WiFi sensing anti-interference method and WiFi device

Abstract

The application relates to the field of communication and perception integration, in particular to a WiFi perception anti-interference method, which is used for solving the problem of perception performance decline caused by interference in the same frequency band of wireless networks with the increase of the number of WiFi devices. The method utilizes variance-based interference measurement and maximum ratio combination-based channel state information optimization method to measure unknown interference levels from the statistical point of view and improve the quality of channel state information, and further improves the performance of WiFi perception.

Description

Technical Field

[0001] This invention relates to the field of integrated communication and sensing, and more particularly to a WiFi sensing anti-interference method and a sensing device. Background Technology

[0002] As the number of WiFi devices increases, interference is common in wireless networks operating on the same frequency band, which can affect WiFi sensing performance.

[0003] Current WiFi sensing research does not consider the impact of interference. In fact, sensing is more sensitive to interference than communication. Even if data transmission is not affected at a certain level of interference, interference can still affect sensing performance. Summary of the Invention

[0004] The purpose of this invention is to solve the problem of decreased sensing performance caused by interference in wireless networks on the same frequency band in Integrated Sensing Communication (ISAC) as the number of WiFi devices increases.

[0005] To achieve the above technical objectives, the specific technical solutions are as follows.

[0006] In a first aspect, the present invention proposes a WiFi sensing anti-interference method, the method comprising the following steps: acquiring the transmission and reception signals of pilot symbols in each subcarrier, and acquiring the reception signals of data symbols; constructing a transmission signal matrix and a reception signal matrix, the matrix size being M×N, where N is the number of symbols and M represents the number of subcarriers; based on the i-th reception signal of the j-th subcarrier... and the i-th transmitted signal Calculate the channel state information of the i-th symbol of the j-th subcarrier. Based on the fact that the noise distribution is a Gaussian distribution with a mean of 0, the mean of the channel state information of the j-th subcarrier is estimated. Then, the variance of the channel state estimation mean is obtained; based on the variance and a preset threshold, the number of symbols K of the highest quality is determined; based on the channel state information... and channel state information estimation mean Obtain quality metrics Constructing the channel state information quality matrix Then select the K symbols with the highest quality from the quality matrix Q; based on the K symbols with the highest quality, use maximum ratio combining to obtain the optimal channel state information for each subcarrier; where i = 1, 2, ..., N, j = 1, 2, ..., M.

[0007] In one embodiment of the above technical solution, the preset threshold is the L variance threshold. At the L variance threshold There exists a value greater than the variance. When the value is [value], the number of signs Otherwise, K is a set value; where l∈[1,L], and L is a preset value. for The corresponding preset value.

[0008] In one embodiment of the above technical solution, the estimated mean of the channel state information of the j-th subcarrier is... The steps to obtain the variance of the channel state estimation mean include: the estimated channel state information value on the j-th subcarrier is... based on It follows a Gaussian distribution with a mean of 0. Expectations based on Expected value, obtain variance The variance is used to estimate the interference level of symbols on the subcarrier; where, The noise of the i-th symbol on the j-th subcarrier.

[0009] In one embodiment of the above technical solution, the quality measurement value

[0010] In one embodiment of the above technical solution, the step of obtaining the optimal channel state information of each subcarrier using maximum ratio combining includes: constructing the transmission signal on the j-th subcarrier using the K highest quality symbols in each subcarrier. Get x j Corresponding received signal Solving optimization problems Obtain the optimal weight w, w = [w1, w2, ..., w K ] T Λ is a diagonal matrix reflecting the uncorrelated characteristics of interference between different symbols; based on weights w and y j The optimal channel state information of the j-th subcarrier is

[0011] In one embodiment of the above technical solution, the method further includes: obtaining a channel state information vector of the highest quality from each data packet, and representing it as h = [h1, h2, ..., h...]. M ], where h j This represents the optimized channel state information on the j-th subcarrier. Within a set time period, a set h is extracted from multiple data packets, preprocessed, and used to construct the channel state information matrix for each activity, serving as the input to the perception model for classification or regression.

[0012] Secondly, this invention proposes a WiFi device that employs any of the above methods for anti-interference processing of channel state information to improve sensing performance.

[0013] The beneficial technical effects of the present invention are as follows: The method first measures the interference level of each subcarrier based on a variance method to determine the symbols that can be used to improve channel state information; then it optimizes using multiple symbol signals to improve the sensing performance under interference conditions. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 Standard WiFi transmission process and WiFi packet structure in time and frequency domains.

