CN110417699A - A method of the ofdm system timing synchronization based on machine learning - Google Patents
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- H04L27/00—Modulated-carrier systems
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- H—ELECTRICITY
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- H04L27/00—Modulated-carrier systems
- H04L27/26—Systems using multi-frequency codes
- H04L27/2601—Multicarrier modulation systems
- H04L27/2647—Arrangements specific to the receiver only
- H04L27/2655—Synchronisation arrangements
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Abstract
The method for the ofdm system timing synchronization based on machine learning that the present invention relates to a kind of.The training of signal-to-noise ratio prediction is carried out using multilayer perceptron model when the receiving end OFDM is synchronized, subsequent synchronous circuit is selected according to resulting signal-to-noise ratio, suitable synchronous circuit is selected for different signal-to-noise ratio conditions, guarantees to reduce implementation complexity while precision.A point synchronous method for situation discussion is carried out to the related parameter in synchronized algorithm in varied situations according to deriving the case where being divided into the high s/n ratio situation, SNR<-5dB low signal-to-noise ratio situation and -5dB<SNR<5dB of SNR>5dB according to the size of signal-to-noise ratio.
Description
Technical Field
The invention belongs to the field of wireless communication, and particularly relates to a method for symbol timing synchronization of an OFDM system based on machine learning.
Background
Orthogonal Frequency Division Multiplexing (OFDM) technology has become the most common modulation technology in Digital Video Broadcasting (DVB), Digital Audio Broadcasting (DAB) and Wireless Local Area Networks (WLAN) and will play an important role in future 5G communications. The essence of OFDM is multi-carrier modulation, which addresses the distortion caused by frequency selective channels by modulating high-speed data streams onto orthogonal subcarriers. Since the narrow band channel can be considered as flat fading, the receiving end compensates for the channel-induced distortion by a simple equalizer. The effect of time-frequency synchronization determines the decoding effect on communication information, but the OFDM system is very sensitive to the variation of the following parameters: (1) intersymbol interference (ISI) due to the starting sample or due to the channel; (2) inter-carrier interference (ICI) due to frequency mismatch between the transmit and receive local oscillators or due to doppler shift. In order to obtain good communication performance, an efficient and accurate synchronization method is required in a communication system.
At present, a great deal of research is carried out on the synchronization algorithm of the OFDM system, and different synchronization algorithms aim at different communication conditions and sub-band parameters and have targeted improvement. The synchronization is fast, the synchronization algorithm is simple to implement, the estimation deviation of the synchronization error is large under the condition of low signal-to-noise ratio, the synchronization algorithm improved on the synchronization algorithm needs a more complex hardware implementation scheme to achieve the expected synchronization estimation accuracy, the signal-to-noise ratio of a channel has a large influence on the synchronization result, a great deal of research is already carried out on the signal-to-noise ratio estimation of the OFDM system, and the traditional methods for estimating the signal-to-noise ratio are mainly divided into a maximum likelihood estimation method, a statistic estimation method, a spectrum analysis estimation method and the like. The performance is very poor in a low signal-to-noise ratio scene based on the maximum likelihood estimation method, and the calculation amount is large; the method based on statistics is simple in calculation, but needs enough data, and when the modulation order is increased, the estimation error under the scene of high signal-to-noise ratio is increased; the method based on the spectrum analysis has high estimation precision, but has large calculation amount and is not easy to realize. In recent years, deep learning has been rapidly developed in the fields of image and video processing, natural language processing, and the like, but the conventional network structure has not been studied in a large scale in the communication field because of insufficient ability to extract communication feature values. By constructing a data set of OFDM communication signals and training a network model by using the data set, the application of a deep learning training model in a communication system is researched, and the method has important significance.
Disclosure of Invention
Step one, defining a sampling observation window, and y [ n ] in the observation window]The number of samples of (M +1) NOFDM-1, where M is the number of symbols, and M is contained with certainty in these samplesThe entire OFDM symbol. First consider the position of the starting symbol, which should be in the range of 1 to NOFDMBut the exact location is not known because the channel delay is not known. The index set is defined as follows:
wherein M is the [1, N ]F-OFDM]Indicating the timing synchronization position required to be searched; q is an element of [0, N ∈G-1]Representing the q-th sample value after the current timing position; p is an element of [0, M-1 ]]Indicating the p-th symbol after the current timing synchronization position. Let SδIs a cyclic prefix index, S, from the first symbol to the Mth symbolδ+NIndicating the location corresponding to the original index.
