WO2021175344A2 - Procédé et dispositif de mesure et de reconstruction dynamiques de réponse impulsionnelle de canal sans fil - Google Patents

Procédé et dispositif de mesure et de reconstruction dynamiques de réponse impulsionnelle de canal sans fil Download PDF

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WO2021175344A2
WO2021175344A2 PCT/CN2021/091940 CN2021091940W WO2021175344A2 WO 2021175344 A2 WO2021175344 A2 WO 2021175344A2 CN 2021091940 W CN2021091940 W CN 2021091940W WO 2021175344 A2 WO2021175344 A2 WO 2021175344A2
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channel
impulse response
module
data
signal
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WO2021175344A3 (fr
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朱秋明
毛开
宁本哲
陈小敏
李瀚芃
仲伟志
黄洋
吴启晖
张小飞
华博宇
鲍军伟
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南京航空航天大学
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  • the present invention relates to the technical field of wireless communication, in particular to a method for dynamic measurement and reconstruction of wireless channel impulse response and a device for dynamic measurement and reconstruction of wireless channel impulse response.
  • Time domain channel measurement is the direct measurement of the impulse response of the channel, which can be divided into pulse-based channel measurement technology and related channel measurement technology; frequency domain channel measurement can directly measure the transmission function of the channel, and use frequency domain and time domain Correspondence to obtain the channel impulse response.
  • frequency domain channel measurement can directly measure the transmission function of the channel, and use frequency domain and time domain Correspondence to obtain the channel impulse response.
  • the receiving and sending signal ends are susceptible to interference from other signals at the same frequency. When the power of the transmitted signal is limited, the obtained multipath signal strength is low.
  • Frequency domain channel measurement requires accurate time positioning and strict synchronization at the transceiver end, which limits the test distance to a certain extent, and the transmitter and receiver are in the same physical device, so it is mainly suitable for short distances in static environments Channel measurement.
  • Correlation-based channel measurement technology uses the good autocorrelation of pseudo-random sequences, reduces the average noise power and average interference power in the receiving bandwidth, and has good anti-interference characteristics.
  • this method is difficult to perform real-time analysis of fast time-varying non-stationary scenes.
  • using traditional signal processing methods to estimate channel impulse response is more complicated, and it is difficult to solve the problem of real-time reproduction of real channel characteristics.
  • the purpose of the present invention is to provide a wireless channel impulse response dynamic measurement reconstruction method and device, which can dynamically measure and obtain channel impulse response data of fast time-varying non-stationary scenes, and use it to train a neural network based on a propagation model to obtain Channel impulse response characteristic parameters, thereby realizing real-time reproduction of real channel impulse response characteristics, which can be applied to dynamic measurement and reconstruction of wireless channel impulse response in complex non-stationary scenarios, as well as hardware realistic simulation of wireless channel propagation characteristics , And then improve the performance test and evaluation of communication equipment.
  • an embodiment of the present invention provides a method for dynamic measurement and reconstruction of a wireless channel impulse response.
  • the method includes:
  • Disconnect the radio frequency cable use the GPS module to perform clock synchronization calibration of the transceiver device, and send a channel measurement signal based on the input channel measurement parameter via the channel measurement transmitting unit;
  • the channel parameter training unit extracts the characteristic parameters in the original channel impulse response data, determines and trains the neural network based on the original channel impulse response data and the characteristic parameters, and generates each path based on the trained neural network The delay value and power value;
  • the channel response reconstruction unit reconstructs and reproduces the channel impulse response based on the delay value and power value of each path, and obtains the channel output data based on the superposition of the reproduced channel impulse response and the channel input analog data.
  • the acquiring system additive noise and calculating the system response coefficient includes:
  • the transmission signal is set as a single tone signal, the transmission signal power is 1 dBm, and the system response coefficient is calculated by the ratio of the received signal power to the transmission signal power.
  • the completion of signal processing based on the system additive noise and the system response coefficient to obtain the original channel impulse response data includes:
  • Q is the additive noise of the system
  • k is the system response coefficient
  • y r [n] is the channel measurement data corresponding to the channel measurement signal and actually received by the measurement
  • y [n] is the elimination Channel measurement data affected by the hardware system itself
  • the channel impulse response r[n] is the data processed by a single receiving antenna element in the receiving antenna array, and the remaining receiving antenna elements of the receiving antenna array are processed
  • the channel impulse response matrix of the receiving antenna array is obtained by integrating the channel impulse responses of the v receiving antenna array elements at u coherent moments Channel impulse response matrix The original data for the channel impulse response.
