A method and apparatus for scene adaptive multi-path time delay alignment in a wireless communication system

Abstract

The application discloses a wireless communication system scene adaptive multipath time delay alignment method and device, the method uses SISO channel detection system to measure channel data, adopts a rectangular or RRC shaping filter to generate a CAZAC oversampling sequence as a template function, calculates the cross-correlation matrix of the IQ signal and the template function, calculates the noise threshold based on the median power and false alarm rate, executes dynamic threshold noise filtering, generates the cross-correlation matrix after noise reduction, and completes the preliminary estimation of the time domain impulse response; the SAGE algorithm is used to extract the multipath time delay and complex gain from the cross-correlation matrix; the channel impulse response is drawn to draw a power delay profile (PDP); and the PDP is aligned in combination with the geographic position information of the transceiver during actual measurement. The application solves the defects of traditional alignment in a bad scene, improves the statistical modeling accuracy, and can effectively align the multipath time delay through experiment verification, thereby providing reliable support for channel modeling.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and specifically to a method and apparatus for adaptive multipath delay alignment in wireless communication systems. Background Technology

[0002] In wireless communication systems, the wireless channel, as the only uncontrollable factor, has a significant impact on communication quality. To accurately establish channel models for various scenarios, actual detection experiments are usually required, and the channel model is constructed based on the channel detection data obtained from actual channel detection. In channel detection systems, statistical modeling requires aligning the power delay spectrum (PDP) at different times to ensure that signals at different times are statistically modeled under the same standard.

[0003] Currently, the two commonly used delay alignment methods in channel modeling are strongest path alignment and first path alignment. However, both alignment methods have certain problems in harsh non-line-of-sight (NLOS) scenarios. When strongest path alignment is used in an NLOS scenario, there is no direct path in the receiving path. In this case, the strongest path used for alignment is a reflection path, and the strongest reflection path is not fixed. For continuously measured data, the delay corresponding to the strongest reflection path is not continuous, resulting in unreasonable alignment effects.

[0004] When using first-path alignment, if the signal-to-noise ratio is poor, a small amount of noise will be present in the extracted path. If the delay of the noise path is slightly less than the delay of the signal path at a certain moment, the noise path will affect the alignment effect when first-path alignment is used. The small amount of noise present before the signal path will cause the signal path to shift backward, resulting in poor alignment. Summary of the Invention

[0005] To address this, the present invention provides a multipath delay alignment method and apparatus for adaptive wireless communication system scenarios, which solves the PDP alignment problem obtained by using the SISO detection system in complex urban channel scenarios, overcomes the shortcomings of the strongest path and first path alignment in harsh NLOS scenarios, and improves the accuracy of subsequent statistical modeling.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multipath delay alignment method for adaptive wireless communication system scenarios, comprising the following steps:

[0007] Using measured channel data from the SISO channel sounding system, a CAZAC oversampled sequence was generated using rectangular or RRC shaping filters as a template function. The cross-correlation matrix between the IQ signal and the template function was calculated. The noise threshold was calculated based on the median power and false alarm rate. Dynamic threshold denoising was performed to generate the denoised cross-correlation matrix and complete the preliminary estimation of the time-domain impulse response.

[0008] The SAGE algorithm is used to extract multipath delay and complex gain from the cross-correlation matrix.

[0009] A power delay spectrum (PDP) is plotted for the channel impulse response. In the power delay spectrum (PDP), the x-axis represents the delay, the y-axis represents the number of snapshots / packets, and the z-axis represents the power value.

[0010] Alignment is performed on the PDP based on the actual geographical location information of the transceiver during measurement.

[0011] As a preferred method for adaptive multipath delay alignment in wireless communication systems, the extraction of multipath delay and complex gain from the cross-correlation matrix using the SAGE algorithm specifically includes:

[0012] Initialize multipath parameters, use a dual threshold strategy to determine the criteria for valid path discrimination, calculate the dynamic noise threshold by combining the peak amplitude and the median noise power, filter valid data points, and construct an initial parameter set that includes the amplitude and phase characteristics of each path and the time delay position parameters;

[0013] For each path, the expectation-maximization iterative optimization of the EM algorithm is performed. In the expectation calculation stage, based on the template function obtained from the autocorrelation of the probe sequence, the synthetic signals of other paths are reconstructed using the current parameter space, the components of other paths are subtracted from the observed signals, and the residual of the current path is extracted. In the maximization stage, maximum likelihood estimation is performed in the residual domain, the maximum value of the correlation peak is searched to determine the optimal time delay, the complex gain is calculated based on the normalized template function, the current path parameters are updated, and the signal is reconstructed synchronously.

[0014] The joint optimal solution is gradually approximated through multiple rounds of global iteration. After the iteration is completed, paths with the same time delay in the parameter space are merged and the extracted results are stored in the form of a multipath delay-complex gain parameter matrix.

