Scene-adaptive multipath time delay alignment method and device for wireless communication system

Abstract

The invention discloses a scene-adaptive multipath time delay alignment method and device for a wireless communication system, and the method comprises the steps: employing an SISO channel detection system to actually measure channel data, employing rectangular or RRC shaping filtering to generate a CAZAC oversampling sequence as a template function, calculating a cross-correlation matrix of an IQ signal and the template function, and enabling the CAZAC oversampling sequence to serve as the template function; a noise threshold is calculated based on the median power and the false alarm rate, dynamic threshold filtering is executed, a cross-correlation matrix after denoising is generated, and preliminary estimation of the time domain impulse response is completed; extracting multipath time delay and complex gain from the cross-correlation matrix by using an SAGE algorithm; drawing a power delay spectrum PDP for the channel impulse response; and performing alignment operation on the PDP in combination with the geographical location information of the transceiver during actual measurement. According to the method, the defect of traditional alignment in a severe scene is overcome, the statistical modeling accuracy is improved, the multipath time delay can be effectively aligned through experimental verification, and reliable support is provided for channel modeling.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a scenario-adaptive multipath delay alignment method and device for a wireless communication system. Background Art

[0002] In wireless communication systems, the wireless channel, as the only uncontrollable factor, significantly impacts communication quality. To accurately establish channel models for various scenarios, actual sounding experiments are typically required. Channel models are constructed based on the channel sounding data obtained from actual sounding. In a channel sounding system, statistical modeling requires aligning the power delay profiles (PDPs) at different times. This ensures that signals at different times are statistically modeled under the same standard.

[0003] Currently, two common delay alignment methods are used in channel modeling: strongest path alignment and first path alignment. However, both methods present challenges in harsh non-line-of-sight (NLOS) scenarios. When using strongest path alignment in NLOS scenarios, there are no direct paths in the receiving path. The strongest path used for alignment is a specific reflection path, which is not fixed. For continuously measured data, the delay corresponding to the strongest reflection path is not continuous, resulting in inappropriate alignment results.

[0004] When using first-path alignment, if the signal-to-noise ratio is poor, a small amount of noise may be present in the extracted paths. 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 when using first-path alignment. The small amount of noise that precedes the signal path will cause the signal path to shift backward, resulting in poor alignment. Summary of the Invention

[0005] To this end, the present invention provides a scenario-adaptive multipath delay alignment method and device for a wireless communication system, which solves the PDP alignment problem obtained using a 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 object, the present invention provides the following technical solution: a scenario-adaptive multipath delay alignment method for a wireless communication system, comprising the following steps:

[0007] 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 filtering is performed to generate a denoised cross-correlation matrix, completing a preliminary estimate of the time domain impulse response.

[0008] Extracting multipath delay and complex gain from the cross-correlation matrix using a SAGE algorithm;

[0009] Draw a power delay profile (PDP) for the channel impulse response, where the x-axis represents delay, the y-axis represents the number of snapshots / packets, and the z-axis represents power.

[0010] The PDP is aligned based on the geographic location information of the transceiver during actual measurement.

[0011] As a preferred solution of the scenario-adaptive multipath delay alignment method for a wireless communication system, the use of the SAGE algorithm to extract the multipath delay and complex gain from the cross-correlation matrix specifically includes:

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

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

[0014] The joint optimal solution is gradually approached through multiple rounds of global iteration. After the iteration, the paths with the same 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 solution for the scenario-adaptive multipath delay alignment method for wireless communication systems, when drawing the power delay profile (PDP) of the channel impulse response, the complex amplitude of the channel impulse response matrix is ​​converted into power using the formula:

[0016]

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

[0018] As a preferred solution of the scenario-adaptive multipath delay alignment method for wireless communication systems, the PDP alignment operation is performed in combination with the geographic location information of the transceiver during actual measurement, specifically including:

[0019] Extract GPS location information of the transceiver;

[0020] The motion between adjacent GPS points is considered as uniform linear motion, and linear averaging is used for interpolation. The number of interpolations is determined by the quotient of the time interval between adjacent GPS points and the time interval between adjacent snapshots to obtain the GPS positions of all snapshots.

