CN109617847B - OFDM receiving method without cyclic prefix based on model-driven deep learning - Google Patents
OFDM receiving method without cyclic prefix based on model-driven deep learning Download PDFInfo
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Abstract
The invention discloses a cyclic prefix-free OFDM receiving method based on model-driven deep learning, which comprises the following steps: (1) receiving a pilot signal y in a signal ypTransforming to obtain frequency domain pilot signal, performing least square estimation initialization with local frequency domain pilot signal to obtain least square channel estimation result, inputting to fully-connected first deep neural network for improvement, and performing Fourier inversion on the improved result to obtain time domain channel estimation signal
(2) The received signal y is converted into a real number domain received signal after eliminating intersymbol interference
(3) Receiving a signal in the real number domain
As input, a signal is estimated from a time domain channel
Iterative solution is carried out by adopting a second deep neural network to obtain a finally estimated modulation signal
(4) Will modulate the signal
Obtaining transmitted information bits after demodulation
The invention has the advantages of less time consumption and high detection performance.
Description
Technical Field
The invention relates to a communication technology, in particular to a cyclic prefix-free OFDM receiving method based on model-driven deep learning.
Background
In recent years, deep learning has been a fundamental technique in artificial intelligence, and has been a great success in subjects such as computer vision and natural language processing. Deep learning is a branch of the field of machine learning, and is a supervised learning method, and a group of optimal neural network parameters are obtained by minimizing a loss function between a predicted value and a true value of a deep neural network, so that the deep neural network can perform accurate prediction.
Deep learning has been studied in the wireless communication physical layer, including channel estimation, signal detection, encoder, decoder, channel feedback information reconstruction, and end-to-end deep learning communication system. However, the examples of integrating the algorithm knowledge in the field of wireless communication into the neural network design are few at present, most of the functions of the existing neural network regard a wireless communication system or a module as a black box, the training of the neural network completely depends on a large amount of data driving, the parameter number of the neural network is large, and the training speed is slow. In OFDM without cyclic prefix, inter-symbol interference and inter-carrier interference caused by multipath channels make channel estimation and channel detection very challenging. The bit error rate of the data-driven neural network is high under high-order modulation, and the performance is expected to be further improved by using the model-driven neural network.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems in the prior art, the invention provides a cyclic prefix-free OFDM receiving method based on model-driven deep learning.
The technical scheme is as follows: the OFDM receiving method without the cyclic prefix based on the model-driven deep learning comprises the following steps:
(1) receiving a pilot signal y in a signal ypTransforming to obtain frequency domain pilot signal, performing least square estimation initialization with local frequency domain pilot signal to obtain least square channel estimation result, inputting to fully-connected first deep neural network for improvement, and performing Fourier inversion on the improved result to obtain time domain channel estimation signal
(2) The received signal y is converted into a real number domain received signal after eliminating intersymbol interference
(3) Receiving a signal in the real number domain
As input, a signal is estimated from a time domain channel
Iterative solution is carried out by adopting a second deep neural network expanded by the OAMP algorithm to obtain a finally estimated modulation signal
(4) Will modulate the signal
Obtaining transmitted information bits after demodulation
Further, the step (1) specifically comprises:
(1.1) receiving the signaly pilot signal ypFourier transform is carried out to obtain a frequency domain pilot signal Yp；
(1.2) mixing the frequency domain pilot signal YpAnd a local frequency domain pilot signal XpPerforming least square estimation initialization to obtain least square channel estimation result
Wherein, the least square channel estimation result of the k sub-carrier
Comprises the following steps:
in the formula, Yp(k)、Xp(k) Receiving frequency domain pilot signals and local frequency domain pilot signals of the kth subcarrier respectively, wherein N is the number of the subcarriers;
(1.3) estimating the result of least squares channel
The real part and the imaginary part of the vector are connected in series to form a vector
Improving a first deep neural network input to a full connection, wherein the first deep neural network comprises a layer network for performing the following calculations:
in the formula, WLMMSETo use the weight matrix for linear minimum mean square error channel estimation,
is WLMMSEA real-valued matrix of, and
re { }, Im { } denote taking a real part and an imaginary part respectively,
improving the result for the output of the first deep neural network;
(1.4) will improve the results
Performing inverse Fourier transform to obtain a time domain channel estimation signal
Further, the weight matrix WLMMSEObtained through training, the training process is as follows:
obtaining a plurality of samples, each sample comprising a true frequency domain channel H and a corresponding least squares channel estimation result
Will be provided with
As parameters to be trained, an Adam optimizer and a small-batch gradient descent method are adopted to perform multi-round training, and the learning rate is dynamically adjusted to obtain parameters
The optimum value of (d); loss function using squared error loss
Further, the step (2) specifically comprises:
(2.1) eliminating intersymbol interference from the received signal y according to the following formula:
in the formula,
for eliminating the received signal of intersymbol interference, A is an intersymbol interference and intersymbol interference matrix, and the expression is
(2.2) received Signal to be interference cancelled between symbols
Conversion to a real number domain received signal according to
Further, in the step (3), the second deep neural network is formed by connecting LS layer network layers in series, the structures of all the layers are the same, and the input of the l layer network layer is the output of the l-1 layer
And receiving signals in real number domain
Output is as
The operations performed are:
in the formula,
is a linear MMSE matrix and is a linear MMSE matrix,
estimating a signal for a time domain channel
F is a normalized fourier transform matrix,
is the noise variance, I is the identity matrix, β is the update parameter, λl、γlFor training parameters, N is the number of carriers, and ε isPositive number less than preset threshold, modulation symbol u from real modulation set
Indicates that the modulation symbol is judged as amProbability of (u)nA symbol is modulated for the nth sub-carrier,
is the nth subcarrier symbol received for the l layer.