And step two, signal-to-noise ratio estimation. Constructing a MLP with a three-layer structure, the training sequence input layer obtains a segmented data stream for a sampling window, which can be expressed as a dimension of (M +1) NOFDMA vector X of-1, the hidden layer is fully connected to the input layer, the output of the hidden layer is f (W)1X+b1)，W1Is the coefficient of connection, b1Is a bias and the function f is an activation function, which may be tanh, sigmoid, etc. The output of the output layer is
softmax(W2X1+b2) (2)
Wherein X1Is the output of the hidden layer. In order to minimize the loss value, the connection coefficient and the offset value are updated after training each time, and the SNR is finally obtained and then enters different synchronous estimation steps through a judgment selection circuit.
And step three, synchronous estimation under high signal-to-noise ratio. According to the observation window obtained in the step one, the symbol timing offset and the carrier frequency offset of the observation sample point are given, and by using the implicit correlation in the observation data, the following log-likelihood function can be obtained:
since the symbol timing offset and carrier frequency offset are independent of each other, we can ignore the second term in the above equation when maximizing Λ (δ, ε). Also, since Y is a gaussian vector, we can also ignore all additions and growing multiplication constants in the log-likelihood function expression, since they do not affect the maximum of the result. The following formula can then be obtained:
wherein γ andis defined as follows:
ρ is the correlation coefficient between y [ N ] and y [ N + N ], and can be further written as
When the signal-to-noise ratio SNR >5dB, ρ can be approximately seen as 1, then Λ (δ, ε) can be written as:
thus, the maximum value of Λ (δ, ε) can be first determined by maximizing cos (2 π ε + < γ [ δ [ + ] - γ ]]) To solve the problem. When coarse symbol synchronization has been performed, then ε at which Λ (δ, ε) is maximized can be obtained byML：
The log-likelihood function at this time can be written as:
the STO and CFO estimates are the following:
obtaining the correlation value and energy value of the signal in the sampling window in the step one, and obtaining the delta when the difference value is maximum, namely the obtained STO estimated deltaMLThen, the estimated value of STO is used to obtain epsilonML。
And step four, synchronous estimation under low signal-to-noise ratio. When the signal-to-noise ratio SNR is < -5dB, ρ can be approximately seen as 0, then Λ (δ, ε) can be written as:
Λ(δ，ε)＝|γ[δ]|cos(2πε+∠γ[δ]) (13)
thus, the maximum value of Λ (δ, ε) can be first determined by maximizing cos (2 π ε + < γ [ δ [ + ] - γ ]]) To solve the problem. When coarse symbol synchronization has been performed, then ε at which Λ (δ, ε) is maximized can be obtained byML：
The log-likelihood function at this time can be written as:
Λ(δ，εML[δ])＝|γ[δ]| (15)
the STO and CFO estimates are the following:
δML＝argmax{|γ[δ]|} (16)
namely the sampling window in the step oneThe correlation value and energy value of the signals in the mouth make the delta when the difference value takes the maximum value be the calculated STO estimated deltaMLAnd then obtaining delta through STO estimated valueML。
Step five, when the signal to noise ratio is less than SNR (minus 5 dB) and less than 5dB, the formula of the lambda (delta, epsilon) is as follows for accurate estimation and calculation:
thus, the maximum value of Λ (δ, ε) can be first determined by maximizing cos (2 π ε + < γ [ δ [ + ] - γ ]]) To solve the problem. When coarse symbol synchronization has been performed, then ε at which Λ (δ, ε) is maximized can be obtained byML：
The log-likelihood function at this time can be written as:
Λ(δ，εML[δ])＝|γ[δ]| (20)
the STO and CFO estimates are the following:
wherein,
therefore, rho value can be obtained according to SNR obtained in the second step, and then correlation value and energy value of the signal in the sampling window in the first step are obtained, so that delta when the difference value is maximum is the obtained STO estimation deltaMLThen, the estimated value of STO is used to obtain epsilonML。
Drawings
Fig. 1 is a flow chart of the present invention for selecting a synchronization scheme based on signal-to-noise ratio estimation.
