Dynamic statistical channel model construction method and application thereof

Abstract

The invention provides a dynamic statistical channel model construction method and application thereof, and the method comprises the steps: determining the positions of a current transmitting end and a receiving end, obtaining the channel measurement data and GPS information data of the receiving end in a process of driving from a preset position to the transmitting end, and recording a measurement frequency band; obtaining a channel impulse response matrix; calculating a channel stationary interval; calculating the average power delay spectrum of each channel stationary interval, and calculating the root-mean-square delay spread of each channel stationary interval; performing statistical fitting on the root-mean-square delay spread of each channel stationary interval and the distance between the receiving end and the transmitting end by adopting a linear function to obtain a fitting relation curve which is used as a dynamic statistical channel model; and reselecting the current positions of the transmitting end and the receiving end, and reconstructing a real channel model at the current position by using the obtained dynamic statistical channel model. According to the invention, dynamic adjustment can be carried out according to the position of the transmitting and receiving end, and the universality and accuracy of the statistical channel model are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless channel modeling, and in particular to a dynamic statistical channel model construction method and application thereof. Background Art

[0002] In wireless communication systems, the wireless channel is the only uncontrollable factor that has a significant impact on the quality of communication. In order to accurately establish the channel model for each scenario, actual detection experiments are often carried out to establish the channel model based on the channel detection data obtained from the actual channel detection. Wireless channel modeling is a key tool for the design, evaluation, and optimization of wireless communication systems. It helps predict signal propagation characteristics, optimize network performance, and provide a scientific basis for the formulation of communication standards. It also supports algorithm development, equipment testing, and evaluation of emerging technologies, and is essential to improving communication quality and promoting technological progress.

[0003] The existing channel modeling methods can be divided into statistical channel modeling and deterministic channel modeling. Although deterministic modeling has higher accuracy, it is rarely used in communication systems due to its high complexity. Traditional statistical modeling methods often simply perform statistical averaging on the channel data in a scene to obtain the final channel model. The advantage of this approach is that it is simple to process and has wide applicability. However, since only statistical averaging is performed, the model accuracy is low.

[0004] In the channel measurement of actual urban scenes, the statistical characteristics of the channel increase linearly with the distance between the transmitter and the receiver. In this case, if the traditional statistical modeling method is used, it will be difficult to correctly characterize the real channel. Therefore, in order to solve the above problems, a technical solution is needed to achieve more accurate channel modeling. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a dynamic statistical channel model construction method and application thereof.

[0006] In order to achieve the above technical objectives, the specific technical solutions adopted by the present invention are as follows: In one aspect, the present invention provides a method for constructing a dynamic statistical channel model, comprising the following steps: Determine the current positions of the transmitter and receiver, obtain the channel measurement data and GPS information data of the receiver during the process of driving from the preset position to the transmitter, and record the measurement frequency band used for channel measurement; Obtaining a channel impulse response matrix based on channel measurement data; Calculate the channel stable interval based on the measurement frequency band; The average power delay spectrum of each channel stationary interval is calculated based on the channel impulse response matrix, and the root mean square delay spread of each channel stationary interval is calculated according to the average power delay spectrum; A linear function is used to fit the root mean square delay spread and the distance between the transmitting and receiving ends in the stable interval of each channel to obtain a fitting relationship curve as a dynamic statistical channel model; The current transmitting end and receiving end positions are reselected, and the real channel model at the current position is reconstructed using the obtained dynamic statistical channel model.

[0007] Further, the obtained dynamic statistical channel model is used to reconstruct a real channel model at the current position, including: Reselect the positions of the transmitter and the receiver, and obtain the distance between the transmitter and the receiver after the reselected positions; Using the obtained dynamic statistical channel model and the distance between the transmitting and receiving ends after the re-selection of the positions, the root mean square delay spread after the re-selection of the transmitting end and the receiving end is calculated as the target root mean square delay spread; The channel model is constructed by taking advantage of the linear attenuation of the multipath power of the channel model, and the root mean square delay spread of the channel model is calculated traversally when the number of multipaths is different. The channel model corresponding to the RMS delay spread that has the smallest difference from the target RMS delay spread is selected as the true channel model at the current position.