  • the extraction of the characteristic parameters in the original channel impulse response data by the channel parameter training unit includes:
  • I the initialization matrix of the angle parameter
  • I the value of the nth discrete time delay point of the channel impulse response matrix of the receiving antenna array at time t;
  • D AOA (c i ,c j ), D ⁇ (c i ,c j ) and D P (c i ,c j ) are the distances of the i-th element and the j-th element with respect to the angle of arrival AOA , Regarding the distance of the delay ⁇ and the distance of the power P, i and j are positive integers, respectively expressed as:
  • ⁇ std is the standard deviation of the multipath component time delay
  • ⁇ max is the maximum value of the multipath component time delay difference
  • P std is the standard deviation of the multipath component power
  • ⁇ P max is the maximum value of the multipath component power difference
  • the determining and training a neural network based on the original channel impulse response data and the characteristic parameters includes:
  • the network scale of the neural network is determined to be L'based on the subset set ⁇ C 1 , C 2 ,..., C L' ⁇ , where the neural network includes an input layer, and the input layer is input with a narrow pulse signal ⁇
  • the neural network further includes a delay control layer with L'neurons and a hidden layer with L'neurons in sequence after the input layer, and the neural network further includes a delay control layer with L'neurons.
  • the output layer, the output layer is expressed as:
  • ⁇ l' represents the pulse delay coefficient of each path
  • ⁇ l' represents the weight of each path
  • f out is the activation function of the output layer.
  • r(t) is the channel impulse response of the single receiving antenna array element at time t and is used as training set data.
  • the weight ⁇ l' and the delay coefficient ⁇ l' are adjusted by the gradient descent method.
  • the delay value of each path at time t is The power value of each path is Obtain the delay value and power value of each path, and form a time-varying input set
  • the embodiment of the present invention also provides a wireless channel impulse response dynamic measurement and reconstruction device, which includes:
  • the channel measurement transmitting unit is used to send channel measurement signals based on the input channel measurement parameters
  • a channel measurement receiving unit configured to receive the channel measurement signal, complete signal processing based on the system additive noise and the system response coefficient, and obtain the original channel impulse response data
  • the channel model training unit is used to extract characteristic parameters in the original channel impulse response data, determine and train a neural network based on the original channel impulse response data and the characteristic parameters, and generate a neural network based on the trained neural network The delay value and power value of each path;
  • the channel response reconstruction unit is used to reconstruct and reproduce the channel impulse response based on the delay value and power value of each path, and obtain the channel output based on the superposition of the reproduced channel impulse response and the channel input analog data data.
  • the channel measurement transmission unit includes a transmission control module, a signal generation module, a power amplification module, and a transmission antenna;
  • the transmission control module is used to control the parameters of the transmission signal and configure the type of the transmission signal
  • the signal generation module is used to complete the radio frequency movement of the baseband signal based on the configuration of the transmission control module to generate a radio frequency signal;
  • the power amplifying module is used to amplify the signal power of the radio frequency signal and transmit it through a transmitting antenna.
  • the channel measurement receiving unit includes a low-noise amplification module, a signal conditioning module, a receiving control module, a preprocessing module, and a receiving antenna array;
  • the low-noise amplifying module is used to amplify the signal received by the receiving antenna array through low-noise power and transmit it to the signal conditioning module;
  • the signal conditioning module is used for conditioning the transmitted and received signal
  • the receiving control module is used to control the parameter setting of the received signal, control the signal conditioning module and the preprocessing module;
  • the preprocessing module is used to perform real-time correlation operations on the conditioned signal to obtain the channel impulse response.
  • the channel model training unit includes a data storage module, a feature parameter extraction module, a network scale determination module, and a neural network training module;
  • the data storage module is used to store channel impulse response data and channel impulse response original data
  • the characteristic parameter extraction module is configured to extract characteristic parameters based on the original channel impulse response data and transfer them to the network size determination module;
  • the network scale determination module is used to process the characteristic parameters, determine the network scale of the neural network, and transmit the network scale to the neural network training module;
  • the neural network training module is configured to use channel impulse response data as the training set data of the neural network, train the neural network based on the input layer of the neural network and the network scale, and train the neural network based on the trained The neural network generates the delay value and power value of each path.
