Channel estimation method and wireless perception device

Abstract

The application provides a channel estimation method and a wireless sensing device. The channel estimation method is applied to the wireless sensing device, the wireless sensing device comprises a sending node and a receiving node, and the method comprises the following steps: the receiving node determines a weighting matrix based on a channel matrix; the sending node acquires the weighting matrix; the sending node sends a time-domain sensing signal of beamforming to the receiving node through an air interface based on the weighting matrix; and the receiving node performs channel estimation by using the time-domain sensing signal, and determines channel state information, wherein the channel state information represents the channel state of the time-domain sensing signal in the process of propagation in a physical space environment. The application can improve the accuracy of channel estimation while reducing the cost of channel estimation.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a channel estimation method and a wireless sensing device. Background Technology

[0002] During wireless signal transmission, the wireless channel has a significant impact on the communication performance of the wireless communication system. Due to the low predictability and high randomness of the wireless channel, channel estimation (CHE) is required to obtain channel state information (CSI). This allows the receiver (RX) to accurately recover the signal transmitted by the transmitter (TX) based on the channel state information, thereby ensuring the communication performance of the wireless communication system.

[0003] Currently, the transmitting device sends probe frames to the receiving device. These probe frames include a long training sequence. The receiving device performs channel estimation based on the long training sequence to obtain channel state information.

[0004] However, in related technologies, the crystal oscillators of the transmitting and receiving devices are usually out of sync, resulting in different residual frequency offsets in multiple measurements by the receiving device and crystal oscillator frequency drift. Furthermore, the receiving device jitters periodically when performing channel estimation, affecting its receiving performance. This leads to poor accuracy of the determined channel state information, affecting the accuracy of the signal recovery by the receiving device, and thus impacting the communication performance of the wireless communication system. Summary of the Invention

[0005] This application provides a channel estimation method and a wireless sensing device that can improve the accuracy of channel estimation while reducing the cost of channel estimation.

[0006] In a first aspect, this application provides a channel estimation method applied to a wireless sensing device, the wireless sensing device including a transmitting node and a receiving node, the method comprising: the receiving node determining a weighting matrix based on a channel matrix; the transmitting node acquiring the weighting matrix; the transmitting node transmitting a beamformed time-domain sensing signal to the receiving node via an air interface based on the weighting matrix; and the receiving node using the time-domain sensing signal to perform channel estimation and determine channel state information, the channel state information representing the channel state of the time-domain sensing signal during propagation in the physical space environment.

[0007] In this method, both the transmitting node and the receiving node are located in the wireless sensing device. Therefore, the crystal oscillators of the transmitting node and the receiving node are synchronized, eliminating the effects of frequency offset, crystal frequency drift and phase noise caused by asynchronous crystal oscillators during the channel estimation process. Furthermore, the receiving node will not jitter at regular intervals, thereby improving the accuracy of the determined channel state information.

[0008] Furthermore, beamforming time-domain sensing signals can make the energy of the main path of the time-domain sensing signal received by the receiving node extremely small or even zero. Even if the physical distance between the transmitting and receiving nodes is small, it can reduce the impact of unwanted signals on the receiving node, thereby further improving the accuracy of channel state information.

[0009] The receiving node can decompose the channel matrix to obtain a weighted matrix. Time-domain aware signals can include Wireless Fidelity (WiFi) frames and radar signals, such as pulse signals and frequency-modulated continuous waves. Beamforming methods include multi-antenna precoding, phased array antennas, and directional antennas.

[0010] In one possible implementation, a beamformed time-domain aware signal is transmitted to a receiving node over the air interface via multi-antenna precoding. The transmitting node transmits the beamformed time-domain aware signal to the receiving node over the air interface based on the weighting matrix, comprising: the transmitting node determining a precoding matrix based on the weighting matrix; and the transmitting node transmitting the time-domain aware signal to the receiving node over the air interface based on the precoding matrix.

[0011] Weighting matrices can be used to weight signals in either the frequency or time domain. When used to weight signals in the frequency domain, multiple weighting matrices are used, with each matrix corresponding to a specific subcarrier. When used to weight signals in the time domain, only one weighting matrix is ​​used.

[0012] In one possible implementation, there are multiple weighting matrices. There are also multiple channel matrices, each corresponding one-to-one with a subcarrier. The receiving node determines the weighting matrix based on the channel matrices, including: the receiving node determining the weighting matrix corresponding to the subcarrier based on the channel matrix of the subcarrier. Optionally, the receiving node can perform Singular Value Decomposition (SVD) on the channel matrix of the subcarrier to obtain the weighting matrix of that subcarrier.

[0013] In one possible implementation, the number of weighting matrices is one. The number of channel matrices is multiple, with each channel matrix corresponding one-to-one with a subcarrier. The receiving node determines the weighting matrix based on the channel matrices, including: multiplying the channel matrix of each subcarrier by its conjugate to obtain a first matrix corresponding to the subcarrier; summing the first matrices corresponding to each of the multiple subcarriers to obtain a second matrix; and determining the weighting matrix based on the second matrix.

[0014] In one possible implementation, there are multiple weighting matrices. The transmitting node determines the precoding matrix based on the weighting matrices, including: the transmitting node determining at least one column of the weighting matrix corresponding to the subcarrier as a third matrix; the transmitting node calculating the null space of the third matrix to obtain the precoding matrix corresponding to the subcarrier. In this case, there are multiple precoding matrices, and multiple precoding matrices correspond to multiple subcarriers.

[0015] The third matrix can have one or more null spaces. When there are multiple null spaces, all null spaces can be used as precoding matrices, or mathematical processing can be performed on the multiple null spaces, and the matrix obtained from the mathematical processing can be used as the precoding matrix. This application does not limit the specific implementation of this method. Mathematical processing includes summation or averaging, etc.

[0016] Alternatively, the null space of the third matrix can be computed through block diagonalization based on SVD or orthogonal triangular decomposition (also known as QR decomposition) or generalized zero-forcing channel based on SVD or QR decomposition.

[0017] When the number of antennas in a receiving node is large, the dimension of the weighting matrix is ​​large, and therefore its rank is also large. Solving the null space of a weighting matrix with a large rank is more difficult. The antennas of the receiving node can have strong correlation, thereby reducing the rank of the weighting matrix and allowing for the acquisition of more zero vectors even with a large dimension. Optionally, the antennas in the transmitting node can have the same transmission direction, and the antennas in the receiving node can have the same reception direction. This makes the channel matrix of each receiving antenna almost identical, thus resulting in strong correlation among the antennas of the receiving node.

[0018] In one possible implementation, there are multiple weighting matrices. The transmitting node determines the precoding matrix based on the weighting matrices, including: the transmitting node obtaining the average signal-to-noise ratio (SNR) corresponding to each of the multiple subcarriers; the transmitting node determining a fourth matrix based on the weighting matrices and the average SNR corresponding to each of the multiple subcarriers; the transmitting node determining at least one column of the fourth matrix as a fifth matrix; and the transmitting node calculating the null space of the fifth matrix to obtain the precoding matrix. At this point, only one precoding matrix is ​​obtained.

[0019] For example, the transmitting node can multiply the weighted matrix corresponding to the subcarrier, the conjugate of the weighted matrix, and the average signal-to-noise ratio, and then sum the values ​​of the multiplication of the weighted matrices, the conjugates of the weighted matrices, and the average signal-to-noise ratio of multiple subcarriers to obtain the target weighted matrix. The target weighted matrix is ​​then decomposed to obtain a fourth matrix.

[0020] In one possible implementation, the number of corresponding weighted matrices is one. The transmitting node determines the precoding matrix based on the weighted matrices, including: the transmitting node determining at least one column of the weighted matrices as a sixth matrix; the transmitting node calculating the null space of the sixth matrix to obtain the precoding matrix. In this case, only one precoding matrix is ​​obtained.

[0021] In one possible implementation, there are multiple precoding matrices. The transmitting node transmits the time-domain aware signal to the receiving node through the air interface based on the precoding matrices. This includes: the transmitting node precoding the subcarriers of the initial frequency domain signal using the precoding matrices corresponding to the subcarriers to obtain the frequency-domain aware signal; the transmitting node converting the frequency-domain aware signal into the time-domain aware signal; and the transmitting node transmitting the time-domain aware signal to the receiving node through the air interface. For example, the transmitting node can multiply multiple subcarriers by their corresponding precoding matrices to obtain the frequency-domain aware signal.

