CN112787962A - Channel time domain feature extraction method and system based on pilot frequency reference signal - Google Patents
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Abstract
The invention provides a channel time domain feature extraction method and a system based on a pilot frequency reference signal, which comprises the following steps: step M1: estimating channel impulse response at a pilot frequency reference signal according to a frequency domain baseband signal received by a communication receiver; step M2: estimating channel impulse response at the digital subcarrier according to a frequency domain baseband signal received by a communication receiver; step M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier from the frequency domain into a time domain, and extracting time domain characteristics; step M4: and identifying and classifying the channel environment by the time domain characteristics 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 characteristic extraction module, and finally sends the impulse response to the channel classification algorithm module, thereby completing the identification and classification of the channel environment where the communication transceiver is located.
Description
Technical Field
The present invention relates to the field of channel classification, and in particular, to a method and a system for extracting time domain characteristics of a channel based on a pilot reference signal, and more particularly, to a method for extracting characteristics of a pilot reference signal and a method for sensing channel classification.
Background
Due to the fact that channel environments in wireless communication are complex and changeable, the signal transmission process is affected by surrounding complex physical environments, and therefore signals under different channel scenes have obvious differences in the aspects of 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 feature extraction and channel classification method based on the pilot frequency reference signal.
The signal characteristic extraction method mainly comprises the steps of sometimes obtaining frequency domain characteristics such as time delay and frequency deviation matching degree characteristics, high-order statistics, cyclostationarity, energy ratio of each order component of wavelet transform extraction signals and the like; and training the learning features to recognize based on the DNN or CNN neural network. The neural network automatically learns the characteristics of data or samples through a deep network, avoids the process of extracting the characteristics on line by nodes, transfers the complex information extraction process to offline network training, avoids the influence of artificially extracting characteristic setting parameters, and has high identification accuracy and remarkable performance advantages. The characteristic extraction to be adopted by the invention is to perform cross correlation on the pilot frequency reference signal in the received signal and the local reference sequence, and perform operations such as normalization, gain combination, interception and the like.
And carrying out classification training on the data subjected to feature extraction by using a machine learning method, so that the physical environment where the wireless communication transceiver is located can be sensed in a new environment. The methods to be adopted are KNN, CNN, SVM, random forest and the like. Typical channel models exist as follows: low altitude channel, urban channel, rural channel and mountainous channel models. And according to the channel models obtained by classification, the communication system is adjusted by changing decision-making libraries 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 time domain pilot sequence, which includes a separator, a channel information estimation unit, a time domain pilot cyclic utilization control unit, a step size adaptive unit, a time domain adaptive filter, an adder, a time domain interpolation unit; automatically adjusting the tap updating step length of the time domain adaptive filter according to the estimated speed of the channel change; estimating the channel response of the time domain pilot frequency sequence; subtracting a tap filtering summation operation result output by the time domain adaptive filter by utilizing a time domain pilot frequency cyclic utilization control unit to obtain an estimation error, and using the estimation error 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 a channel time domain feature extraction system based on a pilot frequency reference signal.
The invention provides a channel time domain feature extraction method based on a pilot frequency reference signal, which comprises the following steps:
step M1: estimating channel impulse response at a pilot frequency reference signal according to a frequency domain baseband signal received by a communication receiver;
step M2: estimating channel impulse response at the digital subcarrier according to a frequency domain baseband signal received by a communication receiver;
step M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier from the frequency domain into a time domain, and extracting 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 characteristics in the channel through a channel classification algorithm.
Preferably, the step M2 includes: the channel impulse response at the digital subcarriers is estimated by interpolation and/or filtering methods based on the 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 subcarriers from the frequency domain to the time domain by including a fast fourier transform, a discrete cosine transform, or a wavelet transform.
Preferably, the time domain feature extraction in the step M3 includes: the time domain is subjected to operations including normalization, gain combining, and clipping.
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 invention provides a channel time domain feature extraction system based on a pilot frequency reference signal, which comprises the following steps:
module M1: estimating channel impulse response at a pilot frequency reference signal according to a frequency domain baseband signal received by a communication receiver;
module M2: estimating channel impulse response at the digital subcarrier according to a frequency domain baseband signal received by a communication receiver;
module M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier from the frequency domain into a time domain, and extracting 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 characteristics in the channel through a channel classification algorithm.
