CN115834300B - Dynamic threshold channel estimation method and system based on minimum cost - Google Patents
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Abstract
The invention provides a dynamic threshold channel estimation method and a system based on minimum cost, which relate to the technical field of wireless communication, and specifically comprise the following steps: based on the acquired pilot frequency data, carrying out channel estimation to obtain an initial channel impulse response; preprocessing the initial channel impulse response according to the time correlation of the wireless channel; based on the preprocessed channel impulse response, analyzing the relation between the overall error cost and the threshold value, and calculating an optimal threshold value; determining a denoising matrix according to the optimal threshold value, and performing noise suppression on the initial channel impulse response to obtain a final channel impulse response; the invention does not need prior channel information, estimates the channel impulse response by utilizing the sparsity and the time correlation of the wireless channel, improves the channel estimation precision and ensures the operation complexity.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a dynamic threshold channel estimation method and system based on minimum cost.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the rapid development of wireless communication technology, the demand for real-time, high-speed and high-quality digital communication is increasing; in extremely complex spatial environments, radio waves arrive at the receiving end from the transmitter mainly via reflection, diffraction and scattering, so that the received signal is severely faded; in order to ensure the performance of the communication system, the channel state information can be obtained by a channel estimation technology, and the time-frequency domain response of the channel is provided for the receiving end, so that the recovery quality of the received signal is greatly improved. However, accurate channel estimation is difficult due to noise in the wireless channel and its time-varying and multipath propagation characteristics.
The Cyclic Prefix (CP) is adopted as a guard interval in an orthogonal frequency division multiplexing (Orthogonal Frequency Division Multiplexing, OFDM) system, so that multipath effects can be well overcome; by means of sparse characteristics of wireless channels, various threshold denoising methods are presented, and an optimal threshold is obtained by minimizing mean square error, however, in order to achieve optimal denoising performance, the prior channel information is often needed to assist or perform high-complexity iterative operation; the confidence denoising strategy and the weighted average denoising strategy in the existing method are not well applicable to dynamic channels by virtue of the time correlation of wireless channels, although the method works well in static environments.
Therefore, how to perform high-precision channel estimation in a dynamic environment without prior channel information is still one of the problems to be solved at present.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a dynamic threshold channel estimation method and a system based on minimum cost, which do not need prior channel information, utilize the sparsity of wireless channels and the time correlation thereof to estimate channel impulse response, improve channel estimation precision and ensure operation complexity.
To achieve the above object, one or more embodiments of the present invention provide the following technical solutions:
the first aspect of the invention provides a dynamic threshold channel estimation method based on minimum cost;
a dynamic threshold channel estimation method based on minimum cost, comprising:
Based on the acquired pilot frequency data, carrying out channel estimation to obtain an initial channel impulse response;
Preprocessing the initial channel impulse response according to the time correlation of the wireless channel;
based on the preprocessed channel impulse response, analyzing the relation between the overall error cost and the threshold value, and calculating an optimal threshold value;
and determining a denoising matrix according to the optimal threshold value, and performing noise suppression on the initial channel impulse response to obtain a final channel impulse response.
Further, the step of obtaining the initial channel impulse response specifically includes:
performing channel estimation on the acquired pilot frequency data by adopting a least square method to obtain a channel frequency response;
and performing M-point inverse fast Fourier transform on the obtained channel frequency response to obtain a channel impulse response, namely an initial channel impulse response.
Further, the channel estimation is expressed as:
Where H LS (M, K) is the channel frequency response, Y (M, K) and X (M, K) are pilot symbols of the M (m=1, 2, …, M) th subcarrier in the received and transmitted kth (k=1, 2, …, K) OFDM symbol, respectively.
Further, the preprocessing is performed on the initial channel impulse response, and the P-frame averaging is performed on the initial channel impulse response by adopting an adjacent frame averaging method, wherein P is a preset adjacent frame number.