Fig. 2 is a block diagram of a hardware design for selecting a synchronization scheme based on snr estimation in accordance with the present invention.
Detailed Description
The invention will be further described with reference to fig. 2, but this example should not be construed as limiting the invention.
1. Initial state
Resetting all FIFOs and emptying the shift register;
2. coarse synchronization detection
Continuously detecting the gate line until the flag bit jumps to a high level when the data is not written into the FIFO;
3. timed synchronous wait
Since the data is written into FIFO _1 and FIFO _2, the accumulated length is Nc and the search length is Ns, the number of signal points that are collectively calculated is 2Nc + Ns. Signal data is buffered in the FIFO2 from the start point and is input to the arithmetic module;
4. synchronous circuit selection
Calculating the signal-to-noise ratio according to the signal-to-noise ratio estimation method provided by the step two, and selecting different circuits according to the obtained result;
5. calculation of correction values
When SNR is greater than 5dB, carrying out synchronous estimation operation according to equation (11); when SNR < -5dB, the synchronization estimation operation is performed according to the formula (16), and when-5 dB < SNR <5dB, the synchronization estimation operation is performed according to the formula (21). In the operation module part, only three parameter operations are involved, namely a correlation coefficient, a correlation value and an energy value, wherein the correlation coefficient can be obtained according to the signal-to-noise ratio obtained in the step 4.
The correlation value can be calculated by calculating the difference Δ Rn between Rn and Rn-1, followed by recursive accumulation. Firstly, the input data to be processed is temporarily stored by the input buffer, and then the relevant operation is carried out on the data. Then, the synchronous shift register and the subtracter obtain the required delta R [ n ], so as to reduce the calculation amount brought by accumulation and summation.
Similar to the recursive design of correlation values, the energy value can be calculated by calculating the difference Δ En between E n and E n-1, and then accumulating the recursions. Firstly, the input data to be processed is temporarily stored through an input buffer, and then the input data to be processed is subjected to square sum operation. Then, the synchronous shift register and the subtracter obtain the required delta R [ n ], so as to reduce the calculation amount brought by accumulation and summation. The energy detection module mainly comprises a squaring module and a recursion accumulation module, wherein the squaring module is obtained by respectively squaring the real part and the imaginary part of data and then adding the squares of absolute values, so that hardware realization resources can be effectively saved.
6. Signal recovery
Based on the resulting correction value STO for the timing synchronization. The data points of the STO plus the length of the leader sequence in the FIFO _2 are discarded, and the data with the fixed length of the signal data of one frame is extracted, so that the effective signal data is obtained.
Claims (3)
1. A method for selecting OFDM wireless communication synchronization circuit according to channel signal-to-noise ratio includes the following steps:
A) defining a sampling observation window, y [ n ] in the observation window]The number of samples of (M +1) NOFDM-1, where M is the number of symbols, and M complete OFDM symbols are certainly contained in these samples. First consider the position of the starting symbol, which should be in the range of 1 to NOFDMBut the exact location is not known because the channel delay is not known. The index set is defined as follows:
wherein m is [1, N ]F-OFDM]Indicating the timing synchronization position required to be searched; q is an element of [0, N ∈G-1]Representing the q-th sample value after the current timing position; p is an element of [0, M-1 ]]Indicating the p-th symbol after the current timing synchronization position. Let SδIs a cyclic prefix index, S, from the first symbol to the Mth symbolδ+NIndicating the location corresponding to the original index.
B) And (5) estimating the signal-to-noise ratio. Constructing a MLP with a three-layer structure, the training sequence input layer obtains a segmented data stream for a sampling window, which can be expressed as a dimension of (M +1) NOFDMA vector X of-1, the hidden layer is fully connected to the input layer, the output of the hidden layer is f (W)1X+b1)，W1Is the coefficient of connection, b1Is a bias and the function f is an activation function, which may be tanh, sigmoid, etc. The output of the output layer is
softmax(W2X1+b2) (2)
Wherein X1Is the output of the hidden layer. In order to minimize the loss value, the connection coefficient and the offset value are updated after training each time, and the SNR is finally obtained and then enters different synchronous estimation steps through a judgment selection circuit.