[0008] Further, obtaining a channel impulse response matrix based on the channel measurement data includes: Based on the channel measurement data, a correlation peak matrix is ​​obtained with the measurement waveform length as rows and the channel snapshot as columns; The correlation peak matrix obtained by SAGE algorithm is used to obtain the channel impulse response corresponding to each channel snapshot to form a channel impulse response matrix.

[0009] Furthermore, the channel stable interval is 40 λ ,in λ To measure the signal wavelength.

[0010] Furthermore, suppose that there are The average power delay profile of each channel stable interval is calculated according to the following formula:

[0011] in, For time delay; is the channel impulse response; To measure time.

[0012] Furthermore, the root mean square delay spread of each channel stationary interval is calculated according to the following formula:

[0013] in, Lis the PDP length.

[0014] Furthermore, the fitting relationship curve is:

[0015] in, is the RMS delay spread; is the distance between the transmitting and receiving ends; A parameter of a function.

[0016] Furthermore, in the channel multipath, j The power of the strip diameter is:

[0017] in, A parameter of a function.

[0018] Furthermore, let the channel multipath number be M , the root mean square delay spread of the channel model at different multipath numbers is calculated according to the following formula: .

[0019] On the other hand, the present invention also provides an application of a dynamic statistical channel model construction method, which uses the dynamic statistical channel model construction method to construct a channel model for an urban scene.

[0020] Compared with the prior art, the present invention has the following beneficial technical effects: The dynamic statistical channel model construction method provided by the present invention establishes a dynamic statistical model related to the distance between the transceiver and the transmitter by statistically fitting the relationship between the root mean square delay spread and the distance between the transceiver and the transmitter in the stable interval of each channel, thereby greatly improving the accuracy of the statistical channel model. The traditional statistical modeling method is to statistically average the power delay spectrum in a scene to reflect the delay spread of the scene. In the ultra-short wave frequency band of the urban scene, the delay spread of the channel is related to the distance between the transceiver and the transmitter. Therefore, the traditional statistical modeling error is large. Compared with traditional statistical modeling, the present invention can restore the real channel more efficiently and accurately.

[0021] Furthermore, the dynamic statistical channel model provided by the present invention can be dynamically adjusted according to the position of the transmitting and receiving ends, thereby improving the versatility and accuracy of the dynamic statistical channel model. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0023] Figure 1 A flow chart of a method for constructing a dynamic statistical channel model provided by an embodiment; Figure 2 A satellite image of a channel measurement scenario for experimental verification provided in an embodiment, wherein A and B are Figure 5 , Figure 6 In the embodiment, TX is the transmitting end and RX is the receiving end; Figure 3 A graph showing a trend of the root mean square delay spread of measured channel data versus the distance between the transmitting and receiving ends provided in an embodiment; Figure 4 Channel model diagram obtained by modeling the traditional statistical model; Figure 5 A comparison diagram of a dynamic statistical channel model, a traditional statistical channel model and a real channel model when the distance between the transmitting and receiving ends is relatively close provided by an embodiment; Figure 6 A comparison diagram of a dynamic statistical channel model, a traditional statistical channel model and a real channel model when the distance between the transmitting and receiving ends is far provided in an embodiment. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Reference Figure 3 In one embodiment, the root mean square delay spread of the channel data is measured as the distance between the transmitting and receiving ends changes. Figure 3 It can be seen that in the same scenario, there is a correlation between the channel characteristics and the distance between the transmitter and receiver, and from the fitting results of the RMS delay spread of the channel data and the distance between the transmitter and receiver, it can be seen that there is a linear relationship between the RMS delay spread and the distance between the transmitter and receiver.

[0026] Reference Figure 1 In one embodiment, a method for constructing a dynamic statistical channel model is provided, comprising the following steps: Determine the current positions of the transmitter and receiver, obtain the channel measurement data and GPS information data of the receiver in the process of driving from the preset position to the transmitter, and record the measurement frequency band used for channel measurement; obtain the channel impulse response matrix based on the channel measurement data; Calculate the channel stable interval based on the measurement frequency band; The average power delay spectrum of each channel stationary interval is calculated based on the channel impulse response matrix, and the root mean square delay spread of each channel stationary interval is calculated according to the average power delay spectrum; A linear function is used to fit the root mean square delay spread and the distance between the transmitting and receiving ends in the stable interval of each channel to obtain a fitting relationship curve as a dynamic statistical channel model; The current transmitting end and receiving end positions are reselected, and the real channel model at the current position is reconstructed using the obtained dynamic statistical channel model.