Preferably, said module M2 comprises: the channel impulse response at the digital subcarriers is estimated by interpolation and/or filtering methods based on the 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 subcarriers 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 of the time domain features in the module M3 includes: the time domain is subjected to operations including normalization, gain combining, and clipping.
Preferably, the channel classification algorithm in the module M4 includes KNN algorithm, SVM algorithm, random forest algorithm, neural network algorithm or CNN algorithm.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can effectively recover the channel characteristics after extracting the time domain characteristics;
2. the invention can carry out classification training on modeling data or collected real environment data, and channel information can be effectively obtained after the modeling data or the collected real environment data pass through the channel classification module.
3. The invention can change the impulse response of the channel from the frequency domain to the time domain through the time domain characteristic extraction module, and finally sends the impulse response to the channel classification algorithm module, thereby completing the identification and classification of the channel environment where the communication transceiver is located.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
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 diagram of an originating reference signal;
FIG. 4 is a normalized channel time domain impulse response;
FIG. 5 is a schematic frequency domain diagram of a reference signal after a receiving end passes a channel;
FIG. 6 is a diagram illustrating an estimated impulse response after channel passing;
FIG. 7 is a diagram illustrating the impulse responses estimated for different sub-carriers in the Pilot 1 mode;
fig. 8 is a diagram illustrating the impulse responses estimated for different subcarriers in the 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 invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1
A channel time domain characteristic extraction method based on pilot frequency reference signal belongs to the classification of wireless channels, and the method mainly comprises the steps of obtaining time domain channel response after cross-correlation with a local sequence through an unknown channel based on the known pilot frequency reference signal, and obtaining channel correlation characteristics after truncation.
The channel time domain feature extraction method based on the pilot reference signal provided by the invention, as shown in fig. 1-8, includes:
step M1: estimating channel impulse response at a pilot frequency reference signal according to a frequency domain baseband signal received by a communication receiver;
step M2: estimating channel impulse response at the digital subcarrier according to a frequency domain baseband signal received by a communication receiver;
step M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier from the frequency domain into a time domain, and extracting 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 characteristics in the channel through a channel classification algorithm.
Specifically, as shown in fig. 3, the frequency domain response of the reference signal after passing through the channel is shown in fig. 5, and the obtained frequency domain channel response is subjected to IFFT transformation to obtain the estimated time domain impulse after the channel is passed. The collected data (as shown in fig. 6) is classified and trained, and the channel information can be effectively obtained after the data passes through the channel classification module.
Specifically, the step M2 includes: the channel impulse response at the digital subcarriers is estimated by interpolation and/or filtering methods based on the 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 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 of the time domain feature in step M3 includes: the time domain is subjected to operations including normalization, gain combining, and clipping.
Specifically, the pilot frequency reference signal in the received signal is cross-correlated with the local reference sequence, normalized, and the received channel frequency domain impulse response is converted into the time domain and then truncated, so as to obtain the time domain characteristics of the channel.
Specifically, the channel classification algorithm in step M4 includes a KNN algorithm, an SVM algorithm, a random forest algorithm, a neural network algorithm, or a CNN algorithm. The better communication system adjustment can be performed according to the classification accuracy obtained by different classification algorithms.
The KNN algorithm determines the category of the classified sample according to the category of the nearest sample or samples, and the selected neighbors are all the objects which are classified correctly;
the SVM algorithm is a binary classification model, a basic model is defined as a linear classifier with the maximum interval on a feature space, and a learning strategy of the SVM algorithm is interval maximization and can be finally converted into the 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 division.
The neural network algorithm means that a neuron receives input signals transmitted by eta other neurons, the input signals are transmitted through weighted connections, total input values received by the neuron are compared with threshold values of the neuron, and then the total input values are processed through an activation function to generate output of the neuron.
After the CNN algorithm is processed by networks such as a convolutional layer and a pooling layer, an image becomes smaller and smaller, but the convolutional layer becomes deeper and deeper, a conventional feedforward neural network consisting of a plurality of fully-connected layers is added at the top of a stack, and the final layer outputs prediction.
The invention provides a channel time domain feature extraction system based on a pilot frequency reference signal, which comprises the following steps:
module M1: estimating channel impulse response at a pilot frequency reference signal according to a frequency domain baseband signal received by a communication receiver;
module M2: estimating channel impulse response at the digital subcarrier according to a frequency domain baseband signal received by a communication receiver;
module M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier from the frequency domain into a time domain, and extracting 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 characteristics in the channel through a channel classification algorithm.