[0016] Figure 2 Interference of WiFi data packets in the time and frequency domains.

[0017] Figure 3 , one A flowchart illustrating a method for improving WiFi sensing performance under interference by enhancing the quality of Channel State Information (CSI) in one implementation.

[0018] Figure 4 , one A diagram illustrating the signal collection process during correct demodulation of data packets in one implementation method.

[0019] Figure 5 , one CSI of symbols on subcarriers under two interference levels in one implementation.

[0020] Figure 6 , one A schematic diagram illustrating the impact of K on CSI quality in one implementation method.

[0021] Figure 7 , one A schematic diagram of the CSI enhancement algorithm for the j-th subcarrier in one implementation method. Detailed Implementation

[0022] Wireless sensing technology has emerged as a promising sensing paradigm in recent years. It utilizes the transmission and reception of wireless signals to detect and measure physical characteristics and changes in the environment. Common applications include home security monitoring and healthcare monitoring. Current research primarily focuses on extracting changes from the channel state information (CSI) of WiFi signals to capture target movement in the surrounding environment, such as human activity recognition and gesture recognition. These studies are implemented on a dedicated set of WiFi devices and specific channels for sensing, without considering the network characteristics of the WiFi devices.

[0023] With the development of 6G communication and sensing integration, some researchers have begun to focus on ISAC design in WiFi systems. In the past two years, some related work has been carried out, the basic idea of ​​which is to achieve sensing using communication data packets. For example, the IEEE 802.11bf task group attempted to revise the IEEE 802.11 standard to support WiFi sensing, mainly through MAC (Media Access Control) layer protocol design. Another example is extracting CSI from WiFi communication data packets sent by APs (Access Points) to identify target activity, primarily addressing the CSI calibration problem under different WiFi communication modes when the target of the data packet is not a sensing device. Yet another example is focusing on millimeter-wave WiFi systems based on the IEEE 802.11ad standard, first using the main lobe signal for communication, and then using the side lobe signals for sensing to achieve communication and sensing integration.

[0024] The following description, in conjunction with the accompanying drawings, clearly and completely describes how the technical solution of this case is implemented. Obviously, the described embodiments are only a part of the embodiments of this case, and not all of them. Based on the embodiments in this case, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this application.

[0025] (I) Basic Knowledge

[0026] (1.1) WiFi transmission process

[0027] Figure 1(a) describes the transmission process of standard WiFi data bits. Data bits first pass through a channel coding module to resist a certain degree of interference, and then are mapped to constellation points according to the modulation method used (such as BPSK (Binary Phase Shift Keying), QPSK (Quadrature Phase Shift Keying), or different levels of QAM (Quadrature Amplitude Modulation), such as QAM-16, QAM-64, and QAM-256). The constellation points are mapped to OFDM subcarriers after the S / P (serial-to-parallel) module and output as time-domain OFDM symbols after IFFT (Inverse Fast Fourier Transform) and P / S (parallel-to-serial) processes. Each OFDM symbol is inserted with a CP (Cyclic Prefix) to eliminate inter-symbol interference. Finally, the signal is transmitted through the RF front end.

[0028] Figure 1 (b) shows the simplified WiFi packet structure after expansion from the time and frequency domains. In the frequency domain, OFDM divides the WiFi channel into multiple orthogonal subcarriers, carrying data in parallel. Specifically, each 20MHz WiFi channel is divided into 64 subcarriers, including 48 data subcarriers, 4 pilot subcarriers, and 12 empty subcarriers. Pilot subcarriers are used for fine-grained channel estimation. Here, they are ignored. Figure 1 The empty subcarrier in (b) is excluded because it does not affect the implementation of this design. In the time domain, the WiFi signal consists of a preamble symbol followed by a set of data OFDM symbols.

[0029] (1.2) WiFi Sensing Process

[0030] The principle of WiFi sensing lies in the fact that the movement of a target affects the WiFi channel; therefore, target motion information can be obtained by analyzing the resulting channel changes. Most research achieves WiFi sensing by analyzing the Common Interface Sequence (CSI) extracted from the preamble of WiFi data packets, such as... Figure 1 As shown in (b) above. The preamble is intended for synchronization and channel estimation during the demodulation process. Its transmitted signal is a known symbol. Where j represents the index of the subcarrier, and when there are M subcarriers, j∈[1,M]. Using The interference and background noise on the j-th subcarrier of the leading edge are represented by h. j If CSI is represented, then it corresponds to the received signal of the j-th subcarrier. for:

[0031]

[0032] The receiver can obtain the CSI vector [h1, h2, ..., h] from the preamble of each data packet. M ] T Based on CSI collected from a set of data packets over a period of time, the basic process of WiFi detection is similar, including CSI preprocessing to remove noise, activity range detection, and feature extraction from CSI for classification or regression, depending on the application.

