Pilot-based enhanced in-service channel parameter estimation method and device, and storage medium

Abstract

The present application relates to a kind of based on pilot enhancement live network channel parameter estimation method, device and storage medium, by utilizing the pilot information in the downlink signal of commercial mobile network, using high-precision estimation algorithm is preliminarily estimated to the transmission channel between signal from base station to the collection user, and based on the channel result of estimation, the data part in received signal is channel equalization, reconstruct the original data sent by symbol decision, then with the reconstructed data as original transmitting data, high-precision parameter estimation algorithm is applied again to carry out channel parameter estimation, compared with prior art, the present application has by the reconstruction and reuse of the data part of known transmission standard commercial signal, can reduce the data overhead for channel estimation, increase the air interface bandwidth that can be used for user data transmission, while transmission channel state still can obtain real reliable estimation result, with the advantages of accurate channel estimation, low data overhead and controllable calculation complexity.

Description

Technical Field

[0001] This invention relates to the field of wireless channel parameter estimation, and in particular to a method, apparatus, and storage medium for estimating existing network channel parameters based on pilot enhancement. Background Technology

[0002] With the continuous development of wireless communication, new communication applications in the 5G era require massive amounts of data and greater bandwidth. The broadband propagation characteristics of these new scenarios present challenges to communication technologies and transceiver design. To improve communication quality, channel parameter estimation is essential. Typical channel parameter estimation relies on the combined participation of transceivers, i.e., transmitters and receivers. However, in many new scenarios, such as high-speed rail and aviation communications, the high cost and legal issues associated with installing self-deployed transceivers make active channel measurement difficult for researchers. Passive measurement methods can bypass the limitations of deploying transmitting equipment and utilize commercially transmitted signals to measure and characterize the channel.

[0003] However, in existing technologies, usually only the reference signal in the commercial signal can be used by the receiver for channel estimation. The reference signal is only a small part of the commercial signal, and most of the data signal, which is the main body of transmission, cannot be effectively utilized. Therefore, the accuracy of this channel parameter estimation needs to be improved. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, and storage medium for estimating existing network channel parameters based on pilot enhancement.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for estimating existing network channel parameters based on pilot enhancement includes:

[0007] Step S1: Acquire the received signal and extract the pilot portion of the commercial signal from the received signal as the pilot signal;

[0008] Step S2: Using the acquired pilot signal and the standard transmission value of the pilot signal of the commercial signal, estimate the multipath component parameters to obtain the amplitude, phase, time delay and Doppler shift, so as to reconstruct the time-varying channel impulse response;

[0009] Step S3: Based on the reconstructed time-varying channel impulse response, perform equalization processing on the data part of the commercial signal to obtain the predicted value of the symbol domain of the original data part;

[0010] Step S4: Based on the predicted values ​​of the symbol domain of the obtained original data portion, map them onto the constellation diagram according to the modulation scheme specified in the commercial signal transmission standard, perform symbol decision on them, and finally obtain the reconstructed values ​​of the data portion;

[0011] Step S5: Use the reconstructed value of the data part as the true value of the original transmitted data to reconstruct the original transmitted signal and obtain the reconstructed value of the original transmitted signal. Use the obtained multipath component parameters as the channel parameter estimation result.

[0012] The commercial signal is a downlink broadcast signal transmitted by the base station, wherein each frame of the time-frequency resource network contains M. rs pilot subcarriers and M d One data subcarrier, and N rs pilot symbols and N d Data symbols.

[0013] The mathematical expression for the reconstructed time-varying channel impulse response in step S2 is:

[0014]

[0015] Where: h rs (t, τ) represents the time-varying channel impulse response, α rs，l v is the amplitude of the l-th propagation path. rs，l The Doppler translation of the l-th propagation path, where t is the current time and τ is the time delay. rs，l Let L be the time delay of the l-th propagation path, and L be the number of propagation paths.

[0016] The process of estimating the multipath component parameters to obtain amplitude, phase, time delay, and Doppler shift is achieved using a high-precision channel parameter estimation algorithm.

[0017] The high-precision channel parameter estimation algorithm is a channel parameter estimation algorithm based on spatial alternation generalized expectation maximization, a channel parameter estimation algorithm based on matrix eigenspace decomposition, or a channel parameter estimation algorithm based on rotation invariant techniques.

[0018] The equalization process employs either zero-forcing equalization or minimum mean square error equalization.

