A dynamic statistical channel model construction method and its application
By constructing a dynamic statistical channel model and using channel measurement data and GPS information to fit the distance relationship between the transmitter and receiver, the problem of low channel modeling accuracy in urban scenarios is solved, and a channel model with higher accuracy and versatility is achieved.
Patent Information
- Application Number
- CN202510030601.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-08
- Publication Date
- 2025-10-03
- Estimated Expiration
- 2045-01-08
AI Technical Summary
Existing statistical channel modeling methods have low accuracy in urban scenarios and are difficult to accurately characterize channel characteristics, especially when the distance between the transmitter and receiver changes, the error is large.
By acquiring channel measurement data and GPS information, the channel impulse response matrix is constructed, the root mean square delay spectrum of the channel stationary interval is calculated, and a linear function is used to fit the distance between the transmitter and receiver to establish a dynamic statistical channel model. The channel model is dynamically adjusted to adapt to position changes.
The accuracy and versatility of the channel model are improved, which can more accurately restore the real channel characteristics and reduce the error when the distance between the transmitter and receiver changes in urban scenarios.
Smart Images
Figure CN119814201B_ABST
Abstract
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, as the only uncontrollable factor, significantly impacts communication quality. To accurately establish channel models for various scenarios, actual channel probing experiments are often conducted. Channel models are built based on the channel probing data obtained during actual channel probing. 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 development of communication standards. It also supports algorithm development, device testing, and the evaluation of emerging technologies, and is crucial for improving communication quality and promoting technological advancement.
[0003] Existing channel modeling methods can be categorized as statistical and deterministic. Although deterministic modeling offers greater accuracy, it is rarely used in communication systems due to its high complexity. Traditional statistical modeling methods often simply average the channel data in a scenario to produce a final channel model. This approach offers advantages in terms of simplicity and wide applicability. However, due to the simple statistical averaging, the model accuracy is relatively low.
[0004] In real-world channel measurements in urban scenarios, the statistical characteristics of the channel increase linearly with the distance between the transmitter and receiver. In this case, using traditional statistical modeling methods will make it difficult to accurately characterize the actual channel. Therefore, to address this issue, 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:
[0007] In one aspect, the present invention provides a method for constructing a dynamic statistical channel model, comprising the following steps:
[0008] Determine the current location of the transmitter and receiver, obtain channel measurement data and GPS information data from the receiver as it moves from the preset location to the transmitter, and record the measurement frequency band used for channel measurement;
[0009] obtaining a channel impulse response matrix based on channel measurement data;
[0010] Calculate the channel stationary interval based on the measurement frequency band;
[0011] 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 based on the average power delay spectrum;
[0012] A linear function is used to fit the root mean square delay spread and the distance between the transmitter and receiver in the stable interval of each channel to obtain a fitting relationship curve as a dynamic statistical channel model;
[0013] 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.
[0014] Furthermore, reconstructing a true channel model at the current position using the obtained dynamic statistical channel model includes:
[0015] Reselect the transmitter and receiver positions and obtain the transmitter-receiver distance after the reselection;
[0016] Using the obtained dynamic statistical channel model and the distance between the transmitter and receiver after repositioning, the root mean square delay spread after repositioning the transmitter and receiver is calculated as the target root mean square delay spread.
[0017] 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 for different multipath numbers.
[0018] 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.
[0019] Furthermore, obtaining a channel impulse response matrix based on the channel measurement data includes:
[0020] Based on the channel measurement data, a correlation peak matrix is obtained, with the measurement waveform length as the row and the channel snapshot as the column;
[0021] The correlation peak matrix obtained by SAGE algorithm is processed to obtain the channel impulse response corresponding to each channel snapshot to form a channel impulse response matrix.
[0022] Furthermore, the channel stable interval is 40 λ ,in λ To measure the signal wavelength.
[0023] Furthermore, suppose that there are a total of The average power delay profile of each channel stationary interval is calculated according to the following formula:
[0024]
[0025] in, is the time delay; is the channel impulse response; To measure time.
[0026] Furthermore, the root mean square delay spread of each channel stationary interval is calculated according to the following formula:
[0027]
[0028] in, L is the PDP length.
[0029] Furthermore, the fitting relationship curve is:
[0030]
[0031] in, is the rms delay spread; is the distance between the transmitting and receiving ends; A parameter of a function.
[0032] Furthermore, in the channel multipath, j The power of the strip diameter is:
[0033]
[0034] in, A parameter of a function.
[0035] Furthermore, let the channel multipath number be M The root mean square delay spread of the channel model for different multipath numbers is calculated according to the following formula:
[0036] .
[0037] On the other hand, the present invention also provides an application of a dynamic statistical channel model construction method, which uses the above dynamic statistical channel model construction method to construct a channel model for an urban scene.
[0038] Compared with the prior art, the present invention has the following beneficial technical effects:
[0039] 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 relatively large. Compared with traditional statistical modeling, the present invention can restore the real channel more efficiently and accurately.
[0040] Furthermore, the dynamic statistical channel model provided by the present invention can be dynamically adjusted according to the positions of the transmitting and receiving ends, thereby improving the versatility and accuracy of the dynamic statistical channel model. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] In order 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 use in the embodiments or the description of the prior art. 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 any creative work.
[0042] Figure 1 A flow chart of a method for constructing a dynamic statistical channel model provided by an embodiment;
[0043] Figure 2 Satellite image of a channel measurement scenario for experimental verification provided in an embodiment, where A and B are Figure 5 、 Figure 6 In the embodiment, TX is the transmitting end and RX is the receiving end.
