A method for estimating bandwidth of a WiFi signal and a receiving device

Abstract

A method and receiving device for estimating WiFi signal bandwidth are disclosed, relating to the field of wireless communication technology. In this method, the receiving device uses a long training sequence in the preamble of the received WiFi signal for bandwidth estimation, avoiding reliance on signaling fields. This allows the receiver to obtain WiFi signal bandwidth information before decrypting the signaling fields, enabling it to employ optimal multi-channel combining configuration during the signaling processing stage, thereby improving the combining gain of the WiFi signal.

Description

Technical Field

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

[0002] With the rapid development of wireless communication technology, users' demands for data transmission rates and network capacity are increasing daily. To meet this demand, Wireless Local Area Network (WLAN) standards, such as IEEE 802.11ac and 802.11ax (i.e., WiFi 5 and WiFi 6), have introduced channel bonding and wider channel bandwidth technologies. These technologies allow multiple basic 20MHz channels to be aggregated to form wider transmission channels of 40MHz, 80MHz, or even 160MHz, thereby significantly increasing data throughput. Therefore, in modern WiFi communication scenarios, although the receiving device operates on a preset wide channel (e.g., 80MHz), it needs to accurately identify the actual transmission bandwidth occupied by each received signal frame (which could be 20MHz, 40MHz, or 80MHz). This is the prerequisite and foundation for correctly demodulating subsequent data.

[0003] In relevant technical protocols, a broadband channel (e.g., 80MHz) consists of a pre-configured 20MHz primary channel and several secondary channels. The primary channel serves as the communication anchor point for the entire broadband channel. When the sending end transmits a data packet, it encodes key transmission parameters, including the actual bandwidth, into a signaling field and transmits this signaling field on the primary channel. Correspondingly, the standard workflow of the receiving end is as follows: first, it listens on the known primary channel and demodulates the received signaling field to obtain the actual bandwidth of the transmission from the decoded information; then, it adjusts its configuration based on this bandwidth information to receive subsequent complete data.

[0004] However, the bandwidth information of WiFi signals is carried in the signaling field. Only by decoding the signaling field can the signal bandwidth be known. This makes it difficult for related technologies to obtain the bandwidth information of WiFi signals before decoding the signaling field, thereby reducing the combining gain of WiFi signals. Summary of the Invention

[0005] This application provides a WiFi signal bandwidth estimation method and receiving device. By using a long training sequence (L-LTF) to estimate the signal bandwidth, the bandwidth information of the WiFi signal can be obtained before decrypting the signaling field, thereby improving the combining gain of the WiFi signal.

[0006] Firstly, a WiFi signal bandwidth estimation method is provided, applied to a receiving device. The method includes: the receiving device performing cross-correlation calculations between a long training sequence in the received WiFi signal preamble and a locally known long training sequence to obtain channel estimation results for each 20MHz channel in the WiFi signal; the receiving device determining the signal-to-noise ratio (SNR) of each 20MHz channel based on the channel estimation results; the receiving device determining whether the SNR of the main channel is less than a preset first threshold; if so, the receiving device sets a seventh threshold as a preset second threshold; if not, the receiving device sets the seventh threshold as the maximum value between a preset third threshold and a preset sixth threshold, where the sixth threshold is the maximum value of the main channel's SNR. The receiving device calculates the signal-to-noise ratio (SNR) multiplied by a preset fifth threshold. It then determines whether the SNR of the sub-channel is greater than a seventh threshold. If yes, the receiving device determines that the sub-channel has a signal; otherwise, it determines that the sub-channel has no signal. For a channel with a total bandwidth of 80MHz, the receiving device determines whether the number of sub-channels with signals is not less than a preset fourth threshold. If yes, the receiving device determines that the bandwidth of the WiFi signal is 80MHz; otherwise, it determines whether the sub-channels on the same side of the main channel have signals. If the sub-channels on the same side have signals, the receiving device determines that the bandwidth of the WiFi signal is 40MHz; if the sub-channels on the same side have no signals, the receiving device determines that the bandwidth of the WiFi signal is 20MHz.

[0007] By adopting the above technical solution, the receiving device avoids dependence on signaling fields by using long training sequences for bandwidth estimation, thereby obtaining the bandwidth information of the WiFi signal before decrypting the signaling fields. This enables the receiving device to adopt the optimal multi-channel combining configuration during the signaling processing stage, thereby improving the combining gain of the WiFi signal.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the receiving device determines the signal-to-noise ratio (SNR) of each 20MHz channel based on the channel estimation results of each 20MHz channel, specifically including: the receiving device determining the time-domain impulse response sequence corresponding to each 20MHz channel based on the channel estimation results of each 20MHz channel; and the receiving device calculating the SNR of each 20MHz channel based on the time-domain impulse response sequence.

[0009] By employing the aforementioned technical solution, the frequency-domain channel estimation results are transformed into a time-domain impulse response sequence, providing a superior data foundation for signal-to-noise ratio (SNR) calculation. The time-domain representation effectively separates the temporal distribution characteristics of signal and noise. In the time-domain impulse response sequence, signal components are typically concentrated in the first few sampling points, while noise components are relatively uniformly distributed throughout the time domain. This inherent time-domain separation characteristic creates favorable conditions for power separation. The SNR calculation method based on the time-domain response can more accurately identify the main propagation path and delay spread characteristics of the signal, exhibiting better signal-to-noise separation capabilities compared to frequency-domain analysis. The high-precision SNR data obtained through time-domain analysis can significantly improve the accuracy and reliability of subsequent bandwidth determination.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the receiving device determines the time-domain impulse response sequence corresponding to each 20MHz channel based on the channel estimation results of each 20MHz channel. Specifically, this includes: the receiving device performing linear interpolation processing on the DC subcarrier positions in the channel estimation results of each 20MHz channel to obtain completed frequency-domain channel response data; the receiving device applying a Hamming window function to the frequency-domain channel response data for windowing processing; the receiving device performing an inverse fast Fourier transform operation on the windowed frequency-domain channel response data to obtain the time-domain impulse response sequence corresponding to each 20MHz channel; and the receiving device calculating the power values ​​of the sampling points in the time-domain impulse response sequence to form a time-domain power distribution sequence, where the sampling points are the output points of the inverse fast Fourier transform.

[0011] By employing the above technical solution, linear interpolation is performed on the DC subcarrier positions in the channel estimation results, making the input data for the subsequent inverse fast Fourier transform more complete. The windowing process using the Hamming window function suppresses discontinuities at frequency domain edges and spectral leakage, improving the quality and accuracy of the time-domain transform, thereby enhancing the accuracy of subsequent signal-to-noise ratio calculations.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, before the step of linear interpolation processing of the DC subcarrier positions in the channel estimation results of each 20MHz channel by the receiving device, the method further includes: the receiving device calculating the amplitude value, mean value, and standard deviation of all subcarriers in each 20MHz channel; the receiving device marking subcarrier data points whose amplitude values ​​exceed the range of the mean plus or minus three times the standard deviation as outliers; and the receiving device replacing the outliers with the average value of adjacent normal subcarrier data.