[0019] Step S3 specifically includes:

[0020] Step S31: Perform a Fourier transform on the reconstructed time-varying channel impulse response to obtain the time-varying transfer function;

[0021] Step S32: Assuming the transmitted signal travels through the channel with a flat and constant time range within the corresponding frame, calculate the optimal matrix G using the time-varying transfer function:

[0022]

[0023] Wherein: H rs For time-varying transfer functions, (·) HHere, P is the Hermite operator, P is the transmitted signal power in scalar form, σ is the variance of the noise, and I is the unit diagonal matrix.

[0024] Step S33: Based on the obtained optimal matrix and combined with the received values ​​of the data portion of the commercial signal, obtain the predicted values ​​of the symbol field of the original data portion:

[0025]

[0026] in: Y is the predicted value of the symbolic field of the original data. d The received value is the data portion of the commercial signal.

[0027] Step S4 specifically includes:

[0028] Step S41: Map the predicted values ​​of the symbol domain of the obtained raw data onto the constellation diagram, and map the corresponding phase shift keying symbol standard values ​​onto the constellation diagram according to the transmission standard;

[0029] Step S42: Traverse all points in the predicted values ​​of the symbol domain of all data portions, and calculate the Euclidean distance between each point and the standard value of the phase shift keying symbol. Select the standard value of the phase shift keying symbol with the closest Euclidean distance as the estimated value of that point. The calculation formula is as follows:

[0030]

[0031] in: Let be the estimated value of the i-th point in the predicted values ​​of the symbol field in the data portion, min(·) is used to obtain the minimum value in the set, m is the total number of symbols in the data portion, and n is the number of standard value symbols of phase shift keying symbols. Let s(j) be the i-th point in the predicted value of the symbol field of the original data, and s(j) be the standard value of the j-th phase shift keying symbol.

[0032] A network channel parameter estimation device based on pilot enhancement includes a memory, a processor, and a program stored in the memory. When the processor executes the program, it implements the method described above.

[0033] A storage medium having a program stored thereon, characterized in that the program, when executed, implements the above-described method.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. By using 5G commercial signals for passive channel detection, the problem of complex deployment of active channel measurement transceivers in some scenarios is solved, and the overhead is reduced without reducing the accuracy of channel estimation.

[0036] 2. The reconstructed data portion of the commercial signal is assumed to be the original transmitted signal, and the pilot information is combined with the reconstructed data portion to apply the HRPE estimation algorithm, which increases the amount of estimated data and expands the estimated data bandwidth, thereby improving the accuracy of channel parameter estimation (by 7-15dB) at medium to high signal-to-noise ratios (5-25dB). Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0038] Figure 2 This is a schematic diagram illustrating the composition and distribution of various components of a 5G commercial signal in one frame, as shown in the embodiment.

[0039] Figure 3 This is a schematic diagram of the flight path corresponding to the measured channel of the UAV in the NLOS (Non-Line Of Sight) scenario used in the embodiment;

[0040] Figure 4 This is a time-frequency domain schematic diagram of the received commercial signal in the embodiment;

[0041] Figure 5 This is a schematic diagram in the time-frequency domain of the UAV channel reconstructed using pilot information to estimate channel parameters in the embodiment.

[0042] Figure 6 The original data in the example contains the predicted symbol field values. A schematic diagram of the time-frequency domain;

[0043] Figure 7 This is a constellation diagram of the symbol domain predicted values ​​of the original data in the embodiment;

[0044] Figure 8 The reconstructed values ​​of the data portion in the embodiment A constellation chart;

[0045] Figure 9 This is a time-frequency domain schematic diagram of the reconstructed value X′ of the original transmitted signal in the embodiment;

[0046] Figure 10 The example shows the UAV estimated channel time-frequency diagram results obtained based on the method of the present invention.

[0047] Figure 11 The figure shows the correlation curves between MSE and SNR in the example. Detailed Implementation

[0048] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0049] A method for estimating existing network channel parameters based on pilot enhancement, such as... Figure 1 As shown, it includes:

[0050] Step S1: Obtain the received signal Y. Based on the receiver processing chain method, using the known 5G commercial signal transmission standard, extract the pilot portion of the commercial signal from the received signal as the pilot signal Y. rs ;

[0051] Commercial signals are broadcast signals transmitted downlink from base stations, where each frame of the time-frequency resource network contains M. rs pilot subcarriers and M d One data subcarrier, and N rs pilot symbols and N d Data symbols.