[0021] Calculate the transceiver distance corresponding to all snapshot moments;

[0022] Calculate the time delay of each snapshot by dividing the distance of each snapshot by the speed of light;

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

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

[0025]

[0026] d=R×b

[0027] Where a and b are intermediate variables in the calculation formula, lat R ,lon R are the latitude and longitude of the receiver, lat T ,lon T are the latitude and longitude of the transmitter, respectively, and R is the radius of the earth;

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

[0029]

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

[0031] As a preferred solution for the scenario-adaptive multipath delay alignment method for wireless communication systems, each snapshot is moved to the same time based on the calculated delay. The alignment formula is:

[0032]

[0033] Where, P(t n ,τ′) represents the n The power of the channel impulse response after time alignment, h(t n ,τ i -Δτ n ) indicates that at t n At this moment, for all delays τ i Corrected Δτ n The subsequent channel impulse response.

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

[0035] The data preprocessing module is used to measure the channel data using the SISO channel sounding system, generate a CAZAC oversampling sequence as a template function using rectangular or RRC shaping filtering, calculate the cross-correlation matrix between the IQ signal and the template function, calculate the noise threshold based on the median power and false alarm rate, perform dynamic threshold noise filtering, generate a denoised cross-correlation matrix, and complete a preliminary estimate of the time domain impulse response;

[0036] A multipath parameter extraction module, configured to extract multipath delay and complex gain from the cross-correlation matrix using a SAGE algorithm;

[0037] A PDP drawing module is used to draw a power delay profile (PDP) for the channel impulse response, wherein the power delay profile (PDP) has an x-axis representing delay, a y-axis representing the number of snapshots / packets, and a z-axis representing power value;

[0038] The PDP alignment module is used to perform an alignment operation on the PDP in combination with the geographical location information of the transceiver during actual measurement.

[0039] As a preferred solution of the scenario-adaptive multipath delay alignment device for wireless communication systems, the multipath parameter extraction module includes:

[0040] The multipath parameter initialization submodule is used to initialize the multipath parameters. It uses a dual-threshold strategy to determine the effective path discrimination standard, calculates the dynamic noise threshold by combining the amplitude peak and the median noise power, selects valid 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 each path using the EM algorithm. In the expectation calculation phase, based on the template function obtained from the autocorrelation of the detection sequence, the synthetic signals of other paths are reconstructed using the current parameter space. The components of other paths are subtracted from the observed signal to extract the residual of the current path. In the maximization phase, maximum likelihood estimation is performed in the residual domain, the maximum value of the correlation peak is searched to determine the optimal 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 approach the joint optimal solution through multiple rounds of global iteration. After the iteration, the paths with the same 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 of the scenario-adaptive multipath delay alignment device for wireless communication systems, in the PDP drawing module, the complex amplitude of the channel impulse response matrix is ​​converted into power, and the formula is:

[0044]

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

[0046] As a preferred solution of the scenario-adaptive multipath delay alignment device for wireless communication systems, the PDP alignment module includes:

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

[0048] The GPS interpolation submodule is used to treat the movement between adjacent GPS points as uniform linear motion, use linear averaging to perform 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;

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

[0050] The delay calculation submodule is used to calculate the delay 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 according to the calculated delay to complete the alignment.

[0052] As a preferred solution for the scenario-adaptive multipath delay alignment device of the wireless communication system, the distance calculation submodule adopts the Haversine formula, specifically:

[0053]

[0054] d=R×b

[0055] Where a and b are intermediate variables in the calculation formula, lat R ,lon R are the latitude and longitude of the receiver, lat T ,lon T are the latitude and longitude of the transmitter, and R is the radius of the earth.

[0056] As a preferred solution of the scenario-adaptive multipath delay alignment device for wireless communication systems, in the delay calculation submodule, the delay formula for each snapshot is calculated by dividing the distance of each snapshot by the speed of light:

[0057]

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

[0059] As a preferred solution for the scenario-adaptive multipath delay alignment device of the wireless communication system, in the alignment execution submodule, each snapshot is moved to the same time according to the calculated delay, and the alignment formula is:

[0060]

[0061] Where, P(t n ,τ′) represents the n The power of the channel impulse response after time alignment, h(t n ,τ i -Δτ n ) indicates that at t n At this moment, for all delays τ i Corrected Δτ n The subsequent channel impulse response.