Further, the calculating the optimal threshold value specifically includes:
Calculating path power and noise power based on the preprocessed channel impulse response, and constructing a cumulative distribution function of the path power and the noise power;
based on the cumulative distribution function, calculating the false alarm cost and the missing alarm cost under the denoising threshold value by introducing cost factors, and constructing the whole error cost;
and (3) solving a first derivative of the overall error cost on the denoising threshold value, and calculating the denoising threshold value when the first derivative is equal to zero, namely the optimal threshold value.
Further, the denoising matrix G (m, k) specifically includes:
Wherein T k OPT is the optimal threshold, and h W (m, k) is the channel impulse response after preprocessing.
Further, the noise suppression of the initial channel impulse response is that the initial channel impulse response is multiplied by a denoising matrix to obtain a final channel impulse response.
The second aspect of the present invention provides a dynamic threshold channel estimation system based on minimum cost.
A dynamic threshold channel estimation system based on minimum cost comprises an initial estimation module, a preprocessing module, a threshold calculation module and a noise suppression module:
an initial estimation module configured to: based on the acquired pilot frequency data, carrying out channel estimation to obtain an initial channel impulse response;
a preprocessing module configured to: preprocessing the initial channel impulse response according to the time correlation of the wireless channel;
A threshold calculation module configured to: based on the preprocessed channel impulse response, analyzing the relation between the overall error cost and the threshold value, and calculating an optimal threshold value;
A noise suppression module configured to: and determining a denoising matrix according to the optimal threshold value, and performing noise suppression on the initial channel impulse response to obtain a final channel impulse response.
A third aspect of the present invention provides a computer readable storage medium having stored thereon a program which when executed by a processor performs steps in a dynamic threshold channel estimation method based on a minimum cost according to the first aspect of the present invention.
A fourth aspect of the invention provides an electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the steps in a dynamic threshold channel estimation method based on a minimum cost according to the first aspect of the invention when the program is executed.
The one or more of the above technical solutions have the following beneficial effects:
Based on the sparsity and time correlation of the wireless channels, the invention provides a dynamic threshold channel estimation method which can be changed along with the change of noise energy based on minimum cost, and under the condition of ensuring the operation complexity and not needing prior channel sparsity, the invention provides the performance close to an ideal channel support set, and improves the channel estimation precision.
Compared with the channel estimation method based on discrete Fourier transform (Discrete Fourier Transform, DFT) with the lowest complexity in the threshold method, the method only increases the average module of the pilot frequency sub-carrier, has lower calculation complexity, is suitable for dynamic channels, and has better practicability in a wireless communication system.
Additional aspects of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention.
Fig. 1 is a flow chart of a method of a first embodiment.
Fig. 2 is a graph of NMSE for a first embodiment 40km/h urban vehicle channel.
Fig. 3 is a diagram of a first embodiment of a 100km/h rural highway channel NMSE.
Fig. 4 is a graph showing the BER of the urban vehicle channel of the first embodiment of 40 km/h.
Fig. 5 is a graph showing the BER of the rural highway channels of 100km/h according to the first embodiment.
Fig. 6 is a system configuration diagram of the second embodiment.
Detailed Description
The invention will be further described with reference to the drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the invention; unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present invention; as used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Example 1
The embodiment discloses a dynamic threshold channel estimation method based on minimum cost;
As shown in fig. 1, a dynamic threshold channel estimation method based on minimum cost includes:
s101, based on the acquired pilot frequency data, carrying out channel estimation to obtain an initial channel impulse response, wherein the method comprises the following specific steps:
(1) Channel estimation is performed on the acquired pilot data by using a Least Square (LS) method to obtain a channel frequency response (Channel Frequency Response, CFR), and the formula is as follows:
Where H LS (M, K) is the channel frequency response, Y (M, K) and X (M, K) are pilot symbols of the M (m=1, 2, …, M) th subcarrier in the received and transmitted kth (k=1, 2, …, K) OFDM symbol, respectively.
In other embodiments, a minimum mean square error estimate may also be employed for channel estimation.
(2) And performing M-point inverse fast Fourier transform on the obtained channel frequency response to obtain a channel impulse response (Channel Impulse Response, CIR), namely an initial channel impulse response.