C) Synchronization estimation at high signal-to-noise ratio. According to the observation window obtained in the step one, the symbol timing offset and the carrier frequency offset of the observation sample point are given, and by using the implicit correlation in the observation data, the following log-likelihood function can be obtained:
since the symbol timing offset and carrier frequency offset are independent of each other, we can ignore the second term in the above equation when maximizing Λ (δ, ε). Also, since Y is a gaussian vector, we can also ignore all additions and growing multiplication constants in the log-likelihood function expression, since they do not affect the maximum of the result. The following formula can then be obtained:
wherein γ andis defined as follows:
ρ is the correlation coefficient between y [ N ] and y [ N + N ], and can be further written as
When the signal-to-noise ratio SNR >5dB, ρ can be approximately seen as 1, then Λ (δ, ε) can be written as:
thus, the maximum value of Λ (δ, ε) can be first determined by maximizing cos (2 π ε + < γ [ δ [ + ] - γ ]]) To solve the problem. When coarse symbol synchronization has been performed, then ε at which Λ (δ, ε) is maximized can be obtained byML：
The log-likelihood function at this time can be written as:
the STO and CFO estimates are the following:
obtaining the correlation value and the correlation value of the signal in the sampling window in the step oneThe energy value is such that the delta when the difference value takes the maximum value is the estimated delta of STOMLThen, the estimated value of STO is used to obtain epsilonML。
D) Synchronization estimation at low signal-to-noise ratio. When the signal-to-noise ratio SNR is < -5dB, ρ can be approximately seen as 0, then Λ (δ, ε) can be written as:
Λ(δ，ε)＝|γ[δ]|cos(2πε+∠γ[δ]) (13)
thus, the maximum value of Λ (δ, ε) can be first determined by maximizing cos (2 π ε + < γ [ δ [ + ] - γ ]]) To solve the problem. When coarse symbol synchronization has been performed, then ε at which Λ (δ, ε) is maximized can be obtained byML：
The log-likelihood function at this time can be written as:
Λ(δ，εML[δ])＝|γ[δ]| (15)
the STO and CFO estimates are the following:
δML＝argmax{|γ[δ]|} (16)
obtaining the correlation value and energy value of the signal in the sampling window in the step one, and obtaining the delta when the difference value is maximum, namely the obtained STO estimated deltaMLThen, the estimated value of STO is used to obtain epsilonML。
E) When signal-to-noise ratio-5 dB < SNR <5dB, then the formula for Λ (δ, ε) is:
thus, the maximum value of Λ (δ, ε) can be first determined by maximizing cos (2 π ε + < γ [ δ [ + ] - γ ]]) To solve the problem. When coarse symbol synchronization has been performed, the maximum value of Λ (δ, ε) can be obtained byεML：
The log-likelihood function at this time can be written as:
Λ(δ，εML[δ])＝|γ[δ]| (20)
the STO and CFO estimates are the following:
wherein,
therefore, rho value can be obtained according to SNR obtained in the second step, and then correlation value and energy value of the signal in the sampling window in the first step are obtained, so that delta when the difference value is maximum is the obtained STO estimation deltaMLThen, the estimated value of STO is used to obtain epsilonML。
2. The method of claim 1, wherein step a further comprises: defining a new sampling observation window, y [ n ] in the observation window]The number of samples of (M +1) NOFDM-1, where M is the number of symbols, and defines the index set as follows:
wherein m is [1, N ]F-OFDM]Indicating the timing synchronization position required to be searched; q is an element of [0, N ∈G-1]Representing the q-th sample value after the current timing position; p ∈ [0, M-1]Indicating the p-th symbol after the current timing synchronization position. Let SδIs a cyclic prefix index, S, from the first symbol to the Mth symbolδ+NIndicating the location corresponding to the original index.
3. The method of claim 1, wherein step B further comprises: and constructing an MLP with a three-layer structure, wherein a training sequence input layer is a sampling window to obtain a segmented data stream, the connection coefficient and the offset value are updated after training each time, and finally, the SNR is obtained after training, and then, the different synchronous estimation steps are performed through a judgment selection circuit.
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