[0013] By adopting the above technical solution, outliers are identified based on statistical principles. The identification and replacement of outliers reduces data anomalies caused by factors such as channel estimation errors, hardware defects, or transient interference, providing more reliable input data for subsequent DC interpolation processing.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, the receiving device calculates the signal-to-noise ratio (SNR) of each 20MHz channel based on the time-domain impulse response sequence. Specifically, this includes: the receiving device determining the window length and sliding search range of the signal power detection window according to the length of the guard interval in the WiFi protocol; the receiving device moving the signal power detection window point by point within the time-domain impulse response sequence according to the sliding search range, and calculating the sum of the power values ​​of all sampling points within each window length; the receiving device selecting the maximum value from the sums of power within all window lengths as the signal power of each 20MHz channel; the receiving device selecting a continuous sampling point region outside the sliding search range, and taking the average power value of the sampling points within the sampling point region as the noise power; and the receiving device using the ratio of signal power to noise power as the SNR of each 20MHz channel.

[0015] By adopting the above technical solution, the signal power detection window design based on the WiFi protocol guard interval length enables more accurate separation of signal and noise. The sliding window power detection mechanism captures the region of strongest power concentration by moving point by point and accumulating calculations, identifying the main propagation path and delay components of the signal. The selection strategy for the maximum power accumulation sum improves the accuracy and reliability of signal power estimation and reduces the deviation that may be caused by fixed-position detection. The selection of noise power estimation regions outside the sliding search range improves the purity of noise power calculation and reduces the contamination of noise estimation by signal components. The signal-to-noise ratio calculated by the ratio of signal power to noise power has higher accuracy and better environmental adaptability.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after the receiving device determines that there is no signal on the sub-channel, the method further includes: for a channel with a total bandwidth of 40MHz, the receiving device determines whether the number of sub-channels with signals is 2; if yes, the receiving device determines that the bandwidth of the WiFi signal is 40MHz; if no, the receiving device determines that the bandwidth of the WiFi signal is 20MHz.

[0017] By adopting the above technical solution, the specialized processing logic for the 40MHz channel scenario improves the coverage of the bandwidth estimation method. Combined with the judgment logic for the 80MHz scenario, a more complete multi-level bandwidth detection system is formed, enhancing the applicability of the bandwidth estimation method.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after the receiving device determines the bandwidth of the WiFi signal, the method further includes: the receiving device configuring signaling merging and demodulation parameters according to the bandwidth of the WiFi signal, the signaling merging and demodulation parameters including at least a merging weighting coefficient and a phase correction parameter; the receiving device performing maximum ratio merging processing on the signaling fields in the sub-channels with signals according to the merging weighting coefficient; the receiving device performing demodulation and decoding operations on the merged signaling fields to obtain signaling information including the actual transmission bandwidth; the receiving device comparing and verifying the estimated bandwidth of the WiFi signal with the decoded actual transmission bandwidth, and dynamically adjusting the threshold parameter according to the comparison result.

[0019] By adopting the above technical solution, the signaling combining and demodulation parameter optimization based on estimated bandwidth configuration can leverage multi-channel diversity gain, thereby improving the demodulation performance of the signaling field. Maximum ratio combining (MRC) processing allocates optimal weights according to the signal quality of each sub-channel, maximizing the signal-to-noise ratio (SNR) of the combined signal. The comparison and verification between the actual transmission bandwidth information obtained from demodulation and decoding operations and the estimated results forms a closed-loop feedback optimization mechanism. The dynamic adjustment function of the threshold parameters based on the comparison results improves the accuracy of the threshold parameters, thus enhancing the accuracy of WiFi signal bandwidth estimation.

[0020] In a second aspect, embodiments of this application provide a receiving device, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the receiving device to perform the method as described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a receiving device, cause the receiving device to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a receiving device, cause the receiving device to perform the method described in the first aspect and any possible implementation thereof.

[0023] It is understood that the receiving device provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. Because the receiving device uses a long training sequence for bandwidth estimation, it avoids dependence on the signaling field. Thus, it obtains the bandwidth information of the WiFi signal before decrypting the signaling field, enabling the receiving device to adopt the optimal multi-channel combining configuration during the signaling processing stage, thereby improving the combining gain of the WiFi signal.

[0026] 2. Because the receiving equipment performs linear interpolation on the DC subcarrier positions in the channel estimation results, the input data for the subsequent inverse fast Fourier transform is more complete. The windowing process of the Hamming window function suppresses discontinuities at frequency domain edges and spectral leakage, improving the quality and accuracy of the time-domain transform, thereby enhancing the accuracy of subsequent signal-to-noise ratio calculations.

[0027] 3. Because the receiving device optimizes the signaling combining and demodulation parameters based on the estimated bandwidth configuration, it can leverage multi-channel diversity gain, thereby improving the demodulation performance of the signaling field. Maximum ratio combining (MRC) processing allocates optimal weights according to the signal quality of each sub-channel, maximizing the signal-to-noise ratio of the combined signal. The comparison and verification between the actual transmission bandwidth information obtained from the demodulation and decoding operations and the estimated results forms a closed-loop feedback optimization mechanism. The dynamic adjustment function of the threshold parameters based on the comparison results improves the accuracy of the threshold parameters, thus enhancing the accuracy of WiFi signal bandwidth estimation. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of a frame structure of a WiFi signal in an embodiment of this application.

[0029] Figure 2 This is a flowchart illustrating a WiFi signal bandwidth estimation method in an embodiment of this application.

[0030] Figure 3 This is a schematic diagram of an interpolation and windowing method in an embodiment of this application.

[0031] Figure 4 This is another flowchart illustrating a WiFi signal bandwidth estimation method in an embodiment of this application.

[0032] Figure 5 This is a schematic diagram of the physical device structure of a receiving device in an embodiment of this application. Detailed Implementation

[0033] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0034] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0035] Since the embodiments of this application involve the application of wireless communication technology, for ease of understanding, the relevant terms and concepts involved in the embodiments of this application will be introduced below.

[0036] Combined gain

[0037] Combining gain refers to the improvement in signal-to-noise ratio (SNR) of the combined signal relative to the SNR of a single signal after coherently combining multiple received signals in a wireless communication system. Combining gain is typically expressed in decibels (dB) and reflects the signal quality improvement effect of multi-channel information fusion compared to single-channel processing.