The channel frequency response H LS (M, k) is subjected to an M-point inverse fast fourier transform (INVERSE FAST Fourier Transform, IFFT), which is converted from the frequency domain to the time domain, and the CIR can be expressed specifically as:
Wherein, C is CIR support set, which is the point set with CIR median value not 0, namely multipath in equidistant sampling; h (m, k) is the noiseless CIR at the radial sampling point, and can be modeled as 0 in the mean and 0 in the variance in the Rayleigh fading channel Is a complex gaussian random variable; n LS (m, k) is 0 as the mean and 0 as the variance/>Is a complex gaussian white noise of (c).
Since h (m, k) is independent of n LS (m, k), h LS (m, k) conforms to the complex gaussian distribution CN, specifically:
s102, preprocessing an initial channel impulse response according to the time correlation of the wireless channel;
The preprocessing aims to improve the distinguishing degree of the path sampling points and the noise sampling points through an inter-frame averaging technology, so that the threshold value has better denoising effect, in other words, even if the denoising threshold value is large, the paths with smaller energy are not filtered out while the noise is removed greatly.
The purpose of the approach is different from the traditional multi-frame averaging, and is not directly used for average denoising of the initial channel impulse response CIR, because the multi-frame averaging technology can distort the channel coefficient at the sampling point in the dynamic channel.
P frame average is carried out on the initial channel impulse response, specifically:
where P is a preset number of adjacent frames, and k is a start frame number for P frame averaging.
Based on the time correlation of dynamic channels, the adjacent OFDM frame channel information still has similarity, and the radial power becomesNoise power is/>
The method comprises the steps of carrying out average denoising on an initial channel impulse response in a static channel, and then removing noise sampling points on the basis; in a dynamic channel, the aim is to improve the degree of differentiation between the path and the noise; the more the average frame number is in the static channel, the better the channel estimation performance is; in a dynamic channel, the faster the time-varying speed of the channel, the lower the correlation between adjacent frames, the fewer the number of allowed average frames, and P typically takes 5-10 frames.
S103, analyzing the relation between the overall error cost and the threshold value based on the preprocessed channel impulse response, and calculating an optimal threshold value;
Analyzing the distribution characteristics of the path power and the noise power, introducing cost factors, calculating the missed alarm cost and the false alarm cost under a certain threshold value, and analyzing the relation between the overall error cost and the threshold value to obtain an optimal threshold value, wherein the method specifically comprises the following steps:
(1) Calculating path power and noise power based on the preprocessed channel impulse response, and constructing a cumulative distribution function of the path power and the noise power;
The square of the envelope is obtained for the preprocessed channel impulse response h W (m, k) to obtain |h W(m,k)|2 as the path power and the noise power, and the path power and the noise power are subjected to exponential distribution, and then cumulative distribution functions (Cumulative Distribution Function, CDF) F r (x) and F n (x) of the path power and the noise power are respectively:
(2) Based on the cumulative distribution function, calculating the false alarm cost and the missing alarm cost under the denoising threshold value by introducing cost factors, and constructing the whole error cost;
Assuming that the denoising threshold is T k, when the power of the m-th subcarrier in the kth OFDM frame is smaller than T k, the power is considered as a noise sampling point, and conversely, the power is considered as a path sampling point; the functional properties according to the cumulative distribution function CDF are available, when x=t k, the path removal probability is F r(Tk), the noise removal probability is F n(Tk); thus, the false alarm (noise path) and false alarm (noise path) are F r(Tk)、1–Fn(Tk respectively), the overall error cost W (T k) can be expressed as:
wherein, alpha is a false alarm cost factor, 1-alpha is a false alarm cost factor, and the channel estimation is greatly influenced by misjudgment of the path as noise, so that the value of alpha is between 0.5 and 1.
(3) And (3) solving a first derivative of the overall error cost on the denoising threshold value, and calculating the denoising threshold value when the first derivative is equal to zero, namely the optimal threshold value.