[0052] In this embodiment, the transmission and reception signals are generated using simulation. The specific process is as follows:

[0053] Step S11: First, use MATLAB simulation to generate the downlink transmission broadcast signal of the base station BS, such as... Figure 2 As shown, specifically, the base station BS is a 5G base station. Figure 2 This represents the component distribution of a 5G commercial signal generated by MATLAB simulation. The transmitted commercial signal is considered to consist of a pilot section and a data section, where the white Cell RS represents the pilot section and the green PDSCH represents the data section, represented as X = (X... rs X d ), X∈C M×N Each frame of the time-frequency resource network contains M (M = M rs +M a ) subcarriers, including pilot M rs and data section M d Subcarriers, and N (N = N rs +N d ) symbols, each containing pilot N rs and data part N d symbol.

[0054] Step S12: The generated 5G commercial signal first needs to be processed by adding a channel. The simulation uses the measured drone channel. Figure 3 This demonstrates the measured drone flight paths corresponding to the actual drone channel, specifically measured under non-line-of-sight conditions and at a drone flight altitude of 15m. The multipath parameter α of the measured drone channel is used. uav，l , τ uav，l v uav，l The channel response is constructed according to the Whittaker-Shannon interpolation formula.

[0055]

[0056] Step S13: The constructed channel response is understood as a filter that filters the transmitted signal to obtain the received signal transmitted through the UAV channel as measured in the experiment. To simulate and analyze different signal-to-noise ratios, Gaussian white noise is added to the received signal transmitted through the channel to obtain the simulated received signal Y = HX + N, Y ∈ C. M×N , Figure 4 This is a time-frequency diagram showing the received commercial signal. The x-axis represents the carrier frequency, the y-axis represents the symbol, and the z-axis represents the signal amplitude. Noise power is calculated based on the signal-to-noise ratio using the formula shown below:

[0057]

[0058] Among them, P tx P represents the transmitted signal power. rx N represents the received signal power. fft N represents the number of FFT points. txgrid SNR represents the number of frequency points in the transmitted signal resource block, and is the signal-to-noise ratio expressed in dB.

[0059] S14. Based on the known pilot subcarrier M in known commercial signal transmission standards rs and pilot symbol N rs Information, extracting the pilot portion Y from each frame's time-frequency resource network. rs (i)=Y(M rs (i), N rs (i)),

[0060] Step S2: Utilize the acquired pilot signal Y rs Combined with the standard transmission value X of the pilot signal in commercial signals rs , The high-resolution parameter estimation algorithm (HRPE) is used to estimate the multipath component parameters. When the algorithm converges, the estimated amplitude, time delay, and Doppler shift are used to reconstruct the instantaneous propagation multipath components of the commercial signal, specifically the time-varying channel impulse response. The mathematical expression of the reconstructed time-varying channel impulse response is as follows:

[0061]

[0062] Where: h rs (t, τ) represents the time-varying channel impulse response, α rs，l v is the amplitude of the l-th propagation path. rs，lThe Doppler translation of the l-th propagation path, where t is the current time and τ is the time delay. rs，l Let L be the time delay of the l-th propagation path, and L be the number of propagation paths.

[0063] High-precision channel parameter estimation algorithms include SAGE (Estimation of Signal Parameters via Rotational Invariance Techniques), MUSIC (multiple signal classification algorithm) based on matrix eigenspace decomposition, and ESPRIT (estimating signal parameter via rotational invariance techniques) based on rotational invariance techniques.

[0064] Step S3: Based on the reconstructed time-varying channel impulse response h rs (t, τ), for the data portion Y of the commercial signal d After performing equalization processing, the predicted values ​​of the symbolic domain of the original data are obtained. The equalization process employs either zero-forcing equalization or minimum mean square error equalization. In this embodiment, it specifically includes:

[0065] Step S31: Perform a Fourier transform on the reconstructed time-varying channel impulse response to obtain the time-varying transfer function. Figure 5 It is a UAV channel time-frequency diagram reconstructed by estimating channel parameters using pilot information;

[0066] Step S32: Assuming the transmitted signal travels through the channel with a flat and constant time range within the corresponding frame, calculate the optimal matrix G using the time-varying transfer function:

[0067]

[0068] Wherein: H rs For time-varying transfer functions, (·) H Here, P is the Hermite operator, P is the transmitted signal power in scalar form, σ is the noise variance, and I is the unit diagonal matrix. In particular, when the noise variance is equal to 0, it can be regarded as using zero-forcing (ZF) equalization.

