CN112787962B - Channel time domain feature extraction method and system based on pilot frequency reference signal - Google Patents
Channel time domain feature extraction method and system based on pilot frequency reference signal Download PDFInfo
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Abstract
The invention provides a channel time domain feature extraction method and a system based on pilot frequency reference signals, wherein the method comprises the following steps: step M1: estimating channel impulse response at a pilot reference signal according to a frequency domain baseband signal received by a communication receiver; step M2: estimating channel impulse response at the digital sub-carrier according to the frequency domain baseband signal received by the communication receiver; step M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier into a time domain, and extracting the characteristics of the time domain; step M4: and identifying and classifying the channel environment by the time domain features in the channel through a channel classification algorithm. The invention can change the impulse response of the channel from the frequency domain to the time domain through the time domain feature extraction module, and finally send the impulse response into the channel classification algorithm module, thereby completing the identification and classification of the channel environment where the communication transceiver is positioned.
Description
Technical Field
The invention relates to the field of channel classification, in particular to a channel time domain feature extraction method and a system based on pilot reference signals, and more particularly relates to a feature extraction method and a channel classification sensing method of pilot reference signals.
Background
Because the channel environment in wireless communication is complex and changeable, the signal transmission process is influenced by the surrounding complex physical environment, so that the signals in different channel scenes have obvious differences in energy, power, time delay, channel response and the like. Therefore, in order to realize intelligent perception of the communication environment, the invention focuses on a characteristic extraction and channel classification method based on pilot reference signals.
The signal characteristic extraction method mainly comprises the steps of extracting the energy ratio of each order component of the signal by frequency domain characteristics such as time delay and frequency offset matching degree characteristics, high-order statistics, cyclostationary characteristics, wavelet transformation and the like; recognition is performed based on DNN or CNN neural network training learning features. The neural network automatically learns the characteristics of data or samples through the deep network, avoids the process of extracting the characteristics on line by the nodes, transfers the complex information extraction process to offline network training, avoids the influence of manually extracting characteristic setting parameters, and has higher identification accuracy and more remarkable performance advantage. The invention adopts the characteristic extraction that pilot frequency reference signals in the received signals are subjected to cross-correlation with local reference sequences, normalization, gain combination, interception and other operations.
The data after feature extraction is subjected to classification training by using a machine learning method, so that the physical environment where the wireless communication transceiver is located can be perceived in a new environment. The method to be adopted includes KNN, CNN, SVM, random forest, etc. Typical channel models exist: low-altitude channels, urban channels, rural channels, and mountain channel models. And according to the channel model obtained by classification, carrying out communication system adjustment by changing a decision base such as a system modulation order, a modulation mode and the like.
Patent document CN101997790a (application number: 200910057756.9) discloses a channel estimation device based on a time domain pilot sequence, which comprises a separator, a channel information estimation unit, a time domain pilot cyclic utilization control unit, a step length adaptive unit, a time domain adaptive filter, an adder and a time domain interpolation unit; automatically adjusting the tap update step length of the time domain adaptive filter according to the estimated speed of channel variation; estimating the channel response of the time domain pilot sequence; the time domain pilot frequency sequence generated by the time domain pilot frequency cyclic utilization control unit is subtracted from the tap filtering summation operation result output by the time domain adaptive filter to obtain an estimated error, and the estimated error is used as an error of tap updating of the time domain adaptive filter; and recovering the channel response value of the current frame.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a channel time domain feature extraction method and system based on pilot frequency reference signals.
The channel time domain feature extraction method based on the pilot frequency reference signal provided by the invention comprises the following steps:
Step M1: estimating channel impulse response at a pilot reference signal according to a frequency domain baseband signal received by a communication receiver;
step M2: estimating channel impulse response at the digital sub-carrier according to the frequency domain baseband signal received by the communication receiver;
step M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier into a time domain, and extracting the time domain characteristics to obtain the time domain characteristics of the channel;
step M4: and identifying and classifying the channel environment by the time domain features in the channel through a channel classification algorithm.