To obtain the optimal denoising threshold, the overall error cost W (T k) may be first derivative of the denoising threshold T k, i.e.:
Calculating a denoising threshold value when the first derivative is equal to zero, and obtaining the denoising threshold value of the minimum error cost as follows:
Wherein, the radial power And noise power/>The maximum value and the median of the envelope square in the channel impulse response h W (m, k) after preprocessing can be taken, specifically:
s104, determining a denoising matrix according to the optimal threshold value, and performing noise suppression on the initial channel impulse response to obtain a final channel impulse response, wherein the method specifically comprises the following steps:
(1) The denoising matrix G (m, k) is calculated through the preprocessed channel impulse response h W (m, k) and the optimal threshold T k OPT, specifically:
(2) The initial channel impulse response H LS (m, k) is noise suppressed by the denoising matrix G (m, k), i.e., the initial channel impulse response H LS (m, k) is multiplied by the denoising matrix G (m, k), resulting in the final channel impulse response H OTBS (m, k), which can be expressed as:
hOTBS(m,k)＝hLS(m,k)·G(m,k)
to prove that the present embodiment has higher channel estimation accuracy, the channel estimation performance of the present embodiment is verified in urban vehicle channel and rural highway channel models in Wing-TV test projects in finland.
The method of the embodiment is combined with 3 traditional methods: LS, multi-frame averaging, DFT channel estimation methods (directly regarding sub-carriers outside the CP length as noise sampling points) are compared to the idealities of the support set of known channel CIRs.
When the carrier frequency is set to 800MHz, fig. 2 and 3 show the normalized mean square error (Normalized Mean Square Error, NMSE) performance in the case of 4 methods and ideal support sets, respectively, in the case of 40km/h urban vehicle channel and 100km/h rural highway channel, the x-axis is the signal to noise ratio, and the y-axis is the normalized mean square error, where the value of P and the average frame number in the multi-frame averaging method in the method of this embodiment are 10 and 6 on the urban road and highway, respectively, and generally the faster the time-varying speed of the channel, the lower the correlation between adjacent frames will be, and the fewer the number of frames allowed to average will be.
As can be seen from fig. 2 and 3, the channel estimation method of the present embodiment has better performance than 3 conventional methods, and is closer to the situation of the ideal support set, wherein the multi-frame averaging method shows inapplicability in dynamic channels.
Fig. 4 and 5 show Bit Error Rate (BER) performance in 40km/h urban vehicle channels and 100km/h rural highway channels, respectively, for 4 methods and ideal support sets, with the x-axis being the signal-to-noise ratio and the y-axis being the Bit Error Rate, it can be seen that the method of the present embodiment has Bit Error Rate performance that is closer to that of the known ideal support set.
Example two
The embodiment discloses a dynamic threshold channel estimation system based on minimum cost;
As shown in fig. 6, a dynamic threshold channel estimation system based on minimum cost includes an initial estimation module, a preprocessing module, a threshold calculation module, and a noise suppression module:
an initial estimation module configured to: based on the acquired pilot frequency data, carrying out channel estimation to obtain an initial channel impulse response;
a preprocessing module configured to: preprocessing the initial channel impulse response according to the time correlation of the wireless channel;
A threshold calculation module configured to: based on the preprocessed channel impulse response, analyzing the relation between the overall error cost and the threshold value, and calculating an optimal threshold value;
A noise suppression module configured to: and determining a denoising matrix according to the optimal threshold value, and performing noise suppression on the initial channel impulse response to obtain a final channel impulse response.
Example III
An object of the present embodiment is to provide a computer-readable storage medium.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps in a dynamic threshold channel estimation method based on minimum cost as described in one embodiment of the present disclosure.
Example IV
An object of the present embodiment is to provide an electronic apparatus.