[0033] Current research on WiFi sensing does not consider the impact of interference, which means that in formula (1) when When only representing negligible background noise, h j Only then can it be approximated as

[0034] However, when interference is present, For h j However, the estimated impact cannot be ignored. In fact, sensing is more sensitive to interference than communication; even if data transmission can be completed normally under a certain level of interference, sensing performance may still be severely affected.

[0035] (1.3) Interference in WiFi networks

[0036] Interference in WiFi networks is complex. Different levels of interference may exist in various symbols and subcarriers of WiFi data packets, which can be divided into time-domain interference and frequency-domain interference.

[0037] In the time domain, because wireless devices use the CSMA / CA mechanism for channel access in the ISM band, the start time of data packets is random. At the same time, the length of the data packets will also vary.

[0038] For example, in Figure 2 In (a) of the diagram, the target WiFi data packet is the data packet for which we need to extract CSI for sensing. IF packets represent those packets that interfere with the target data packet; these could be WiFi data packets or other types of data packets. The target data packet may encounter various interference scenarios, such as interference from IF packet 1 at the beginning of a symbol, interference from IF packet 2 in the middle of a symbol, and interference from IF packet 3 at the end of a symbol. We note that multiple IF packets may interfere with the target data packet simultaneously. Considering that the signal power of each IF packet may differ on the sensing device, a WiFi data packet may experience different degrees of interference in different symbols. In this case, the sensing device is a WiFi device.

[0039] In the frequency domain, a WiFi channel may overlap with a ZigBee channel with a bandwidth of 2 MHz and a Bluetooth channel with a bandwidth of 1 MHz. It may also partially overlap with other WiFi channels. Figure 2 (b) illustrates an example where a target 20MHz WiFi channel overlaps with ZigBee, Bluetooth, and other WiFi channels. Intermediate frequency (IF) data packets from different channels will interfere with different subcarriers of the target WiFi data packets. Therefore, WiFi data packets may encounter varying degrees of interference on different subcarriers.

[0040] (II) WiFi Sensing Anti-interference Design Method

[0041] (2.1) Overview

[0042] This case improves WiFi sensing performance under interference by enhancing the quality of Channel State Information (CSI). The method flow is as follows: Figure 3 As shown.

[0043] The WiFi device performing the sensing task first collects the transmitted and received signals of each data packet and reconstructs the channel state information. Subsequently, anti-interference processing is performed to optimize CSI, including two general methods: variance-based interference measurement and maximum ratio combining (MRC)-based CSI optimization. These methods statistically measure unknown interference levels and improve CSI quality. Based on these two methods, a practical CSI enhancement process is proposed to adapt to the complex interference situations in real-world networks. The device then extracts high-quality CSI from each data packet and inputs it into a CSI preprocessing module to remove outliers caused by burst noise and obtain sample indices for each activity at the start and end of the CSI to achieve activity window detection. Finally, the CSI associated with each activity is input into the sensing model.

[0044] Figure 3 The gray blocks represent newly added modules in this invention, while the white blocks represent existing modules currently related to CSI-based WiFi sensing. This solution can be seen as an extension of the current WiFi sensing work; the optimized CSI can be used as input to the current work to improve its sensing performance under interference. Here, X represents the transmitted signal, and Y represents the received signal.

[0045] (2.2) Signal Acquisition

[0046] According to our design, the sensing device should collect the transmitted and received signals of each data packet to optimize channel state information under interference. The signal collection process for each data packet is as follows: Figure 4As shown, since the preamble and pilot symbols are known to the receiver, the transmitted and received signals of the preamble symbols can be acquired during preamble synchronization, while the transmitted and received signals of the pilot symbols, as well as the received signals of the data symbols, can be acquired during OFDM demodulation. After successful demodulation, the transmitted data signals can be acquired. That is, for each WiFi data packet, compared to existing technologies that only use the preamble symbols to calculate CSI, this method additionally extracts the pilot symbols and data symbols to improve the quality of channel state information and enhance the performance of WiFi sensing.