[0022] In one possible implementation, the number of precoding matrices is one. The transmitting node transmits the time-domain-aware signal to the receiving node through the air interface based on the precoding matrix, including: the transmitting node precoding the initial time-domain signal using the precoding matrix to obtain the time-domain-aware signal; and the transmitting node transmitting the time-domain-aware signal to the receiving node through the air interface. For example, the transmitting node can multiply the initial time-domain signal by the precoding matrix to obtain the time-domain-aware signal.

[0023] In one possible implementation, a beamformed time-domain-aware signal is transmitted to a receiving node via an air interface using a directional antenna. The transmitting node includes at least one antenna, and the transmission of the beamformed time-domain-aware signal to the receiving node via the air interface based on the weighting matrix includes: the transmitting node adjusting the angle of the at least one antenna based on the weighting matrix; and the transmitting node using the adjusted at least one antenna to transmit the time-domain-aware signal to the receiving node via the air interface. Optionally, the transmitting node can determine a target codebook corresponding to the weighting matrix from multiple codebooks, and then adjust the angle of the at least one antenna according to the target codebook.

[0024] In one possible implementation, before the receiving node determines the weighting matrix based on the channel matrix, the method further includes: the receiving node determining a calibration weighting matrix based on a calibration channel matrix; the transmitting node acquiring the calibration weighting matrix; the transmitting node transmitting a beamforming time-domain calibration signal to the receiving node via the air interface based on the calibration weighting matrix; the receiving node using the time-domain calibration signal to perform channel estimation and determine calibration channel state information; after determining the channel state information, the method further includes: the receiving node updating the channel state information using the calibration channel state information to obtain updated channel state information. Using calibration channel state information for background signal cancellation can improve detection sensitivity.

[0025] Optionally, the receiving node can subtract the calibration channel state information from the channel state information to obtain the updated channel state information.

[0026] The aforementioned processes of determining channel state information, acquiring calibration channel state information, and updating channel state information based on calibration channel state information can all be executed cyclically, with the cycle period customizable. After each determination of channel state information, the most recently determined calibration channel state information can be subtracted from the existing channel state information to update the channel state information.

[0027] In one possible implementation, the method further includes: the transmitting node sending a probe frame to the receiving node, the probe frame including a long training sequence; and the receiving node determining the channel matrix based on the long training sequence.

[0028] Among them, the probe frame can be a null data packet (NDP) probe frame, which only contains a preamble field and does not include a data field. The preamble field includes a Long Training Field (LTF).

[0029] Optionally, before sending a probe frame to the receiving node, the sending node may also send a control frame to the receiving node. This control frame instructs the receiving node to perform channel estimation. Upon receiving the control frame, the receiving node stops its current transmission process and waits to receive subsequent probe frames.

[0030] Secondly, this application provides a channel estimation device applied to a wireless sensing device. The device includes: a first determining module for determining a weighting matrix based on a channel matrix; a first acquiring module for acquiring the weighting matrix; a first transmitting module for transmitting a beamformed time-domain sensing signal over an air interface based on the weighting matrix; and a first channel estimation module for performing channel estimation using the time-domain sensing signal to determine channel state information, wherein the channel state information represents the channel state of the time-domain sensing signal during its propagation in the physical space environment.

[0031] In one possible implementation, the first transmitting module includes: a determining unit, configured to determine a precoding matrix based on the weighting matrix; and a transmitting unit, configured to transmit the time-domain aware signal to the receiving node through the air interface based on the precoding matrix.

[0032] In one possible implementation, there are multiple channel matrices, each corresponding to a different subcarrier. The first determining module is specifically used to determine the weighting matrix corresponding to the subcarrier based on the channel matrix of the subcarrier.

[0033] In one possible implementation, there are multiple channel matrices, each corresponding to a different subcarrier. The first determining module is specifically used to multiply the channel matrix of the subcarrier by the conjugate of the channel matrix of the subcarrier to obtain a first matrix corresponding to the subcarrier; to sum the first matrices corresponding to the multiple subcarriers to obtain a second matrix; and to determine the weighting matrix based on the second matrix.

[0034] In one possible implementation, the determining unit is specifically used to determine at least one column of the weighted matrix corresponding to the subcarrier as a third matrix; calculate the null space of the third matrix to obtain the precoding matrix corresponding to the subcarrier.

[0035] In one possible implementation, the determining unit is specifically used to obtain the average signal-to-noise ratio corresponding to each of the plurality of subcarriers; determine a fourth matrix based on the weighting matrix and average signal-to-noise ratio corresponding to each of the plurality of subcarriers; determine at least one column of the fourth matrix as a fifth matrix; and calculate the null space of the fifth matrix to obtain the precoding matrix.

[0036] In one possible implementation, the determining unit is specifically used to determine at least one column of the weighted matrix as the sixth matrix; calculate the null space of the sixth matrix to obtain the precoding matrix.

[0037] In one possible implementation, the transmitting unit is specifically configured to precode the subcarrier of the frequency domain initial signal using the precoding matrix corresponding to the subcarrier to obtain a frequency domain sensing signal; convert the frequency domain sensing signal into the time domain sensing signal; and transmit the time domain sensing signal to the receiving node through the air interface.

[0038] In one possible implementation, the transmitting unit is specifically used to precode the initial time-domain signal using the precoding matrix to obtain the time-domain sensing signal; and to transmit the time-domain sensing signal to the receiving node through the air interface.

[0039] In one possible implementation, the transmitting node includes at least one antenna, and the first transmitting module is specifically configured to adjust the angle of the at least one antenna based on the weighting matrix; and to transmit the time-domain sensing signal to the receiving node through the air interface using the adjusted at least one antenna.

[0040] In one possible implementation, the apparatus further includes: a second determining module, configured to determine a calibration weighting matrix based on a calibration channel matrix; a second acquiring module, configured to acquire the calibration weighting matrix; a second transmitting module, configured to transmit a beamforming time-domain calibration signal through the air interface based on the calibration weighting matrix; a second channel estimation module, configured to perform channel estimation using the time-domain calibration signal to determine calibration channel state information; and an updating module, configured to update the channel state information using the calibration channel state information to obtain updated channel state information.

[0041] In one possible implementation, the apparatus further includes: a third transmitting module for transmitting a probe frame, the probe frame including a long training sequence; and a third determining module for determining the channel matrix based on the long training sequence.

[0042] Thirdly, this application provides a wireless sensing device, comprising: one or more processors; a memory for storing one or more computer programs or instructions; wherein when the one or more computer programs or instructions are executed by the one or more processors, the one or more processors implement the method as described in any one of the first aspects.

[0043] Fourthly, this application provides a wireless sensing device, including a processor for performing the method as described in any of the first aspects.

[0044] Fifthly, this application provides a computer-readable storage medium including a computer program or instructions that, when executed on a computer, cause the computer to perform the method described in any one of the first aspects. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the structure of a MIMO system provided in an embodiment of this application;

[0046] Figure 2 This is a schematic diagram of the structure of a wireless sensing device provided in an embodiment of this application;

[0047] Figure 3 This is a schematic diagram of the structure of a transmitting node provided in an embodiment of this application;

[0048] Figure 4 This is a schematic diagram of the structure of a receiving node provided in an embodiment of this application;

[0049] Figure 5 A flowchart illustrating a channel estimation method provided in an embodiment of this application;

[0050] Figure 6 A flowchart illustrating another channel estimation method provided in an embodiment of this application;

[0051] Figure 7 This application provides a schematic diagram of signal transmission between a transmitting node and a receiving node.

[0052] Figure 8 This is a schematic diagram of signals transmitted between a transmitting node and a receiving node, provided in an embodiment of this application.

[0053] Figure 9 A schematic diagram of signals transmitted between a transmitting node and a receiving node, provided in an embodiment of this application;

[0054] Figure 10 A schematic diagram of a sensing process provided in an embodiment of this application;

[0055] Figure 11 A block diagram of a channel estimation device provided in an embodiment of this application;

[0056] Figure 12 A block diagram of a first transmitting module provided in an embodiment of this application;

[0057] Figure 13 A block diagram of another channel estimation apparatus provided in the embodiments of this application;

[0058] Figure 14 A block diagram of another channel estimation apparatus provided in the embodiments of this application;

[0059] Figure 15 This is a schematic diagram of the structure of a wireless sensing device provided in an embodiment of this application;

[0060] Figure 16This is a schematic diagram of a channel estimation device provided in an embodiment of this application. Detailed Implementation

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0063] The terms "first" and "second," etc., used in the specification and claims of this application are used to distinguish different objects, rather than to describe a specific order of objects. For example, "first range" and "second range," etc., are used to distinguish different ranges, rather than to describe a specific order of ranges.