[0069] Step S33: Based on the obtained optimal matrix and combined with the received values ​​of the data portion of the commercial signal, obtain the predicted values ​​of the symbol field of the original data portion:

[0070]

[0071] in: Y is the predicted value of the symbolic field of the original data. d The received value for the data portion of the commercial signal. Figure 6 Represents the symbol field predicted value of the original data. The time-frequency plot shows the carrier frequency on the x-axis, the sign on the y-axis, and the signal amplitude on the z-axis. It can be seen that most data amplitudes are around 1, close to the standard value, but some predicted values ​​have significant deviations, corresponding to... Figure 7 Outliers;

[0072] Step S4: Predicted values ​​based on the partial symbolic domain of the obtained raw data Based on the modulation scheme specified in commercial signal transmission standards, the data is mapped onto a constellation diagram, and symbol determination is performed to ultimately obtain the reconstructed values ​​of the data portion.

[0073] Specifically, step S4 includes:

[0074] Step S41: Calculate the symbol domain prediction values ​​of the original data obtained in S3. Mapping these values ​​onto a constellation diagram, and according to the transmission standard, mapping the corresponding phase shift keying symbol standard values ​​onto the constellation diagram. Figure 7 This represents the mapping of the symbol domain prediction values ​​of the original data constellation diagram. The blue dots correspond to the complex phase points of the received signal, and the red dots correspond to the standard values ​​and zeros of the phase shift keying symbols of QPSK modulation. It can be seen that the equalized symbols are basically clustered near the phase points and zeros of the original constellation diagram.

[0075] Step S42: Traverse all points in the predicted values ​​of the symbol domain of all data portions, and calculate the Euclidean distance between each point and the standard value of the phase shift keying symbol. Select the standard value of the phase shift keying symbol with the closest Euclidean distance as the estimated value of that point. The calculation formula is as follows:

[0076]

[0077] in: Let be the estimated value of the i-th point in the predicted values ​​of the symbol field in the data portion, min(·) is used to obtain the minimum value in the set, m is the total number of symbols in the data portion, and n is the number of standard value symbols of phase shift keying symbols. Let s(j) be the i-th point in the predicted value of the symbol field of the original data, and s(j) be the standard value of the j-th phase shift keying symbol.

[0078] Reconstructed values ​​of the data section constellation charts as Figure 8 As shown, most of the symbol values ​​have been accurately mapped, compared to the partial symbol domain prediction values ​​of the original data. Outliers were filtered out, and errors in most points relative to the keying symbol standard values ​​were removed through mapping.

[0079] Step S5: Use the reconstructed value of the data portion as the original true value of the transmitted data, that is, let The reconstructed original transmitted signal is obtained by reconstructing the original transmitted signal. X′∈C M×N A high-precision channel parameter estimation algorithm is applied to estimate the multipath component parameters. When the algorithm converges, the channel parameter estimation result is obtained.

[0080] After all estimation processes are completed, the algorithm outputs the time delay, Doppler shift, and complex amplitude results for all estimated paths, as well as the estimated channel reconstructed based on the HRPE algorithm estimation parameters. The time-frequency diagram of the UAV estimated channel obtained by the algorithm is shown below. Figure 10 As shown.

[0081] Estimation accuracy analysis. We measure estimation accuracy by comparing the mean squared error (MSE). MSE refers to the average expected value of the difference between the estimated and true values ​​of the parameter, and its calculation method is shown in the formula below:

[0082]

[0083] Among them, y i This represents the actual measured value of the UAV air-to-ground communication channel. The MSE was calculated using the results obtained from passive channel estimation using only pilot information and the results obtained from channel parameter estimation using pilot enhancement, respectively, to compare the accuracy improvement of this application compared with the traditional method at different signal-to-noise ratios.

[0084] The accuracy of the scheme was simulated by varying the signal-to-noise ratio (SNR) of the added noise from 0 dB to 45 dB. The correlation curves between MSE and SNR are shown below. Figure 11 As shown in the figure, the green line represents the MSE (Reconstructed Significance) curve of the proposed method, while the red line represents the MSE (Reference Signal) curve of the traditional passive measurement scheme using a reference signal. The figure shows that when the SNR is less than 4dB, the green curve is higher than the red curve, indicating a larger channel estimation error in this case. This may be due to the increased error introduced by the reconstructed signal at low SNR, making the proposed method unsuitable. When the SNR is greater than 4dB, the green curve remains lower than the red curve, indicating that the proposed method can be used reliably at high SNR. In medium-to-high SNR conditions, the proposed method effectively improves the accuracy of channel estimation compared to traditional pilot-based passive channel estimation schemes.

