specifically, the method of obtaining the second information corresponding to the time window Wkbased on one or more eigenvalues or singular values may be to select any one of the eigenvalues or singular values , other than the maximum eigenvalue or singular value, as the second information corresponding to the time window Wk ;or, to select, a sum of on the remaining eigenvalues or singular values other than the maximum eigenvalue or singular value, as the second information corresponding to the time window Wk,wherein 1 < c1< c2≤ K, c1and c2are positive integers. Alternatively, the eigenvalues or singular values are normalized, e.g., , and the above processing are performed on normalized eigenvalues to obtain the second information corresponding to the time window.

[0111] Optionally, in addition to the phase information based on the channel state, the following data may also be based on amplitude information of the channel state, strength indication (RSSI) of the received signal, antenna gain information, and so on. Alternatively, the second information set is obtained by pre-processing the phase information of the channel state and the above-described data as described in the first embodiment, second embodiment, third embodiment, and then performing the signal processing process as described in the present embodiment.

[0112] [The Sixth embodiment]

[0113] A data outlier processing method is proposed below, and it should be noted that the outlier processing method proposed in the present invention, features extracted with respect to channel state information and / or based on the channel state information (e.g., amplitude / phase / processed phase) and / or other parameters of the received signal (e.g., RSSI, antenna gain, MCS, etc.), can perform the outlier processing on the entire data at the sampling point level. It is also possible to perform inter-segment outlier processing, i.e., after segmentation from the time dimension, a feature is extracted from each segment of the data, and the outlier processing is performed on the features corresponding to different segments. It is also possible to perform intra-segment outlier processing, i.e., after segmentation from the time dimension, the outlier processing is performed at the sampling point level within each segment of the data.

[0114] FIG. 11 illustrates a schematic diagram of a method performed by a node according to at least one embodiment of the present disclosure.

[0115] For the step S502, or options in the step S505, a possible method of outlier identification and processing on the channel state information, as shown in FIG. 11, includes: performing the channel estimation on the received signal to obtain the channel state information; obtaining a first information based on the channel state information; and performing an outlier processing on the first information to obtain a third information. The first information may be obtained based on the segmented channel state information. the performing of the outlier processing on the first information to obtain the third information includes, obtaining the first information difference based on the first information, performing an over-threshold determination on the elements in the first information, and when an element exceeds the threshold, it is determined to be an outlier, and performing the outlier processing. The obtaining of the first information difference value based on the first information includes, performing a sliding averaging on the elements contained in the first information to obtain a smoothed first information, determining a difference between the first information and the smoothed first information and obtaining the modulo value of the difference to obtain the first information difference value. The performing of an over-threshold determination on an element in the first information includes, obtaining a threshold based on the first information difference value, for example, taking the mean value of the first information difference value as the threshold, and when an element in the first information is larger than the threshold, the element is recognized as an outlier. It should be noted that the method of identifying outliers is not limited to the over-threshold determination method described above, and other methods, such as a median absolute deviation (MAD) method, may also be used in the actual data processing. The outlier processing, including, replacing an outlier in the first information, the replacement content may be a non-outlier that is closest to the outlier in time or sampling moment, and the replaced sequence is the third information.

[0116] The data outlier processing method proposed in the present embodiment may be used in combination with one or more other embodiments in the present invention, and a specific example is that, according to the method of the six embodiment, the third information is obtained based on the first information at a sampling point level, and then the third information is segmented according to time, and the mean / variance of the amplitude / phase / phase after preprocessing is obtained based on the segmented third information as the second information.

[0117] [The Seventh embodiment]

[0118] In some examples, the device performs the target detection or the target state / action recognition by a neural network, e.g., recognizing the presence or absence of a person / machine / animal, mapping movement path, detecting the number of persons, etc. The method for detecting the number of persons is to select the number of persons corresponding to a output unit having the largest value among all output units of the neural network.

[0119] Specifically, the structure of the "neural network" for detecting the number of persons includes, but is not limited to, a Multilayer Perceptron (MLP), a Convolutional Neural Network (CNN), a 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 and so on.

[0120] Specifically, the input to the neural network is a second information set, which contains the second information for the 1st to the Tth consecutive time windows.

[0121] Specifically, the output of the neural network is a detection result for the time corresponding to the Tth time window. When the detection target is the number of persons in the space, each output of the neural network corresponds to all possible numbers of persons in the particular space to be detected, respectively, and the output of the detector may be the probability or value of number of each type of persons.