[0047] It is important to note that we only use WiFi data packets that can be correctly demodulated; data packets that fail to be demodulated will be discarded and will not be used for detection.

[0048] The sensing device constructs a transmitted signal matrix X and a received signal matrix Y for each received data packet. X is represented as:

[0049]

[0050] in This represents the transmitted signal of the i-th symbol in the data packet on the j-th subcarrier, where M represents the number of subcarriers and N represents the number of symbols. The structure of Y is the same as X. For each... have:

[0051]

[0052] in This represents the interference and background noise of the i-th symbol in the data packet on the j-th subcarrier. The background noise is usually much smaller than the interference, so we also consider it as a component of the interference.

[0053] In the following sections, h will be used j Let represent the CSI on the j-th subcarrier. Considering that the duration of a data packet does not exceed a few milliseconds, the CSI can be assumed to remain constant between different symbols within a data packet.

[0054] (2.3) Variance-based interference measurement

[0055] The first step in anti-interference is to measure the interference level of each subcarrier. For formula (3), obtaining an accurate h is nearly impossible due to the randomness of unknown interference. Therefore, a variance-based method can be used to statistically measure the interference level of each subcarrier. The CSI on the j-th subcarrier is estimated using the following formula:

[0056]

[0057] in, It is the estimated CSI, which includes h jand additional interference components

[0058] make This represents the CSI estimated from all symbols on the j-th subcarrier. It can be observed that... It is a set of random variables, and the variance of each element depends on the SINR (signal-to-noise ratio). For each element calculated by formula (4) It is known, and It is unknown, so we treat it as a random variable.

[0059] We assume interference It follows a Gaussian distribution with a mean of 0. Due to the channel coefficient h... j With interference components It is independent, therefore The mean is:

[0060]

[0061] in It is equivalent interference. Because The mean of is zero, which can be verified. It is h j An unbiased estimate, therefore we can obtain The expectation is:

[0062]

[0063] The variance can be calculated as follows:

[0064]

[0065] When the number of symbols is sufficiently large, the variance of the estimated CSI is negatively correlated with SINR. Therefore, we can estimate the interference level of symbols on any subcarrier by calculating the variance of the CSI. Figure 5 The real part of the CSI extracted from a set of symbols on a subcarrier is shown under both low and high interference conditions. Similarly, the imaginary part exhibits similar characteristics. It can be seen that with an SINR of 30 dB, the variance of the CSI is significantly lower than that with an SINR of 10 dB, and the CSI will have more identifiable features.

[0066] It is important to note that there is typically a CFO (Center Frequency Offset) Δ between the transmitter and receiver. f This can lead to phase deviation. A solution widely used in WiFi communication scenarios can be used, namely... Compensation is performed. Therefore, we ignore this process in the analysis and use formula (3) to perform compensation. It is considered as the received signal after frequency offset compensation.

[0067] (2.4) CSI Optimization Based on Maximum Ratio Combining (MRC)

[0068] In this section, we propose CSI optimization based on maximum ratio combining (MRC), which improves the CSI quality of each subcarrier by combining signals from multiple symbols.

[0069] Assumption Let be a set of symbols transmitted on the j-th subcarrier, which will be used to optimize CSI quality. We have:

[0070] y j =h j ·x j +n j (8)

[0071] in This indicates the corresponding received signal. This indicates the corresponding interference. h j These symbols are considered to remain unchanged during their duration.

[0072] According to the MRC method, a higher SINR will result in higher CSI estimation accuracy. To maximize SINR, we assign weights w = [w1, w2, ..., w...] to this set of symbols. K ] T And convert formula (8) to:

[0073] y′ j =w T ·y j =h j ·w T ·x j +w T ·n j (9)

[0074] The power of the received target signal is:

[0075]

[0076] Since the interference is unknown, we can only consider the interference n. j Treat them as a set of random variables and obtain their power statistically:

[0077]

[0078] Where Λ is a diagonal matrix, reflecting the uncorrelated nature of interference between different symbols, and

[0079]

[0080] in

[0081] The design goal here is to obtain the weight w to maximize SINR, i.e., P. s / p n Therefore, determining w is equivalent to solving the following optimization problem:

[0082]

[0083] Since the probability of each symbol being interfered with is usually the same, and the interference signals have the same statistical distribution, assuming that the interference level of each symbol is statistically consistent, then each element σ in formula (12) is... kk They have the same value. Based on this assumption, the optimal weight w can be calculated.