[0015] As a preferred method for adaptive multipath delay alignment in wireless communication systems, during the process of plotting the power delay spectrum (PDP) of the channel impulse response, the complex amplitude of the channel impulse response matrix is ​​converted into power, as shown in the formula:

[0016]

[0017] In the formula, P(t) n ,τ) represents the time t n The power of the channel impulse response at time t, h(t) n ,τ i ) indicates that at t n Time and delay are τ i The channel impulse response.

[0018] As a preferred method for adaptive multipath delay alignment in wireless communication systems, the step of aligning the PDP by incorporating the transceiver's geographical location information during actual measurement specifically includes:

[0019] Extract the GPS location information of the transceiver;

[0020] The motion between adjacent GPS points is treated as uniform linear motion, and interpolation is performed using a linear averaging method. The number of interpolations is determined based on the quotient of the time interval between adjacent GPS points and the time interval between adjacent snapshots, and the GPS positions of all snapshots are obtained.

[0021] Calculate the transceiver distance for all snapshot times;

[0022] The latency of each snapshot is calculated by dividing the distance of each snapshot by the speed of light;

[0023] Based on the calculated latency, each snapshot is moved to the same time to complete the alignment.

[0024] As a preferred method for adaptive multipath delay alignment in wireless communication systems, the Haversine formula is used in calculating the transceiver distance d corresponding to all snapshot times:

[0025]

[0026] d = R × b

[0027] In the formula, a and b are intermediate variables in the calculation formula, and lat R lon R These are the latitude and longitude of the receiver, respectively. T lon T These are the latitude and longitude of the transmitter, respectively, and R is the Earth's radius;

[0028] The formula for calculating the time delay of each snapshot by dividing the distance of each snapshot by the speed of light is:

[0029]

[0030] In the formula, Δτ n Indicates t n The time delay of a snapshot at time d relative to the first snapshot n Indicates t n The distance between the transceivers at time d1 represents the distance between the transceivers at the time of transmitting the first snapshot, and c is the speed of light.

[0031] As a preferred method for adaptive multipath delay alignment in wireless communication systems, each snapshot is moved to the same time based on the calculated delay, and the alignment formula is as follows:

[0032]

[0033] In the formula, P(t) n ,τ′) represents at t n The power of the channel impulse response after time alignment, h(t) n ,τ i -Δτ n ) indicates that at t n At any given time, for all time delays τ i Correction Δτ n The channel impulse response afterward.

[0034] The present invention also provides a multipath delay alignment device for adaptive wireless communication system scenarios, comprising:

[0035] The data preprocessing module is used to generate CAZAC oversampled sequences as template functions using measured channel data from the SISO channel sounding system. It employs rectangular or RRC shaping filters to calculate the cross-correlation matrix between the IQ signal and the template function, calculates the noise threshold based on the median power and false alarm rate, performs dynamic threshold denoising, generates the denoised cross-correlation matrix, and completes the preliminary estimation of the time-domain impulse response.

[0036] The multipath parameter extraction module is used to extract multipath delay and complex gain from the cross-correlation matrix using the SAGE algorithm.

[0037] The PDP plotting module is used to plot the power delay spectrum (PDP) of the channel impulse response. In the power delay spectrum (PDP), the x-axis represents the delay, the y-axis represents the number of snapshots / packets, and the z-axis represents the power value.

[0038] The PDP alignment module is used to align the PDP by combining the transceiver's geographical location information during actual measurement.

[0039] As a preferred embodiment of a multipath delay alignment device for adaptive wireless communication system scenarios, the multipath parameter extraction module includes:

[0040] The multipath parameter initialization submodule is used to initialize multipath parameters. It adopts a dual threshold strategy to determine the effective path discrimination criteria, calculates the dynamic noise threshold by combining the peak amplitude and the median noise power, filters effective data points, and constructs an initial parameter set containing the amplitude and phase characteristics of each path and the time delay position parameters.

[0041] The path parameter iterative optimization submodule is used to perform expectation-maximization iterative optimization of the EM algorithm on each path. In the expectation calculation stage, based on the template function obtained by the autocorrelation of the probe sequence, the synthetic signals of other paths are reconstructed using the current parameter space, the components of other paths are subtracted from the observed signals, and the residual of the current path is extracted. In the maximization stage, maximum likelihood estimation is performed in the residual domain, the maximum value of the correlation peak is searched to determine the optimal time delay, the complex gain is calculated based on the normalized template function, the current path parameters are updated, and the signal is reconstructed synchronously.

[0042] The parameter merging and storage submodule is used to gradually approximate the joint optimal solution through multiple rounds of global iteration. After the iteration is completed, the paths with the same time delay in the parameter space are merged and the extracted results are stored in the form of a multipath delay-complex gain parameter matrix.

[0043] As a preferred solution for an adaptive multipath delay alignment device in wireless communication systems, the PDP plotting module converts the complex amplitude of the channel impulse response matrix into power using the following formula:

[0044]

[0045] In the formula, P(t) n ,τ) represents the time t n The power of the channel impulse response at time t, h(t) n ,τ i ) indicates that at t n Time and delay are τ i The channel impulse response.