[0122] Specifically, after the neural network completes the detection for the Tth time window, the second information set will be updated chronologically on a sliding scale in steps of one time window. For example, when detection is performed for the (T+1)th moment, the second information set will include the second information for the 2nd to (T+1)th time windows. By inputting the information included in the second information set to the neural network, the neural network obtains the detection result for the (T+1)th moment.

[0123] Possible implementations corresponding to some of the embodiments above will be given below. The features in the following may be in the form of vectors or matrices.

[0124] An implementation method corresponding to the first embodiment and the second embodiment includes: dividing channel state information received by M antennas, e.g., M=4, into N groups, e.g., dividing data received by the nth antenna and (n+1)th antenna into a group, and then performing a quotient operation between the channel state information received by the nth antenna and that corresponding to the (n+1)th antenna, and then performing an operation to find an angle or a phase on the result of the quotient to obtain the processed frequency domain feature information. Optionally, an operation of abandoning outliers may be performed on the frequency domain feature information, such as abandoning data corresponding to subcarriers in the two guard intervals and / or intermediate DC subcarriers since they do not carry useful information.

[0125] An implementation method corresponding to the sixth embodiment, including: inputting a certain feature related to the channel state information, obtaining a median of the feature, obtaining a difference between the feature and the median, and squaring the difference to obtain a first intermediate result, obtaining a median of the first intermediate result to obtain a second intermediate result, performing a quotient operation between the feature and the second intermediate result to obtain a third intermediate result, finding all the numbers in the third intermediate result that are numbers less than the first threshold and taking the mean of the numbers as the fourth intermediate result, finding the indexes corresponding to all numbers greater than or equal to the second threshold in the third intermediate result, selecting the numbers corresponding to the indexes from the third intermediate result, and replacing them with the fourth intermediate result to obtain the features after removing the outliers. The second threshold and the first threshold may be equal or unequal.

[0126] An implementation method corresponding to the third embodiment, including: determining an index based on a certain time range, and obtaining channel state information within a time window associated with the time range based on the index and the channel state information. For example, if the channel state information of the antenna p is , and the time window t1 corresponds to the kth column to the (k+n)th column of , the channel state information of all subcarriers corresponding to the antenna p within the time window t1 may be expressed as . and in the physical aspect, it means that the kth column to the (k+n)th column of is extracted to form a new matrix as the channel state information of all subcarriers corresponding to the antenna p within the time window t1.

[0127] An implementation method corresponding to the sixth embodiment, including: inputting features associated with the channel state information, obtaining a mean value for the features, obtaining a modulo values of the differences between the features and their mean value, sorting the modulo values, filtering the sorted sequence according to a certain condition, returning indexes of the data that satisfy the condition, and finding the variance of the features within each time window based on the indexes and the features. The features of a total of T consecutive time windows are obtained.

[0128] An implementation method corresponding to the seventh embodiment, including: inputting the obtained features of all T time windows into a neural network to obtain detection results of the Tth time window. Repeating the above feature extraction process to obtain the features for the 2nd to the (T+1)th time windows and inputting them into the neural network to obtain detection results for the (T+1)th time window. Repeating the above process to obtain the detection results for any (T+x) th time window. When the detection target is the number of persons in the space, each output of the neural network corresponds to all possible numbers of persons in the particular space to be detected, respectively, and the output of the detector may be the probability or value of number of each type of persons. The method of obtaining the detection results for the Tth time window is to select the number of persons corresponding to the largest output unit of the neural network.

[0129] The above methods described in the present disclosure may be performed by a first node device including a transceiver and a processor, the first node device, for example, may be a receiver, e.g., a base station, a UE, a relay node, and the like. FIG. 12 illustrates an exemplary structure of the first node device according to the present disclosure. As shown in FIG. 12, the first node device includes a transceiver 1210 and a processor 1220 coupled to the transceiver 1210. The transceiver 1210 is configured to send and receive signals. The processor 1220 is configured to perform a user selection method as described in the present disclosure. The present disclosure may also be implemented as a computer storage medium. The computer storage medium is stored with computer-executable instructions, and when the stored computer-executable instructions are executed by the processor, the processor performs the method as previously described in the present disclosure.

[0130] FIG. 13 illustrates an example diagram of a user equipment, according to embodiments of the present disclosure. FIG. 13 corresponds to the example of the UE of FIG.1 and the UE of FIG.12.