[0084]

[0085] Using the weight w and formula (9), we can obtain the optimized CSI on the j-th subcarrier:

[0086]

[0087] This paper theoretically analyzes how this CSI optimization process improves CSI quality. We calculate h′ j =w T ·y j variance:

[0088]

[0089] in This is the variance of the interference, representing the energy level of the interference in the environment. As can be seen, the optimized variance is much lower than the value calculated from the original received signal, equivalent to... The results show that the optimization process can significantly improve the quality of CSI.

[0090] To further analyze the impact of the K value on the optimization process, we can take the derivative along K, thus obtaining:

[0091]

[0092] This means that as K increases, the suppression of interference will decrease. Figure 6As shown, with an SINR of 10 dB, even at K=5, the CSI quality is significantly improved compared to the original quality at K=1, and the quality is even higher when K increases to 35. We can also see that as K increases, the improvement in quality becomes smaller and smaller, and when K exceeds 35, the improvement becomes insignificant. Therefore, in practical networks, a suitable K value needs to be found to ensure the effectiveness of interference resistance with low computational complexity.

[0093] (2.5) CSI Enhancement Process for Practical Systems

[0094] The preceding design provides a theoretical approach to interference measurement and CSI optimization from a statistical perspective. However, in real-world networks, different symbols may exhibit inconsistent interference patterns, making the selection of symbols for CSI optimization a challenge. In this section, we generate a CSI quality matrix for each packet to measure interference at a finer-grained level, and then propose a general algorithm to adapt to different interference scenarios.

[0095] Equations (5) and (6) show that the mean CSI on the j-th subcarrier approximates the actual CSI for a relatively large number of symbols. Meanwhile, based on the previous analysis of variance, we see that the distance between each symbol and the mean can indicate the SINR situation. A smaller distance indicates a larger SINR, and the CSI quality calculated for that symbol is higher. Therefore, based on this observation, we further estimate the CSI quality calculated for each symbol. For each received data packet, we will... We consider it as the benchmark for the j-th subcarrier because it is an unbiased estimate with small variance. We generate a CSI quality matrix for each symbol on each subcarrier, i.e.:

[0096]

[0097] in and Therefore, larger This indicates that the CSI extracted from the i-th symbol on the j-th subcarrier has a high quality.

[0098] We then propose a general algorithm for CSI optimization of the j-th subcarrier, the pseudocode of which is as follows: Figure 7 As shown. The sensing device first uses variance to roughly measure the SINR level of this subcarrier to determine the required K value. Since variance is inversely proportional to SINR, we use the L variance threshold. The SINR situation is divided into several levels, the first element The highest value indicates the lowest SINR threshold. The L variance threshold is then applied. As a preset threshold, l∈[1,L], At the L variance threshold There exists a value greater than the variance. When the value is [value], the number of signs Otherwise, K is a set value. Where L is a preset value. Represents the l-th variance threshold The larger the value of K, the better. corresponding The larger the value, the greater the variance. Note that the SINR level and K value related to variance are determined through experimental experience. Afterwards, the sensing device selects the K symbols with the highest quality using the CSI quality matrix, calculates the weight w according to equation (14), and then calculates h using equation (15). j When the variance exceeds the highest threshold At that time, we considered this subcarrier to be severely interfered with and set K to 100 to improve CSI quality as much as possible.

[0099] After anti-interference processing, a high-quality CSI vector can be obtained from each data packet, denoted as h = [h1, h2, ..., h...]. M ], where h j Let represent the optimized CSI on the j-th subcarrier. Based on a set h extracted from multiple data packets over a period of time, CSI preprocessing is performed to construct the CSI matrix for each activity, which serves as input to a perception model for classification or regression.

[0100] The implementation process of the above technical solution includes: measuring the interference level of each subcarrier using a variance-based method; optimizing the CSI using a general MRC-based method by utilizing multiple symbol signals; and improving CSI quality under complex interference conditions in real-world networks through CSI enhancement. During implementation, interference issues in the integrated WiFi communication and sensing system have been fully considered, improving the performance of WiFi sensing. The above technical solution has been verified on the USRP software radio platform, proving its complete feasibility.

[0101] Through the above description of the embodiments, those skilled in the art can clearly understand that this disclosure can be implemented using software plus necessary general-purpose hardware, or it can be implemented using dedicated hardware including dedicated integrated circuits, dedicated CPUs, dedicated memory, dedicated components, etc. Generally, any function performed by a computer program can be easily implemented using corresponding hardware, and the specific hardware structure used to implement the same function can be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for this disclosure, software implementation is more often a preferred implementation method.