[0064] In the embodiments of this application, the words "in one example," "examplely," or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "in one example," "examplely," or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the words "in one example," "examplely," or "for example" is intended to present the relevant concepts in a specific manner.

[0065] In the description of the embodiments in this application, unless otherwise stated, "at least one" means one or more, and "multiple" means two or more. For example, multiple processing units refer to two or more processing units; multiple systems refer to two or more systems.

[0066] In the field of information and communication, wireless communication technology is developing rapidly and its applications are becoming increasingly widespread. WiFi technology is a wireless communication technology based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard. Currently, WiFi technology has become the preferred method for more and more user devices to access the Internet. Its speed and spectrum utilization are constantly improving with the updates of the IEEE 802.11 standard to adapt to new business applications and narrow the gap with wired network bandwidth.

[0067] Users can connect to an access point (AP) using WiFi-enabled wireless devices to access the WiFi communication system. Wireless devices can include computers, mobile phones, and digital audio players. An AP can consist of one AP or multiple interconnected APs. The coverage area of ​​a WiFi network composed of APs is related to the overlap range of the APs, and its coverage area can reach several square miles.

[0068] With the increasing prevalence of applications such as video conferencing, wireless interactive virtual reality (VR), and mobile teaching, WiFi communication systems are connecting to a growing number of wireless devices. These systems need to improve transmission efficiency while still being able to support a large number of connected devices. The application of Multiple-Input Multiple-Output (MIMO) systems, Orthogonal Frequency Division Multiplexing (OFDM) technology, and beamforming technology in WiFi can enhance the capacity and transmission efficiency of WiFi communication systems.

[0069] A MIMO system includes multiple transmit antennas at a transmitting device and at least one receive antenna at a receiving device. When transmitting a signal, the transmitting device encodes the initial signal to obtain at least one independent signal (also called a spatial stream), and then transmits the at least one spatial stream to the receiving device through the multiple transmit antennas. The receiving device receives the at least one spatial stream through the at least one receive antenna, and decodes and combines the at least one spatial stream to obtain the signal. The number of spatial streams cannot exceed the number of transmit antennas or the number of receive antennas; that is, the number of spatial streams is less than or equal to the minimum value of the number of transmit antennas and the number of receive antennas.

[0070] Multiple transmit and receive antennas provide diversity, improving the signal-to-noise ratio of the receiving device and ensuring signal quality even when the distance between the receiving and transmitting devices is large. In a MIMO system, the channels between each transmit and receive antenna are independent, allowing the establishment of multiple parallel spatial channels. Transmitting signal streams independently through multiple parallel spatial channels reduces the signal throughput of a single transmit antenna, increases the signal transmission rate, and improves channel capacity and frequency utilization without increasing bandwidth.

[0071] For example, please refer to Figure 1 , Figure 1 This is a schematic diagram of a MIMO system provided in an embodiment of this application. The MIMO system includes m transmitting antennas (Tx1 to Tx...). m ) and n receiving antennas (Rx1 to Rx nThe number of spatial streams that can be transmitted by M transmitting antennas is less than or equal to the minimum of m and n. For example, assuming m = 4 and n = 4, the number of spatial streams that can be transmitted is less than or equal to 4; assuming m = 4 and n = 2, the number of spatial streams that can be transmitted is less than or equal to 2.

[0072] Orthogonal Frequency Division Multiplexing (OFDM) is a type of multi-carrier modulation (MCM) technique. Modulation and demodulation in OFDM are implemented based on the Inverse Fast Fourier Transform (IFFT) and the Fast Fourier Transform (FFT), respectively.

[0073] When transmitting signals using OFDM technology, the transmitting device divides a given channel into several orthogonal sub-channels in the frequency domain. The high-speed serial original frequency domain signal is converted into multiple low-speed parallel sub-signal streams. Each sub-signal stream is modulated on its corresponding sub-channel using a subcarrier, resulting in multiple modulated subcarriers. Then, IFFT is used to convert these multiple modulated subcarriers into the time domain, yielding multiple OFDM symbols. These OFDM symbols are then converted from parallel to serial and a guard interval (also known as a cyclic prefix) is inserted to obtain the time-domain signal, which is finally transmitted.

[0074] For the receiving device, the guard interval of the received time-domain signal is first removed and serial-to-parallel conversion is performed to obtain multiple OFDM symbols. The OFDM symbols are then converted to the frequency domain using FFT to obtain multiple modulated subcarriers. These modulated subcarriers are then demodulated to obtain multiple low-speed parallel sub-signal streams. Finally, these low-speed parallel sub-signal streams are converted back to a high-speed serial frequency-domain original signal.

[0075] In wireless communication systems, the bandwidth provided by a channel is typically much larger than the bandwidth required to transmit a single signal. OFDM technology not only fully utilizes the channel bandwidth, but also, because the signal bandwidth on each sub-channel is smaller than the channel's correlation bandwidth, each sub-channel can be considered as having flat fading, thus eliminating inter-symbol interference (ISI) and effectively combating frequency-selective fading. Furthermore, since the bandwidth of each sub-channel occupies only a small fraction of the original channel bandwidth, channel equalization becomes relatively easy. In addition, the orthogonality between subcarriers allows the spectrum of the sub-channels to overlap, thereby reducing mutual interference between subcarriers and maximizing the utilization of spectrum resources.

[0076] When time-domain signals are transmitted to the receiving device through a wireless channel, inter-symbol interference caused by channel multipath effect causes subcarriers to no longer maintain a good orthogonal state. Therefore, inserting a guard interval after converting the parallel sub-signal stream into a serial signal stream can effectively eliminate inter-symbol interference.

[0077] Beamforming (BF) is a technique that transmits wireless signals in a specific direction. Described in terms of antenna pattern, beamforming creates a beam with a defined direction, transforming an omnidirectional reception pattern into a beam pattern with nulls and a maximum directional lobe. This allows wireless signals to be transmitted in a specific direction, concentrating energy and minimizing interference.

[0078] MIMO systems can effectively combat flat fading channels by utilizing the spatiotemporal frequency domain characteristics of signals. OFDM technology can transform frequency-selective fading into flat fading, while beamforming technology can transmit wireless signals in a specific direction, concentrating energy and minimizing interference. The combination and complementarity of MIMO systems, OFDM technology, and beamforming technology can effectively solve the problems of multipath fading and bandwidth efficiency in wireless communication systems, while also improving system capacity and transmission reliability.

[0079] The communication performance of a wireless communication system is greatly affected by the wireless channel, such as shadowing fading and frequency-selective fading. Signals are inevitably subject to interference during transmission from the transmitting device to the receiving device. Therefore, when demodulating the received signal, the receiving device needs to restore the original signal sent by the transmitting device as much as possible to ensure the accuracy of signal transmission.

[0080] In order for the receiving device to accurately recover the original signal transmitted by the transmitting device when demodulating the received signal, the receiving device needs to estimate the wireless channel to obtain channel state information. Then, based on the channel state information, the receiving signal is recovered to the original signal transmitted by the transmitting device, thus ensuring the communication performance of the wireless communication system. In related technologies, the transmitting device sends probe frames including a long training sequence to the receiving device, and the receiving device performs channel estimation based on the long training sequence to obtain the channel state information.

[0081] However, in related technologies, the crystal oscillators of the transmitting and receiving devices are often out of sync, leading to inconsistent residual frequency offsets measured multiple times by the receiving device and crystal oscillator frequency drift. Residual frequency offset refers to the residual value remaining after the receiving device compensates for the frequency offset of the received signal. Furthermore, the receiving device experiences periodic jitter during channel estimation, affecting its receiving performance. This results in poor accuracy of the determined channel state information, impacting the accuracy of signal recovery and consequently affecting the communication performance of the wireless communication system. In addition, the transmitting and receiving devices need to maintain a certain physical distance, and their relative positions cannot change during channel estimation; that is, the transmitting and receiving devices need to be dedicated to channel estimation, increasing the cost of channel estimation.

[0082] This application provides a channel estimation method applicable to wireless sensing devices employing MIMO systems, OFDM technology, and beamforming technology. The wireless sensing device includes at least three antennas, with at least two configured in transmit mode and at least one configured in receive mode. The at least two antennas configured in transmit mode belong to transmitting nodes, and the at least one antenna configured in receive mode belongs to receiving nodes. The antennas of the transmitting and receiving nodes share a common crystal oscillator. For example, please refer to... Figure 2 , Figure 2 This is a schematic diagram of the structure of a wireless sensing device provided in an embodiment of this application. The wireless sensing device includes three antennas, wherein the transmitting node includes two antennas and the receiving node includes one antenna.