[0131] As shown in FIG. 13, the UE according to an embodiment may include a transceiver 1310, a memory 1320, and a processor 1330. The transceiver 1310, the memory 1320, and the processor 1330 of the UE may operate according to a communication method of the UE described above. However, the components of the UE are not limited thereto. For example, the UE may include more or fewer components than those described above. In addition, the processor 1330, the transceiver 1310, and the memory 1320 may be implemented as a single chip. Also, the processor 1330 may include at least one processor.

[0132] The transceiver 1310 collectively refers to a UE receiver and a UE transmitter, and may transmit / receive a signal to / from a base station or a network entity. The signal transmitted or received to or from the base station or a network entity may include control information and data. The transceiver 1310 may include a RF transmitter for up-converting and amplifying a frequency of a transmitted signal, and a RF receiver for amplifying low-noise and down-converting a frequency of a received signal. However, this is only an example of the transceiver 1310 and components of the transceiver 1310 are not limited to the RF transmitter and the RF receiver.

[0133] Also, the transceiver 1310 may receive and output, to the processor 1330, a signal through a wireless channel, and transmit a signal output from the processor 1330 through the wireless channel.

[0134] The memory 1320 may store a program and data required for operations of the UE. Also, the memory 1320 may store control information or data included in a signal obtained by the UE. The memory 1320 may be a storage medium, such as read-only memory (ROM), random access memory (RAM), a hard disk, a CD-ROM, and a DVD, or a combination of storage media.

[0135] The processor 1330 may control a series of processes such that the UE operates as described above. For example, the transceiver 1310 may receive a data signal including a control signal transmitted by the base station or the network entity, and the processor 1330 may determine a result of receiving the control signal and the data signal transmitted by the base station or the network entity.

[0136] FIG. 14 illustrates an example diagram of a base station, according to embodiments of the present disclosure. FIG. 14 corresponds to the example of the gNB of FIG.1 and the base station of FIG.12.

[0137] As shown in FIG. 14, the base station according to an embodiment may include a transceiver 1410, a memory 1420, and a processor 1430. The transceiver 1410, the memory 1420, and the processor 1430 of the base station may operate according to a communication method of the base station described above. However, the components of the base station are not limited thereto. For example, the base station may include more or fewer components than those described above. In addition, the processor 1430, the transceiver 1410, and the memory 1420 may be implemented as a single chip. Also, the processor 1430 may include at least one processor.

[0138] The transceiver 1410 collectively refers to a base station receiver and a base station transmitter, and may transmit / receive a signal to / from a terminal or a network entity. The signal transmitted or received to or from the terminal or a network entity may include control information and data. The transceiver 1410 may include a RF transmitter for up-converting and amplifying a frequency of a transmitted signal, and a RF receiver for amplifying low-noise and down-converting a frequency of a received signal. However, this is only an example of the transceiver 1410 and components of the transceiver 1410 are not limited to the RF transmitter and the RF receiver.

[0139] Also, the transceiver 1410 may receive and output, to the processor 1430, a signal through a wireless channel, and transmit a signal output from the processor 1430 through the wireless channel.

[0140] The memory 1420 may store a program and data required for operations of the base station. Also, the memory 1420 may store control information or data included in a signal obtained by the base station. The memory 1420 may be a storage medium, such as read-only memory (ROM), random access memory (RAM), a hard disk, a CD-ROM, and a DVD, or a combination of storage media.

[0141] The processor 1430 may control a series of processes such that the base station operates as described above. For example, the transceiver 1410 may receive a data signal including a control signal transmitted by the terminal, and the processor 1430 may determine a result of receiving the control signal and the data signal transmitted by the terminal.

[0142] In one example, a method for performing target detection in a wireless communication system, the method comprising: grouping channel state information received by M antennas, to obtain N groups of channel state information, wherein the M and the 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.

[0143] In another example, wherein the grouping of the channel state information received by the M antennas comprises: grouping the channel state information received by the M antennas according to the correspondence of the M antennas.

[0144] In another example, wherein the grouping of the channel status information received by the M antennas according to the correspondence of the M antennas comprises at least one of: combining the M antennas two by two to obtain the correspondence of the M antennas, and grouping the channel state information received by the M antennas according to the correspondence of the M antennas; combining any one of the M antennas with any other one of the M antennas to obtain the correspondence of the M antennas, and grouping the channel state information received by the M antennas according to the correspondence of the M antennas.