  • the channel response reconstruction unit includes an impulse response reconstruction module and a channel superposition module;
  • the impulse response reconstruction module is configured to reproduce an analog channel based on the delay value and power value of each path, and generate a reconstructed channel impulse response based on the analog channel;
  • the channel superimposing module is used to superimpose channel input data on the analog channel to obtain channel output data, and the channel output data is used as analog recurring channel measurement data.
  • the invention realizes the channel measurement work in the fast time-varying non-stationary scene, and is particularly suitable for applications such as base station layout and wireless network optimization.
  • the invention obtains the accurate reconstructed channel impulse response through the training and reconstruction of the actual measurement data based on the neural network, and performs the hardware reconstruction simulation reproduction of the actual measurement channel characteristics, which greatly saves the consumption of the actual measurement resources in the field, and improves the channel measurement and measurement. Simulate the efficiency of reproduction.
  • FIG. 1 is a schematic diagram of the structure of an exemplary wireless channel impulse response dynamic measurement and reconstruction device according to an embodiment of the present invention
  • FIG. 2 is a schematic flow chart of an exemplary wireless channel impulse response dynamic measurement reconstruction method according to an embodiment of the present invention
  • Fig. 3 is a schematic diagram of an exemplary neural network layer structure according to an embodiment of the present invention.
  • the embodiment of the present invention provides a wireless channel impulse response dynamic measurement reconstruction (measurement and reconstruction) device.
  • the device may be a system that combines channel measurement hardware and software.
  • the device may include a channel measurement transmitting unit 1- 1.
  • the channel model training unit 1-3 is connected to the channel response reconstruction unit 1-4, and the three can share a receiving control module 1-10.
  • the channel measurement transmission unit 1-1 may include a transmission control module 1-5, a signal generation module 1-6, a power amplification module 1-7, and a (omnidirectional) transmission antenna.
  • the transmission control module 1-5 controls the configuration of the parameters of the transmission signal and the type of the transmission signal
  • the signal generation module 1-6 completes the radio frequency transfer of the baseband signal based on the configuration of the transmission control module, and generates the radio frequency signal
  • the power amplifier module 1-7 responsible for amplifying the signal power and transmitting the signal through the transmitting antenna.
  • the channel measurement receiving unit 1-2 may include a low-noise amplifying module 1-8, a signal conditioning module 1-9, a receiving control module 1-10, a pre-processing module 1-11, and a receiving antenna array;
  • the low-noise amplifying module 1-8 is responsible for transmitting the signal received by the receiving antenna array to the signal conditioning module 1-9 through low-noise power amplification;
  • the signal conditioning module 1-9 is responsible for real-time correction, denoising and other conditioning operations on the received signal;
  • the receiving control module 1-10 is responsible for controlling the parameter setting of the received signal, the control signal conditioning module 1-9, and the (data) preprocessing module 1-11.
  • the preprocessing module 1-11 is responsible for performing real-time correlation operations on the conditioned signal , And then get the channel impulse response.
  • the channel model training unit 1-3 may include a data storage module 1-12, a feature parameter extraction module 1-13, a network size determination module 1-14, and a neural network training module 1-15;
  • the data storage module 1-12 It can realize high-rate data storage, real-time storage of channel parameters or third-party offline data import and received signals and data obtained based on received signal processing;
  • the characteristic parameter extraction module 1-13 is responsible for measuring data or channels from the channel The impulse response extracts parameters such as time delay, power, and angle, and transmits them to the network size determination module 1-14;
  • the network size determination module 1-14 is responsible for processing feature parameters to determine the neural network training scale, and calculate the neural network input layer Parameters, and then pass them to the neural network training module 1-15;
  • the neural network training module 1-15 is responsible for using channel measurement (received) data or channel impulse response as neural network training set data, and training the neural network, Until the reconstruction of the channel model parameters in the neural network tends to be stable.