[0038] In WiFi communication, when the transmitter uses a wideband channel (such as 40MHz or 80MHz) to transmit signaling fields, the same information is transmitted simultaneously on multiple 20MHz sub-channels. If the receiver can accurately identify these sub-channels carrying the same signaling content and perform maximum ratio combining on them, the signal-to-noise ratio can be improved.

[0039] This application provides a WiFi signal bandwidth estimation method and receiving device. By using a long training sequence (L-LTF) to estimate the signal bandwidth, the bandwidth information of the WiFi signal can be obtained before decrypting the signaling field, thereby improving the combining gain of the WiFi signal.

[0040] To better understand the advantages of this application, please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic diagram of the frame structure of a WiFi signal.

[0041] In the WiFi protocol, the signaling field (SIG field) carries key parameter information, including the actual transmission bandwidth. In broadband transmission mode, this signaling field is repeatedly transmitted on multiple 20MHz sub-channels (as shown in the figure, DuplicateSIG).

[0042] According to the standard processing flow of existing technology, the receiving device needs to first demodulate the signaling field on the main channel to obtain bandwidth information, and then configure subsequent data reception parameters based on the decoded bandwidth. However, during the demodulation of the signaling field, the receiving device has not yet learned the actual transmission bandwidth, so it cannot determine which sub-channels carry valid duplicate signaling information. This results in the inability to coherently combine the SIG of the main channel with the Duplicate SIG of other sub-channels, thus losing potential combining gain.

[0043] This application embodiment utilizes a long training sequence (L-LTF) to achieve advance bandwidth estimation, allowing the actual transmission bandwidth of the signal to be known before signaling field demodulation. Based on this estimated bandwidth information, the receiving device can identify each sub-channel carrying repetitive signaling content and configure corresponding combining weight coefficients and phase correction parameters to achieve maximum ratio combining of multi-channel signaling fields, thereby improving the combining gain.

[0044] The following describes a WiFi signal bandwidth estimation method from an embodiment of this application:

[0045] Please see Figure 2 This is a flowchart illustrating a WiFi signal bandwidth estimation method in an embodiment of this application.

[0046] S201. The receiving device performs cross-correlation calculations between the long training sequence in the preamble of the received WiFi signal and the locally known long training sequence to obtain the channel estimation results for each 20MHz channel in the WiFi signal.

[0047] The WiFi signal preamble represents the synchronization and training field sequence at the beginning of the WiFi data frame, used by the receiving device for signal detection, time-frequency synchronization, and channel estimation. The Long Training Format (L-LTF) sequence refers to a known sequence in the WiFi preamble specifically used for channel estimation, possessing a fixed frequency domain structure and time domain waveform in the IEEE 802.11 standard. The locally known L-LTF sequence represents a standard L-LTF sequence pre-stored by the receiving device, serving as a reference for cross-correlation calculations. Cross-correlation calculations refer to the process of calculating the correlation between two signal sequences, extracting channel characteristic information by measuring the similarity between the received signal and a known signal. Each 20MHz channel indicates that in broadband WiFi transmission, the total bandwidth is divided into multiple basic 20MHz spectral units, each undergoing independent channel estimation. The channel estimation result refers to the complex response function obtained through signal processing algorithms, describing the transmission characteristics of the wireless channel and reflecting the amplitude attenuation and phase changes of the signal during transmission.

[0048] Specifically, the receiving device first extracts the L-LTF sequences from each 20MHz sub-channel of the received WiFi signal. These sequences carry the transmission characteristic information of their respective channels. Then, it performs a cross-correlation operation between the received L-LTF sequence on each 20MHz channel and the corresponding locally known L-LTF sequence. The influence of the channel on the signal is quantified by calculating the correlation between the two. According to the basic principle of channel estimation, the received L-LTF sequence can be expressed as: R_LTF(k) = H_LTF(k) × LTF(k) + N(k). Here, R_LTF(k) represents the L-LTF sequence received at frequency k, H_LTF(k) represents the channel frequency response at frequency k, LTF(k) represents the known L-LTF sequence at frequency k, and N(k) represents the noise component. In the IEEE 802.11 standard, in order to improve the accuracy of channel estimation, the preamble contains two identical L-LTF symbols (L-LTF1 and L-LTF2). The receiving device averages these two symbols to reduce the impact of noise: R_LTF(k) = [R_LTF1(k) + R_LTF2(k)] / 2.

[0049] During the cross-correlation operation, the receiving device demodulates the received sequence using the locally known L-LTF sequence LTF(k), and the channel estimation result is calculated using the following formula:

[0050] H_LTF(k) = R_LTF(k) × LTF * (k) = H_LTF(k) × |LTF(k)|²

[0051] Among them, LTF *(k) represents the conjugate complex number of the locally known L-LTF sequence, and |LTF(k)|² represents the power of the L-LTF sequence. Since the L-LTF sequences in the IEEE 802.11 standard are normalized and satisfy the condition |LTF(k)|²=1, the above formula simplifies to:

[0052] H_LTF(k) = R_LTF(k) × LTF * (k)

[0053] Through this cross-correlation operation, the difference between the received signal and the known sequence is mainly caused by the channel's frequency response. The channel's transmission function can be deduced through mathematical operations. The final channel estimation result H_LTF(k) is represented in complex form, with its real and imaginary parts describing the amplitude and phase response characteristics of the channel at each frequency point k, providing accurate basic data for subsequent channel analysis and bandwidth determination.

[0054] S202, The receiving device determines the signal-to-noise ratio of each 20MHz channel based on the channel estimation results of each 20MHz channel.

[0055] Signal-to-noise ratio (SNR) is the ratio of signal power to noise power, usually expressed in decibels, and is an important indicator used to quantify signal quality.

[0056] Specifically, the receiving device first analyzes the channel estimation results for each 20MHz channel, extracting the signal power and noise power information contained therein. For signal power calculation, the receiving device can convert the frequency-domain channel estimation into a time-domain impulse response using a time-domain transformation method. Then, it detects the main propagation paths of the signal in the time domain and calculates the cumulative power along these paths as the signal power. For noise power estimation, the receiving device can select regions in the time-domain response far from the main signal paths, as these regions primarily contain noise components, and obtain the noise power level through statistical analysis. The receiving device then calculates the ratio between the calculated signal power and noise power to obtain the signal-to-noise ratio (SNR) for each 20MHz channel.