[0062] The present invention has the following advantages:

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

[0064] Second, the SAGE algorithm combined with a dual-threshold strategy and EM iterative optimization can accurately extract multipath delay and complex gain in dense scattering environments, solve the problem of multipath overlapping interference, provide structured CIR data, and lay the foundation for statistical modeling.

[0065] Third, combined with the GPS location information of the transceiver, all snapshot locations are obtained through linear interpolation. The Haversine formula is used to calculate the distance, which is divided by the speed of light to convert it into time delay to align the PDP. This is directly based on the actual geographic location and is less affected by signal quality.

[0066] Fourth, compared with the traditional alignment of the strongest path and the first path, this method solves the problem of the unstable strongest path and the susceptibility of the first path to noise interference in NLOS scenarios. It aligns the PDPs at different distances to the same dimension, accurately reflects the delay relationship between the direct path and the reflected path, and provides a more accurate relative delay power relationship between different snapshots for statistical modeling.

[0067] Fifth, experiments show that alignment can eliminate the impact of different positions on delay, aligning the LOS direct path to time 0. The NLOS delay reflects the relative direct delay of the reflection path, providing a reliable basis for subsequent small-scale fading and Doppler dynamic characteristics modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0069] The structures, proportions, sizes, etc. illustrated in this specification are intended solely to complement the contents disclosed herein and to facilitate understanding and reading by persons skilled in the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall remain within the scope of the technical contents disclosed herein.

[0070] Figure 1 A schematic flow chart of a scenario-adaptive multipath delay alignment method for a wireless communication system provided in an embodiment of the present invention;

[0071] Figure 2 A schematic diagram of a technical route for a scenario-adaptive multipath delay alignment method for a wireless communication system provided in an embodiment of the present invention;

[0072] Figure 3 A schematic diagram of a route in the collection process provided in an embodiment of the present invention;

[0073] Figure 4 The actual data collected through the channel detection experiment in the urban scenario provided in the embodiment of the present invention is first subjected to correlation calculation and the multipath delay graph is extracted by 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 a scenario-adaptive multipath delay alignment device for a wireless communication system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0076] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. Obviously, the embodiments described are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0077] Example 1

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

[0079] S1. Use the measured channel data of the SISO channel sounding system and adopt rectangular or RRC shaping filtering to generate the CAZAC oversampling sequence as the template function. Calculate the cross-correlation matrix between the IQ signal and the template function. Calculate the noise threshold based on the median power and false alarm rate. Perform dynamic threshold denoising 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 detection system can obtain the time domain characteristics of the channel. Rectangular or RRC (root raised cosine) shaping filters can optimize the signal spectrum 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 and reflect 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 based on the noise level, retaining the true signal components, thereby achieving a preliminary clean estimate of the time domain impulse response.

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

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

[0083] S21. Initialize the multipath parameters, use a dual-threshold strategy to determine the effective path discrimination standard, calculate the dynamic noise threshold by combining the amplitude peak and the median noise power, screen the effective data points, and construct an initial parameter set containing 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 determine valid 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. These two factors are combined to calculate a dynamic noise threshold, which adaptively adjusts to actual noise conditions and effectively distinguishes signal paths from noise points. After selecting valid data points, an initial set of key parameters, such as amplitude, phase, and delay, for each path is 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 detection sequence, the synthetic signals of other paths are reconstructed using the current parameter space, the components of other paths are deducted from the observed signal, and the residual of the current path is extracted. In the maximization stage, maximum likelihood estimation is implemented in the residual domain, the maximum value of the correlation peak is searched to determine the optimal delay, the complex gain is calculated based on the normalized template function, the current path parameters are updated, and the signal is reconstructed synchronously.

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

[0087] S23. Gradually approach the joint optimal solution through multiple rounds of global iteration. After the iteration is completed, merge the paths with the same delay in the parameter space and store the extracted results in the form of a multipath delay-complex gain parameter matrix.

[0088] Multiple rounds of global iterations continuously optimize the parameters of each path, gradually approaching the joint optimal solution for all paths and improving the accuracy of parameter estimation. Paths with the same latency may represent different representations of the same physical path. Merging them reduces redundancy and makes parameter representation more concise and accurate. Matrix storage facilitates subsequent processing and analysis, providing structured data for subsequent PDP drawing and alignment operations.