[0027] Reconstructing the real channel model at the current position using the obtained dynamic statistical channel model, including: Reselect the positions of the transmitter and the receiver, and obtain the distance between the transmitter and the receiver after the reselected positions; Using the obtained dynamic statistical channel model and the distance between the transmitting and receiving ends after the re-selection of the positions, the root mean square delay spread after the re-selection of the transmitting end and the receiving end is calculated as the target root mean square delay spread; The channel model is constructed by taking advantage of the linear attenuation of the multipath power of the channel model, and the root mean square delay spread of the channel model is calculated traversally when the number of multipaths is different. The channel model corresponding to the RMS delay spread that has the smallest difference from the target RMS delay spread is selected as the true channel model at the current position.

[0028] Reference Figure 2 In one embodiment, the above-mentioned dynamic statistical channel model construction method is applied to construct a dynamic statistical channel model for an urban scene.

[0029] The SISO channel detection system is used to perform channel measurement in urban scenarios. The measurement waveform adopts the ZC sequence.

[0030] Determine the current positions of the transmitter and receiver, obtain channel measurement data (IQ data) and GPS information data of the receiver in the process of moving from the preset position to the transmitter, and record the measurement frequency band used for channel measurement. In this embodiment, the measurement frequency band is ultrashort wave.

[0031] The correlation peak matrix reflecting the channel delay spread is obtained by using the ZC sequence as the correlation of the pseudo-random sequence. The correlation peak matrix has the measured waveform length as the row and the channel snapshot as the column.

[0032] The correlation peak matrix is ​​processed using the SAGE algorithm to obtain a channel impulse response corresponding to each snapshot, thereby obtaining a channel impulse response matrix.

[0033] Based on the measurement frequency band used for channel measurement, the channel stability interval is calculated to be 40 λ ,in λ To measure the signal wavelength.

[0034] Based on the channel impulse response matrix, the average power delay spectrum of each channel stable interval is calculated. Assume that there are a total of The average power delay profile of each channel in the stable interval is calculated according to the following formula:

[0035] in, For time delay; is the channel impulse response; To measure time.

[0036] The RMS delay spread of each channel stationary interval is calculated according to the following formula:

[0037] in, L is the PDP length.

[0038] Calculate the distance between the transmitter and receiver corresponding to each channel stable interval based on the collected GPS information data The relationship between the root mean square delay spread and the distance between the transmitting and receiving ends in the stable interval of each channel is plotted, and the root mean square delay spread and the distance between the transmitting and receiving ends in the stable interval of each channel are fitted with a linear function to obtain a fitting relationship curve as a dynamic statistical channel model. The fitting relationship curve is:

[0039] in, is the RMS delay spread; is the distance between the transmitting and receiving ends; A parameter of a function.

[0040] The positions of the transmitting end and the receiving end are reselected, and the real channel model at the current position is reconstructed using the obtained dynamic statistical channel model.

[0041] Calculate the distance between the transmitter and receiver after reselection. Calculate the RMS delay spread after reselecting the transmitter and receiver positions based on the fitting relationship curve between the RMS delay spread in the stable interval of each channel and the distance between the transmitter and receiver. .

[0042] In urban scenes, there are abundant scatterers and dense multipaths. Except for the first path, the power attenuation can be roughly expressed by a linear function. The second path is about -10dB, and the last path is about -30dB. It can be seen that the multipath attenuation trend of the channel model is a linear function. j The power of the strip diameter is:

[0043] in, A parameter of a function.

[0044] Assume the channel multipath number is M , the RMS delay spread of the channel model with different multipath numbers is calculated according to the following formula: .

[0045] Select Zhongyu The channel model corresponding to the RMS delay spread with the smallest difference is taken as the real channel model at the current position.

[0046] Reference Figure 4 , which is the channel model diagram obtained by the traditional statistical model. It can be seen from the figure that the result of traditional statistical channel modeling is a constant value and cannot correctly represent the actual channel situation.