[0057] In some embodiments, the signal-to-noise ratio (SNR) of each 20MHz channel can be calculated in several ways: Optionally, the receiving device can employ a frequency-domain power spectrum analysis method, directly calculating the power spectral density of the channel estimation results for each 20MHz channel in the frequency domain. It distinguishes between signal power and noise power by analyzing the statistical characteristics of the frequency-domain amplitude response, including steps such as power spectrum smoothing, signal region identification, and noise floor estimation. Finally, the SNR is obtained by calculating the ratio of signal power to noise power. This method has low computational complexity and is suitable for real-time processing. Optionally, the receiving device can also employ a time-domain impulse response analysis method. First, the frequency-domain channel estimation of each 20MHz channel is converted into a time-domain impulse response sequence through IFFT transformation. Then, signal and noise separation is performed based on the time-domain power distribution characteristics, including processing steps such as time-domain power calculation, signal window detection, noise region selection, and power statistical analysis. A more accurate SNR is obtained by calculating the ratio of time-domain signal power to noise power. This method provides higher estimation accuracy and better signal-to-noise separation performance.

[0058] It is understandable that other methods can be used to calculate the signal-to-noise ratio of each 20MHz channel, and no limitation is made here.

[0059] Five preset threshold values ​​were used in steps S203-S211, namely the first preset threshold value, the second preset threshold value, the third preset threshold value, the fourth preset threshold value, and the fifth preset threshold value.

[0060] The preset second threshold value is less than the preset first threshold value, and the preset third threshold value is greater than the preset first threshold value.

[0061] The preset first threshold is the dividing line between good and bad channel environment. It is set based on the minimum signal-to-noise ratio (SNR) requirement for stable operation of a WiFi system, typically 2dB. This value is derived from the theoretical demodulation thresholds of different modulation methods in the WiFi protocol, combined with the noise margin of the actual system. When the SNR of the main channel is lower than this value, it indicates a poor channel environment, and the detection requirements need to be reduced.

[0062] A second preset threshold is specifically designed for harsh channel environments and is set to a relatively low value (e.g., -0.5dB). This setting is based on the minimum threshold at which the receiver can still distinguish between signal and pure noise in low signal-to-noise ratio (SNR) conditions. This value was determined through extensive experimentation: when the SNR of the main channel is very low, the SNR of other sub-channels will also decrease accordingly. If a normal threshold is used, valid signals will be missed; therefore, a more lenient detection standard is needed.

[0063] The preset third threshold is used in good channel environments and is set to a higher value than the preset first threshold (e.g., 3dB). When the channel environment is good, even if the sixth threshold is not high enough due to the moderate signal-to-noise ratio of the main channel, the preset third threshold can still provide a relatively strict detection standard, reducing the possibility of misjudging weak interference or noise as valid signals.

[0064] The preset fourth threshold value represents the threshold for the number of sub-channels used to distinguish between 80MHz transmission and lower bandwidth transmission, and is usually set to 3 or 4.

[0065] The preset fifth threshold value is usually a coefficient between 0.2 and 0.25.

[0066] S203. The receiving device determines whether the signal-to-noise ratio of the main channel is less than a preset first threshold value.

[0067] In this context, the main channel refers to the pre-configured 20MHz reference channel in WiFi broadband communication, serving as the anchor point of the entire communication link and the primary carrier for control information transmission. The signal-to-noise ratio (SNR) is the ratio of signal power to noise power on the main channel, calculated through step S202, and reflects the transmission quality of the main channel.

[0068] This step is executed immediately after the signal-to-noise ratio (SNR) calculation for each 20MHz channel. At this point, the receiving device has obtained the SNR information for all sub-channels, including the main channel, and needs to determine a suitable decision strategy based on the quality of the main channel. The evaluation result of the main channel quality will directly affect the threshold setting for subsequent sub-channel signal detection, because the main channel, as the reference channel for communication, reflects the overall channel environment. Specifically, the receiving device extracts the SNR value corresponding to the main channel from the SNR results calculated in step S202 and then compares it with a pre-set first threshold value. The purpose of this comparison is to identify whether the current channel environment is a high-quality or low-quality scenario, providing a basis for subsequent adaptive threshold setting. If the main channel SNR is less than the first threshold value, it indicates that the current channel environment is relatively poor, and a conservative decision strategy needs to be adopted; if the main channel SNR is not less than the first threshold value, it indicates that the channel environment is relatively good, and a more sensitive adaptive decision strategy can be adopted.

[0069] S204. The receiving device sets the seventh threshold value as the preset second threshold value.

[0070] The seventh threshold value represents the decision threshold used for subsequent sub-channel signal detection. The preset second threshold value refers to a pre-configured fixed threshold value.

[0071] This step is executed when the judgment result of step S203 is "yes" (i.e., the main channel signal-to-noise ratio is less than the preset first threshold value). This indicates that the current channel environment quality is relatively poor, requiring a conservative decision strategy to improve the reliability of subsequent signal detection. In harsh channel environments, received signals are generally affected by strong noise interference and fading. Using an overly aggressive threshold setting may lead to noise being misjudged as valid signals, thus affecting the accuracy of bandwidth estimation. Specifically, the receiving device confirms a low signal-to-noise ratio environment based on the judgment result of step S203 and then directly sets the seventh threshold value to the preset second threshold value. The preset second threshold value is usually determined based on extensive experimental data and theoretical analysis, set to a value that ensures a low misjudgment rate even in harsh environments.

[0072] S205. The receiving device determines the seventh threshold value as the maximum value among the preset third threshold value and the sixth threshold value.

[0073] The sixth threshold value represents an adaptive threshold value dynamically calculated based on the main channel signal-to-noise ratio, reflecting the correlation between threshold setting and channel quality.

[0074] This step is executed if the result of step S203 is negative (i.e., the signal-to-noise ratio of the main channel is not less than the preset first threshold value), indicating that the current channel environment quality is relatively good. A more sensitive adaptive decision strategy can be adopted to improve signal detection accuracy. In a good channel environment, noise interference is relatively weak, and the receiving device can set a more refined decision threshold to distinguish between valid signals and background noise, thereby improving the accuracy of bandwidth estimation. Specifically, the receiving device first calculates the sixth threshold value by multiplying the signal-to-noise ratio of the main channel by the preset fifth threshold value to obtain a dynamic threshold value proportional to the channel quality. The preset fifth threshold value is typically a coefficient between 0.2 and 0.25. Then, this dynamic threshold value is compared with the preset third threshold value, and the maximum value is selected as the final seventh threshold value. This strategy of taking the maximum value not only allows the threshold value to be adaptively adjusted according to the channel quality but also ensures that the threshold value will not fall below the preset minimum safety value, maintaining basic noise immunity while improving detection sensitivity.

[0075] S206. The receiving device determines whether the signal-to-noise ratio of the sub-channel is greater than the seventh threshold value.

[0076] This step is performed after steps S204 and S205 are completed.

[0077] S207. The receiving device confirms that there is a signal in the sub-channel.

[0078] "Signal present" refers to the logical state in which the sub-channel is confirmed to carry a valid WiFi signal, indicating that the transmitting end has transmitted a signal within this spectrum range.