[0089] S3. Draw a power delay profile PDP for the channel impulse response. In the power delay profile PDP, the x-axis is the delay, the y-axis is the number of snapshots / packets, and the z-axis is the power value.

[0090] The channel impulse response reflects the signal propagation in the channel. Converting its complex amplitude to power can visually display the power distribution under different delays. The PDP is presented in three dimensions: the x-axis represents the difference in signal arrival time, the y-axis represents the number of snapshots / packets at different measurement moments or data groups, and the z-axis represents the signal energy intensity at the corresponding delay and time. This facilitates observation of the time-varying characteristics of the channel and multipath power distribution.

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

[0092]

[0093] Where, P(t n ,τ) represents the time at t n The power of the channel impulse response at time t n ,τ i ) indicates that at t n Time, delay is τ i The square of the complex modulus is the power. For each time t n and time delay τ iBy taking the square modulo the channel impulse response and summing them, we can obtain the total power at that time and delay. This is a common method for converting complex-valued signals into power values. It conforms to the physical definition of power and accurately reflects the distribution of signal energy in the delay domain.

[0094] S4. Align the PDPs based on the actual transceiver location information during measurement. Changes in the transceiver's location can cause changes in signal propagation distance, which in turn causes delay variations. Directly using the original PDPs for statistical modeling can introduce errors due to distance differences. Aligning the PDPs based on location information eliminates the delay effects of distance variations, allowing PDPs at different times to be compared and analyzed under the same standard, improving modeling accuracy.

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

[0096] S41. Extract the GPS location information of the transceiver. GPS can provide accurate longitude and latitude coordinates to determine the specific location of the transceiver at different times. This is the basic data for subsequent distance and delay calculations.

[0097] S42: Consider the motion between adjacent GPS points as uniform linear motion, perform interpolation using linear averaging, 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] Because the GPS sampling interval is typically greater than the snapshot interval, linear interpolation is a simple and effective method for obtaining the position at each snapshot, assuming uniform linear motion between adjacent GPS points. By calculating the quotient of the time interval to determine the number of interpolations, the time interval between the interpolated points can be matched to the snapshot interval, resulting in the GPS position corresponding to each snapshot, ensuring accurate distance calculation.

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

[0100] Specifically, in step S43, the Haversine formula is used to calculate the transceiver distance d corresponding to all snapshot moments, specifically:

[0101]

[0102] d=R×b

[0103] In the formula, a and b are intermediate variables in the calculation formula, lat R ,lonR are the latitude and longitude of the receiver, lat T ,lon T where a is the latitude and longitude of the transmitter, and R is the radius of the Earth. 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. The angular difference between the two points is calculated using the latitude and longitude, and then converted into distance. The intermediate variable a calculates the haversine of the radian angle between the two points, and b is the radian value. Multiplying this value by the Earth's radius, R, yields the actual distance. This formula accurately calculates the distance between any two points on the Earth's surface and is suitable for use when the transceiver is located on the Earth's surface.

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

[0105]

[0106] Where Δτ n Indicates t n The delay of the snapshot at time relative to the first snapshot, d n Indicates t n The distance between the transceiver at the moment of the first snapshot, d1 represents the distance between the transceiver at the moment of the first snapshot, and c is the speed of light. The speed of signal propagation in vacuum is the speed of light c, and the distance difference d n -d1 divided by the speed of light is the time difference, or the delay of this snapshot relative to the first. Using the first snapshot as a reference point, we calculate the relative delays of other snapshots, unifying delays at different times into the same reference frame and preparing for subsequent alignment.

[0107] S45. Based on the calculated delay, each snapshot is moved to the same time. The alignment formula is:

[0108]

[0109] Where, P(t n ,τ′) represents the n The power of the channel impulse response after time alignment, h(t n ,τ i -Δτ n ) indicates that at t n At this moment, for all delays τ i Corrected Δτ n The channel impulse response after τ is calculated. i Subtract the corresponding relative delay Δτ nThis is equivalent to adjusting the time base of the snapshot to match that of the first snapshot, thereby shifting the PDPs of different snapshots along the time axis. This process places the PDPs of different snapshots in the same time dimension, eliminating latency differences caused by distance changes. This facilitates subsequent statistical modeling and analysis, and more accurately reflects the true characteristics of the channel.