  • the channel response reconstruction unit 1-4 may include an impulse response reconstruction module 1-16 and a channel superposition module 1-17;
  • the impulse response reconstruction module 1-16 is mainly a neural network training module 1-15 Training stable reconstructed data is used for the realization of analog channels to generate a reconstructed channel impulse response;
  • the channel superimposing module 1-17 is responsible for superimposing the generated channel with channel input data, and then outputting the simulated and reproduced channel measurement data.
  • the aforementioned modules or units can be used in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), chip It is implemented in a system on a system (SoC), a load programmable logic device (CPLD), computer hardware, firmware, software, and/or a combination thereof.
  • FPGA field programmable gate arrays
  • ASIC application-specific integrated circuits
  • ASSP application-specific standard products
  • SoC system on a system
  • CPLD load programmable logic device
  • computer hardware firmware, software, and/or a combination thereof.
  • the embodiment of the present invention also provides a wireless channel impulse response dynamic measurement and reconstruction method under the same inventive concept, and the method includes:
  • Disconnect the radio frequency cable use the GPS module to perform clock synchronization calibration of the transceiver device, and send a channel measurement signal based on the input channel measurement parameter via the channel measurement transmitting unit;
  • the channel parameter training unit extracts the characteristic parameters in the original channel impulse response data, determines and trains the neural network based on the original channel impulse response data and the characteristic parameters, and generates each path based on the trained neural network The delay value and power value;
  • the channel response reconstruction unit reconstructs and reproduces the channel impulse response based on the delay value and power value of each path, and obtains the channel output data based on the superposition of the reproduced channel impulse response and the channel input analog data.
  • the first step is to use the radio frequency cable to directly connect the channel to measure the transceiver equipment of the hardware system.
  • the transceiver equipment includes the transmitter equipment and the receiver equipment.
  • the transmitter equipment such as the transmitter, etc.
  • the receiver For equipment such as receivers, first ground the transmitting end (equipment) and send a zero-load signal.
  • the transmitting end (equipment)
  • the system response coefficient k can be calculated as follows
  • the second step is to disconnect the radio frequency cable and use the GPS module to synchronize the clock of the transceiver;
  • the channel measurement parameters include the channel measurement parameters input by the user such as the center frequency and bandwidth, and the signal generation module 1-6 is configured through the transmission control module 1-5 , Generate a channel measurement signal, and send it out through the power amplification module 1-7 and the transmitting antenna.
  • the transmission sequence corresponding to the channel measurement signal used in the present invention is the ZC sequence, and the ZC sequence has the following form:
  • the channel measurement transmission signal passes through the receiving antenna array (there are multiple receiving antenna elements in the receiving antenna array.
  • the receiving antenna array may be referred to as multiple antennas in the embodiment of the present invention.
  • the signal y r [n] is preprocessed to obtain the original channel impulse response data.
  • y r [n] is the channel measurement data received by the actual measurement
  • y [n] is the channel measurement data after eliminating the influence of the hardware system itself.
  • Equation (4) Represents the inverse sequence of the channel measurement signal transmission sequence.
  • the channel impulse response r[n] is the data processed by the single antenna of the receiving antenna array, and the other antennas are processed in the same way.
  • the channel of u coherent moments and v receiving antenna elements Impulse response integration can get the multi-antenna channel impulse response matrix
  • the channel storage module 1-12 can also accept third-party measurement data (data equivalent to the aforementioned channel measurement data) to import, and the characteristic parameter extraction module 1-13 uses the stored actual measurement data or third-party measurement data to determine Feature parameters, the network scale determination module 1-14 determines the neural network scale by processing the feature parameters, and then the neural network training module 1-15 starts training according to the network scale, input layer data and selected training set data;
  • third-party measurement data data equivalent to the aforementioned channel measurement data
  • the characteristic parameter extraction module 1-13 uses the stored actual measurement data or third-party measurement data to determine Feature parameters
  • the network scale determination module 1-14 determines the neural network scale by processing the feature parameters
  • the neural network training module 1-15 starts training according to the network scale, input layer data and selected training set data
  • the angle parameters can include the angle of arrival azimuth and the angle of arrival pitch, and the angle of arrival and pitch of the nth path at time t
  • the angles ⁇ n (t) and ⁇ n (t) are obtained according to the following formula:
  • I the initialization matrix of the angle parameter
  • I the value of the nth discrete time delay point of the channel impulse response matrix of the receiving antenna array at time t.