[0079] This step is executed if the result of step S206 is "yes" (i.e., the sub-channel signal-to-noise ratio is greater than the seventh threshold). This indicates that the signal strength of the sub-channel is strong enough to exceed the noise level, and it can be confirmed as a valid signal transmission channel. This confirmation is based on the statistical decision result of signal detection theory, distinguishing between signal and noise by comparing the relative magnitudes of signal power and noise power. In WiFi broadband transmission, the transmitting end will send the same or related signal content on a corresponding number of sub-channels according to the actual required transmission bandwidth. Specifically, the receiving device confirms, based on the comparison result of step S206, that the signal-to-noise ratio of the current sub-channel exceeds the set threshold, which means that the received power on this channel mainly comes from the useful signal rather than background noise. The receiving device then marks the state of the sub-channel as having a signal and stores this information in the corresponding data structure for subsequent processing.

[0080] S208, The receiving device determines that there is no signal on the sub-channel.

[0081] The "no signal" state refers to the logical state in which the subchannel is confirmed to consist mainly of background noise, indicating that the transmitter is not transmitting effective signals within this spectrum range.

[0082] This step is executed if the result of step S206 is negative (i.e., the signal-to-noise ratio of the sub-channel is not greater than the seventh threshold). This indicates that the signal strength of the sub-channel is weak and has not exceeded the preset noise threshold, thus confirming it as a non-signal transmission channel mainly composed of background noise. This judgment is based on the hypothesis testing principle in signal detection theory, distinguishing between signal and noise hypotheses through statistical analysis. In WiFi broadband transmission systems, the transmitting end does not always use all available sub-channels; unused sub-channels only contain background noise components such as thermal noise and environmental interference from the receiving end. Specifically, the receiving device confirms, based on the comparison result of step S206, that the signal-to-noise ratio of the current sub-channel has not exceeded the set threshold, indicating that the received power on this channel mainly comes from various noise sources rather than useful WiFi signals. The receiving device then marks the state of the sub-channel as no signal and records this information in the corresponding state management structure. Sub-channels with no signal will not participate in subsequent bandwidth estimation calculations, thereby avoiding noise interference with the bandwidth decision results and ensuring the accuracy of bandwidth estimation.

[0083] S209. For a channel with a total bandwidth of 80MHz, the receiving device determines whether the number of sub-channels with signals is not less than a preset fourth threshold value.

[0084] The 80MHz total bandwidth channel represents a broadband spectrum configuration consisting of four 20MHz sub-channels, a common high-speed transmission mode in the WiFi standard. The number of sub-channels with signals refers to the total number of 20MHz channels carrying valid signals confirmed through step S207, reflecting the scale of spectrum resources used for the current transmission.

[0085] This step is executed after steps S207 and S208. At this point, the receiving device has obtained the signal status information of each sub-channel and needs to infer the actual transmission bandwidth of the transmitting end based on the distribution of the number of signaled sub-channels. For a wideband channel configured as 80MHz, theoretically it contains four 20MHz sub-channels. If the transmitting end does indeed use 80MHz bandwidth for transmission, then all four sub-channels should be detected as signaled. However, considering the complexity of the actual channel environment and the fault tolerance requirements of the detection algorithm, a certain detection error is usually allowed. Specifically, the receiving device counts the number of all sub-channels marked as signaled and then compares the value with a preset fourth threshold. If the number of signaled sub-channels reaches or exceeds the preset fourth threshold, it indicates that most or all sub-channels are participating in signal transmission, and it can be inferred that the transmitting end is using 80MHz transmission bandwidth. If the number of signaled sub-channels is lower than the preset fourth threshold, it indicates that only some sub-channels are participating in transmission, and further analysis is needed to determine whether it is a 40MHz or 20MHz bandwidth mode.

[0086] In some embodiments, the receiving device may employ a step-by-step elimination decision method, adding a dedicated decision stage with a 40MHz bandwidth after steps S207 and S208 and before step S209. For a channel configuration with a total bandwidth of 40MHz, the receiving device first counts the number of all sub-channels with signals, and then determines whether the number is equal to 2. If it is equal to 2, the bandwidth of the WiFi signal is determined to be 40MHz and the bandwidth decision process ends. If it is not equal to 2, the bandwidth of the WiFi signal is determined to be 20MHz. This hierarchical decision method can complete the identification of the medium bandwidth mode before entering the 80MHz decision stage, thus improving decision efficiency.

[0087] S210, The receiving device determines that the bandwidth of the WiFi signal is 80MHz.

[0088] This step is executed under the condition that the judgment result of step S209 is yes (i.e., the number of signal sub-channels is not less than the preset fourth threshold value), indicating that the number of detected signal sub-channels is sufficient and conforms to the characteristic mode of 80MHz transmission. In 80MHz WiFi transmission, the transmitting end will transmit signals simultaneously on four 20MHz sub-channels, and the receiving end should be able to detect valid signals on most or all sub-channels. When the number of signal sub-channels reaches the preset threshold, it can be reasonably inferred that the transmitting end has indeed used an 80MHz transmission bandwidth. Specifically, the receiving device confirms that the current transmission mode is 80MHz bandwidth based on the statistical comparison result of step S209, and then sets the WiFi signal bandwidth parameter to 80MHz.

[0089] S211. The receiving device determines whether there is a signal in the sub-channel on the same side of the main channel.

[0090] This step is executed if the result of step S209 is negative (i.e., the number of signal sub-channels is less than the preset fourth threshold), indicating that the current transmission is not in 80MHz mode and further differentiation is needed between 40MHz and 20MHz transmission modes. 40MHz WiFi transmission uses channel bonding technology, combining the main channel with an adjacent sub-channel to form a continuous 40MHz spectrum. According to the WiFi protocol, this bonding must maintain spectrum continuity; that is, the two 20MHz channels involved in the bonding must be adjacent in frequency. Specifically, the receiving device first determines the position of the main channel within the entire 80MHz spectrum, and then checks the signal status of the adjacent sub-channel located on the same side of the main channel. If the adjacent sub-channel is also detected as having a signal, it indicates that the transmitting end has used a bonded transmission of the main channel and the adjacent channel, which conforms to the spectrum usage characteristics of 40MHz transmission. If the adjacent sub-channel has no signal, it indicates that only the main channel is involved in the transmission, and it should be determined as 20MHz mode.

[0091] S212. The receiving device determines that the bandwidth of the WiFi signal is 40MHz.

[0092] This step is executed under the condition that the judgment result of step S211 is yes (i.e., there is a signal on the sub-channel on the same side of the main channel), indicating that the detected sub-channel signal distribution conforms to the typical characteristics of 40MHz transmission. The 40MHz transmission mode uses channel bonding technology, combining the main channel with a sub-channel with an adjacent spectrum. This combination must meet the spectrum continuity requirement. When the receiving device detects signals on both the main channel and its adjacent channel on the same side, it can be reasonably inferred that the transmitting end has adopted the bonded transmission method of these two channels. Specifically, based on the detection result of step S211, the receiving device confirms that the current transmission mode conforms to the spectrum usage mode of 40MHz bandwidth, and then configures the WiFi signal bandwidth parameter to 40MHz.