[0145] In another example, wherein the obtaining of the features based on the N groups of channel state information comprises: 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 a time window, and the performing of the target detection based on the features comprises: performing the target detection based on the N groups of frequency domain features.

[0146] In another example, wherein the obtaining of the frequency domain features of the group of channel state information based on the frequency domain features and the time information corresponding to the time window comprises: obtaining, based on the frequency domain features and the time information corresponding to the time window, the frequency domain features corresponding to the time information within each time window; and performing the feature extraction based on the frequency domain features corresponding to the time information within each time window.

[0147] In another example, wherein the extracting of the frequency domain features for each group of channel state information comprises: performing a quotient operation and / or performing an outlier processing for each group of channel state information, and extracting the frequency domain features.

[0148] In another example, wherein the performing of the feature extraction based on the frequency domain features corresponding to the time information within each time window comprises: performing an outlier processing on the frequency domain features corresponding to the time information within each time window, and performing at least one of statistical feature extraction, maximum eigenvalue extraction, and extraction of eigenvalues other than the maximum eigenvalue.

[0149] In another example, wherein the obtaining of the features based on the N groups of channel state information comprises: for each group of channel state information, obtaining the time domain channel state information based on the channel state information; obtaining time domain features based on the time domainchannel state information ; and obtaining the time domain features within each time window based on the time domain features and the time information corresponding to the time window.

[0150] In another example, wherein the obtaining of the time domain features based on the time domain channel state information comprises: performing a quotient operation and / or performing an outlier processing on the time domain channel state information, and extracting the time domain features.

[0151] In another example, wherein the obtaining of the time domain features within each time window based on the time domain features and time information corresponding to the time window comprises: obtaining, based on the time domain features and the time information corresponding to the time window, the time domain features corresponding to the time information within each time window; performing the feature extraction on the time domain features corresponding to the time information within each time window.

[0152] In another example, wherein the performing of the feature extraction on the time domain features corresponding to the time information within each time window comprises: performing the outlier processing on the time domain features corresponding to the time information within each time window, and performing at least one of statistical feature extraction, maximum eigenvalue extraction, and extraction of eigenvalues other than the maximum eigenvalue.

[0153] In one example, a computer-readable storage medium storing computer executable instructions, wherein when the computer executable instructions are executed by a processor, the processor performs the method in any one of above.

[0154] It can be understood that "at least one / at least one" described in this disclosure includes any and / or all possible combinations of listed items, various embodiments described in this disclosure and various examples in embodiments can be changed and combined in any suitable form, and " / "described in this disclosure means "and / or".

[0155] The illustrative logical blocks, modules, and circuits described in this disclosure may be implemented in a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic devices, 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 the alternative, the processor may be any processor of the related art, controller, microcontroller, or state machine. A 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.

[0156] The steps of a method or algorithm described in this disclosure may be embodied directly in hardware, in a software module performed by a processor, or in a combination of the both. Software modules may reside in a RAM memory, a flash memory, a ROM memory, an erasable programmable ROM (EPROM) memory, an electrically EPROM (EEPROM) memory, registers, hard disks, removable disks, or any other form of storage media known in the art. A storage medium is coupled to a processor to enable the processor to read and write information from / to the storage medium. In the alternative, the storage medium may be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in the user equipment terminal. In the alternative, the processor and the storage medium may reside as separate components in the user equipment terminal.

[0157] In one or more designs, the described functions may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, each function can be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, and the latter includes any media that facilitates the transfer of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0158] The description set forth herein, taken in conjunction with the drawings, describes example methods and devices, and does not represent all examples that can be realized or are within the scope of the claims. As used herein, the term "example" means "serving as an example, instance or illustration" rather than "preferred" or "superior to other examples". The detailed description includes specific details in order to provide an understanding of the described technology. 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.

[0159] Although this specification contains many specific implementation details, these should not be interpreted as limitations on any embodiment or the scope of the claimed protection, but as descriptions of specific features of specific embodiments. Some features described in this specification in the context of separate embodiments can also be combined in a single embodiment. On the contrary, various features described in the context of a single embodiment can also be implemented separately in multiple embodiments or in any suitable sub-combination. Furthermore, although features may be described above as functioning 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 subcombination or a variation of a subcombination.