[0083] The following describes the structure of the sending and receiving nodes. Please refer to [the documentation / reference]. Figure 3 , Figure 3 This is a schematic diagram of the structure of a transmitting node provided in an embodiment of this application. The transmitting node 10 includes an encoding module 101, an OFDM system, and multiple antennas 102. The OFDM system includes multiple OFDM subsystems corresponding one-to-one with the multiple antennas 102. Figure 3 Two antennas and two OFDM subsystems are shown. (See diagram.) Figure 3 As shown, each OFDM subsystem includes a serial-to-parallel conversion module 103, a subcarrier mapping module 104, an IFFT module 105, a parallel-to-serial conversion module 106, a digital front end (DFE) module 107, a digital / analog (D / A) conversion module 108, and an up-conversion module 109.

[0084] Please refer to Figure 4 , Figure 4This is a schematic diagram of the structure of a receiving node provided in an embodiment of this application. The receiving node includes at least one antenna 201, an OFDM system, and a decoding module 202. The OFDM system includes at least one OFDM subsystem corresponding to at least one antenna 201. Figure 4 An antenna and an OFDM subsystem are shown. (Example) Figure 4 The OFDM subsystem includes a downconversion module 203, an analog-to-digital (A / D) conversion module 204, a serial-to-parallel conversion module 205, an FFT module 206, a MIMO processing module 207, a subcarrier mapping module 208, and a parallel-to-serial conversion module 209.

[0085] When transmitting the original frequency domain signal, the encoding module 101 encodes the original frequency domain signal and outputs at least one independent spatial stream. The number of spatial streams is less than or equal to the minimum value of the number of antennas included in the transmitting node and the number of antennas included in the receiving node. Therefore, the encoding module 101 encodes the signal and outputs a single spatial stream. This single spatial stream is input to the corresponding OFDM subsystem. The serial-to-parallel conversion module 103 converts the input spatial stream into multiple low-speed parallel sub-signal streams. The subcarrier mapping module 104 modulates the multiple low-speed parallel sub-signal streams onto their respective sub-channels using a subcarrier, resulting in multiple modulated subcarriers. The IFFT module 105 converts the multiple modulated subcarriers into the time domain, resulting in multiple OFDM symbols. The parallel-to-serial conversion module 106 performs parallel-to-serial conversion on the multiple OFDM symbols to obtain OFDM symbols. The digital front-end (DFE) module 107 performs digital domain filtering, spectrum shifting, power adjustment, and pre-compensation on the OFDM symbols, and obtains a time-domain analog signal through the D / A conversion module 108. The upconversion module 109 converts the time-domain analog signal into a high-frequency time-domain analog signal, which is then transmitted through the corresponding antenna.

[0086] After receiving a high-frequency time-domain analog signal through an antenna, the down-conversion module 203 converts the high-frequency time-domain analog signal into a low-frequency time-domain analog signal, and the A / D conversion module 204 converts the low-frequency time-domain analog signal into a time-domain digital signal to obtain OFDM symbols. The OFDM symbols are then converted into multiple OFDM symbols by the serial-to-parallel conversion module 205. The FFT module 206 converts the multiple OFDM symbols into the frequency domain to obtain multiple modulated subcarriers. The MIMO processing module 105 performs channel estimation based on the multiple modulated subcarriers. The subcarrier mapping module 208 demodulates the multiple modulated subcarriers to obtain multiple low-speed parallel sub-signal streams. The parallel-to-serial conversion module 209 converts the multiple low-speed parallel sub-signal streams into a high-speed serial spatial stream. The decoding module 202 decodes the spatial stream to obtain the original frequency-domain signal.

[0087] This application provides a channel estimation method, which can be applied to the transmitting and receiving nodes included in a wireless sensing device, for example, to... Figure 3 The sending node shown and Figure 4 The receiving node is shown below. Please refer to it. Figure 5 , Figure 5 This is a flowchart illustrating a channel estimation method provided in an embodiment of this application. The method may include the following steps:

[0088] 301. The receiving node determines the weighting matrix based on the channel matrix.

[0089] The receiving node can decompose the channel matrix to obtain a weighted matrix.

[0090] 302. The sending node obtains the weighted matrix.

[0091] 303. The transmitting node transmits beamforming time-domain sensing signals to the receiving node via the air interface based on the weighted matrix.

[0092] Time-domain sensing signals can include WiFi frames and radar signals, such as pulse signals and frequency-modulated continuous waves.

[0093] Beamforming methods include multi-antenna precoding, phased array antennas, and directional antennas. When the beamformed time-domain sensing signal is transmitted from the transmitting node to the receiving node, the energy of the main path of the time-domain sensing signal received by the receiving node is extremely small or even zero. At this point, the energy of the time-domain sensing signal received by the receiving node mainly comes from the echo signal. Thus, even if the receiving node is located in the same device as the transmitting node, it will not be affected by unwanted signals, thereby improving the accuracy of channel estimation.

[0094] 304. The receiving node uses the time-domain sensing signal to perform channel estimation and determine the channel state information.

[0095] Channel state information represents the channel state of a time-domain sensed signal during its propagation in the physical space environment.

[0096] In summary, this application provides a channel estimation method. The receiving node determines a weighting matrix based on the channel matrix. After obtaining the weighting matrix, the transmitting node transmits a beamformed time-domain sensing signal to the receiving node via the air interface. The receiving node uses the time-domain sensing signal to perform channel estimation and determine channel state information. Since both the transmitting and receiving nodes are located in a wireless sensing device, their crystal oscillators are synchronized, eliminating the effects of frequency offset, crystal frequency drift, and phase noise caused by asynchronous crystal oscillators during channel estimation. Furthermore, the receiving node does not jitter periodically, thereby improving the accuracy of the determined channel state information. In addition, the beamformed time-domain sensing signal can minimize or even eliminate the energy of the main path of the time-domain sensing signal received by the receiving node. Even if the physical distance between the transmitting and receiving nodes is small, it can reduce the impact of unwanted signals on the receiving node, thereby further improving the accuracy of the channel state information.

[0097] This application provides a channel estimation method, which can be applied to the transmitting and receiving nodes included in a wireless sensing device, for example, to... Figure 3 The sending node shown and Figure 4 The receiving node is shown below. Please refer to it. Figure 6 , Figure 6 A flowchart illustrating another channel estimation method provided in this application embodiment, the method may include the following steps:

[0098] 401. The sending node sends a probe frame to the receiving node. The probe frame includes a long training sequence.

[0099] The probe frame can be an NDP probe frame, which contains only a preamble field and does not include a data field. The preamble field includes the LTF.

[0100] Optionally, before sending probe frames to the receiving node, the sending node may also send a control frame to the receiving node. This control frame instructs the receiving node to perform channel estimation. Upon receiving the control frame, the receiving node stops its current transmission process and waits to receive subsequent probe frames. The control frame can be a null data packet announcement (NDPA) frame, such as a very high throughput (VHT) NDPA.

[0101] The transmitting node can send beamforming probe frames and control frames to the receiving node. Figure 3 For example, the sending node can process and send probe frames or control frames according to each module.

[0102] 402. The receiving node determines the channel matrix based on the long training sequence in the probe frame.

[0103] As mentioned above Figure 3 and Figure 4 As shown, the receiving node converts the probe frame from the time domain to the frequency domain using FFT. The frequency domain probe frame includes multiple subcarriers, and the receiving node can determine the channel matrix H for each subcarrier based on a long training sequence. For example, assuming the transmitting node has N antennas and the receiving node has M antennas, forming an M×N channel matrix H between the transmitting and receiving nodes, the channel matrix for any subcarrier at any time t can be as follows:

[0104]

[0105] For example, assuming the transmitting node has 2 antennas and the receiving node has 1 antenna, the channel matrix H of any subcarrier at any given time can be:

[0106] [-1.3617-0.8487i 0.4550-0.3349i]

[0107] 403. The receiving node determines the weighting matrix based on the channel matrix.

[0108] Weighting matrices can be used to weight signals in either the frequency or time domain. When used to weight signals in the frequency domain, multiple weighting matrices are used, with each matrix corresponding to a specific subcarrier. When used to weight signals in the time domain, only one weighting matrix is ​​used.