[0110] See also Figure 3 This figure illustrates the route used to collect actual data during a channel probing experiment in an urban setting. "TX" represents the transmitter location, and the red dot represents the receiver (or test point). The coverage area reflects the distribution of the test route / sampling points, providing a visual representation of the geographic range of signal propagation. Longitude and latitude, along with a 200m scale, help quantify the transmit and receive distances. Combined with PDP latency data, this verifies the theoretical distance-delay relationship (delay ≈ distance / speed of light) and analyzes the impact of the geographic environment (such as buildings and roads) on multipath.

[0111] See also Figure 4 The actual data collected through the channel detection experiment in the urban scenario is first subjected to correlation calculations and the multipath delay diagram extracted by the SAGE algorithm is shown. The horizontal axis represents the delay and the vertical axis represents the data packet number collected at different times. A snapshot is generated every 0.2048ms, 10,000 snapshots constitute a packet, and a packet is collected every 2.048s.

[0112] See also Figure 5 This is the effect of GPS alignment. It can be observed that alignment effectively eliminates the impact of varying distances on latency. The direct path of LOS is aligned to time zero, while the NLOS latency reflects the absence of the direct path and the delay of the reflected path relative to the direct path. Statistical modeling based on this can better reflect the accurate latency relationships at various locations.

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

[0114] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0115] Example 2

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

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

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

[0119] The PDP drawing module 300 is used to draw a power delay profile (PDP) for the channel impulse response, wherein the power delay profile (PDP) has an x-axis representing delay, a y-axis representing the number of snapshots / packets, and a z-axis representing power.

[0120] The PDP alignment module 400 is used to perform an alignment operation on the PDP in combination with the geographical location information of the transceiver during actual measurement.

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

[0122] Initialize multipath parameters submodule 201, which is used to initialize multipath parameters, use a dual-threshold strategy to determine the effective path discrimination standard, calculate the dynamic noise threshold based on the amplitude peak and the noise power median, screen valid data points, and construct an initial parameter set containing the amplitude and phase characteristics and time delay position parameters of each path;

[0123] The path parameter iterative optimization submodule 202 is configured to perform expectation-maximization iterative optimization of the EM algorithm on each path. In the expectation calculation phase, based on the template function obtained from the autocorrelation of the detection sequence, the synthetic signals of other paths are reconstructed using the current parameter space, the components of other paths are subtracted from the observed signal, and the residual of the current path is extracted. In the maximization phase, maximum likelihood estimation is performed in the residual domain, the maximum value of the correlation peak is searched to determine the optimal 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 storing submodule 203 is used to gradually approach the joint optimal solution through multiple rounds of global iterations. After the iterations are completed, the paths with the same delay in the parameter space are merged and the extraction results are stored in the form of a multipath delay-complex gain parameter matrix.

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

[0126]

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

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

[0129] GPS information extraction submodule 401, for extracting GPS location information of the transceiver;

[0130] The GPS interpolation submodule 402 is used to treat the motion between adjacent GPS points as uniform linear motion, perform interpolation using a linear average 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] The distance calculation submodule 403 is used to calculate the transceiver distances corresponding to all snapshot moments;

[0132] The delay calculation submodule 404 is configured to calculate the delay 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 according to the calculated delay to complete the alignment.

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

[0135]

[0136]

[0137] d=R×b

[0138] Where a and b are intermediate variables in the calculation formula, lat R ,lon R are the latitude and longitude of the receiver, lat T ,lon T are the latitude and longitude of the transmitter, and R is the radius of the earth.

[0139] In this embodiment, the delay calculation submodule 404 calculates the delay of each snapshot by dividing the distance of each snapshot by the speed of light using the formula:

[0140]

[0141] Where Δτ n Indicates t n The delay of the snapshot at time relative to the first snapshot, d n Indicates t n The distance between the transceivers at the moment d1 is the distance between the transceivers at the moment 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 according to the calculated delay, and the alignment formula is:

[0143]

[0144] Where, P(t n ,τ′) represents the n The power of the channel impulse response after time alignment, h(t n ,τ i -Δτ n ) indicates that at t n At this moment, for all delays τ i Corrected Δτ n The subsequent channel impulse response.