[0046] As a preferred embodiment of a multipath delay alignment device for adaptive wireless communication system scenarios, the PDP alignment module includes:

[0047] The GPS information extraction submodule is used to extract the GPS location information of the transceiver;

[0048] The GPS interpolation submodule is used to treat the motion between adjacent GPS points as uniform linear motion, perform interpolation using a linear averaging method, determine the number of interpolations based on the quotient of the time interval between adjacent GPS points and the time interval between adjacent snapshots, and obtain the GPS positions of all snapshots.

[0049] The distance calculation submodule is used to calculate the transceiver distance at all snapshot times;

[0050] The latency calculation submodule is used to calculate the latency of each snapshot by dividing the distance of each snapshot by the speed of light;

[0051] The alignment execution submodule is used to move each snapshot to the same time based on the calculated latency, thus completing the alignment.

[0052] As a preferred solution for an adaptive multipath delay alignment device in wireless communication systems, the distance calculation submodule employs the Haversine formula, specifically:

[0053]

[0054] d = R × b

[0055] In the formula, a and b are intermediate variables in the calculation formula, and lat R lon R These are the latitude and longitude of the receiver, respectively. T lon T Here, R represents the latitude and longitude of the transmitter, respectively, and R is the Earth's radius.

[0056] As a preferred embodiment of the adaptive multipath delay alignment device for wireless communication systems, the delay calculation submodule uses the following formula to calculate the delay of each snapshot by dividing the distance of each snapshot by the speed of light:

[0057]

[0058] In the formula, Δτ n Indicates t n The time delay of a snapshot at time d relative to the first snapshot n Indicates t n The distance between the transceivers at time d1 represents the distance between the transceivers at the time of transmitting the first snapshot, and c is the speed of light.

[0059] As a preferred solution for an adaptive multipath delay alignment device in wireless communication systems, the alignment execution submodule moves each snapshot to the same time based on the calculated delay, and the alignment formula is as follows:

[0060]

[0061] In the formula, P(t) n ,τ′) represents at t n The power of the channel impulse response after time alignment, h(t) n ,τ i -Δτ n ) indicates that at t n At any given time, for all time delays τ i Correction Δτ n The channel impulse response afterward.

[0062] The present invention has the following advantages:

[0063] First, using measured data from the SISO channel sounding system, a CAZAC oversampled sequence is generated using rectangular or RRC shaping filtering as a template function. The noise threshold is calculated by combining median power and false alarm rate for dynamic threshold denoising, which effectively improves the signal-to-noise ratio of the initial estimation of the time-domain impulse response and provides high-quality input for subsequent parameter extraction.

[0064] Second, by using the SAGE algorithm combined with a dual-threshold strategy and EM iterative optimization, multipath delay and complex gain can be extracted with high accuracy in dense scattering environments, solving the multipath overlap interference problem, providing structured CIR data, and laying the foundation for statistical modeling.

[0065] Third, by combining the GPS location information of the transceiver, all snapshot locations are obtained through linear interpolation. The distance is calculated using the Haversine formula, and then divided by the speed of light to convert it into time delay for PDP alignment. This alignment is based directly on the actual geographical location and is less affected by signal quality.

[0066] Fourth, compared with the traditional alignment of the strongest path and the first path, it solves the problems of the strongest path not being fixed and the first path being easily affected by noise in NLOS scenarios. It aligns the PDPs at different distances to the same dimension, accurately reflects the time delay relationship between the direct path and the reflection path, and provides a more accurate relative time delay power relationship between different snapshots for statistical modeling.

[0067] Fifth, experiments show that alignment can eliminate the influence of different positions on the time delay. Aligning the LOS direct path to time 0, the NLOS time delay reflects the relative direct path time delay, providing a reliable basis for subsequent small-scale fading and Doppler dynamic characteristic modeling. Attached Figure Description

[0068] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0069] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0070] Figure 1 This is a schematic diagram of the adaptive multipath delay alignment method for wireless communication system scenarios provided in this embodiment of the invention;

[0071] Figure 2 This is a schematic diagram of the technical route of the adaptive multipath delay alignment method for wireless communication system scenarios provided in this embodiment of the invention;

[0072] Figure 3 This is a schematic diagram of the data acquisition process provided in this embodiment of the invention;

[0073] Figure 4 The multipath delay map is obtained by first performing relevant operations on the actual data collected by the channel sounding experiment in the urban scenario provided in this embodiment of the invention, and then extracting it through the SAGE algorithm.

[0074] Figure 5 Provided in the embodiments of the present invention Figure 3 The effect after GPS alignment;

[0075] Figure 6 This is a schematic diagram of the architecture of the adaptive multipath delay alignment device for wireless communication system scenarios provided in this embodiment of the invention. Detailed Implementation

[0076] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0077] Example 1

[0078] See Figure 1 and Figure 2 Embodiment 1 of the present invention provides an adaptive multipath delay alignment method for wireless communication system scenarios, comprising the following steps:

[0079] S1. Using the measured channel data from the SISO channel sounding system, a CAZAC oversampled sequence is generated using rectangular or RRC shaping filtering as a template function. The cross-correlation matrix between the IQ signal and the template function is calculated. The noise threshold is calculated based on the median power and false alarm rate. Dynamic threshold denoising is performed to generate the denoised cross-correlation matrix and complete the preliminary estimation of the time-domain impulse response.