[0109] As mentioned above, there are multiple channel matrices, each corresponding one-to-one with a subcarrier. In the first implementation, the receiving node can determine the weighting matrix corresponding to the subcarrier based on its channel matrix. In this case, there are multiple weighting matrices, each corresponding one-to-one with a subcarrier. Optionally, the receiving node can perform Singular Value Decomposition (SVD) on the channel matrix of the subcarrier to obtain its weighting matrix. Taking an M×N channel matrix H of any subcarrier as an example, the formula for Singular Value Decomposition is as follows:

[0110] SVD(H)=U m×m Σ m×n V n×n

[0111] Among them, U m×m and V n×n For unitary matrices, Σ m×n For a diagonal matrix, Σ m×n Values ​​are only present on the main diagonal; all other elements are 0. V n×n That is, the weighting matrix of any subcarrier.

[0112] Assuming the channel matrix of any subcarrier is [-1.3617-0.8487i 0.4550-0.3349i], the following matrix is ​​obtained by decomposing the channel matrix:

[0113] U = [1]

[0114] Σ=[1.7011 0]

[0115]

[0116] In another implementation, the receiving node can first multiply the channel matrix of the subcarrier by its conjugate to obtain a first matrix corresponding to the subcarrier. The first matrices corresponding to multiple subcarriers are then summed to obtain a second matrix. The second matrix is ​​then decomposed to obtain a weighted matrix, where there is only one weighted matrix. The process of decomposing the second matrix can refer to the aforementioned process of decomposing the subcarrier matrix, and will not be elaborated upon in this embodiment.

[0117] For example, taking n subcarriers as an example, the second matrix is ​​calculated as follows:

[0118] H total =H1 H H1+H2 H H2+…+H n H H n

[0119] The formula for decomposing the second matrix is ​​as follows:

[0120] V = svd(H) total )

[0121] Among them, H total H represents the second matrix. n H represents the channel matrix of the nth subcarrier. n H H represents n The conjugate of . V represents the weighted matrix, svd(H total ) indicates that for H total Perform SVD.

[0122] 404. The sending node obtains the weighted matrix determined by the receiving node.

[0123] After determining the weighted matrix, the receiving node can feed it back via a sounding report frame. In one example, the receiving node can feed back the weighted matrix over the air interface; that is, the receiving node transmits the weighted matrix through its included antenna, and the sending node receives the weighted matrix through its included antenna. In another example, since the sending and receiving nodes are located in the same device, the receiving node can transmit the weighted matrix to the receiving node via internal circuitry. For example, the receiving node can store the determined weighted matrix in its device, and the sending node can retrieve the weighted matrix stored in the device, thus effectively reducing the overhead of transmitting the weighted matrix.

[0124] 405. The transmitting node transmits beamforming time-domain sensing signals to the receiving node via the air interface based on a weighted matrix.

[0125] Beamforming methods include multi-antenna precoding, phased array antennas, and directional antennas. The following sections will explain multi-antenna precoding and directional antenna methods.

[0126] For multi-antenna precoding, the transmitting node can determine the precoding matrix based on the weighting matrix, and then transmit the time-domain sensing signal to the receiving node via the air interface based on the precoding matrix. When transmitting the time-domain sensing signal to the receiving node via the air interface based on the precoding matrix, the precoding matrix can be multiplied with the frequency domain signal or the time domain signal to obtain the time-domain sensing signal.

[0127] In one implementation, a precoding matrix is ​​used to multiply the frequency-domain signal. The transmitting node can use the precoding matrix to precode multiple subcarriers of the initial frequency-domain signal to obtain a frequency-domain sense signal. This frequency-domain sense signal is then converted into a time-domain sense signal and transmitted to the receiving node via the air interface. The initial frequency-domain signal is obtained based on a spatial stream, which is the encoded initial sense signal. As described above... Figure 3 As shown, the initial frequency domain signal can be the signal output by the subcarrier mapping module 104. After obtaining the frequency domain sensing signal, the frequency domain sensing signal can be converted into a time domain sensing signal by the IFFT module 106. Then, subsequent modules can process and send the time domain sensing signal in sequence.

[0128] For this implementation, multiple weighting matrices are required, as mentioned above, with each weighting matrix corresponding one-to-one with a subcarrier of the initial frequency domain signal. Correspondingly, multiple precoding matrices are also required, each corresponding one-to-one with a subcarrier. For example, the transmitting node can determine at least one column of the weighting matrix corresponding to each subcarrier as the third matrix. The null space of the third matrix is ​​calculated to obtain the precoding matrix corresponding to the subcarrier. Then, the precoding matrix corresponding to the subcarrier is used to precode the subcarriers of the initial frequency domain signal to obtain the frequency-domain sensing signal. For instance, multiple subcarriers can be multiplied by their corresponding precoding matrices to obtain the frequency-domain sensing signal.

[0129] The third matrix can have one or more null spaces. When there are multiple null spaces, all null spaces can be used as precoding matrices, or mathematical processing can be performed on the multiple null spaces, and the matrix obtained from the mathematical processing can be used as the precoding matrix. This application does not limit the specific implementation of this method. Mathematical processing includes summation or averaging, etc.

[0130] The number of columns in the third matrix can be related to the number of spatial streams included in the initial sensing signal. For example, the number of columns in the third matrix can be the same as the number of spatial streams included in the initial sensing signal. Taking an initial sensing signal including one spatial stream as an example, one column V0 in the weighted matrix V corresponding to any subcarrier can be determined as the third matrix. The null space of the third matrix has only one column W0, which is then determined as the precoding matrix corresponding to any subcarrier. Taking an initial sensing signal including two spatial streams as an example, two columns V0 and V1 in the weighted matrix V corresponding to any subcarrier can be determined as the third matrix. The null space of the third matrix has two columns W0 and W1, which are then determined as the precoding matrix.

[0131] The precoding matrix is ​​calculated as follows:

[0132] W = null(V′) = {W∈C} n :VW=0}

[0133] Where W represents the precoding matrix, C represents the complex number set, V′ represents the third matrix, null(V′) represents solving the null space of V′, and V′W=0.

[0134] Alternatively, the null space of the third matrix can be computed through block diagonalization based on SVD or orthogonal triangular decomposition (also known as QR decomposition) or generalized zero-forcing channel based on SVD or QR decomposition.

[0135] For example, the method for computing the null space of the third matrix using SVD-based block diagonalization is as follows:

[0136] svd(V′) = [U, S, V1]

[0137] W=V1(:,rank(V1):length(size(V1)))

[0138] The method for calculating the null space of the third matrix based on block diagonalization of QR decomposition is as follows:

[0139] qr(V′) = qr: [q, r]

[0140] W=q(:,rank(q):length(size(q)))

[0141] Assuming the time-domain sensing signal comprises a spatial stream, the weighting matrix for any subcarrier is:

[0142]

[0143] Choosing one column of V as the third matrix, the null space of the third matrix is ​​obtained as follows:

[0144]

[0145] When a receiving node includes a large number of antennas, the dimension of the weighting matrix is ​​large, and therefore its rank is also large. Solving for the null space of a weighted matrix with a large rank is more difficult. For example, if a receiving node has four antennas, a weighting matrix with a rank of 3 will have only one column of null space, while a weighting matrix with a rank of 4 will have no null space. The antennas of a receiving node can have strong correlation, thereby reducing the rank of the weighting matrix and allowing for more zero vectors to be obtained even when the dimension of the weighting matrix is ​​large. Optionally, the antennas of a transmitting node can have the same transmission direction, and the antennas of a receiving node can have the same reception direction. This makes the channel matrix of each receiving antenna almost identical, thus making the antennas of the receiving node have strong correlation. For an example, please refer to [reference needed]. Figure 7 , Figure 7 This application provides a schematic diagram of signal transmission between a transmitting node and a receiving node, as shown in the embodiment of the present application. Figure 7 As shown, both the transmitting and receiving nodes include four antennas. The four antennas of the transmitting node have the same transmission direction, and the four antennas of the receiving node have the same reception direction. In the radiation pattern, the solid beam represents the beam-shaped signal transmitted by the transmitting node, and the dashed beam represents the signal canceled out by the beam-shaping process. Figure 7 It can be seen that the signal canceled by beamforming is the signal that is aimed at the antennas of all receiving nodes.

[0146] In another implementation, the precoding matrix is ​​used to multiply the time-domain signal. The transmitting node can use the precoding matrix to precode the initial time-domain signal to obtain the time-domain sensing signal, and then send the time-domain sensing signal to the receiving node through the air interface.