[0093] S213. The receiving device determines that the bandwidth of the WiFi signal is 20MHz.

[0094] This step is executed if the result of step S211 is negative (i.e., there is no signal on the sub-channel on the same side of the main channel). This indicates that after the elimination process of all the aforementioned bandwidth decision steps, the current transmission mode has been determined to be the most basic 20MHz single-channel transmission. Through the preceding decision process, the receiving device has eliminated the possibility of 80MHz transmission (no result in step S209) and also eliminated the possibility of 40MHz transmission (no result in step S211). Therefore, it can be determined that the transmitting end only uses the main channel for signal transmission.

[0095] In some embodiments, after the receiving device determines the bandwidth of the WiFi signal through steps S210-S213, the receiving device can adopt a direct demodulation verification method. Based on the determined WiFi signal bandwidth, signaling merging and demodulation parameters are configured. These parameters include at least a merging weighting coefficient and a phase correction parameter. Then, the signaling fields in the sub-channels with signals are processed by maximum ratio merging according to the merging weighting coefficient. Next, demodulation and decoding operations are performed on the merged signaling fields to obtain signaling information including the actual transmission bandwidth. Finally, the estimated WiFi signal bandwidth is compared and verified with the decoded actual transmission bandwidth, and the threshold parameter is dynamically adjusted based on the comparison result. This method enables real-time verification of bandwidth estimation accuracy and continuous optimization of system performance. Optionally, the receiving device can also employ an adaptive parameter adjustment method. First, it configures the optimal signaling processing parameters based on the currently determined bandwidth, including dedicated merging weights and phase correction strategies for different bandwidth modes. Then, it performs merging and demodulation processing of the signaling fields to obtain high-quality decoding results. Next, it evaluates the algorithm performance by comparing the consistency between the estimated bandwidth and the actual bandwidth. Finally, it intelligently adjusts the threshold parameters at each level based on the verification results to improve the accuracy of subsequent bandwidth estimation. This method provides stronger environmental adaptability and better long-term performance stability.

[0096] It is understandable that other signaling processing and verification methods can also be used to apply and optimize bandwidth estimation results, such as parameter adjustment based on machine learning and multi-frame joint verification, etc., which are not limited here.

[0097] In the above embodiments, the receiving device estimates the signal bandwidth by using a long training sequence (L-LTF), thereby obtaining the bandwidth information of the WiFi signal before decrypting the signaling field, and thus improving the combining gain of the WiFi signal.

[0098] However, the original channel estimation results often suffer from missing DC components in the frequency domain, manifesting as zero or anomalous data at DC subcarrier positions. Furthermore, discontinuities may exist at the edges of the frequency domain data, introducing errors and distortions into subsequent time-domain transformations.

[0099] As attached Figure 3 As shown, the channel estimation results exhibit a typical amplitude distribution pattern in the frequency domain. (See attached image) Figure 3 The DC position marked in the figure corresponds to the zero frequency point, which is usually set to zero to avoid DC offset issues, creating a noticeable dip in the frequency domain response. The frequency axis shows the complete spectral range from DC to the highest frequency, while the amplitude axis reflects the intensity variation of the channel response at each frequency point. (See attached...) Figure 3 It can be seen that the frequency domain response maintains a relatively stable amplitude level in most frequency ranges, but there are obvious amplitude variations and discontinuities near DC and at the edge of the spectrum.

[0100] In some embodiments, to reduce errors and distortion in subsequent time-domain transformations, the receiving device may perform the following frequency-domain preprocessing and time-domain transformation methods during the process of determining the time-domain impulse response sequence corresponding to each 20MHz channel based on the channel estimation results of each 20MHz channel. Time-domain impulse response sequence

[0101] The following describes another implementation method for determining the time-domain impulse response sequence corresponding to each 20MHz channel based on the channel estimation results of each 20MHz channel in a WiFi signal bandwidth estimation method according to an embodiment of this application:

[0102] Please see Figure 4 This is another flowchart illustrating a WiFi signal bandwidth estimation method in an embodiment of this application.

[0103] S401. The receiving device performs linear interpolation on the DC subcarrier positions in the channel estimation results of each 20MHz channel to obtain the completed frequency domain channel response data.

[0104] In this context, the DC subcarrier position refers to the subcarrier position with a frequency of zero in the frequency domain. In WiFi systems, this is typically set to zero to avoid DC offset issues, corresponding to the zero frequency point in the frequency domain index. Linear interpolation is a mathematical method that uses adjacent known data points to calculate the value of missing data points through a linear function, maintaining data continuity and smoothness. The completed frequency domain channel response data represents the set of frequency domain data containing complete subcarrier information after interpolation. For example, in the IEEE 802.11 standard, the DC subcarrier (index 0) is usually set to zero and needs to be completed by linear interpolation using the values ​​of adjacent subcarriers -1 and +1. The "obtain" operation represents the processing result of obtaining complete frequency domain data through interpolation calculation.

[0105] Specifically, the receiving device first locates the position of the DC subcarrier in each 20MHz channel, which typically corresponds to the zero frequency point in the frequency domain index. Then, it identifies the nearest valid subcarriers on either side of the DC subcarrier; these subcarriers contain reliable channel estimation information. The receiving device extracts the complex channel estimates of these two reference subcarriers, including real and imaginary parts. Next, it performs linear interpolation calculations on the real and imaginary parts respectively, taking the arithmetic mean of the two reference values ​​as the estimate of the DC subcarrier. The interpolated complex value is then filled into the DC subcarrier position, thus forming complete frequency domain channel response data.

[0106] In some embodiments, linear interpolation of DC subcarriers can be implemented in several ways: Optionally, the receiving device can use a simple linear interpolation method, directly selecting two adjacent subcarriers (index -1 and +1) of the DC subcarrier, extracting the real and imaginary parts of their channel estimates respectively, then calculating the arithmetic mean of the real and imaginary parts, and filling the corresponding position with the obtained complex result as the estimated value of the DC subcarrier. This method is simple to calculate and applicable to most scenarios. Optionally, the receiving device can also use a preprocessing-enhanced interpolation method, performing anomaly detection and correction processing before performing DC interpolation. First, the amplitude, mean, and standard deviation of all subcarriers in each 20MHz channel are calculated. Then, subcarrier data points whose amplitude exceeds the mean plus or minus three times the standard deviation are marked as anomalies. Next, the average value of adjacent normal subcarrier data is used to replace the anomalies. Finally, linear interpolation of the DC subcarrier is performed based on the replaced data. This method can improve the reliability of the interpolation results and reduce the impact of abnormal data on time-domain transformation.