[0080] Among them, the SISO (Single-Input Single-Output) channel sounding system can acquire the time-domain characteristics of the channel. Rectangular or RRC (Root Raised Cosine) shaping filters can optimize the signal spectral characteristics and reduce inter-symbol interference. CAZAC (Constant Envelope Zero Autocorrelation) sequences have good autocorrelation and cross-correlation characteristics, and oversampling can improve time resolution. Cross-correlation matrix calculation can highlight the similarity between the signal and the template, reflecting the channel impulse response. Median power can resist the influence of outliers, false alarm rate is used to set a reasonable noise judgment threshold, and dynamic threshold noise filtering can adaptively remove interference according to the noise level, retain the true signal components, and thus achieve a preliminary clean estimate of the time-domain impulse response.

[0081] S2. Use the SAGE algorithm to extract the multipath delay and complex gain from the cross-correlation matrix. The cross-correlation matrix contains the delay and amplitude / phase information of the multipath signal. The SAGE (Spatial Alternating Generalized Expectation-Maximization) algorithm is an efficient parameter estimation method suitable for parameter extraction in multipath channel environments.

[0082] Specifically, in step S2, the extraction of multipath delay and complex gain from the cross-correlation matrix using the SAGE algorithm includes:

[0083] S21. Initialize multipath parameters, use a dual-threshold strategy to determine the criteria for valid paths, calculate the dynamic noise threshold by combining the peak amplitude and the median noise power, filter valid data points, and construct an initial parameter set that includes the amplitude and phase characteristics of each path and the time delay position parameters.

[0084] The dual-threshold strategy uses two different thresholds to more accurately identify effective paths, avoiding the limitations of a single threshold. The peak amplitude reflects signal strength, while the median noise power serves as a reference for noise levels. Combining these two metrics to calculate a dynamic noise threshold allows for adaptive adjustment based on actual noise conditions, effectively distinguishing between signal paths and noise points. After selecting effective data points, an initial set containing key parameters such as amplitude, phase, and delay for each path can be constructed, providing a foundation for subsequent iterative optimization.

[0085] S22. Perform expectation-maximization iterative optimization of the EM algorithm on each path. In the expectation calculation stage, based on the template function obtained by the autocorrelation of the probe sequence, reconstruct the synthetic signal of other paths using the current parameter space, subtract the components of other paths from the observed signal, and extract the residual of the current path. In the maximization stage, perform maximum likelihood estimation in the residual domain, search for the maximum value of the correlation peak to determine the optimal time delay, calculate the complex gain based on the normalized template function, update the current path parameters and reconstruct the signal synchronously.

[0086] The EM (Expectation-Maximization) algorithm is an iterative optimization method. In the expectation phase, signals from other paths are reconstructed using the currently estimated parameters and subtracted from the observed signals to obtain the residual of the current path. This reduces interference from other paths, allowing focus on parameter estimation for the current path. In the maximization phase, maximum likelihood estimation is used in the residual domain to find the most probable parameters. The maximum value of the correlation peak corresponds to the optimal time delay. A normalized template function is used to accurately calculate the complex gain. After updating the parameters, the signal is reconstructed, gradually approximating the true value.

[0087] S23. The joint optimal solution is gradually approximated through multiple rounds of global iteration. After the iteration is completed, the paths with the same time delay in the parameter space are merged and the extracted results are stored in the form of a multipath delay-complex gain parameter matrix.

[0088] The multi-round global iteration allows for continuous optimization of path parameters, gradually approaching the joint optimal solution of all paths and improving the accuracy of parameter estimation. Paths with the same time delay may be different representations of the same physical path; merging them reduces redundancy and makes the parameter representation more concise and accurate. Storing the data in matrix form facilitates subsequent processing and analysis, providing structured data for subsequent PDP drawing and alignment operations.

[0089] S3. Plot the power delay spectrum (PDP) of the channel impulse response. In the power delay spectrum (PDP), the x-axis represents the delay, the y-axis represents the number of snapshots / packets, and the z-axis represents the power value.

[0090] The channel impulse response (PPD) reflects the propagation of the signal in the channel. After converting its complex amplitude into power, the power distribution under different time delays can be visually displayed. The PDP is presented in three dimensions: the x-axis represents the time delay, the y-axis represents the number of snapshots / packets, which represents different measurement times or data groups, and the z-axis represents the signal energy intensity at the corresponding time delay. This facilitates the observation of the time-varying characteristics of the channel and the multipath power distribution.

[0091] Specifically, in step S3, during the process of plotting the power delay spectrum (PDP) of the channel impulse response, the complex amplitude of the channel impulse response matrix is ​​converted into power, as shown in the formula:

[0092]

[0093] In the formula, P(t) n ,τ) represents the time t n Power of the channel impulse response at time t, h(t) n ,τ i ) indicates that at t n Time and delay are τ i The channel impulse response. The square of the modulus of the complex number is the power for each time t. n and delay τ iThe total power at that moment and time delay can be obtained by taking the square of the modulus of the channel impulse response and summing the results. This is a common method for converting complex signals into power values, which conforms to the physical definition of power and accurately reflects the distribution of signal energy in the time delay domain.