[0145] It should be noted that the information interaction, execution process, etc. between the modules of the above-mentioned device are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and will not be repeated here.

[0146] Example 3

[0147] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which a program code of a multipath delay alignment method for scenario adaptation of a wireless communication system is stored. The program code includes instructions for executing the multipath delay alignment method for scenario adaptation of a wireless communication system of embodiment 1 or any possible implementation thereof.

[0148] Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media 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 through a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the multipath delay alignment method for scenario-adaptive wireless communication system of embodiment 1 or any possible implementation thereof.

[0152] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in a memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0153] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part 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, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable systems. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0154] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing system. They can be centralized on a single computing system or distributed across a network of multiple computing systems. Alternatively, they can be implemented using program code executable by a computing system, and thus, they can be stored in a storage system and executed by the computing system. In some cases, the steps shown or described herein can be performed in a different order than that shown, or they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into 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 using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A scenario-adaptive multipath delay alignment method for a wireless communication system, characterized in that: The following steps are involved: 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 filtering is performed to generate a denoised cross-correlation matrix, completing a preliminary estimate of the time domain impulse response. Extracting multipath delay and complex gain from the cross-correlation matrix using a SAGE algorithm; Draw a power delay profile (PDP) for the channel impulse response, where the x-axis represents delay, the y-axis represents the number of snapshots / packets, and the z-axis represents the power value; The PDP is aligned based on the geographic location information of the transceiver during actual measurement.

2. The method for scenario-adaptive multipath delay alignment in a wireless communication system according to claim 1, wherein: The extracting multipath delay and complex gain from the cross-correlation matrix using the SAGE algorithm specifically includes: Initialize the multipath parameters, use a dual-threshold strategy to determine the effective path discrimination criteria, calculate the dynamic noise threshold by combining the peak amplitude and median noise power, screen valid data points, and construct an initial parameter set containing the amplitude and phase characteristics and time delay position parameters of each path; The expectation-maximization iterative optimization of the EM algorithm is performed on each path. In the expectation calculation phase, based on the template function obtained from the autocorrelation of the detection sequence, the synthetic signals of other paths are reconstructed in the current parameter space. The components of other paths are subtracted from the observed signal, and the residual of the current path is extracted. In the maximization phase, maximum likelihood estimation is performed in the residual domain, searching for the maximum value of the correlation peak to determine the optimal delay. The complex gain is calculated based on the normalized template function, the parameters of the current path are updated, and the signal is reconstructed synchronously. The joint optimal solution is gradually approached through multiple rounds of global iteration. After the iteration, the paths with the same 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 scenario-adaptive multipath delay alignment method for a wireless communication system according to claim 1, wherein: When drawing the power delay profile (PDP) of the channel impulse response, the complex amplitude of the channel impulse response matrix is ​​converted to power using the formula: Where, P(t n ,τ) represents the time at t n The power of the channel impulse response at time h(t n ,τ i ) indicates that at t n Time, delay is τ i The channel impulse response.

4. The scenario-adaptive multipath delay alignment method for a wireless communication system according to claim 1, wherein: The step of aligning the PDP based on the actual measurement of the geographical location information of the transceiver specifically includes: Extract GPS location information of the transceiver; The motion between adjacent GPS points is considered as uniform linear motion, and linear averaging is used for interpolation. The number of interpolations is determined by the quotient of the time interval between adjacent GPS points and the time interval between adjacent snapshots to obtain the GPS positions of all snapshots. Calculate the transceiver distance corresponding to all snapshot moments; Calculate the time delay of each snapshot by dividing the distance of each snapshot by the speed of light; Based on the calculated delay, each snapshot is moved to the same time to complete the alignment.

5. The scenario-adaptive multipath delay alignment method for a wireless communication system according to claim 4, characterized in that: The Haversine formula is used to calculate the transceiver distance d corresponding to all snapshot times, specifically: d=R×b Where a and b are intermediate variables in the calculation formula, lat R ,lon R are the latitude and longitude of the receiver, lat T ,lon T are the latitude and longitude of the transmitter, respectively, and R is the radius of the earth; The formula for calculating the delay of each snapshot by dividing the distance of each snapshot by the speed of light is: Where Δτ n Indicates t n The delay of the snapshot at time relative to the first snapshot, d n Indicates t n The distance between the transceivers at the moment d1 is the distance between the transceivers at the moment of transmitting the first snapshot, and c is the speed of light.