Further, the training parameter λl、γlObtained through training, the training process is as follows:
obtaining a plurality of samples, each sample comprising a modulated signal u and a corresponding real number domain received signal
Will be lambdal、γlAs a parameter to be trained, an Adam optimizer and a small batch gradient descent method are adopted to perform multi-round training, and the learning rate is dynamically adjusted to obtain a parameter lambdal、γlThe optimum value of (d); loss function using squared error loss
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: the invention reasonably integrates the OAMP algorithm into the neural network design, and the performance of the bit error rate is obviously improved in the OFDM without the cyclic prefix, the number of network training parameters is greatly reduced, and the training time is greatly shortened. The invention trains the neural network by the OFDM receiver in modules, and inputs the result of the traditional communication algorithm as an initial value into the neural network for optimization and improvement, thereby having less time consumption of network training and high detection performance.
Drawings
FIG. 1 is a schematic flow diagram of one embodiment of the present invention;
FIG. 2 is a flow chart of channel estimation of the present invention;
fig. 3 is a schematic flow chart of signal detection according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples of 64-subcarrier cyclic prefix-free OFDM systems.
First, the channel model suitable for this embodiment
In an OFDM system with 64 sub-carriers, the data frame format is one pilot OFDM symbol and one data OFDM symbol, with the pilot and data occupying 64 sub-carriers. The constellation modulation mode of the pilot frequency is QPSK, and the constellation modulation mode of the data adopts 64QAM of the LTE standard. At a transmitting end, data bits are 64 multiplied by 6 to 384 bits, and are converted into time domain sending signals through 64QAM constellation modulation, pilot frequency adding framing and IFFT; after passing through a multipath channel, time domain receiving pilot frequency and data are obtained at a receiving end and are sent to the OFDM system receiver without the cyclic prefix based on the model-driven deep learning, and 384-bit recovery is obtained.
Assuming that white gaussian noise w exists in the channel, the time domain signal y obtained at the receiving end is as follows:
where N denotes the number of subcarriers, u is the modulated signal, q and q-1Respectively representing a current OFDM symbol and a previous OFDM symbol. F is an N × N normalized fourier transform matrix and H is an N × N cyclic channel matrix, represented as follows:
a represents the channel matrix of the inter-carrier interference and the inter-symbol interference, and the expression is
The formula (1) can be further converted into
The transmitted signal received in the time domain is denoted as s ═ H1FHu, then SNR is expressed as
Wherein,
is the variance of the noise w.
Second, the detailed steps of this embodiment
As shown in fig. 1, the neural network is applied to a wireless communication receiver, instead of the functions of two modules of channel estimation and signal equalization of the original OFDM system, and specifically includes two sub-modules of channel estimation and signal detection, the network is trained by a supervised learning method on line, the trained network is used for forward prediction in actual application, the receiver specifically includes four steps of channel estimation, signal calculation, signal detection and signal demodulation, a first deep neural network in the channel estimation step is trained first and then a second deep neural network in the signal detection step is trained in the training, and a channel estimation result output in the channel estimation step is obtained first and then signal detection is performed in the application.