Specifically, as shown in fig. 3, the frequency domain response of the reference signal after passing through the channel is shown in fig. 5, and the obtained frequency domain channel response is subjected to IFFT transformation to obtain the estimated time domain impulse after the channel is passed. The collected data (as shown in fig. 6) is classified and trained, and the channel information can be effectively obtained after the data passes through the channel classification module.
Specifically, the module M2 includes: the channel impulse response at the digital subcarriers is estimated by interpolation and/or filtering methods based on the 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 of the time domain feature in the module M3 includes: the time domain is subjected to operations including normalization, gain combining, and clipping.
Specifically, the pilot frequency reference signal in the received signal is cross-correlated with the local reference sequence, normalized, and the received channel frequency domain impulse response is converted into the time domain and then truncated, so as to obtain the time domain characteristics of the channel.
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 better communication system adjustment can be performed according to the classification accuracy obtained by different classification algorithms.
The KNN algorithm determines the category of the classified sample according to the category of the nearest sample or samples, and the selected neighbors are all the objects which are classified correctly;
the SVM algorithm is a binary classification model, a basic model is defined as a linear classifier with the maximum interval on a feature space, and a learning strategy of the SVM algorithm is interval maximization and can be finally converted into the 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 division.
The neural network algorithm means that a neuron receives input signals transmitted by eta other neurons, the input signals are transmitted through weighted connections, total input values received by the neuron are compared with threshold values of the neuron, and then the total input values are processed through an activation function to generate output of the neuron.
After the CNN algorithm is processed by networks such as a convolutional layer and a pooling layer, an image becomes smaller and smaller, but the convolutional layer becomes deeper and deeper, a conventional feedforward neural network consisting of a plurality of fully-connected layers is added at the top of a stack, and the final layer outputs prediction.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. A channel time domain feature extraction method based on a pilot frequency reference signal is characterized by comprising the following steps:
step M1: estimating channel impulse response at a pilot frequency reference signal according to a frequency domain baseband signal received by a communication receiver;
step M2: estimating channel impulse response at the digital subcarrier according to a frequency domain baseband signal received by a communication receiver;
step M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier from the frequency domain into a time domain, and extracting 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 characteristics in the channel through a channel classification algorithm.
2. The method for extracting time domain characteristics of channel based on pilot reference signal as claimed in claim 1, wherein said step M2 comprises: the channel impulse response at the digital subcarriers is estimated by interpolation and/or filtering methods based on the frequency domain baseband signals received by the communication receiver.
3. The method of claim 1, wherein the step M3 comprises 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 a process comprising fast Fourier transform, discrete cosine transform or wavelet transform.
4. The method for extracting time domain features of a channel based on a pilot reference signal as claimed in claim 1, wherein the time domain feature extraction in step M3 comprises: the time domain is subjected to operations including normalization, gain combining, and clipping.
5. The method for extracting time domain features of channel based on pilot reference signal as claimed in claim 1, wherein the channel classification algorithm in step M4 includes KNN algorithm, SVM algorithm, random forest algorithm, neural network algorithm or CNN algorithm.
6. A system for extracting time domain characteristics of a channel based on a pilot reference signal, comprising:
module M1: estimating channel impulse response at a pilot frequency reference signal according to a frequency domain baseband signal received by a communication receiver;
module M2: estimating channel impulse response at the digital subcarrier according to a frequency domain baseband signal received by a communication receiver;
module M3: converting the channel impulse response at the pilot frequency reference signal and the channel impulse response at the digital subcarrier from the frequency domain into a time domain, and extracting 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 characteristics in the channel through a channel classification algorithm.
7. The pilot reference signal based channel time domain feature extraction system according to claim 6, wherein the module M2 comprises: the channel impulse response at the digital subcarriers is estimated by interpolation and/or filtering methods based on the frequency domain baseband signals received by the communication receiver.
8. The pilot-reference-signal-based channel time-domain feature extraction system according to claim 6, wherein the module M3 comprises 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 a method comprising fast Fourier transform, discrete cosine transform or wavelet transform.
9. The pilot-reference-signal-based channel time-domain feature extraction system according to claim 6, wherein the time-domain feature extraction in the module M3 comprises: the time domain is subjected to operations including normalization, gain combining, and clipping.
10. The pilot-reference-signal-based channel time-domain feature extraction system according to claim 6, wherein the channel classification algorithm in the module M4 comprises a KNN algorithm, a SVM algorithm, a random forest algorithm, a neural network algorithm or a CNN algorithm.
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