Preferably, the step M2 includes: channel impulse responses at the digital subcarriers are estimated by interpolation and/or filtering methods based on frequency domain baseband signals received by the communication receiver.
Preferably, the step M3 includes converting the channel impulse response at the pilot reference signal and the channel impulse response at the digital sub-carrier from the frequency domain to the time domain by including a fast fourier transform, a discrete cosine transform, or a wavelet transform.
Preferably, the extracting the time domain feature in the step M3 includes: the time domain is subjected to operations including normalization, gain combination and interception.
Preferably, the channel classification algorithm in the step M4 includes a KNN algorithm, an SVM algorithm, a random forest algorithm, a neural network algorithm, or a CNN algorithm.
The channel time domain feature extraction system based on the pilot frequency reference signal provided by the invention comprises:
module M1: estimating channel impulse response at a pilot reference signal according to a frequency domain baseband signal received by a communication receiver;
Module M2: estimating channel impulse response at the digital sub-carrier according to the frequency domain baseband signal received by the communication receiver;
module M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier into a time domain, and extracting the time domain characteristics to obtain the time domain characteristics of the channel;
Module M4: and identifying and classifying the channel environment by the time domain features in the channel through a channel classification algorithm.
Preferably, the module M2 comprises: channel impulse responses at the digital subcarriers are estimated by interpolation and/or filtering methods based on frequency domain baseband signals received by the communication receiver.
Preferably, the module M3 includes converting the channel impulse response at the pilot reference signal and the channel impulse response at the digital sub-carrier from the frequency domain to the time domain by including a fast fourier transform, a discrete cosine transform, or a wavelet transform.
Preferably, the extracting the time domain features in the module M3 includes: the time domain is subjected to operations including normalization, gain combination and interception.
Preferably, the channel classification algorithm in the module M4 comprises a KNN algorithm, an SVM algorithm, a random forest algorithm, a neural network algorithm or a CNN algorithm.
Compared with the prior art, the invention has the following beneficial effects:
1. The channel characteristics can be effectively recovered after the time domain characteristics are extracted;
2. According to the invention, modeling data or acquired real environment data can be subjected to classification training, and channel information can be effectively obtained after the channel classification module is used.
3. The invention can change the impulse response of the channel from the frequency domain to the time domain through the time domain feature extraction module, and finally send the impulse response into the channel classification algorithm module, thereby completing the identification and classification of the channel environment where the communication transceiver is positioned.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a schematic diagram of a channel feature extraction process;
Fig. 2 is a schematic diagram of a pilot pattern;
Fig. 3 is a frequency domain schematic diagram of an originating reference signal;
FIG. 4 is a normalized channel time domain impulse response;
Fig. 5 is a frequency domain schematic diagram of a reference signal after a receiving end passes through a channel;
FIG. 6 is a graph showing the estimated impulse response after channel crossing;
Fig. 7 is a schematic diagram of impulse response estimated by different subcarriers in pilot 1 mode;
fig. 8 is a graph showing the impulse response estimated for different subcarriers in pilot 2 mode.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
Example 1
A channel time domain feature extraction method based on pilot frequency reference signals belongs to the classification of wireless channels, and the method is mainly based on the process of obtaining time domain channel response after cross-correlation with a local sequence after passing through an unknown channel and obtaining channel related features after cutting off the time domain channel response.
According to the channel time domain feature extraction method based on the pilot reference signal, as shown in fig. 1-8, the method comprises the following steps:
Step M1: estimating channel impulse response at a pilot reference signal according to a frequency domain baseband signal received by a communication receiver;
step M2: estimating channel impulse response at the digital sub-carrier according to the frequency domain baseband signal received by the communication receiver;
step M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier into a time domain, and extracting the time domain characteristics to obtain the time domain characteristics of the channel;
step M4: and identifying and classifying the channel environment by the time domain features in the channel through a channel classification algorithm.
Specifically, as shown in fig. 3, the reference signal is an originating reference signal, the frequency domain response of the reference signal after passing through a channel is shown in fig. 5, and the obtained frequency domain channel response is subjected to IFFT to obtain the estimated time domain impact after passing through the channel. The acquired data (as shown in fig. 6) is subjected to classification training, and channel information can be effectively obtained after the data passes through a channel classification module.