(1) Channel estimation
As shown in fig. 2, the pilot signal y in the signal y is receivedpFourier transform is carried out to obtain a frequency domain pilot signal YpThe frequency domain pilot signal YpAnd a local frequency domain pilot signal XpPerforming least squares estimation initialization, wherein the least squares channel estimation result of the k sub-carrier
Comprises the following steps:
then, the real part and the imaginary part of the least square estimation result are connected in series to form a vector with the length of 128, the vector is input into a first neural network which is fully connected for improvement, and a channel estimation result with the length of 128 is output
Will improve the results
Performing inverse Fourier transform to obtain a time domain channel estimation signal
The deep neural network is a one-layer fully-connected network, an activation function is not adopted, multiplicative parameters of the layer network are initialized by adopting real values of a weight matrix of linear minimum mean square error channel estimation, initial values of additive parameters are set to be all zero, and all the parameters are set to be trainable. Wherein the minimum mean square error channel estimate
Weight matrix WLMMSEReal value matrix of
Is composed of
The dimension is 128 × 128. The deep neural network training process comprises the steps of obtaining a plurality of samples, wherein each sample comprises a real frequency domain channel H and a corresponding least square channel estimation result
The loss function is the loss of squared error
The optimizer is an Adam optimizer, small-batch gradient descent is adopted during training, 50 batches are adopted in each round, the size of each batch is 1000 samples, the training is carried out for 2000 rounds, the learning rate of the first 1000 rounds is 0.001, and the learning rate of the last 1000 rounds is 0.0001.
(2) Signal calculation
Firstly, the received signal y is eliminated with intersymbol interference according to the following formula:
in the formula,
in order to cancel the received signal of the inter-symbol interference,
is an estimate of A, qi-1Is a pilot OFDM symbol; secondly, the received signal with intersymbol interference eliminated
Conversion to a real number domain received signal according to
(3) Signal detection
Iterative solution is carried out by adopting a second deep neural network to obtain a finally estimated modulation signal
And the second deep neural network expands the iterative OAMP algorithm and introduces trainable parameters to further improve the signal detection performance. To apply the OAMP algorithm, the second term in equation (2) needs to be removed. Since the data frame format is one pilot OFDM symbol and one data OFDM symbolThe previous OFDM symbol qi-1Are pilot OFDM symbols. The pilot symbols are known to the receiving end and thus the inter-symbol interference can be removed. As shown in fig. 2, the receiver obtains the time domain channel estimate by channel estimation
The received signal after removal of the intersymbol interference is then
Wherein,
are all estimated by time domain channel
And (4) obtaining.
In order to increase the computation speed and reduce the complexity, the complex domain OFDM system needs to be converted into the real domain before signal detection. The equivalent real number domain of equation (4) is expressed as follows
Wherein
As shown in FIG. 3, the second deep neural network is formed by connecting LS layer network layers in series, each layer has the same structure, and the input of the l-layer network layer is the output of the l-1 layer
And receiving signals in real number domain
Output is as
The operations performed are:
in the formula,
is a linear MMSE matrix and is a linear MMSE matrix,
i is the identity matrix, beta is the update parameter, set to 0.5, lambdal、γlFor training parameters, epsilon is a positive number smaller than a preset threshold, and modulation symbols u are from a real number modulation set
For a 64QAM, the number of bits in the symbol is,
indicates that the modulation symbol is judged as amProbability of (u)nA symbol is modulated for the nth sub-carrier,
is the nth subcarrier symbol received for the l layer. r islAnd
respectively, affecting the modulation symbol estimates
A priori mean and variance of precision.
As can be seen from FIG. 3, in the second neural network, each layer involves 2 trainable variables (λ)l,γl) The total amount of training parameters is 2 LS. Moreover, the number of training parameters is independent of the number of subcarriers, and is only related to the number of network layers. For large-scale OFDM, this structure shows great advantages.
The deep neural network is trained by obtaining a plurality of samples, each sample comprising a modulation signal u and a corresponding real number domain received signal
The loss function is the loss of squared error
The optimizer is an Adam optimizer, batch gradient descent is adopted during training, the size of each batch is 1000 samples, namely 1000 data are adopted in each round of training together, 10000 rounds of training are performed, and the learning rate is 0.001.