[0047] Reference Figure 5 , Figure 6 , respectively when the distance between the transmitting and receiving ends is close (that is, the receiving end is located at Figure 2 When the receiving end is at point A in the figure), and when the distance between the transmitting and receiving ends is far (that is, the receiving end is at Figure 2 ), a comparison diagram of the dynamic statistical channel model, the traditional statistical channel model and the real channel model. As can be seen from the figure, the traditional statistical channel model has a large error compared to the real channel model, while the dynamic statistical channel model provided by the present invention can be dynamically adjusted according to the position of the transmitting and receiving ends, and can better restore the real channel.

[0048] Compared with the traditional statistical channel model, the dynamic statistical channel model established by the present invention can restore the real channel more accurately, and greatly improves the accuracy of the statistical channel model.

[0049] Matters not covered by the present invention are known technologies.

[0050] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0051] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

[0052] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for constructing a dynamic statistical channel model, characterized in that: The following steps are involved: Determine the current positions of the transmitter and receiver, obtain the channel measurement data and GPS information data of the receiver during the process of driving from the preset position to the transmitter, and record the measurement frequency band used for channel measurement; Obtaining a channel impulse response matrix based on channel measurement data; Calculate the channel stable interval based on the measurement frequency band; The average power delay spectrum of each channel stationary interval is calculated based on the channel impulse response matrix, and the root mean square delay spread of each channel stationary interval is calculated according to the average power delay spectrum; A linear function is used to statistically fit the root mean square delay spread and the distance between the transmitting and receiving ends in each channel stable interval to obtain a fitting relationship curve as a dynamic statistical channel model; The current transmitting end and receiving end positions are reselected, and the real channel model at the current position is reconstructed using the obtained dynamic statistical channel model.

2. The method for constructing a dynamic statistical channel model according to claim 1, characterized in that: Reconstructing the real channel model at the current position using the obtained dynamic statistical channel model, including: Reselect the positions of the transmitter and the receiver, and obtain the distance between the transmitter and the receiver after the reselected positions; Using the obtained dynamic statistical channel model and the distance between the transmitting and receiving ends after the re-selection of the positions, the root mean square delay spread after the re-selection of the transmitting end and the receiving end is calculated as the target root mean square delay spread; The channel model is constructed by taking advantage of the linear attenuation of the multipath power of the channel model, and the root mean square delay spread of the channel model is calculated traversally when the number of multipaths is different. The channel model corresponding to the RMS delay spread that has the smallest difference from the target RMS delay spread is selected as the true channel model at the current position.

3. The method for constructing a dynamic statistical channel model according to claim 1, wherein: The obtaining of a channel impulse response matrix based on the channel measurement data comprises: Based on the channel measurement data, a correlation peak matrix is ​​obtained with the measurement waveform length as rows and the channel snapshot as columns; The correlation peak matrix obtained by SAGE algorithm is used to obtain the channel impulse response corresponding to each channel snapshot to form a channel impulse response matrix.

4. The method for constructing a dynamic statistical channel model according to claim 1, wherein: The channel stable interval is 40 λ ,in λ To measure the signal wavelength.

5. The method for constructing a dynamic statistical channel model according to claim 1, wherein: Assume that there are a total of The average power delay profile of each channel stable interval is calculated according to the following formula: in, For time delay; is the channel impulse response; To measure time.

6. The method for constructing a dynamic statistical channel model according to claim 1, wherein: The root mean square delay spread of each channel stationary interval is calculated according to the following formula: in, L is the PDP length.

7. The method for constructing a dynamic statistical channel model according to claim 1, wherein: The fitting relationship curve is: in, is the RMS delay spread; is the distance between the transmitting and receiving ends; A parameter of a function.

8. The method for constructing a dynamic statistical channel model according to claim 2, wherein: In the multipath channel, j The power of the strip diameter is: in, A parameter of a function.

9. The method for constructing a dynamic statistical channel model according to claim 8, wherein: Assume the channel multipath number is M , the root mean square delay spread of the channel model at different multipath numbers is calculated according to the following formula: 。 10. An application of a dynamic statistical channel model construction method, characterized in that: A channel model for an urban scene is constructed using the dynamic statistical channel model construction method described in any one of claims 1 to 9.

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