Specifically, the step M2 includes: channel impulse responses at the digital subcarriers are estimated by interpolation and/or filtering methods based on frequency domain baseband signals received by the communication receiver.
Specifically, the step M3 includes converting the channel impulse response at the pilot reference signal and the channel impulse response at the digital subcarrier from the frequency domain to the time domain by including a fast fourier transform, a discrete cosine transform, or a wavelet transform.
Specifically, the extracting the time domain features in the step M3 includes: the time domain is subjected to operations including normalization, gain combination and interception.
Specifically, a pilot frequency reference signal in a received signal is subjected to cross-correlation with a local reference sequence, normalization is performed, and a received channel frequency domain impulse response is converted into a time domain and then truncated, so that the time domain characteristics of a channel are obtained.
Specifically, the channel classification algorithm in the step M4 includes a KNN algorithm, an SVM algorithm, a random forest algorithm, a neural network algorithm, or a CNN algorithm. The classification accuracy obtained according to different classification algorithms can be subjected to more optimal communication system adjustment.
The KNN algorithm only decides the category of the classified sample according to the category of one or more samples which are nearest to each other, and the selected neighbors are objects which are already classified correctly;
The SVM algorithm is a binary classification model, the basic model is defined as a linear classifier with the largest interval on the feature space, the learning strategy is that the interval is maximized, and the method can be finally converted into a solution of a convex quadratic programming problem.
The random forest algorithm randomly selects a subset containing k attributes from the attribute set of the node, and then selects an optimal attribute from the subset for partitioning.
The neural network algorithm refers to that a neuron receives input signals transmitted from eta other neurons, the input signals are transmitted through weighted connection, the total input value received by the neuron is compared with the threshold value of the neuron, and then the output of the neuron is generated through an activating function.
After the CNN algorithm is processed by the convolutional layer, the pooling layer and other networks, the image becomes smaller and smaller, but the convolutional layer becomes deeper and deeper, and a conventional feedforward neural network consisting of a plurality of fully connected layers is added at the top of the stack, and the final layer outputs the prediction.
The channel time domain feature extraction system based on the pilot frequency reference signal provided by the invention comprises:
module M1: estimating channel impulse response at a pilot reference signal according to a frequency domain baseband signal received by a communication receiver;
Module M2: estimating channel impulse response at the digital sub-carrier according to the frequency domain baseband signal received by the communication receiver;
module M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier into a time domain, and extracting the time domain characteristics to obtain the time domain characteristics of the channel;
Module M4: and identifying and classifying the channel environment by the time domain features in the channel through a channel classification algorithm.
Specifically, as shown in fig. 3, the reference signal is an originating reference signal, the frequency domain response of the reference signal after passing through a channel is shown in fig. 5, and the obtained frequency domain channel response is subjected to IFFT to obtain the estimated time domain impact after passing through the channel. The acquired data (as shown in fig. 6) is subjected to classification training, and channel information can be effectively obtained after the data passes through a channel classification module.
Specifically, the module M2 includes: channel impulse responses at the digital subcarriers are estimated by interpolation and/or filtering methods based on frequency domain baseband signals received by the communication receiver.
In particular, the module M3 includes converting the channel impulse response at the pilot reference signal and the channel impulse response at the digital subcarriers from the frequency domain to the time domain by including a fast fourier transform, a discrete cosine transform, or a wavelet transform.
Specifically, the extracting the time domain features in the module M3 includes: the time domain is subjected to operations including normalization, gain combination and interception.
Specifically, a pilot frequency reference signal in a received signal is subjected to cross-correlation with a local reference sequence, normalization is performed, and a received channel frequency domain impulse response is converted into a time domain and then truncated, so that the time domain characteristics of a channel are obtained.
Specifically, the channel classification algorithm in the module M4 includes a KNN algorithm, an SVM algorithm, a random forest algorithm, a neural network algorithm, or a CNN algorithm. The classification accuracy obtained according to different classification algorithms can be subjected to more optimal communication system adjustment.