  • the distance calculation method is as follows:
  • D AOA (c i ,c j ), D ⁇ (c i ,c j ) and D P (c i ,c j ) are the distances of the i-th element and the j-th element with respect to the angle of arrival AOA , Regarding the distance of the time delay ⁇ and the distance of the power P, i and j are positive integers, and the three can be expressed as:
  • ⁇ std is the standard deviation of the multipath component time delay
  • ⁇ max is the maximum value of the multipath component time delay difference
  • P std is the standard deviation of the multipath component power
  • ⁇ P max is the maximum value of the multipath component power difference
  • the fifth step is to construct a neural network with a network scale of L'in the neural network training module 1-15, use the narrow pulse signal as the data of the neural network input layer, and use the channel impulse response data and output layer obtained after the measurement data processing Calculate the error function value of the neural network from the data, and back-propagate training the various parameters of the neural network until the neural network reaches a steady state;
  • the specific implementation of the fifth step of neural network training is as follows:
  • ⁇ l' represents the pulse delay coefficient of each path
  • ⁇ l' represents the weight of each path
  • f out is the activation function of the output layer
  • its threshold is determined by the difference between the maximum power value of the channel impulse response and the noise floor Decide.
  • r(t) is the channel impulse response of a single receiving antenna at time t.
  • the weight ⁇ l' and pulse delay coefficient ⁇ l' are adjusted by the gradient descent method to minimize the error and obtain stability
  • the pulse delay coefficient the delay value of each path at time t is The power value of each path is At this point, the time-varying input set required by the final impulse response reconstruction module 1-16 is obtained
  • the impulse response reconstruction module 1-16 uses the training results of the Shenjiang network training module 1-15 to dynamically generate the corresponding channel impulse response network to reproduce the real channel conditions, and then the channel input simulation data is passed through the channel superposition module 1-17 Complete the superposition of the input signal and the analog channel, and finally output the analog reproduced channel output data;
  • the channel measurement and reconstruction equipment completes the task and turns off the power of all equipment.
  • the embodiment of the present invention can be used for spectrum situational cognitive mapping based on a drone platform, and the implementation process (shown in FIG. 2) is as follows.
  • Channel measurement transmitter coordinates are latitude 31.9375800°, longitude 118.7956968°, height is 15m, channel measurement receiver coordinates are latitude 31.93609234°, longitude 118.7947329°, height 1m, transmit signal frequency band is 1.5GHz, bandwidth is 100MHz, it should be noted that, This example is not intended as a limited implementation of the embodiment of the present invention.
  • the first step is to use the radio frequency cable to directly connect the transmitter and receiver equipment of the channel measurement system. First, ground the transmitter and send a zero-load signal. At this time, save the receiver data as system additive noise Q; then set the transmitter signal as a single tone signal.
  • the second step is to disconnect the radio frequency cable and use the GPS module to perform clock synchronization calibration at the transceiver; use the channel measurement parameters input by the user, including the center frequency and bandwidth, and configure the signal generation module 1-6 through the transmission control module 1-5 to generate
  • the channel measurement signal is sent through the power amplification module 1-7 and the omnidirectional transmitting antenna.
  • the channel measurement signal used in the embodiment of the present invention is a ZC sequence.
  • the channel measurement transmission signal passes through the receiving antenna array and the low-noise amplification module 1-8, the signal conditioning module 1-13 performs filtering and other processing according to the parameter configuration of the receiving control module 1-10, and the data preprocessing module 1-11
  • the conditioned channel measurement signal y r [n] is preprocessed to obtain the original channel impulse response data y[n];
  • the channel impulse response r[n] is the data processed by the single antenna of the receiving antenna array, and the other antennas are processed in the same way. Integrating the channel impulse responses of v receiving antenna elements at u coherent moments can obtain a multi-antenna channel impulse response matrix
  • the channel storage module 1-12 can also accept the import of third-party measurement data
  • the characteristic parameter extraction module 1-13 uses the stored actual measurement data or third-party measurement data to obtain the neural network characteristic parameters
  • the network scale determination module 1-14 Determine the neural network scale by processing the feature parameters, and then the neural network training module 1-15 starts training according to the network scale, input layer and selected training set data;
  • the distance calculation method is as formula (6);
  • the input layer of the neural network be a narrow impulse signal ⁇ , followed by the input layer is a delay control layer containing L'neurons and a hidden layer containing L'neurons.