[0147] Optionally, there are multiple weighting matrices. As mentioned above, each weighting matrix corresponds one-to-one with a subcarrier of the initial frequency domain signal, and there is one precoding matrix. The transmitting node can obtain the average signal-to-noise ratio (SNR) corresponding to each of the multiple subcarriers. Based on the weighting matrices and average SNR corresponding to each subcarrier, a fourth matrix is ​​determined. At least one column of the fourth matrix is ​​determined as the fifth matrix. The null space of the fifth matrix is ​​calculated to obtain the precoding matrix. Then, the initial time-domain signal is multiplied by the precoding matrix to obtain the time-domain sensing signal. The process of calculating the null space of the fifth matrix to obtain the precoding matrix can refer to the aforementioned implementation method, and will not be repeated here in this embodiment.

[0148] For example, the transmitting node can multiply the weighted matrix corresponding to the subcarrier, the conjugate of the weighted matrix, and the average signal-to-noise ratio, and then sum the values ​​of the multiplication of the weighted matrices, conjugates, and average signal-to-noise ratios of multiple subcarriers to obtain the target weighted matrix. The target weighted matrix is ​​then decomposed to obtain a fourth matrix. The process of decomposing the target weighted matrix can refer to the aforementioned process of decomposing the subcarrier matrix, and will not be elaborated upon here in this embodiment.

[0149] For example, taking n subcarriers as an example, the target weighting matrix is ​​calculated as follows:

[0150] V total =V1 H S1V1+V2 H S2V2+…+V n H S n V n

[0151] The formula for decomposing the target weighted matrix is ​​as follows:

[0152] V = svd(V total )

[0153] Among them, V total V represents the target weighting matrix. n V represents the weighting matrix corresponding to the nth subcarrier. n H V represents n The conjugate of V. V represents the weighted matrix, svd(V total ) indicates that for V total Perform SVD.

[0154] Alternatively, the number of weighting matrices is one, and the number of precoding matrices is one. The transmitting node can determine at least one column of the weighting matrix as the sixth matrix, calculate the null space of the sixth matrix, and obtain the precoding matrix. Then, the initial time-domain signal is multiplied by the precoding matrix to obtain the time-domain sensing signal. The process of calculating the null space of the sixth matrix to obtain the precoding matrix can refer to the aforementioned implementation method, and will not be repeated here in the embodiments of this application.

[0155] After beamforming by multi-antenna precoding at the transmitting node, the principal path of the time-domain sensing signal is zero. Taking the multiplication of the precoding matrix W with the first time-domain signal as an example, the principal path of the initial sensing signal is:

[0156] R=HWX=U∑V′WX=U∑(V′W)X=0

[0157] Assume the channel matrix H is:

[0158] [-1.3617-0.8487i 0.4550-0.3349i]

[0159] The precoding matrix W is:

[0160]

[0161] The result of multiplying H and W is as follows:

[0162] HW=[-1.1102e-16+5.5511e-17i]=0

[0163] For beamforming of directional antennas, the transmitting node includes at least one antenna. The transmitting node can adjust the angle of at least one antenna based on a weighting matrix, and then use the adjusted at least one antenna to transmit a time-domain aware signal to the receiving node via an air interface. Optionally, the transmitting node can determine the target codebook corresponding to the weighting matrix from multiple codebooks, and then adjust the angle of at least one antenna according to the target codebook.

[0164] The following is Figure 8 and Figure 9 The effect of beamforming will be explained using an example. Please refer to [link / reference]. Figure 8 and Figure 9 , Figure 8 This is a schematic diagram of signals transmitted between a transmitting node and a receiving node, provided in an embodiment of this application. Figure 9 This is a schematic diagram of signals transmitted between a transmitting node and a receiving node, as provided in an embodiment of this application. Figure 8 The signal shown is not beamforming processed; the signal transmitted by the transmitting node is omnidirectional. Figure 9The signal shown has undergone beamforming; the actual transmitted beam is the beam that appears to be beamed, while the dashed beam is the beam canceled out by beamforming (the dashed beam itself does not exist and is only for illustrative purposes). Figure 8 and Figure 9 It can be seen that the principal diameter energy of the beam-shaped signal received by the receiving node is close to zero. From the radiation pattern, the null point of the beam-shaped signal is aligned with the receiving node.

[0165] 406. The receiving node uses the time-domain sensing signal to perform channel estimation and determine the channel state information.

[0166] Channel state information reflects the information of a channel, representing the channel state during the propagation of a time-domain sensed signal in the physical space environment. For example, it may include at least one of the following: channel matrix, channel profile, multipath delay, Doppler frequency offset, rank beamforming vector of a MIMO channel, channel order, and channel impulse response.

[0167] In this embodiment, before performing channel estimation, calibration channel state information can be obtained first. Optionally, before the transmitting node sends a probe frame to the receiving node, the receiving node can first determine a calibration weighting matrix based on the calibration channel matrix. The transmitting node obtains the calibration weighting matrix and then sends a beamforming time-domain calibration signal to the receiving node over the air interface based on the calibration weighting matrix. The receiving node uses the time-domain calibration signal to perform channel estimation and determine the calibration channel state information. After determining the channel state information, the receiving node can update the channel state information based on the calibration channel state information to obtain the updated channel state information. Optionally, the receiving node can subtract the calibration channel state information from the channel state information to obtain the updated channel state information.

[0168] The process by which the receiving node determines the calibration channel matrix may include: the sending node sending a calibration probe frame to the receiving node, the calibration probe frame including a long training sequence, and the receiving node determining the calibration channel matrix based on the long training sequence in the calibration probe frame. The process of determining the calibration channel state information can refer to the aforementioned processes 401 to 404, and will not be elaborated upon here in the embodiments of this application.

[0169] The aforementioned processes 401 to 406, the process of acquiring calibration channel state information, and the process of updating channel state information based on calibration channel state information can all be executed cyclically, and the cycle period can be customized. After each determination of channel state information, the latest determined calibration channel state information can be subtracted from the channel state information to update the channel state information.

[0170] In recent years, services based on wireless communication technology are no longer limited to the communication field. Currently, various sensing services can be realized based on wireless communication technology, such as indoor scene positioning, driver fatigue monitoring, and sensing services for human position, physiological indicators, posture, and motion recognition.

[0171] Channel state information (CSE) describes the signal transmission process from the transmitting node to the receiving node, and reflects the impact of distance, scattering, fading, and power attenuation on the signal during transmission. It provides a detailed characterization of the channel along multiple paths and also obtains rich information about the indoor environment. CSE not only reflects the amplitude of each subcarrier but also characterizes the phase information of the subcarriers, which can provide richer and more detailed information for sensing services.

[0172] The following explains the process of sensing based on channel state information. Please refer to [link / reference]. Figure 10 , Figure 10 A schematic diagram of a sensing process provided in an embodiment of this application, such as Figure 10 As shown, the principal path energy of the time-domain sensing signal received by the receiving node is close to zero due to beamforming processing. Figure 10 (As shown by the dashed beam in the middle), but the receiving node can also receive other multipath time-domain sensing signals ( Figure 10 (Shown as solid line beam). The receiving node can determine channel state information based on other multipath time-domain sensing signals, and perceive current environmental changes based on changes in channel state information, such as determining whether someone has intruded indoors and the movement status of objects.

[0173] In summary, this application provides a channel estimation method. A transmitting node sends a long sequence of probe frames to a receiving node. The receiving node determines a channel matrix based on the long training sequence in the probe frames and then determines a weighting matrix based on the channel matrix. The transmitting node obtains the weighting matrix determined by the receiving node and then transmits a beamforming time-domain sensing signal to the receiving node via the air interface based on the weighting matrix. The receiving node uses the time-domain sensing signal to perform channel estimation and determine channel state information. Since both the transmitting and receiving nodes are located in a wireless sensing device, their crystal oscillators are synchronized, eliminating the effects of frequency offset, crystal frequency drift, and phase noise caused by asynchronous crystal oscillators during channel estimation. Furthermore, the receiving node does not jitter periodically, thereby improving the accuracy of the determined channel state information. In related technologies, the processing accuracy of the receiving device affects the accuracy of the channel state information determined by the transmitting device. For example, the accuracy of multipath detection and the accuracy of RF calibration of the receiving device both affect the accuracy of the channel state information. In this application embodiment, the transmitting and receiving nodes are located in the same device, avoiding the influence of the receiving device's processing accuracy on the channel state information. The embodiments of this application require only one device to complete channel estimation, which reduces the cost of channel estimation while ensuring the accuracy of channel estimation.