6. The scenario-adaptive multipath delay alignment method for a wireless communication system according to claim 5, characterized in that: Based on the calculated delay, each snapshot is moved to the same time. The alignment formula is: Where, P(t n ,τ′) represents the n The power of the channel impulse response after time alignment, h(t n ,τ i -Δτ n ) indicates that at t n At this moment, for all delays τ i Corrected Δτ n The channel impulse response after .

7. A scenario-adaptive multipath delay alignment device for a wireless communication system, characterized in that: include: The data preprocessing module is used to measure the channel data using the SISO channel sounding system, generate a CAZAC oversampling sequence as a template function using rectangular or RRC shaping filtering, calculate the cross-correlation matrix between the IQ signal and the template function, calculate the noise threshold based on the median power and false alarm rate, perform dynamic threshold noise filtering, generate a denoised cross-correlation matrix, and complete a preliminary estimate of the time domain impulse response; A multipath parameter extraction module, configured to extract multipath delay and complex gain from the cross-correlation matrix using a SAGE algorithm; A PDP drawing module is used to draw a power delay profile (PDP) for the channel impulse response, wherein the power delay profile (PDP) has an x-axis representing delay, a y-axis representing the number of snapshots / packets, and a z-axis representing power value; The PDP alignment module is used to perform an alignment operation on the PDP in combination with the geographical location information of the transceiver during actual measurement.

8. The scenario-adaptive multipath delay alignment device for a wireless communication system according to claim 7, characterized in that: The multipath parameter extraction module includes: The multipath parameter initialization submodule is used to initialize the multipath parameters. It uses a dual-threshold strategy to determine the effective path discrimination standard, calculates the dynamic noise threshold by combining the amplitude peak and the median noise power, selects valid 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 each path using the EM algorithm. In the expectation calculation phase, based on the template function obtained from the autocorrelation of the detection sequence, the synthetic signals of other paths are reconstructed using the current parameter space. The components of other paths are subtracted from the observed signal to extract the residual of the current path. In the maximization phase, maximum likelihood estimation is performed in the residual domain, the maximum value of the correlation peak is searched to determine the optimal 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 approach the joint optimal solution through multiple rounds of global iteration. After the iteration, the paths with the same 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 scenario-adaptive multipath delay alignment device for a wireless communication system according to claim 7, characterized in that: In the PDP drawing module, the complex amplitude of the channel impulse response matrix is ​​converted into power using the formula: Where, P(t n ,τ) represents the time at t n The power of the channel impulse response at time h(t n ,τ i ) indicates that at t n Time, delay is τ i The channel impulse response.

10. The scenario-adaptive multipath delay alignment device for a wireless communication system according to claim 7, characterized in that: The PDP alignment module includes: GPS information extraction submodule, used to extract GPS location information of the transceiver; The GPS interpolation submodule is used to treat the movement between adjacent GPS points as uniform linear motion, use linear averaging to perform 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; The distance calculation submodule is used to calculate the transceiver distance corresponding to all snapshot moments; The delay calculation submodule is used to calculate the delay 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 delay to complete the alignment; In the distance calculation submodule, the Haversine formula is used, specifically: d=R×b Where a and b are intermediate variables in the calculation formula, lat R ,lon R are the latitude and longitude of the receiver, lat T ,lon T are the latitude and longitude of the transmitter, respectively, and R is the radius of the earth; In the delay calculation submodule, the delay formula for each snapshot is calculated by dividing the distance of each snapshot by the speed of light: Where Δτ n Indicates t n The delay of the snapshot at time relative to the first snapshot, d n Indicates t n The distance between the transceivers at the time of the first snapshot, d1 represents the distance between the transceivers at the time of the first snapshot, and c is the speed of light; In the alignment execution submodule, each snapshot is moved to the same time according to the calculated delay. The alignment formula is: Where, P(t n ,τ′) represents the n The power of the channel impulse response after time alignment, h(t n ,τ i -Δτ n ) indicates that at t n At this moment, for all delays τ i Corrected Δτ n The channel impulse response after .

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