CN-116743292-B - Wireless channel modeling method based on measured signal multipath relative amplitude probability

Abstract

A wireless channel modeling method based on the measured signal multipath relative amplitude probability utilizes channel detection equipment to collect and record channel impulse response data of a specific scene propagation link, obtains parameters such as large-scale propagation loss, small-scale multipath number, relative time delay, relative amplitude, amplitude distribution and the like through analysis and processing of the data, establishes a wireless channel model matched with the specific propagation scene, thereby realizing high-precision modeling of complex and changeable electric wave propagation effects and solving the problem of poor matching degree of a simulation model and a real scene.

Inventors

• ZHANG TAOTAO
• HE WENWEN
• WANG MANXI
• TANG YUNGE
• YANG ZHIFEI
• QIAO HUIDONG
• LI YINGDA
• Yang Laitao
• WANG YI
• DAI PENGJU

Assignees

• 中国人民解放军63892部队

Dates

Publication Date

20260505

Application Date

20230627

Claims (2)

1. 1. A wireless channel modeling method based on the measured signal multipath relative amplitude probability is characterized by comprising the following steps: Step 1, selecting a test area, determining the positions of a transmitting antenna and a receiving antenna, respectively connecting a standard antenna to a transmitter and a receiver of channel detection equipment, then placing the standard antenna at the test position, and setting and recording the transmitting power of the channel detection equipment Gain of transmitting antenna Gain of receiving antenna Acquiring and storing channel impulse response data of a propagation link by using a channel detection recording device; step 2, processing the channel impulse response data, and eliminating noise interference in measurement; Step 3, further processing the preprocessed multipath data to respectively obtain large-scale propagation loss, small-scale multipath number, relative time delay, relative amplitude and amplitude distribution, and realizing modeling of the propagation effect of the test scene, wherein the preprocessed multipath data processing method comprises the following steps: (1) Preprocessing channel impulse response data to generate large-scale parameters; (2) Extracting small-scale propagation loss parameters of channel impulse response data; ① With main path signal power of each snapshot The method comprises the steps of taking multi-path data of each snapshot as a reference, carrying out normalization processing, wherein the relative attenuation of a main path signal of a small-scale multi-path parameter after normalization processing is 0dB, and deleting invalid data before the relative attenuation is 0dB when only one data with the relative attenuation of 0dB exists in each snapshot; ② After deleting invalid data, the data in each snapshot is aligned with the relative attenuation of 0dB as the first, the shortest length of each snapshot is used as the column element data length of the multi-path data matrix, and the data beginning with the main path sequence is constructed A row(s), Column multipath information data matrix ; ③ Matrix is formed Middle element Is converted into signal amplitude And generate a matrix ; ④ Matrix is formed The elements of each column are added and averaged, and then the power is calculated to obtain a row Array of individual elements ; ⑤ Searching arrays The first position of the middle position is smaller than the main diameter threshold value Truncating the subsequent data to generate Matrix array ( ); ⑥ Pair matrix Maximum value is taken by each row of data , The following should be satisfied: ⑦ Statistical matrix Probability of column maximum element in each column of data ; ⑧ Setting maximum probability judgment threshold and listing probability of maximum element The data with the probability not smaller than the judgment threshold is listed as effective multipath, and the amplitude of the data is the relative amplitude of the multipath; ⑨ Based on the relative positions of the effective multipaths and the main path Time delay resolution of a measuring device Calculating the relative delay of effective multipaths ; ⑩ Statistical matrix Envelope probability density curves of column data of the effective multipaths are compared with typical envelope distribution types to obtain distribution characteristics of the effective multipaths; (3) And (3) combining the large scale parameters generated in the step (1) with the small scale parameters generated in the step (2) to realize channel propagation loss and multipath number, relative amplitude, relative time delay and amplitude distribution type parameter estimation of a specific propagation scene, thereby completing channel modeling.
2. 2. The method for modeling a wireless channel based on measured signal multipath relative amplitude probability of claim 1, wherein the method for preprocessing channel impulse response data is as follows: ① Extracting the path with the largest multipath signal amplitude in each snapshot between the receiving and transmitting channels as the main path, and recording the main path signal power of each snapshot ; ② According to the transmitted power Gain of transmitting antenna Gain of receiving antenna Calculating the large-scale propagation loss of each snapshot ; ③ Calculation of The value of the cumulative probability of 50% is the large-scale propagation loss of the propagation link 。