[0107] It is understandable that other methods can be used for linear interpolation, and no specific method is specified here.

[0108] S402. The receiving device applies a Hamming window function to the frequency domain channel response data for windowing processing.

[0109] The Hamming window function is a commonly used digital signal processing window function with good spectral leakage suppression characteristics. Its mathematical expression is w(n) = 0.54 - 0.46cos(2πn / N), where n is the sampling point index and N is the window function length. It is specifically used for processing peripheral signals. Figure 3 The problem of discontinuities at the edges of the mid-frequency domain. Windowing is a signal processing operation that multiplies a window function with the frequency domain data point by point to improve spectral characteristics and reduce transform errors. For example, for frequency domain data containing 64 subcarriers, a Hamming window will provide smaller weights (approximately 0.08) at the edges and larger weights (close to 1.0) at the center, thereby achieving a smooth spectral shaping effect and improving the discontinuities at the edges of the frequency domain. Figure 3 Discontinuities at the mid-spectral edges.

[0110] This step is specifically for attachments. Figure 3 The potential discontinuity at the edges of the mid-frequency domain data is addressed after the frequency domain data is completed but before the IFFT transform is performed. At this point, the frequency domain channel response is already complete, but further optimization is still needed to reduce spectral leakage during the time-domain transformation. (See attached...) Figure 3 As shown, the frequency domain response exhibits significant amplitude variations at the spectral edges. This edge effect can cause leakage in digital signal processing, affecting the accuracy of time-domain transformations, particularly by generating spurious oscillations and sidelobes at signal boundaries. Windowing is an effective method to suppress this leakage effect. By smoothing and attenuating the frequency domain data edges, the quality of the time-domain impulse response can be improved. Hamming windows are widely used in such scenarios due to their excellent sidelobe suppression characteristics. Specifically, the receiving device generates a corresponding Hamming window sequence based on the length of the current frequency domain data, with the window sequence length perfectly matching the frequency domain data length. The Hamming window function has an amplitude value close to 1.0 at the data center, gradually decaying towards both ends to a minimum value of approximately 0.08, forming a bell-shaped amplitude distribution used for smoothing the edge effects. Figure 3 The frequency domain edge discontinuities are shown in the diagram. The receiving device then performs a complex multiplication operation between each subcarrier value of the frequency domain channel response data and the corresponding window function value to achieve a windowing effect. The windowed frequency domain data retains the main signal characteristics while smoothing the edge portions, which reduces spectral leakage and sidelobe interference in the subsequent IFFT transform, improving the accuracy and reliability of the time-domain impulse response.

[0111] S403. The receiving device performs an inverse fast Fourier transform operation on the windowed frequency domain channel response data to obtain the time domain impulse response sequence corresponding to each 20MHz channel.

[0112] The Inverse Fast Fourier Transform (IFFT) operation is an efficient digital signal processing algorithm that converts frequency-domain signals into time-domain signals. It is the inverse process of the FFT algorithm and is used to realize the transformation from frequency domain signals to time-domain signals. Figure 3 The diagram shows the conversion from the frequency domain response to the high-quality time-domain impulse response. The time-domain impulse response sequence represents the characteristics of the channel in the time domain, reflecting the propagation delay and power distribution pattern of the signal in the channel. Compared with frequency domain analysis, time-domain analysis has better intuitiveness and accuracy in signal detection, especially in distinguishing between signal power and noise power.

[0113] Specifically, the receiving device independently performs an IFFT operation on the windowed frequency domain data for each 20MHz channel. The IFFT algorithm transforms N frequency domain sampling points into N time domain sampling points through complex number operations. During the transformation, the total signal energy remains unchanged, but the energy is redistributed in the time domain. The result of the time-domain transformation is a complex sequence, with each time-domain sampling point containing real and imaginary parts, corresponding to the channel impulse response value at that moment. In a typical wireless channel, the main energy of the signal is usually concentrated in the first few sampling points of the time-domain response, corresponding to the direct path and the main multipath components, while subsequent sampling points mainly contain noise components and weak multipath echoes. This time-domain distribution characteristic provides a basis for subsequent signal power and noise power separation, allowing the receiving device to obtain WiFi signal bandwidth information before decoding the signaling field. The output sequence of the IFFT transform will serve as input data for power calculation and signal-to-noise ratio analysis.

[0114] In some embodiments, the inverse fast Fourier transform (IFFT) operation can be implemented in several ways: Optionally, the receiving device can employ a hardware accelerator-based IFFT implementation method, utilizing the IFFT hardware core in a dedicated digital signal processor (DSP) or field-programmable gate array (FPGA) for computation. First, the windowed frequency domain data is loaded into a hardware buffer, then the IFFT computation engine is started, and finally, the time domain result is read from the output buffer. This method offers high computation speed and low power consumption. Alternatively, the receiving device can also employ a software library function-based IFFT implementation method, calling IFFT functions from optimized mathematical libraries (such as FFTW, Intel MKL, etc.) for computation, including data format conversion, function parameter configuration, IFFT computation execution, and result data extraction. This method offers high flexibility and ease of debugging.

[0115] It is understandable that other transformation methods can be used to achieve the conversion from the frequency domain to the time domain, and no limitation is made here.

[0116] S404. The receiving device calculates the power values ​​of the sampling points in the time-domain impulse response sequence to form a time-domain power distribution sequence.

[0117] Here, a sampling point refers to a single data element in the time-domain sequence, containing both real and imaginary parts, corresponding to the channel impulse response value at a specific moment. These sampling points are the output points of the inverse Fast Fourier Transform. The power value represents the energy magnitude of each sampling point, obtained by calculating the square of the modulus of the complex value, reflecting the signal power level at that moment. The time-domain power distribution sequence is a real number sequence composed of the power values ​​of all sampling points, describing the distribution pattern of channel energy in the time domain, and providing a power analysis basis for estimating signal bandwidth using long training sequences.

[0118] Specifically, the receiving device iterates through each sampling point in the time-domain impulse response sequence, performing a power calculation operation for each complex sampling point. The power calculation is achieved by adding the square of the real part to the square of the imaginary part of the complex value, i.e., Power = Real. 2 +Imag 2 This is also equivalent to the square of the complex modulus. The calculated power value is a non-negative real number, directly reflecting the signal energy magnitude at that point in time. The power values ​​of all sampling points are arranged in chronological order, forming a time-domain power distribution sequence. This power sequence clearly shows the distribution characteristics of channel energy in the time dimension. Signal components typically form obvious peaks at the beginning of the sequence, while the subsequent part is mainly contributed by noise. The time-domain power distribution sequence will serve as the basic data for subsequent signal power detection and noise power estimation. Its distribution characteristics directly affect the accuracy of signal-to-noise ratio calculation and the reliability of bandwidth determination, ultimately supporting the improvement of WiFi signal combining gain.