[0044] Figure 3 A graph showing the change trend of the root mean square delay spread of measured channel data versus the distance between the transmitting and receiving ends provided in one embodiment;
[0045] Figure 4 Channel model diagram obtained by modeling traditional statistical models;
[0046] 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 in an embodiment;
[0047] Figure 6 This is 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 by an embodiment. DETAILED DESCRIPTION
[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.
[0049] 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 from the figure that in the same scenario, there is a correlation between the channel characteristics and the distance between the transmitter and receiver. 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.
[0050] Reference Figure 1 In one embodiment, a method for constructing a dynamic statistical channel model is provided, comprising the following steps:
[0051] Determine the current locations of the transmitter and receiver, obtain channel measurement data and GPS information data from the receiver as it moves from the preset location 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;
[0052] Calculate the channel stationary interval based on the measurement frequency band;
[0053] 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 based on the average power delay spectrum;
[0054] A linear function is used to fit the root mean square delay spread and the distance between the transmitter and receiver in the stable interval of each channel to obtain a fitting relationship curve as a dynamic statistical channel model;
[0055] 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.
[0056] Reconstructing the real channel model at the current position using the obtained dynamic statistical channel model, including:
[0057] Reselect the transmitter and receiver positions and obtain the transmitter-receiver distance after the reselection;
[0058] Using the obtained dynamic statistical channel model and the distance between the transmitter and receiver after repositioning, the root mean square delay spread after repositioning the transmitter and receiver is calculated as the target root mean square delay spread.
[0059] 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 for different multipath numbers.
[0060] 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.
[0061] 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.
[0062] A SISO channel sounding system was used to perform channel measurements in urban scenarios. The measurement waveform used was a ZC sequence.
[0063] Determine the current positions of the transmitter and receiver, obtain channel measurement data (IQ data) and GPS information data of the receiver as it moves 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.
[0064] A 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.
[0065] The correlation peak matrix is processed using a SAGE algorithm to obtain a channel impulse response corresponding to each snapshot, thereby obtaining a channel impulse response matrix.
[0066] 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.
[0067] Based on the channel impulse response matrix, the average power delay spectrum of each channel stationary interval is calculated. Assume that there are a total of The average power delay profile of each channel in the stationary interval is calculated according to the following formula:
[0068]
[0069] in, is the time delay; is the channel impulse response; To measure time.
[0070] The RMS delay spread of each channel stationary interval is calculated according to the following formula:
[0071]
[0072] in, Lis the PDP length.
[0073] Calculate the transceiver distance 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. 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 the dynamic statistical channel model. The fitting relationship curve is:
[0074]
[0075] in, is the rms delay spread; is the distance between the transmitting and receiving ends; A parameter of a function.
[0076] The transmitter and receiver positions are reselected, and the real channel model at the current position is reconstructed using the obtained dynamic statistical channel model.
[0077] 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 stationary interval of each channel and the distance between the transmitter and receiver. .
[0078] In urban scenarios, 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. In the channel multipath, the power attenuation of the first path is about -10dB. j The power of the strip diameter is:
[0079]
[0080] in, A parameter of a function.
[0081] Assume that 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:
[0082] .
[0083] Select Zhongyu The channel model corresponding to the RMS delay spread with the smallest difference is taken as the true channel model at the current position.
[0084] Reference Figure 4, which is the channel model diagram obtained by traditional statistical modeling. 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.
[0085] 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 A in the figure), and when the distance between the transmitter and receiver is far (that is, the receiver is located at Figure 2 (At point B in the figure), a comparison of the dynamic statistical channel model with the traditional statistical channel model and the true channel model is shown. As can be seen from the figure, the traditional statistical channel model has a significant error compared to the true channel model. However, the dynamic statistical channel model provided by this invention can dynamically adjust based on the location of the transmitter and receiver, better reproducing the true channel.
[0086] The dynamic statistical channel model established by the present invention can restore the real channel more accurately than the traditional statistical channel model, and greatly improves the accuracy of the statistical channel model.
[0087] Matters not covered by the present invention are known technologies.
[0088] The technical features of the above embodiments can 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.
[0089] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and such modifications and improvements are intended to fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.
[0090] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection 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 location of the transmitter and receiver, obtain channel measurement data and GPS information data from the receiver as it moves from the preset location 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 stationary 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 based on the average power delay spectrum; A linear function is used to statistically fit the root mean square delay spread and the distance between the transmitter and receiver in each channel's stable interval, and a fitting relationship curve is obtained as a dynamic statistical channel model. Reselect the current transmitter and receiver positions, and use the obtained dynamic statistical channel model to reconstruct the real channel model at the current position; Reconstructing the real channel model at the current position using the obtained dynamic statistical channel model, including: Reselect the transmitter and receiver positions and obtain the transmitter-receiver distance after the reselection; Using the obtained dynamic statistical channel model and the distance between the transmitter and receiver after repositioning, the root mean square delay spread after repositioning the transmitter and receiver 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 for different multipath numbers. 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.
2. 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 includes: Based on the channel measurement data, a correlation peak matrix is obtained, with the measurement waveform length as the row and the channel snapshot as the column; The correlation peak matrix obtained by SAGE algorithm is processed to obtain the channel impulse response corresponding to each channel snapshot to form a channel impulse response matrix.
3. The method for constructing a dynamic statistical channel model according to claim 1, wherein: The channel stability interval is 40 λ ,in λ To measure the signal wavelength.
4. 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 stationary interval is calculated according to the following formula: in, is the time delay; is the channel impulse response; To measure time.
5. 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.
6. 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.
7. The method for constructing a dynamic statistical channel model according to claim 1, wherein: In the multipath channel, j The power of the strip diameter is: in, A parameter of a function.
8. The method for constructing a dynamic statistical channel model according to claim 7, wherein: Assume that the channel multipath number is M The root mean square delay spread of the channel model for different multipath numbers is calculated according to the following formula: 。