[0094] S4. Align the PDP by incorporating the transceiver's geographical location information during actual measurement. Changes in the transceiver's geographical location will cause changes in signal propagation distance, resulting in changes in latency. Directly using the original PDP for statistical modeling will introduce errors due to distance differences. Aligning the PDP with geographical location information can eliminate the latency effects caused by distance changes, allowing PDPs from different times to be compared and statistically analyzed under the same standard, thus improving modeling accuracy.

[0095] Specifically, in step S4, the alignment operation of the PDP based on the transceiver's geographical location information during actual measurement includes:

[0096] S41. Extract the GPS location information of the transceiver. GPS provides accurate latitude and longitude coordinates to determine the specific location of the transceiver at different times, which is the basis for subsequent calculations of distance and time delay.

[0097] S42. Treat the motion between adjacent GPS points as uniform linear motion, use linear averaging for interpolation, determine the number of interpolations based on the quotient of the time interval between adjacent GPS points and the time interval between adjacent snapshots, and obtain the GPS positions of all snapshots.

[0098] Since GPS sampling intervals are typically longer than snapshot intervals, linear interpolation is a simple and effective method to obtain the position at each snapshot time, assuming that the movement between adjacent GPS points is uniform linear motion. By calculating the quotient of the time intervals to determine the number of interpolation points, the time intervals of the interpolation points can be matched with the snapshot time intervals, thereby obtaining the GPS position corresponding to each snapshot and ensuring accurate distance calculation.

[0099] S43. Calculate the transceiver distances corresponding to all snapshot times. Knowing the location of the transceiver at each snapshot time, calculating the distance between them is key to determining the signal propagation path length, which can then be converted into time delay.

[0100] Specifically, in step S43, during the calculation of the transceiver distance d corresponding to all snapshot times, the Haversine formula is used, as follows:

[0101]

[0102] d = R × b

[0103] In the formula, a and b are intermediate variables in the calculation formula, and lat R lonR These are the latitude and longitude of the receiver, respectively. T lon T Here, latitude and longitude are the transmitter's coordinates, and R is the Earth's radius. The Haversine formula is a classic formula for calculating the great circle distance between two points on the Earth's surface, taking into account the Earth's curvature. It calculates the angular difference between the two points using latitude and longitude, then converts it to distance. The intermediate variable 'a' calculates the semi-versus of the radians between the two points, and 'b' is the radian value. Multiplying these by the Earth's radius R yields the actual distance. This formula can accurately calculate the distance between any two points on the Earth's surface and is suitable for situations where the transceiver is located on the Earth's surface.

[0104] S44. The formula for calculating the time delay of each snapshot by dividing the distance of each snapshot by the speed of light is:

[0105]

[0106] In the formula, Δτ n Indicates t n The time delay of a snapshot at a given moment relative to the first snapshot, d n Indicates t n The distance between the transceivers at time d1 represents the distance between the transceivers at the moment of transmitting the first snapshot, and c is the speed of light. The speed of light in a vacuum is c, and the distance difference d... n -d1 divided by the speed of light gives the time difference, which is the delay of this snapshot relative to the first snapshot. By using the first snapshot as a reference point, the relative delays of other snapshots can be calculated, unifying the delays at different times under the same reference frame, thus preparing for subsequent alignment.

[0107] S45. Based on the calculated delay, move each snapshot to the same time, and complete the alignment using the following formula:

[0108]

[0109] In the formula, P(t) n ,τ′) represents at t n The power of the channel impulse response after time alignment, h(t) n ,τ i -Δτ n ) indicates that at t n At any given time, for all time delays τ i Correction Δτ n The channel impulse response after the snapshot. The delay τ of each snapshot. i Subtract the corresponding relative time delay Δτ nThis is equivalent to adjusting the time base of the snapshot to be consistent with the first snapshot, thereby aligning the PDPs of different snapshots on the time axis. After this processing, the PDPs of different snapshots are in the same time dimension, eliminating the time delay differences caused by distance variations, which facilitates subsequent statistical modeling and analysis, and can more accurately reflect the true characteristics of the channel.

[0110] See Figure 3 This diagram illustrates the route taken during a channel sounding experiment in an urban setting to collect actual data. "TX" represents the transmitter location, and the red dots represent the receiver (or test point) locations. The coverage area reflects the distribution of the test route / sampling points, providing a clear view of the geographical range of signal propagation. Latitude and longitude coordinates and a scale (200m scale) are used to quantify the transmission and reception distances. Combined with PDP delay data, the distance-delay theoretical relationship (delay ≈ distance / speed of light) can be verified, and the impact of geographical environment (such as buildings and roads) on multipath propagation can be analyzed.