The KNN algorithm only decides the category of the classified sample according to the category of one or more samples which are nearest to each other, and the selected neighbors are objects which are already classified correctly;
The SVM algorithm is a binary classification model, the basic model is defined as a linear classifier with the largest interval on the feature space, the learning strategy is that the interval is maximized, and the method can be finally converted into a solution of a convex quadratic programming problem.
The random forest algorithm randomly selects a subset containing k attributes from the attribute set of the node, and then selects an optimal attribute from the subset for partitioning.
The neural network algorithm refers to that a neuron receives input signals transmitted from eta other neurons, the input signals are transmitted through weighted connection, the total input value received by the neuron is compared with the threshold value of the neuron, and then the output of the neuron is generated through an activating function.
After the CNN algorithm is processed by the convolutional layer, the pooling layer and other networks, the image becomes smaller and smaller, but the convolutional layer becomes deeper and deeper, and a conventional feedforward neural network consisting of a plurality of fully connected layers is added at the top of the stack, and the final layer outputs the prediction.
Those skilled in the art will appreciate that the systems, apparatus, and their respective modules provided herein may be implemented entirely by logic programming of method steps such that the systems, apparatus, and their respective modules are implemented as logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc., in addition to the systems, apparatus, and their respective modules being implemented as pure computer readable program code. Therefore, the system, the apparatus, and the respective modules thereof provided by the present invention may be regarded as one hardware component, and the modules included therein for implementing various programs may also be regarded as structures within the hardware component; modules for implementing various functions may also be regarded as being either software programs for implementing the methods or structures within hardware components.
The foregoing describes specific embodiments of the present application. It is to be understood that the application is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the application. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.
Claims (8)
1. A method for extracting channel time domain features based on pilot reference signals, comprising:
Step M1: estimating channel impulse response at a pilot reference signal according to a frequency domain baseband signal received by a communication receiver;
step M2: estimating channel impulse response at the digital sub-carrier according to the frequency domain baseband signal received by the communication receiver;
step M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier into a time domain, and extracting the time domain characteristics to obtain the time domain characteristics of the channel;
step M4: and identifying and classifying the channel environment by the time domain features in the channel through a channel classification algorithm.
2. The method for extracting channel time domain features based on pilot reference signals according to claim 1, wherein said step M2 comprises: channel impulse responses at the digital subcarriers are estimated by interpolation and/or filtering methods based on frequency domain baseband signals received by the communication receiver.
3. The method for extracting time domain features of a channel based on pilot reference signals according to claim 1, wherein the extracting time domain features in step M3 comprises: the time domain is subjected to operations including normalization, gain combination and truncation.
4. The method for extracting channel time domain features based on pilot reference signals according to claim 1, wherein the channel classification algorithm in the step M4 comprises KNN algorithm, SVM algorithm, random forest algorithm, neural network algorithm.
5. A pilot reference signal based channel time domain feature extraction system, comprising:
module M1: estimating channel impulse response at a pilot reference signal according to a frequency domain baseband signal received by a communication receiver;
Module M2: estimating channel impulse response at the digital sub-carrier according to the frequency domain baseband signal received by the communication receiver;
module M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier into a time domain, and extracting the time domain characteristics to obtain the time domain characteristics of the channel;
Module M4: and identifying and classifying the channel environment by the time domain features in the channel through a channel classification algorithm.
6. The pilot reference signal based channel time domain feature extraction system of claim 5, wherein said module M2 comprises: channel impulse responses at the digital subcarriers are estimated by interpolation and/or filtering methods based on frequency domain baseband signals received by the communication receiver.
7. The pilot reference signal based channel time domain feature extraction system of claim 5, wherein the time domain feature extraction in module M3 comprises: the time domain is subjected to operations including normalization, gain combination and truncation.
8. The pilot reference signal based channel time domain feature extraction system of claim 5, wherein the channel classification algorithm in module M4 comprises KNN algorithm, SVM algorithm, random forest algorithm, neural network algorithm.
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