  • the output layer of the neural network can be expressed as (11 );
  • ns is nanoseconds.
  • the impulse response reconstruction module 1-16 uses the training results of the Shenjiang network training module 1-15 to dynamically generate the corresponding channel impulse response network to reproduce the real channel conditions, and then the channel input simulation data passes through the channel superposition module 1-17 Complete the superposition of the input signal and the analog channel, and finally output the analog reproduced channel output data;
  • the channel measurement and reconstruction equipment completes the task and turns off the power of all equipment.
  • the program is stored in a storage medium and includes several instructions to enable the single-chip microcomputer, chip or processor (processor) Execute all or part of the steps of the method described in each embodiment of the present application.
  • the aforementioned storage medium may be non-transitory.
  • the storage medium may include: U disk, hard disk, read-only memory (ROM, Read-Only Memory), flash memory (Flash memory), magnetic disk or optical disk, etc., which can store program code. medium.

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Abstract

La présente invention concerne un procédé et un dispositif de mesure et de reconstruction dynamiques d'une réponse impulsionnelle de canal sans fil, appartenant au domaine technique des communications sans fil. Le dispositif comprend une unité de transmission de mesure de canal, une unité de réception de mesure de canal, une unité d'apprentissage de modèle de canal et une unité de reconstruction de réponse de canal; ladite unité de transmission de mesure de canal comprend un module de commande de transmission; l'unité de réception de mesure de canal et l'unité d'apprentissage de modèle de canal sont connectées à l'unité de reconstruction de réponse de canal, et les trois partagent un module de commande de réception. La présente invention est appropriée pour une mesure et une reconstruction dynamiques de la réponse impulsionnelle d'un canal sans fil dans des scénarios complexes non stationnaires.
PCT/CN2021/091940 2021-05-06 2021-05-06 Procédé et dispositif de mesure et de reconstruction dynamiques de réponse impulsionnelle de canal sans fil WO2021175344A2 (fr)

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Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1645041A2 (fr) * 2003-06-30 2006-04-12 Nokia Corporation Systeme, appareil et procede d'emulation pour emuler un canal radio
CN106126807B (zh) * 2016-06-22 2019-04-09 北京邮电大学 一种基于大数据挖掘的无线信道建模方法
CN107086894A (zh) * 2017-05-12 2017-08-22 重庆大学 一种高速移动下基于马尔科夫链的mimo信道建模方法
CN108512621B (zh) * 2018-03-02 2020-12-29 东南大学 一种基于神经网络的无线信道建模方法
CN111669344B (zh) * 2020-06-01 2022-01-07 西北工业大学 一种基于深度学习的时变ofdm系统信号检测方法

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CN114189302A (zh) * 2021-12-13 2022-03-15 国网山东省电力公司营销服务中心(计量中心) 一种面向用户分散场景的智能电网传输的无线通信信道建模方法
CN114189302B (zh) * 2021-12-13 2023-10-20 国网山东省电力公司营销服务中心(计量中心) 一种面向用户分散场景的智能电网传输的无线通信信道建模方法
CN114362851B (zh) * 2022-01-10 2023-02-21 北京交通大学 一种基于机器学习的无线信道数据去噪方法
CN114362851A (zh) * 2022-01-10 2022-04-15 北京交通大学 一种基于机器学习的无线信道数据去噪方法
CN114422060A (zh) * 2022-03-29 2022-04-29 军事科学院系统工程研究院网络信息研究所 一种无人机通信信道模型的构建方法和系统
CN114900256A (zh) * 2022-05-20 2022-08-12 中国电信股份有限公司 通信场景识别方法和装置
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CN115808701A (zh) * 2023-02-09 2023-03-17 南京航空航天大学 一种多机协同广域空间信道地图测绘装置及方法
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CN118249933A (zh) * 2024-04-01 2024-06-25 北京交通大学 一种非平稳无线信道回放模拟方法、装置、计算机可读存储介质及产品

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