[0111] See Figure 4 In urban scenarios, the actual data collected through channel sounding experiments are first processed and then the multipath delay map is extracted by the SAGE algorithm. The horizontal axis represents the delay and the vertical axis represents the data packet number collected at different times. A snapshot is taken every 0.2048ms, and 10,000 snapshots make up a packet. A packet is collected every 2.048s.

[0112] See Figure 5 This is the effect after GPS alignment. It can be observed that alignment effectively eliminates the impact of different distances on time delay. By aligning the LOS direct path to time 0, the NLOS time delay reflects the absence of a direct path and the time delay of the reflected path relative to the direct path. Therefore, statistical modeling can better reflect the correct time delay relationship at each location.

[0113] It should be noted that the method of this embodiment can be executed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method of this embodiment, and the multiple devices will interact with each other to complete the adaptive multipath delay alignment method for wireless communication system scenarios.

[0114] It should be noted that the above description describes some embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0115] Example 2

[0116] See Figure 6 Embodiment 2 of the present invention also provides a multipath delay alignment device for adaptive wireless communication system scenarios, comprising:

[0117] The data preprocessing module 100 is used to generate a CAZAC oversampled sequence as a template function by using the measured channel data of the SISO channel sounding system, using rectangular or RRC shaping filtering, calculating the cross-correlation matrix between the IQ signal and the template function, calculating the noise threshold based on the median power and false alarm rate, performing dynamic threshold denoising, generating the denoised cross-correlation matrix, and completing the preliminary estimation of the time-domain impulse response.

[0118] The multipath parameter extraction module 200 is used to extract multipath delay and complex gain from the cross-correlation matrix using the SAGE algorithm;

[0119] PDP plotting module 300 is used to plot the power delay spectrum (PDP) of the channel impulse response. In the power delay spectrum (PDP), the x-axis represents the delay, the y-axis represents the number of snapshots / packets, and the z-axis represents the power value.

[0120] The PDP alignment module 400 is used to align the PDP by combining the transceiver's geographical location information during actual measurement.

[0121] In this embodiment, the multipath parameter extraction module 200 includes:

[0122] The multipath parameter initialization submodule 201 is used to initialize multipath parameters. It adopts a dual threshold strategy to determine the effective path discrimination criteria, calculates the dynamic noise threshold by combining the peak amplitude and the median noise power, filters effective data points, and constructs an initial parameter set containing the amplitude and phase characteristics of each path and the time delay position parameters.

[0123] The path parameter iterative optimization submodule 202 is used to perform expectation-maximization iterative optimization of the EM algorithm on each path. In the expectation calculation stage, based on the template function obtained by the autocorrelation of the probe sequence, the synthetic signals of other paths are reconstructed using the current parameter space, the components of other paths are subtracted from the observed signals, and the residual of the current path is extracted. In the maximization stage, maximum likelihood estimation is performed in the residual domain, the maximum value of the correlation peak is searched to determine the optimal time delay, the complex gain is calculated based on the normalized template function, the current path parameters are updated, and the signal is reconstructed synchronously.

[0124] The parameter merging and storage submodule 203 is used to gradually approximate the joint optimal solution through multiple rounds of global iteration. After the iteration is completed, the paths with the same time delay in the parameter space are merged and the extracted results are stored in the form of a multipath delay-complex gain parameter matrix.

[0125] In this embodiment, the PDP plotting module 300 converts the complex amplitude value of the channel impulse response matrix into power using the following formula:

[0126]

[0127] In the formula, P(t) n ,τ) represents the time t n The power of the channel impulse response at time t, h(t) n ,τ i ) indicates that at t n Time and delay are τ i The channel impulse response.

[0128] In this embodiment, the PDP alignment module 400 includes:

[0129] GPS information extraction submodule 401 is used to extract the GPS location information of the transceiver;

[0130] GPS interpolation submodule 402 is used to treat the motion between adjacent GPS points as uniform linear motion, perform interpolation using a linear averaging method, determine the number of interpolations based on the quotient of the time interval between adjacent GPS points and the time interval between adjacent snapshots, and obtain the GPS positions of all snapshots.

[0131] Distance calculation submodule 403 is used to calculate the transceiver distance corresponding to all snapshot times;

[0132] The latency calculation submodule 404 is used to calculate the latency of each snapshot by dividing the distance of each snapshot by the speed of light;

[0133] The alignment execution submodule 405 is used to move each snapshot to the same time based on the calculated delay, thus completing the alignment.

[0134] In this embodiment, the distance calculation submodule 403 uses the Haversine formula, specifically:

[0135]

[0136]

[0137] d = R × b

[0138] In the formula, a and b are intermediate variables in the calculation formula, and lat R lon R These are the latitude and longitude of the receiver, respectively. T lon T Here, R represents the latitude and longitude of the transmitter, respectively, and R is the Earth's radius.

[0139] In this embodiment, the latency calculation submodule 404 uses the formula of dividing the distance of each snapshot by the speed of light to calculate the latency of each snapshot as follows:

[0140]

[0141] In the formula, Δτ n Indicates t n The time delay of a snapshot at time d relative to the first snapshot n Indicates t n The distance between the transceivers at time d1 represents the distance between the transceivers at the time of transmitting the first snapshot, and c is the speed of light.