[0102] Although embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of the present invention, and all of these are within the scope of protection of the present invention.

Claims

1. A WiFi sensing anti-interference method, characterized in that, The method includes the following steps: Obtain the transmitted and received signals of the pilot symbols in each subcarrier, and obtain the received signals of the data symbols. Construct a transmitted signal matrix and a received signal matrix with a size of M×N, where N is the number of symbols and M represents the number of subcarriers. Based on the i-th received signal of the j-th subcarrier and the i-th transmitted signal Calculate the channel state information of the i-th symbol of the j-th subcarrier. ; Based on the fact that the noise distribution is a Gaussian distribution with a mean of 0, the mean of the channel state information of the j-th subcarrier is estimated. This allows us to obtain the variance of the channel state estimation mean. Based on the variance and the preset threshold, determine the number of symbols K of the highest quality; Based on channel state information and channel state information estimation mean Obtain quality metrics Construct the channel state information quality matrix and from the quality matrix Select the K symbols with the highest quality. Based on the K highest quality symbols, the optimal channel state information for each subcarrier is obtained using maximum ratio combining. The steps include: constructing the transmitted signal on the j-th subcarrier using the K highest quality symbols from each subcarrier. ,get Corresponding received signal Solving optimization problems To obtain the optimal weight , , A diagonal matrix reflecting the uncorrelated characteristics of interference between different symbols; based on weights and The optimal channel state information of the j-th subcarrier is ; Where i = 1, 2, ..., N, j = 1, 2, ..., M.

2. The method according to claim 1, characterized in that, The preset threshold is the L variance threshold. At the L variance threshold There exists a value greater than the variance. When the value is , the number of signs K = ; Otherwise, K is a set value; where L is the preset value. for The corresponding preset value.

3. The method according to claim 1, characterized in that, Mean of channel state information estimation for the j-th subcarrier The steps to obtain the variance of the mean and channel state estimates include: The estimated channel state information value on the j-th subcarrier is ; based on It follows a Gaussian distribution with a mean of 0. Expectations ; based on Expected value, obtain variance The variance is used to estimate the interference level of symbols on the subcarrier; in, The noise of the i-th symbol on the j-th subcarrier.

4. The method according to claim 1, characterized in that, Quality metric .

5. The method according to claim 1, characterized in that, The method further includes: Obtain a highest quality channel state information vector from each data packet, and represent it as follows: ,in This represents the optimized channel state information on the j-th subcarrier; Within a set time period, extract a set from multiple data packets. After preprocessing, a channel state information matrix for each activity is constructed, which serves as the input to the perception model for classification or regression.

6. A WiFi device, characterized in that, Channel state information anti-interference processing is performed using any one of claims 1 to 5. The WiFi device includes a signal acquisition module, a variance-based interference measurement module, an MRC-based CSI optimization module, and a practical CSI enhancement process module; wherein: The signal acquisition module is configured to acquire the transmitted and received signals of the pilot symbols in each subcarrier, and acquire the received signals of the data symbols, and construct a transmitted signal matrix and a received signal matrix. The matrix size is M×N, where N is the number of symbols and M represents the number of subcarriers. The variance-based interference measurement module is configured to measure the i-th received signal based on the j-th subcarrier. and the i-th transmitted signal Calculate the channel state information of the i-th symbol of the j-th subcarrier. Based on the fact that the noise distribution is a Gaussian distribution with a mean of 0, the mean of the channel state information of the j-th subcarrier is estimated. This allows us to obtain the variance of the channel state estimation mean. The MRC-based CSI optimization is configured to determine the highest quality number of symbols K based on the variance and a preset threshold; based on channel state information... and channel state information estimation mean Obtain quality metrics Construct the channel state information quality matrix and from the quality matrix Select the K symbols with the highest quality; based on the K symbols with the highest quality, use maximum ratio combining to obtain the optimal channel state information for each subcarrier. The steps include: using the K symbols with the highest quality in each subcarrier to construct the transmitted signal on the j-th subcarrier. ,get Corresponding received signal Solving optimization problems To obtain the optimal weight , , A diagonal matrix reflecting the uncorrelated characteristics of interference between different symbols; based on weights and The optimal channel state information of the j-th subcarrier is ; Where i = 1, 2, ..., N, j = 1, 2, ..., M.

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