(4) Signal demodulation
Obtaining an estimate of the modulated signal by forward propagation after signal detection is complete
Then demodulating to obtain estimated bit data
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (6)
1. A cyclic prefix-free OFDM receiving method based on model-driven deep learning is characterized by comprising the following steps:
(1) receiving a pilot signal y in a signal ypTransforming to obtain frequency domain pilot signal, performing least square estimation initialization with local frequency domain pilot signal to obtain least square channel estimation result, inputting to fully-connected first deep neural network for improvement, and performing Fourier inversion on the improved result to obtain time domain channel estimation signal
(2) The received signal y is converted into a real number domain received signal after eliminating intersymbol interference
(3) Receiving a signal in the real number domain
As input, a signal is estimated from a time domain channel
Iterative solution is carried out by adopting a second deep neural network expanded by the OAMP algorithm to obtain a finally estimated modulation signal
(4) Will modulate the signal
Obtaining transmitted information bits after demodulation
2. The OFDM receiving method without cyclic prefix based on model-driven deep learning of claim 1, wherein: the step (1) specifically comprises the following steps:
(1.1) receiving a pilot signal y in a signal ypFourier transform is carried out to obtain a frequency domain pilot signal Yp；
(1.2) mixing the frequency domain pilot signal YpAnd a local frequency domain pilot signal XpPerforming least square estimation initialization to obtain least square channel estimation result
Wherein, the least square channel estimation result of the k sub-carrier
Comprises the following steps:
in the formula, Yp(k)、Xp(k) Receiving frequency domain pilot signals and local frequency domain pilot signals of the kth subcarrier respectively, wherein N is the number of the subcarriers;
(1.3) estimating the result of least squares channel
The real part and the imaginary part of the vector are connected in series to form a vector
Improving a first deep neural network input to a full connection, wherein the first deep neural network comprises a layer network for performing the following calculations:
in the formula, WLMMSETo use the weight matrix for linear minimum mean square error channel estimation,
is WLMMSEA real-valued matrix of, and
re { }, Im { } denote taking a real part and an imaginary part respectively,
improving the result for the output of the first deep neural network;
(1.4) will improve the results
Performing inverse Fourier transform to obtain a time domain channel estimation signal
3. The OFDM receiving method without cyclic prefix based on model-driven deep learning of claim 2, wherein: the weight matrix WLMMSEObtained through training, the training process is as follows:
obtaining a plurality of samples, each sample comprising a true frequency domain channel H and a corresponding least squares channel estimation result
Will be provided with
As parameters to be trained, an Adam optimizer and a small-batch gradient descent method are adopted to perform multi-round training, and the learning rate is dynamically adjusted to obtain parameters
The optimum value of (d); loss function using squared error loss
H (k) denotes the H value of k subcarriers,
representing k sub-carriers
The value is obtained.
4. The OFDM receiving method without cyclic prefix based on model-driven deep learning of claim 1, wherein: the step (2) specifically comprises the following steps:
(2.1) eliminating intersymbol interference from the received signal y according to the following formula:
in the formula,
for eliminating the received signal of intersymbol interference, A is an intersymbol interference and intersymbol interference matrix, and the expression is
(2.2) received Signal to be interference cancelled between symbols
Conversion to a real number domain received signal according to
5. The OFDM receiving method without cyclic prefix based on model-driven deep learning of claim 1, wherein: the second deep neural net in step (3)The network is formed by connecting LS layer network layers in series, each layer has the same structure, and the input of the l-th layer network layer is the output of the l-1 th layer
And receiving signals in real number domain
Output is as
The operations performed are:
in the formula,
is a linear MMSE matrix and is a linear MMSE matrix,
a is the intercarrier interference and intersymbol interference matrix,
is an estimated value of a and is,
estimating a signal for a time domain channel
F is a normalized fourier transform matrix,
is the noise variance, I is the identity matrix, β is the update parameter, λl、γlInitializing u for training parameters, wherein N is the number of carriers, and epsilon is a positive number smaller than a preset threshold value10, the modulation signal u is from the real modulation set
Indicates that the modulation symbol is judged as amProbability of (u)nA symbol is modulated for the nth sub-carrier,
is the nth subcarrier symbol received for the l layer.
6. The OFDM reception method without cyclic prefix based on model-driven deep learning according to claim 5, wherein: the training parameter lambdal、γlObtained by training, a training processComprises the following steps:
obtaining a plurality of samples, each sample comprising a modulated signal u and a corresponding real number domain received signal
Will be lambdal、γlAs a parameter to be trained, an Adam optimizer and a small batch gradient descent method are adopted to perform multi-round training, and the learning rate is dynamically adjusted to obtain a parameter lambdal、γlThe optimum value of (d); loss function using squared error loss
u (k) denotes the u value of k subcarriers,
representing k sub-carriers
The value is obtained.
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