[0142] In this embodiment, the alignment execution submodule 405 moves each snapshot to the same time based on the calculated delay, and the alignment formula is as follows:

[0143]

[0144] In the formula, P(t) n ,τ′) represents at t n The power of the channel impulse response after time alignment, h(t) n ,τ i -Δτ n ) indicates that at t n At any given time, for all time delays τ i Correction Δτ n The channel impulse response afterward.

[0145] It should be noted that the information interaction and execution process between the modules of the above-mentioned device are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0146] Example 3

[0147] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium storing program code for a scenario-adaptive multipath delay alignment method for wireless communication systems. The program code includes instructions for executing the scenario-adaptive multipath delay alignment method for wireless communication systems as described in Embodiment 1 or any possible implementation thereof.

[0148] Computer-readable storage media 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 magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives, SSDs).

[0149] Example 4

[0150] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0151] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can execute the wireless communication system scenario adaptive multipath delay alignment method of Embodiment 1 or any possible implementation thereof by calling the program instructions.

[0152] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0153] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as 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 the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. 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 via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0154] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing systems. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Optionally, they can be implemented using program code executable by a computing system, thereby storing them in a storage system for execution by the computing system. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0155] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A scenario-adaptive multipath delay alignment method for wireless communication systems, characterized in that, Includes the following steps: Using measured channel data from the SISO channel sounding system, a CAZAC oversampled sequence was generated using rectangular or RRC shaping filters as a template function. The cross-correlation matrix between the IQ signal and the template function was calculated. The noise threshold was calculated based on the median power and false alarm rate. Dynamic threshold denoising was performed to generate the denoised cross-correlation matrix and complete the preliminary estimation of the time-domain impulse response. The SAGE algorithm is used to extract multipath delay and complex gain from the cross-correlation matrix. A power delay spectrum (PDP) is plotted on the channel impulse response. In the power delay spectrum (PDP), the x-axis represents the delay, the y-axis represents the number of snapshots / packets, and the z-axis represents the power value. The PDP is aligned by combining the transceiver's geographical location information during actual measurement.

2. The multipath delay alignment method for adaptive scenarios in a wireless communication system according to claim 1, characterized in that, The extraction of multipath delay and complex gain from the cross-correlation matrix using the SAGE algorithm specifically includes: Initialize multipath parameters, use a dual threshold strategy to determine the criteria for valid path discrimination, calculate the dynamic noise threshold by combining the peak amplitude and the median noise power, filter valid data points, and construct an initial parameter set that includes the amplitude and phase characteristics of each path and the time delay position parameters; For each path, the expectation-maximization iterative optimization of the EM algorithm is performed. In the expectation calculation stage, based on the template function obtained from the autocorrelation of the probe sequence, the synthetic signals of other paths are reconstructed using the current parameter space, the components of other paths are subtracted from the observed signals, and the residual of the current path is extracted. In the maximization stage, maximum likelihood estimation is performed in the residual domain, the maximum value of the correlation peak is searched to determine the optimal time delay, the complex gain is calculated based on the normalized template function, the current path parameters are updated, and the signal is reconstructed synchronously. The joint optimal solution is gradually approximated through multiple rounds of global iteration. After the iteration is completed, paths with the same time delay in the parameter space are merged and the extracted results are stored in the form of a multipath delay-complex gain parameter matrix.

3. The adaptive multipath delay alignment method for wireless communication systems according to claim 1, characterized in that, In the process of plotting the power delay spectrum (PDP) of the channel impulse response, the complex amplitude of the channel impulse response matrix is ​​converted into power, as shown in the formula: In the formula, P(t) n ,τ) represents the time t n The power of the channel impulse response at time t, h(t) n ,τ i ) indicates that at t n Time and delay are τ i The channel impulse response.

4. The adaptive multipath delay alignment method for a wireless communication system according to claim 1, characterized in that, The alignment operation of the PDP based on the transceiver's geographical location information during actual measurement specifically includes: Extract the GPS location information of the transceiver; The motion between adjacent GPS points is treated as uniform linear motion, and interpolation is performed using a linear averaging method. The number of interpolations is determined based on the quotient of the time interval between adjacent GPS points and the time interval between adjacent snapshots, and the GPS positions of all snapshots are obtained. Calculate the transceiver distance for all snapshot times; The latency of each snapshot is calculated by dividing the distance of each snapshot by the speed of light; Based on the calculated latency, each snapshot is moved to the same time to complete the alignment.

5. The adaptive multipath delay alignment method for wireless communication systems according to claim 4, characterized in that, In calculating the transceiver distance d corresponding to all snapshot times, the Haversine formula is used, specifically: d = R × b In the formula, a and b are intermediate variables in the calculation formula, and lat R lon R These are the latitude and longitude of the receiver, respectively. T lon T These are the latitude and longitude of the transmitter, respectively, and R is the Earth's radius; The formula for calculating the time delay of each snapshot by dividing the distance of each snapshot by the speed of light is: In the formula, Δτ n Indicates t n The time delay of a snapshot at time d relative to the first snapshot n Indicates t n The distance between the transceivers at time d1 represents the distance between the transceivers at the time of transmitting the first snapshot, and c is the speed of light.