[0085] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for estimating existing network channel parameters based on pilot enhancement, characterized in that, include: Step S1: Acquire the received signal and extract the pilot portion of the commercial signal from the received signal as the pilot signal; Step S2: Using the acquired pilot signal and the standard transmission value of the pilot signal of the commercial signal, estimate the multipath component parameters to obtain the amplitude, phase, time delay and Doppler shift, so as to reconstruct the time-varying channel impulse response; Step S3: Based on the reconstructed time-varying channel impulse response, perform equalization processing on the data part of the commercial signal to obtain the predicted value of the symbol domain of the original data part; Step S4: Based on the predicted values ​​of the symbol domain of the obtained original data portion, map them onto the constellation diagram according to the modulation scheme specified in the commercial signal transmission standard, perform symbol decision on them, and finally obtain the reconstructed values ​​of the data portion; Step S5: Use the reconstructed value of the data part as the true value of the original transmitted data to reconstruct the original transmitted signal and obtain the reconstructed value of the original transmitted signal. Use the obtained multipath component parameters as the channel parameter estimation result. The commercial signal is a downlink broadcast signal transmitted by the base station, wherein each frame of the time-frequency resource network contains pilot subcarriers and One data subcarrier, and pilot symbols and One data symbol; Step S4 specifically includes: Step S41: Map the predicted values ​​of the symbol domain of the obtained raw data onto the constellation diagram, and map the corresponding phase shift keying symbol standard values ​​onto the constellation diagram according to the transmission standard; Step S42: Traverse all points in the predicted values ​​of the symbol domain of all data portions, and calculate the Euclidean distance between each point and the standard value of the phase shift keying symbol. Select the standard value of the phase shift keying symbol with the closest Euclidean distance as the estimated value of that point. The calculation formula is as follows: in: The first predicted value in the symbol field of the data portion i The estimated value of each point, To find the minimum value in the set, This represents the total number of symbols in the data section. n The standard value for the number of phase shift keying symbols. The predicted value of the symbol field of the original data is the first one. i One point, For the first j Standard values ​​for phase shift keying symbols.

2. The method for estimating existing network channel parameters based on pilot enhancement according to claim 1, characterized in that, The mathematical expression for the reconstructed time-varying channel impulse response in step S2 is: in: For time-varying channel impulse response, For the first l The amplitude of each propagation path, For the first l Doppler translation of the propagation path For the current moment, For time delay, For the first l The time delay of each propagation path, where L is the number of propagation paths.

3. The method for estimating existing network channel parameters based on pilot enhancement according to claim 2, characterized in that, The process of estimating the multipath component parameters to obtain amplitude, phase, time delay, and Doppler shift is achieved using a high-precision channel parameter estimation algorithm.

4. The method for estimating existing network channel parameters based on pilot enhancement according to claim 3, characterized in that, The high-precision channel parameter estimation algorithm is a channel parameter estimation algorithm based on spatial alternation generalized expectation maximization, a channel parameter estimation algorithm based on matrix eigenspace decomposition, or a channel parameter estimation algorithm based on rotation invariant techniques.

5. The method for estimating existing network channel parameters based on pilot enhancement according to claim 1, characterized in that, The equalization process employs either zero-forcing equalization or minimum mean square error equalization.

6. The method for estimating existing network channel parameters based on pilot enhancement according to claim 5, characterized in that, Step S3 specifically includes: Step S31: Perform a Fourier transform on the reconstructed time-varying channel impulse response to obtain the time-varying transfer function; Step S32: Assuming the transmitted signal travels through the channel with a flat and constant time range within the corresponding frame, calculate the optimal matrix using the time-varying transfer function. G : in: It is a time-varying transfer function. For Hermite operators, P The transmitted signal power is expressed as a scalar. Let Variance be the noise level. It is a unit diagonal matrix; Step S33: Based on the obtained optimal matrix and combined with the received values ​​of the data portion of the commercial signal, obtain the predicted values ​​of the symbol field of the original data portion: in: The predicted value is the symbol field of the original data. The received value is the data portion of the commercial signal.

7. A device for estimating existing network channel parameters based on pilot enhancement, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-6.

8. A storage medium having a program stored thereon, characterized in that, When the program is executed, it implements the method as described in any one of claims 1-6.

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