[0160] It should be understood that the specific order or hierarchy of steps in the method of the disclosure is illustrative of a process. Based on the design preference, it can be understood that the specific order or hierarchy of steps in the method can be rearranged to realize the functions and effects disclosed in the disclosure. The appended method claims elements of various steps in an example order, and are not meant to be limited to the particular order or hierarchy presented, unless otherwise specifically stated. Therefore, the disclosure is not limited to the illustrated examples, and any means for performing the functions described herein are included in various aspects of the disclosure.

[0161] While the disclosure has been shown and described with reference to various embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the disclosure as defined by the appended claims and their equivalents.

Claims

1.A method for performing target detection in a wireless communication system, the method comprising:grouping channel state information received by M antennas, to obtain N groups of channel state information, wherein the M and the N are natural numbers greater than 1;obtaining features based on the N groups of channel state information; andperforming target detection based on the features.2.The method of claim 1, wherein the grouping of the channel state information received by the M antennas comprises:grouping the channel state information received by the M antennas according to the correspondence of the M antennas.3.The method of claim 2, wherein the grouping of the channel status information received by the M antennas according to the correspondence of the M antennas comprises at least one of:combining the M antennas two by two to obtain the correspondence of the M antennas, and grouping the channel state information received by the M antennas according to the correspondence of the M antennas;combining any one of the M antennas with any other one of the M antennas to obtain the correspondence of the M antennas, and grouping the channel state information received by the M antennas according to the correspondence of the M antennas.4.The method of claim 1, wherein the obtaining of the features based on the N groups of channel state information comprises: 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 a time window, andthe performing of the target detection based on the features comprises: performing the target detection based on the N groups of frequency domain features.5.The method of claim 4, wherein the obtaining of the frequency domain features of the group of channel state information based on the frequency domain features and the time information corresponding to the time window comprises:obtaining, based on the frequency domain features and the time information corresponding to the time window, the frequency domain features corresponding to the time information within each time window; andperforming the feature extraction based on the frequency domain features corresponding to the time information within each time window.6.The method of claim 4, wherein the extracting of the frequency domain features for each group of channel state information comprises:performing a quotient operation and / or performing an outlier processing for each group of channel state information, and extracting the frequency domain features.7.The method of claim 5, wherein the performing of the feature extraction based on the frequency domain features corresponding to the time information within each time window comprises:performing an outlier processing on the frequency domain features corresponding to the time information within each time window, and performing at least one of statistical feature extraction, maximum eigenvalue extraction, and extraction of eigenvalues other than the maximum eigenvalue.8.The method of claim 1, wherein the obtaining of the features based on the N groups of channel state information comprises:for each group of channel state information, obtaining the time domain channel state information based on the channel state information;obtaining time domain features based on the time domainchannel state information ; andobtaining the time domain features within each time window based on the time domain features and the time information corresponding to the time window.9.The method of claim 8, wherein the obtaining of the time domain features based on the time domain channel state information comprises:performing a quotient operation and / or performing an outlier processing on the time domain channel state information, and extracting the time domain features.10.The method of claim 8, wherein the obtaining of the time domain features within each time window based on the time domain features and time information corresponding to the time window comprises:obtaining, based on the time domain features and the time information corresponding to the time window, the time domain features corresponding to the time information within each time window;performing the feature extraction on the time domain features corresponding to the time information within each time window.11.The method of claim 10, wherein the performing of the feature extraction on the time domain features corresponding to the time information within each time window comprises: performing the outlier processing on the time domain features corresponding to the time information within each time window, and performing at least one of 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; anda processor, coupled to the transceiver and configured to:group channel state information received by M antennas, to obtain N groups of channel state information, wherein the M and the N are natural numbers greater than 1;obtain features based on the N groups of channel state information; andperform target detection based on the features.13.The first node of claim 12, wherein the group the channel state information received by the M antennas comprises:group the channel state information received by the M antennas according to the correspondence of the M antennas.14.The first node of claim 13, wherein the group of the channel status information received by the M antennas according to the correspondence of the M antennas comprises at least one of:combine the M antennas two by two to obtain the correspondence of the M antennas, and group the channel state information received by the M antennas according to the correspondence of the M antennas;combine any one of the M antennas with any other one of the M antennas to obtain the correspondence of the M antennas, and group the channel state information received by the M antennas according to the correspondence of the M antennas.15.The first node of claim 12, wherein the obtain of the features based on the N groups of channel state information comprises:extract frequency domain features for each group of channel state information, and obtain frequency domain features of the group of channel state information based on the frequency domain features and time information corresponding to a time window, andperform the target detection based on the features comprises: perform the target detection based on the N groups of frequency domain features.

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