[0174] Furthermore, when the beamforming time-domain sensing signal is transmitted from the transmitting node to the receiving node, the energy of the main path of the time-domain sensing signal received by the receiving node is extremely small or even zero. At this time, the energy of the time-domain sensing signal received by the receiving node mainly comes from the echo signal. In this way, even if the receiving node is located in the same device as the transmitting node, it will not be affected by unwanted signals, thereby further improving the accuracy of channel state information.

[0175] Furthermore, before performing channel estimation, calibration channel state information can be obtained. After determining the channel state information, the receiving node can update the channel state information based on the calibration information to obtain the updated channel state information. Using the calibration channel state information for background signal cancellation can improve detection sensitivity.

[0176] The order of the methods provided in the embodiments of this application can be adjusted appropriately, and the process can be added or removed as appropriate. For example, the aforementioned process of obtaining calibration channel state information and updating channel state information based on calibration channel state information can be omitted. Any variations that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application, and the embodiments of this application do not limit this.

[0177] The channel estimation method provided in the embodiments of this application has been described above. It is understood that, in order to achieve the above functions, the wireless sensing device includes corresponding hardware structures and / or software modules for performing each function. Those skilled in the art should readily recognize that, based on the algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0178] This application embodiment can divide the channel estimation device into functional modules according to the above method example. For example, each function can be divided into its own functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0179] Figure 11This is a block diagram of a channel estimation device provided in an embodiment of this application. When each functional module is divided according to its corresponding function, the channel estimation device 500 may include a first determining module 501, a first acquiring module 502, a first transmitting module 503, and a first channel estimation module 504. Exemplarily, this channel estimation device can be a terminal device, or a chip or other combined device or component having the aforementioned channel estimation device functions. Figure 11 As shown, the channel estimation device includes:

[0180] The first determining module 501 is used to determine the weighting matrix based on the channel matrix;

[0181] The first acquisition module 502 is used to acquire the weighting matrix;

[0182] The first transmitting module 503 is used to transmit beamforming time-domain sensing signals through the air interface based on the weighting matrix;

[0183] The first channel estimation module 504 is used to perform channel estimation using the time-domain sensing signal and determine channel state information, wherein the channel state information represents the channel state of the time-domain sensing signal during its propagation in the physical space environment.

[0184] Please refer to the above solutions. Figure 12 , Figure 12 A block diagram of a first transmitting module provided in an embodiment of this application, the first transmitting module 503, includes:

[0185] Determining unit 5031 is used to determine the precoding matrix based on the weighting matrix;

[0186] The transmitting unit 5032 is used to transmit the time-domain sensing signal to the receiving node through the air interface based on the precoding matrix.

[0187] In combination with the above scheme, there are multiple channel matrices, and each channel matrix corresponds to a different subcarrier. The first determining module 501 is specifically used to determine the weighting matrix corresponding to the subcarrier based on the channel matrix of the subcarrier.

[0188] In combination with the above scheme, there are multiple channel matrices, and each channel matrix corresponds to a multiple subcarrier. The first determining module 501 is specifically used to multiply the channel matrix of the subcarrier by the conjugate of the channel matrix of the subcarrier to obtain the first matrix corresponding to the subcarrier; to sum the first matrices corresponding to the multiple subcarriers to obtain the second matrix; and to determine the weighting matrix based on the second matrix.

[0189] In conjunction with the above scheme, the determining unit 5031 is specifically used to determine at least one column of the weighted matrix corresponding to the subcarrier as the third matrix; calculate the null space of the third matrix to obtain the precoding matrix corresponding to the subcarrier.

[0190] In conjunction with the above scheme, the determining unit 5031 is specifically used to obtain the average signal-to-noise ratio corresponding to the plurality of subcarriers respectively; determine the fourth matrix based on the weighting matrix and average signal-to-noise ratio corresponding to the plurality of subcarriers respectively; determine at least one column of the fourth matrix as the fifth matrix; calculate the null space of the fifth matrix to obtain the precoding matrix.

[0191] In conjunction with the above scheme, the determining unit 5031 is specifically used to determine at least one column in the weighted matrix as the sixth matrix; calculate the null space of the sixth matrix to obtain the precoding matrix.

[0192] In conjunction with the above scheme, the transmitting unit 5032 is specifically used to precode the subcarrier of the frequency domain initial signal using the precoding matrix corresponding to the subcarrier to obtain the frequency domain sensing signal; convert the frequency domain sensing signal into the time domain sensing signal; and transmit the time domain sensing signal to the receiving node through the air interface.

[0193] In conjunction with the above scheme, the transmitting unit 5032 is specifically used to precode the initial time-domain signal using the precoding matrix to obtain the time-domain sensing signal; and to transmit the time-domain sensing signal to the receiving node through the air interface.

[0194] In conjunction with the above scheme, the transmitting node includes at least one antenna, and the first transmitting module 503 is specifically used to adjust the angle of the at least one antenna based on the weighting matrix; and to transmit the time-domain sensing signal to the receiving node through the air interface using the adjusted at least one antenna.

[0195] Please refer to the above solutions. Figure 13 , Figure 13 A block diagram of another channel estimation device provided in the embodiments of this application, in Figure 11 Based on this, the channel estimation device 500 also includes:

[0196] The second determining module 505 is used to determine the calibration weighting matrix based on the calibration channel matrix;

[0197] The second acquisition module 506 is used to acquire the calibration weighting matrix;

[0198] The second transmitting module 507 is used to transmit a time-domain calibration signal of beamforming through the air interface based on the calibration weighting matrix;

[0199] The second channel estimation module 508 is used to perform channel estimation using the time-domain calibration signal and determine the calibration channel state information.

[0200] The update module 509 is used to update the channel state information using the calibration channel state information to obtain the updated channel state information.

[0201] Please refer to the above solutions. Figure 14 , Figure 14 A block diagram of another channel estimation apparatus provided in the embodiments of this application, in Figure 11 Based on this, the channel estimation device 500 also includes:

[0202] The third sending module 510 is used to send a probe frame, the probe frame including a long training sequence;

[0203] The third determining module 511 is used to determine the channel matrix based on the long training sequence.

[0204] Figure 15 This is a schematic diagram of the structure of a wireless sensing device provided in an embodiment of this application. The wireless sensing device 600 can be a terminal device or a chip or functional module in a terminal device. Figure 15 As shown, the wireless sensing device 600 includes a processor 601, a transceiver 602, and a communication line 603.

[0205] Among them, processor 601 is used to perform such as Figure 5 or Figure 6 In any step of the method embodiment shown, when performing data transmission processes such as sending probe frames, the transceiver 602 and communication line 603 may be invoked to complete the corresponding operation.

[0206] Furthermore, the wireless sensing device 600 may also include a memory 604. The processor 601, memory 604, and transceiver 602 can be connected via a communication line 603.

[0207] The processor 601 can be a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The processor 601 can also be other devices with processing capabilities, such as circuits, devices, or software modules, without limitation.

[0208] Transceiver 602 is used to communicate with other devices or other communication networks, such as Ethernet, radio access network (RAN), wireless local area network (WLAN), etc. Transceiver 602 can be a module, circuit, transceiver, or any device capable of enabling communication.

[0209] The transceiver 602 is mainly used for data transmission and reception, and may include a transmitter and a receiver to send and receive signals respectively; operations other than signal transmission and reception are implemented by the processor, such as information processing and calculation.

[0210] Communication line 603 is used to transmit information between the components included in the wireless sensing device 600.

[0211] In one design, the processor can be viewed as a logic circuit, and the transceiver as an interface circuit.

[0212] Memory 604 is used to store instructions. These instructions can be computer programs.

[0213] The memory 604 can be volatile memory or non-volatile memory, or it can include both. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DRRAM). Memory 604 can also be a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed discs, laser discs, optical discs, digital universal discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices. It should be noted that the memory in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0214] It should be noted that the memory 604 can exist independently of the processor 601, or it can be integrated with the processor 601. The memory 604 can be used to store instructions, program code, or some data, etc. The memory 604 can be located inside or outside the wireless sensing device 600, without limitation. The processor 601 is used to execute the instructions stored in the memory 604 to implement the method provided in the above embodiments of this application.

[0215] In one example, processor 601 may include one or more CPUs, for example Figure 15 CPU0 and CPU1 in the CPU.

[0216] As an optional implementation, the wireless sensing device 600 includes multiple processors, for example, besides Figure 15 In addition to processor 601, it may also include processor 607.