[0119] In some embodiments, the receiving device may also employ a sliding window power detection method to further utilize the time-domain power distribution sequence for more accurate signal-to-noise ratio (SNR) calculation. First, the window length and sliding search range of the signal power detection window are determined based on the length of the guard interval in the WiFi protocol. Then, within the time-domain impulse response sequence, the signal power detection window is moved point by point within the sliding search range, and the sum of the power values ​​of all sampling points within each window length is calculated. Next, the maximum value is selected from the sums of power within all window lengths as the signal power of each 20MHz channel. Simultaneously, a continuous sampling point region is selected outside the sliding search range, and the average power value of the sampling points within the sampling point region is used as the noise power. Finally, the ratio of signal power to noise power is used as the SNR of each 20MHz channel. This method can achieve more accurate signal-to-noise separation and improve the accuracy of SNR calculation.

[0120] The WiFi signal bandwidth estimation method in the embodiments of this application has been described above. The exemplary receiving device 500 provided in the embodiments of this application is described below.

[0121] Figure 5This is a schematic diagram of an exemplary hardware structure of a receiving device 500 provided in an embodiment of this application. In some embodiments, the receiving device 500 is a computer device. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements a WiFi signal bandwidth estimation method according to an embodiment of this application.

[0122] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0123] In some embodiments of this application, a computer-readable storage medium is also provided, including instructions that, when executed on the receiving device 500, cause the receiving device 500 to perform a WiFi signal bandwidth estimation method according to an embodiment of this application.

[0124] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0125] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0126] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0127] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for estimating WiFi signal bandwidth, characterized in that, Applied to a receiving device, the method includes: The receiving device performs cross-correlation calculations between the long training sequence in the received WiFi signal preamble and the locally known long training sequence to obtain the channel estimation results for each 20MHz channel in the WiFi signal. The receiving device determines the signal-to-noise ratio of each 20MHz channel based on the channel estimation results of each 20MHz channel; The receiving device determines the signal-to-noise ratio (SNR) of each 20MHz channel based on the channel estimation results of each 20MHz channel. Specifically, this includes: the receiving device performing linear interpolation on the DC subcarrier positions in the channel estimation results of each 20MHz channel to obtain completed frequency domain channel response data; the receiving device applying a Hamming window function to the frequency domain channel response data for windowing; the receiving device performing an inverse fast Fourier transform (IFFT) operation on the windowed frequency domain channel response data to obtain the time domain impulse response sequence corresponding to each 20MHz channel; the receiving device calculating the power values ​​of the sampling points in the time domain impulse response sequence to form a time domain power distribution sequence, where the sampling points are the output points of the IFFT; and the receiving device calculating the SNR of each 20MHz channel based on the time domain impulse response sequence. The receiving device determines whether the signal-to-noise ratio of the main channel is less than a preset first threshold value; If so, the receiving device determines the seventh threshold value as a preset second threshold value, wherein the preset second threshold value is less than the preset first threshold value; If not, the receiving device determines the seventh threshold value as the maximum value between the preset third threshold value and the sixth threshold value, wherein the sixth threshold value is the product of the signal-to-noise ratio of the main channel and the preset fifth threshold value, and the preset third threshold value is greater than the preset first threshold value; The receiving device determines whether the signal-to-noise ratio of the sub-channel is greater than the seventh threshold value; If so, the receiving device determines that there is a signal in the sub-channel; If not, the receiving device determines that there is no signal in the sub-channel; For a channel with a total bandwidth of 80MHz, the receiving device determines whether the number of sub-channels with signals is not less than a preset fourth threshold value; If so, the receiving device determines that the bandwidth of the WiFi signal is 80MHz; If not, the receiving device determines whether there is a signal in the sub-channel on the same side of the main channel; If there is a signal on the sub-channel on the same side, the receiving device determines that the bandwidth of the WiFi signal is 40MHz; If there is no signal on the sub-channel on the same side, the receiving device determines that the bandwidth of the WiFi signal is 20MHz.

2. The method according to claim 1, characterized in that, Before the step of the receiving device performing linear interpolation processing on the DC subcarrier positions in the channel estimation results of each 20MHz channel, the method further includes: The receiving device calculates the amplitude, mean, and standard deviation of all subcarriers in each 20MHz channel; The receiving device marks subcarrier data points whose amplitude values ​​exceed the mean plus or minus three standard deviations as outliers. The receiving device replaces the abnormal points with the average value of adjacent normal subcarrier data.

3. The method according to claim 2, characterized in that, The receiving device calculates the signal-to-noise ratio of each 20MHz channel based on the time-domain impulse response sequence, specifically including: The receiving device determines the window length and sliding search range of the signal power detection window based on the length of the guard interval in the WiFi protocol; The receiving device moves the signal power detection window point by point within the sliding search range in the time-domain impulse response sequence, and calculates the sum of the power values ​​of all sampling points within each window length; The receiving device selects the maximum value from the sum of power within all window lengths as the signal power of each 20MHz channel; The receiving device selects a continuous sampling point region outside the sliding search range, and uses the average power value of the sampling points within the sampling point region as the noise power; The receiving device uses the ratio of the signal power to the noise power as the signal-to-noise ratio of each 20MHz channel.

4. The method according to claim 1, characterized in that, After the receiving device determines that there is no signal in the sub-channel, the method further includes: For a channel with a total bandwidth of 40MHz, the receiving device determines whether the number of sub-channels with signals is 2; If so, the receiving device determines that the bandwidth of the WiFi signal is 40MHz; If not, the receiving device determines that the bandwidth of the WiFi signal is 20MHz.

5. The method according to claim 4, characterized in that, After the receiving device determines the bandwidth of the WiFi signal, the method further includes: The receiving device configures signaling merging and demodulation parameters according to the bandwidth of the WiFi signal, and the signaling merging and demodulation parameters include at least merging weighting coefficients and phase correction parameters; The receiving device performs maximum ratio combining on the signaling fields in the sub-channel with signal according to the combining weight coefficient; The receiving device performs demodulation and decoding operations on the merged signaling field to obtain signaling information including the actual transmission bandwidth. The receiving device compares and verifies the estimated bandwidth of the WiFi signal with the decoded actual transmission bandwidth, and dynamically adjusts the threshold parameter based on the comparison result.

6. A receiving device, characterized in that, The receiving device includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the receiving device to perform the method as described in any one of claims 1-5.

7. A computer program product containing instructions, characterized in that, When the computer program product is run on the receiving device, the receiving device performs the method as described in any one of claims 1-5.

8. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the receiving device, the receiving device performs the method as described in any one of claims 1-5.

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