An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, the processor implementing the steps in a dynamic threshold channel estimation method based on minimum cost as described in an embodiment of the present disclosure when the program is executed.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A dynamic threshold channel estimation method based on minimum cost, comprising:
Based on the acquired pilot frequency data, carrying out channel estimation to obtain an initial channel impulse response;
Preprocessing the initial channel impulse response according to the time correlation of the wireless channel;
based on the preprocessed channel impulse response, analyzing the relation between the overall error cost and the threshold value, and calculating an optimal threshold value;
determining a denoising matrix according to the optimal threshold value, and performing noise suppression on the initial channel impulse response to obtain a final channel impulse response;
The calculating the optimal threshold value specifically comprises the following steps:
Calculating path power and noise power based on the preprocessed channel impulse response, and constructing a cumulative distribution function of the path power and the noise power;
based on the cumulative distribution function, calculating the false alarm cost and the missing alarm cost under the denoising threshold value by introducing cost factors, and constructing the whole error cost;
And (3) solving a first derivative of the overall error cost on the denoising threshold value, and calculating the denoising threshold value when the first derivative is equal to zero, namely the optimal threshold value, wherein the formula is as follows:
wherein, Is the optimal threshold value/>Is the noise power,/>The path power is the path power, and alpha is a missed warning cost factor;
the method for obtaining the initial channel impulse response comprises the following specific steps:
performing channel estimation on the acquired pilot frequency data by adopting a least square method to obtain a channel frequency response;
performing M-point inverse fast Fourier transform on the obtained channel frequency response to obtain a channel impulse response, namely an initial channel impulse response;
The channel estimate is expressed as:
wherein, Is the channel frequency response, Y (m, k) and X (m, k) are the received and transmitted respectivelyFirst/>, in each OFDM symbolPilot symbols of subcarriers;
The method comprises the steps of preprocessing an initial channel impulse response, and adopting an adjacent frame average method to average P frames of the initial channel impulse response, wherein P is a preset adjacent frame number;
the denoising matrix G (m, k) is specifically:
wherein, Is the optimal threshold value/>Is the channel impulse response after preprocessing.
2. The method of claim 1, wherein the noise suppression of the initial channel impulse response is a multiplication of the initial channel impulse response with a denoising matrix to obtain a final channel impulse response.
3. A dynamic threshold channel estimation system based on minimum cost, which is characterized by comprising an initial estimation module, a preprocessing module, a threshold calculation module and a noise suppression module:
an initial estimation module configured to: based on the acquired pilot frequency data, carrying out channel estimation to obtain an initial channel impulse response;
a preprocessing module configured to: preprocessing the initial channel impulse response according to the time correlation of the wireless channel;
A threshold calculation module configured to: based on the preprocessed channel impulse response, analyzing the relation between the overall error cost and the threshold value, and calculating an optimal threshold value;
A noise suppression module configured to: determining a denoising matrix according to the optimal threshold value, and performing noise suppression on the initial channel impulse response to obtain a final channel impulse response;
The calculating the optimal threshold value specifically comprises the following steps:
Calculating path power and noise power based on the preprocessed channel impulse response, and constructing a cumulative distribution function of the path power and the noise power;
based on the cumulative distribution function, calculating the false alarm cost and the missing alarm cost under the denoising threshold value by introducing cost factors, and constructing the whole error cost;
And (3) solving a first derivative of the overall error cost on the denoising threshold value, and calculating the denoising threshold value when the first derivative is equal to zero, namely the optimal threshold value, wherein the formula is as follows:
wherein, Is the optimal threshold value/>Is the noise power,/>The path power is the path power, and alpha is a missed warning cost factor;
the method for obtaining the initial channel impulse response comprises the following specific steps:
performing channel estimation on the acquired pilot frequency data by adopting a least square method to obtain a channel frequency response;
performing M-point inverse fast Fourier transform on the obtained channel frequency response to obtain a channel impulse response, namely an initial channel impulse response;
The channel estimate is expressed as:
wherein, Is the channel frequency response, Y (m, k) and X (m, k) are the received and transmitted respectivelyFirst/>, in each OFDM symbolPilot symbols of subcarriers;
The method comprises the steps of preprocessing an initial channel impulse response, and adopting an adjacent frame average method to average P frames of the initial channel impulse response, wherein P is a preset adjacent frame number;
the denoising matrix G (m, k) is specifically:
wherein, Is the optimal threshold value/>Is the channel impulse response after preprocessing.
4. A computer readable storage medium having stored thereon a program, which when executed by a processor implements the steps of a minimum cost based dynamic threshold channel estimation method according to any of claims 1-2.
5. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the steps of a minimum cost based dynamic threshold channel estimation method as claimed in any one of claims 1-2 when the program is executed.
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