Description

Wireless channel modeling method based on measured signal multipath relative amplitude probability Technical field: The invention belongs to the technical field of electronic communication, and mainly relates to a wireless channel modeling method based on measured signal multipath relative amplitude probability. The background technology is as follows: the performance of a wireless communication system is primarily limited by the wireless channel. In a mobile communication system, a propagation path between a transmitter and a receiver is very complex, and the propagation path is from a simple line-of-sight path to various complex terrains, such as a large building, a mountain, an obstacle and the like, and the multipath propagation of a wireless signal caused by reflection, diffraction and scattering can affect the propagation of an electromagnetic wave, so that multipath, fading, time delay, attenuation and the like are generated on a received signal, and the received signal has strong randomness in different physical spaces. Channel modeling and simulation serve as important means for channel research, channel propagation characteristics can be rapidly reproduced, and performance evaluation and verification of a complex communication system are realized. The wireless channel simulator can reproduce the wireless channel propagation effect in a controlled laboratory environment, and can simulate typical multipath, fading, time delay, attenuation and other wireless channel propagation effects. The wireless channel simulator realizes the support of the channel simulation model which is highly matched with the independent simulation scene for the accurate simulation of the space radio wave. The accuracy of the channel characteristic simulation influences the reliability of the test result, so that channel simulation environments in different scenes are realistically built, and channel parameter estimation in a specific environment is required to be completed. The wireless channel propagation effect simulation model is divided into a large-scale fading propagation model and a small-scale fading propagation model. At present, a small-scale simulation model used in a wireless channel simulator is a tap time delay model, the recommended model is used for distinguishing the granularity of an application scene from coarse, a propagation scene is distinguished into land and water surface, a propagation area is distinguished into cities, villages, hills, suburban areas and the like, buildings, vegetation coverage and the like on a propagation path are not distinguished, and even under the same distance, the performance of a communication system is different due to different scatterers of the propagation scene. Because the channel model has poor matching degree with the real propagation scene, the simulation precision in the specific propagation scene is affected, and the performance evaluation and verification of the communication system are not facilitated. Therefore, it is necessary to develop wireless channel modeling in a specific scenario. The invention comprises the following steps: In order to establish a wireless channel model matched with a specific propagation scene and realize realistic simulation of the wave propagation effect, the invention provides a wireless channel modeling method based on the multipath relative amplitude probability of an actually measured signal, which can realize accurate estimation of wireless channel parameters such as transmission loss, multipath number, multipath time delay, relative amplitude and amplitude distribution type under the specific scene, thereby realizing high-precision modeling of complex and changeable wave propagation effect and solving the problem of poor matching degree of a simulation model and a real scene. The invention solves the technical problems by adopting the technical scheme that: a wireless channel modeling method based on measured signal multipath relative amplitude probability comprises the following steps: Step 1, selecting a test area, determining the positions of a transmitting antenna and a receiving antenna, respectively connecting a standard antenna to a transmitter and a receiver of channel detection equipment, then placing the standard antenna at the test position, setting and recording the transmitting power P t of the channel detection equipment, the gain G antenna_t of the transmitting antenna and the gain G antenna_r of the receiving antenna, and acquiring and storing channel impulse response data of a propagation link by using the channel detection recording equipment; step 2, processing the channel impulse response data, and eliminating noise interference in measurement; Step 3, further processing the preprocessed multipath data to respectively obtain large-scale propagation loss, small-scale multipath number, relative time delay, relative amplitude and amplitude distribution, and realizing modeling of the propagation effect of the t