[0217] As an optional implementation, the wireless sensing device 600 also includes an output device 605 and an input device 606. For example, the input device 606 is a device such as a keyboard, mouse, microphone, or joystick, and the output device 605 is a device such as a display screen or speaker.

[0218] It should be noted that the wireless sensing device 600 can be a chip system or... Figure 15 Devices with similar structures. The chip system can be composed of chips or include chips and other discrete components. Actions, terminology, etc., involved in the various embodiments of this application can be referenced interchangeably without limitation. The message names or parameter names in the messages used for interaction between nodes in the embodiments of this application are merely examples; other names can be used in specific implementations without limitation. Furthermore, Figure 15 The structural composition shown does not constitute a limitation on the wireless sensing device 600, except... Figure 15 In addition to the components shown, the wireless sensing device 600 may include more than Figure 15 This may indicate more or fewer components, or combinations of certain components, or different component arrangements.

[0219] The processor and transceiver described in this application can be implemented on integrated circuits (ICs), analog ICs, radio frequency integrated circuits, mixed-signal ICs, application-specific integrated circuits (ASICs), printed circuit boards (PCBs), electronic devices, etc. The processor and transceiver can also be manufactured using various IC process technologies, such as complementary metal-oxide semiconductors (CMOS), n-metal-oxide-semiconductor (NMOS), positive-channel metal-oxide semiconductors (PMOS), bipolar junction transistors (BJTs), bipolar CMOS (BiCMOS), silicon germanium (SiGe), gallium arsenide (GaAs), etc.

[0220] Figure 16This is a schematic diagram of a channel estimation device provided in an embodiment of this application. This channel estimation device is applicable to the scenarios shown in the above method embodiments. For ease of explanation, Figure 16 Only the main components of the channel estimation device are shown, including a processor, memory, control circuitry, and input / output devices. The processor is primarily used to process communication protocols and data, execute software programs, and process the data generated by those programs. The memory is mainly used to store the software programs and data. The control circuitry is primarily used for power supply and the transmission of various electrical signals. The input / output devices are primarily used to receive user input data and output data to the user.

[0221] When the channel estimation device is a terminal device, the control circuit can be a motherboard, the memory includes storage media such as hard disks, RAM, and ROM, and the processor can include a baseband processor and a central processing unit. The baseband processor is mainly used to process communication protocols and communication data, while the central processing unit is mainly used to control the entire channel estimation device, execute software programs, and process data from the software programs. Input / output devices include a display screen, keyboard, and mouse. The control circuit can further include or be connected to transceiver circuits or transceivers, such as network cable interfaces, for sending or receiving data or signals, such as for data transmission and communication with other devices. Furthermore, it can also include an antenna for transmitting and receiving wireless signals for data / signal transmission with other devices.

[0222] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes computer program code. When the computer program code is run on a computer, it causes the computer to execute any of the methods described in the embodiments of this application.

[0223] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer or an information processing device, using computer programs or instructions to control related hardware. The computer program or set of instructions can be stored in the computer-readable storage medium. When executed, the computer program or set of instructions can include the processes described in the above method embodiments. The computer-readable storage medium can be any of the wireless sensing devices described in the foregoing embodiments, such as the hard disk or memory of the wireless sensing device. The computer-readable storage medium can also be an external storage device of the wireless sensing device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the wireless sensing device. Further, the computer-readable storage medium can include both internal storage units and external storage devices of the wireless sensing device. The computer-readable storage medium is used to store the computer program or instructions and other programs and data required by the wireless sensing device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0224] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0225] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0226] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0227] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0228] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0229] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0230] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A channel estimation method, characterized in that, Applied to a wireless sensing device, the wireless sensing device including a transmitting node and a receiving node, the method includes: The receiving node determines the weighting matrix based on the channel matrix, wherein there are multiple channel matrices, and each channel matrix corresponds one-to-one with a multiple subcarrier. The receiving node determines the weighting matrix corresponding to the subcarrier based on the channel matrix of the subcarrier. The sending node obtains the weighting matrix; The transmitting node transmits a beamforming time-domain sensing signal to the receiving node via an air interface based on the weighting matrix. The receiving node uses the time-domain sensing signal to perform channel estimation and determine channel state information, which represents the channel state of the time-domain sensing signal during its propagation in the physical space environment. The transmitting node transmits a beamformed time-domain aware signal to the receiving node via an air interface based on the weighting matrix, including: The transmitting node determines the precoding matrix based on the weighting matrix; The transmitting node transmits the time-domain sensing signal to the receiving node through the air interface based on the precoding matrix; The transmitting node determines the precoding matrix based on the weighting matrix, including: The transmitting node determines at least one column of the weighting matrix corresponding to the subcarrier as the third matrix; The transmitting node calculates the null space of the third matrix to obtain the precoding matrix corresponding to the subcarrier.

2. The method according to claim 1, characterized in that, The number of channel matrices is multiple, and each channel matrix corresponds one-to-one with a number of subcarriers. The receiving node determines a weighting matrix based on the channel matrices, and the method further includes: The receiving node multiplies the channel matrix of the subcarrier with the conjugate of the channel matrix of the subcarrier to obtain the first matrix corresponding to the subcarrier; The receiving node sums the first matrices corresponding to the plurality of subcarriers to obtain the second matrix; The receiving node determines the weighting matrix based on the second matrix.

3. The method according to claim 1, characterized in that, The sending node determines the precoding matrix based on the weighting matrix, and further includes: The transmitting node obtains the average signal-to-noise ratio corresponding to each of the plurality of subcarriers; The transmitting node determines the fourth matrix based on the weighting matrix and average signal-to-noise ratio corresponding to the plurality of subcarriers respectively; The sending node determines at least one column of the fourth matrix as the fifth matrix; The transmitting node calculates the null space of the fifth matrix to obtain the precoding matrix.

4. The method according to claim 2, characterized in that, The sending node determines the precoding matrix based on the weighting matrix, and further includes: The transmitting node determines at least one column of the weighted matrix as the sixth matrix; The transmitting node calculates the null space of the sixth matrix to obtain the precoding matrix.

5. The method according to claim 1, characterized in that, The transmitting node transmits the time-domain aware signal to the receiving node through the air interface based on the precoding matrix, including: The transmitting node uses the precoding matrix corresponding to the subcarrier to precode the subcarrier of the initial frequency domain signal to obtain the frequency domain sensing signal; The transmitting node converts the frequency domain sensing signal into the time domain sensing signal; The transmitting node sends the time-domain sensing signal to the receiving node through the air interface.

6. The method according to claim 3 or 4, characterized in that, The transmitting node transmits the time-domain aware signal to the receiving node through the air interface based on the precoding matrix, including: The transmitting node precodes the initial time-domain signal using a precoding matrix obtained by calculating the null space of the fifth matrix or the null space of the sixth matrix, thereby obtaining the time-domain sensing signal. The transmitting node sends the time-domain sensing signal to the receiving node through the air interface.

7. The method according to claim 1, characterized in that, The transmitting node includes at least one antenna, and the transmitting node transmits a beamformed time-domain aware signal to the receiving node via an air interface based on the weighting matrix, including: The transmitting node adjusts the angle of the at least one antenna based on the weighting matrix; The transmitting node uses the adjusted at least one antenna to transmit the time-domain sensing signal to the receiving node through the air interface.

8. The method according to any one of claims 1 to 5, 7, characterized in that, Before the receiving node determines the weighting matrix based on the channel matrix, the method further includes: The receiving node determines the calibration weighting matrix based on the calibration channel matrix; The transmitting node obtains the calibration weighting matrix; The transmitting node transmits a beamforming time-domain calibration signal to the receiving node through the air interface based on the calibration weighting matrix. The receiving node uses the time-domain calibration signal to perform channel estimation and determine the calibration channel state information; After determining the channel state information, the method further includes: The receiving node uses the calibration channel state information to update the channel state information, thereby obtaining the updated channel state information.

9. The method according to any one of claims 1 to 5, 7, characterized in that, The method further includes: The sending node sends a probe frame to the receiving node, the probe frame including a long training sequence; The receiving node determines the channel matrix based on the long training sequence.

10. A wireless sensing device, characterized in that, include: One or more processors; Memory, used to store one or more computer programs or instructions; When the one or more computer programs or instructions are executed by the one or more processors, the one or more processors perform the method as described in any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that, It includes a computer program or instructions that, when executed on a computer, cause the computer to perform the method of any one of claims 1 to 9.

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