6. The adaptive multipath delay alignment method for wireless communication systems according to claim 5, characterized in that, Based on the calculated latency, each snapshot is moved to the same time, and the alignment formula is as follows: In the formula, P(t) n ,τ′) represents at t n The power of the channel impulse response after time alignment, h(t) n ,τ i -Δτ n ) indicates that at t n At any given time, for all time delays τ i Correction Δτ n The channel impulse response afterward.

7. A scenario-adaptive multipath delay alignment device for a wireless communication system, characterized in that, include: The data preprocessing module is used to generate CAZAC oversampled sequences as template functions using measured channel data from the SISO channel sounding system. It employs rectangular or RRC shaping filters to calculate the cross-correlation matrix between the IQ signal and the template function, calculates the noise threshold based on the median power and false alarm rate, performs dynamic threshold denoising, generates the denoised cross-correlation matrix, and completes the preliminary estimation of the time-domain impulse response. The multipath parameter extraction module is used to extract multipath delay and complex gain from the cross-correlation matrix using the SAGE algorithm. The PDP plotting module is used to plot the power delay spectrum (PDP) of the channel impulse response. In the power delay spectrum (PDP), the x-axis represents the delay, the y-axis represents the number of snapshots / packets, and the z-axis represents the power value. The PDP alignment module is used to align the PDP by combining the transceiver's geographical location information during actual measurement.

8. The wireless communication system scenario adaptive multipath delay alignment device according to claim 7, characterized in that, The multipath parameter extraction module includes: The multipath parameter initialization submodule is used to initialize multipath parameters. It adopts a dual threshold strategy to determine the effective path discrimination criteria, calculates the dynamic noise threshold by combining the peak amplitude and the median noise power, filters effective data points, and constructs an initial parameter set containing the amplitude and phase characteristics of each path and the time delay position parameters. The path parameter iterative optimization submodule is used to perform expectation-maximization iterative optimization of the EM algorithm on each path. In the expectation calculation stage, based on the template function obtained by the autocorrelation of the probe sequence, the synthetic signals of other paths are reconstructed using the current parameter space, the components of other paths are subtracted from the observed signals, and the residual of the current path is extracted. In the maximization stage, maximum likelihood estimation is performed in the residual domain, the maximum value of the correlation peak is searched to determine the optimal time delay, the complex gain is calculated based on the normalized template function, the current path parameters are updated, and the signal is reconstructed synchronously. The parameter merging and storage submodule is used to gradually approximate the joint optimal solution through multiple rounds of global iteration. After the iteration is completed, the paths with the same time delay in the parameter space are merged and the extracted results are stored in the form of a multipath delay-complex gain parameter matrix.

9. The adaptive multipath delay alignment device for wireless communication systems according to claim 7, characterized in that, In the PDP plotting module, the complex amplitude value of the channel impulse response matrix is ​​converted into power using the following formula: In the formula, P(t) n ,τ) represents the time t n The power of the channel impulse response at time t, h(t) n ,τ i ) indicates that at t n Time and delay are τ i The channel impulse response.

10. The wireless communication system scenario adaptive multipath delay alignment device according to claim 7, characterized in that, The PDP alignment module includes: The GPS information extraction submodule is used to extract the GPS location information of the transceiver; The GPS interpolation submodule is used to treat the motion between adjacent GPS points as uniform linear motion, perform interpolation using a linear averaging method, determine the number of interpolations based on the quotient of the time interval between adjacent GPS points and the time interval between adjacent snapshots, and obtain the GPS positions of all snapshots. The distance calculation submodule is used to calculate the transceiver distance at all snapshot times; The latency calculation submodule is used to calculate the latency of each snapshot by dividing the distance of each snapshot by the speed of light; The alignment execution submodule is used to move each snapshot to the same time based on the calculated latency, thus completing the alignment. The distance calculation submodule uses the Haversine formula, specifically: d = R × b In the formula, a and b are intermediate variables in the calculation formula, and lat R lon R These are the latitude and longitude of the receiver, respectively. T lon T These are the latitude and longitude of the transmitter, respectively, and R is the Earth's radius; In the latency calculation submodule, the latency of each snapshot is calculated using the formula: the distance to each snapshot is divided by the speed of light. In the formula, Δτ n Indicates t n The time delay of a snapshot at time d relative to the first snapshot n Indicates t n The distance between the transceivers at time d1 represents the distance between the transceivers at the time of transmitting the first snapshot, and c is the speed of light; In the alignment execution submodule, each snapshot is moved to the same time based on the calculated delay, and the alignment formula is as follows: In the formula, P(t) n ,τ′) represents at t n The power of the channel impulse response after time alignment, h(t) n ,τ i -Δτ n ) indicates that at t n At any given time, for all time delays τ i Correction Δτ n The channel impulse response afterward.

Images