CN110099016A - A kind of sparse front channel estimation methods of millimeter wave based on deep learning network - Google Patents

A kind of sparse front channel estimation methods of millimeter wave based on deep learning network Download PDF

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CN110099016A
CN110099016A CN201910397076.5A CN201910397076A CN110099016A CN 110099016 A CN110099016 A CN 110099016A CN 201910397076 A CN201910397076 A CN 201910397076A CN 110099016 A CN110099016 A CN 110099016A
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许威
张雯惠
徐锦丹
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Southeast University
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Abstract

The invention discloses a kind of sparse front channel estimation methods of millimeter wave based on deep learning network, and using millimeter wave channel sparse characteristic as prior information, the full connection deep neural network of training design is used for millimeter wave surface communications channel estimation.First using full connection phase shifter network, isotropic analog transceiver is designed by configuring each phase shifter uniform phase distribution；Then using the channel sparse information of acquisition and the optimal digital estimator of design as the training data of full connection deep learning network.For the condition of sparse channel under each signal-to-noise ratio, the sparse information of channel is inputted into network, obtains corresponding digital estimator, and then obtain channel estimation results.The condition of sparse channel estimator that the present invention provides can reduce low precision analog-digital converter nonlinear quantization bring error, and use deep learning network implementations, to reduce channel estimation complexity, channel estimation methods that inventive can be optimal on approximation theory.

Description

A kind of sparse front channel estimation methods of millimeter wave based on deep learning network
Technical field
The present invention relates to the communications fields, are related to channel estimation methods, are based on depth more particularly relate to one kind Practise the sparse front channel estimation methods of millimeter wave of network.
Background technique
In recent years, the communication technology made breakthrough progress and gradually tend to mature mobile communication in global range Swift and violent development has been obtained in industry.Multiple-input and multiple-output (MIMO) technology is one of key technology of development communication technologies, Improve the message transmission rate of system.More antennas are equipped with by the transmitting terminal and receiving end of system, utilize transmitting-receiving both ends Multiple antennas formed diversity, system stability can be improved.At the same time, due to the independent channel number between transmitting-receiving both ends antenna Greatly increase, the data volume that system is sent in the unit time is also promoted, and then improves the spectrum utilization efficiency of system.
Massive MIMO (i.e. extensive antenna) technology is in transmission rate, energy efficiency, transmission reliability etc. phase Have compared with MIMO technology and is greatly improved, and the extensive MIMO technology of millimeter wave substantially reduces the configuration of extensive antenna array Difficulty, and extensive MIMO technology then solves the problems, such as that the loss of millimeter-wave signal height is easily blocked.It is big in order to reduce millimeter wave The numerical model analysis framework of a small amount of radio frequency link can be used in power consumption and hardware complexity in scale mimo system.
In order to carry out high-property transmission, need to carry out channel estimation first.Traditional extensive multi-antenna technology system Channel estimation inherently a major challenge, the use of low precision analog-digital converter is reducing the same of cost and power consumption under mixed architecture When allow channel estimation to become more difficult.Meanwhile how estimation complexity is reduced while using millimeter wave channel sparsity And pilot-frequency expense, and obtaining high-precision channel estimation is also a major challenge.
Summary of the invention
To solve the above problems, the present invention proposes a kind of condition of sparse channel estimation method, it to be used for low precision analog-digital converter And in the extensive multi-antenna technology system of broadband mmWave of hybrid structure.Believe by priori of millimeter wave channel sparse characteristic Breath, using the selection matrix of condition of sparse channel and its corresponding digital estimator as input to the full connection deep neural network of design It is trained, obtains the deep neural network suitable for different signal-to-noise ratio, be used for millimeter wave surface communications channel estimation.It adopts first With full connection phase shifter network, isotropic analog transceiver is designed by configuring each phase shifter uniform phase distribution；So Afterwards using the channel sparse information of acquisition as priori knowledge, optimal digital estimator is designed, it will be both as full connection depth The training data of learning network.For the condition of sparse channel under each signal-to-noise ratio, the sparse information of channel is inputted into network, can be obtained Corresponding digital estimator, to obtain channel estimation results.The condition of sparse channel estimator that the present invention provides can reduce low essence Analog-digital converter nonlinear quantization bring error is spent, and using deep learning network implementations to reduce channel estimation complexity Degree.In the hybrid structure multiaerial system using low precision analog-digital converter, inventive can be optimal on approximation theory Channel estimation methods.
In order to achieve the above object, the invention provides the following technical scheme:
A kind of sparse front channel estimation methods of millimeter wave based on deep learning network, comprising:
Transmitting terminal pilot signal transmitted connects by the simulation precoder of the phase shifter composition connected entirely via channel arrival Receipts machine, receiving end obtain channel estimation using simulated estimation device, low precision analog-digital converter quantizer and deep learning network；
It is characterized by: channel estimation methods on the basis of the hardware structure the following steps are included:
Step 1: base station design simulates precoding and pilot signal transmitted, simulates precoding FAmIt designs according to the following formula:
Wherein, []ijThe element that i-th row of representing matrix, jth arrange；FAmExpression dimension is Nt×NRFtSimulation precoding Matrix, NtIndicate transmitting antenna number, NRFtIndicate transmitting terminal rf chain number；Indicate the i-th row, the jth of simulation pre-coding matrix The phase of column element；
Step 2: receiving end optimization design simulated estimation device WAm, WAmIt calculates according to the following formula:
Wherein, WAmExpression dimension is Nr×NRFrSimulated estimation matrix, NrIndicate receiving antenna number, NRFrIndicate receiving end Rf chain number；Indicate the i-th row of simulated estimation matrix, the phase of jth column element；
Step 3: receiving end obtains channel by compressed sensing technology, such as classical orthogonal matching pursuit (OMP) technology Sparse features information, i.e. nonzero element position in condition of sparse channel matrix, and in this, as the prior information of subsequent channel estimation；
Step 4: receiving end is for all possible selection matrix P corresponding to condition of sparse channel characteristic informationvThe collection of [k] Conjunction is stored, and according to each selection matrix PvThe corresponding digital estimated matrix of [k] design
Step 5: above-mentioned selection matrix PvThe set of [k] and digital estimated matrix correspondinglyAs complete The training set of Connection Neural Network obtains the feed-forward net connected entirely being suitable under each signal-to-noise ratio by neural metwork training Network.In coherence time, receiving end is according to the corresponding selection matrix P of condition of sparse channelv[k], application network structure obtain network output Digital estimated matrixIt is detected with this data estimated matrix to signal is received, to obtain channel estimation value
Further, selection matrix P in the step 4v[k] is indicated with following formula:
Wherein, eπ(i)(π (i) ∈ { 1,2 ..., NrNt) expression dimension be NrNt× 1 a element of π (i) be 1, remaining The vector that element is 0.NvIndicate that dimension is N on k-th of subcarrierrNt× 1 channel vector projects to the vector in angle domain point Measure hvThe number of nonzero element in [k].
For the nonzero channel element in channel matrix, the selection matrix P of channelvThe form of [k] has N kind possibility, for All possible set { Pv1[k], Pv2[k] ..., PvN[k] }, each Pvi[k] is equal a possibility that (i=1 ..., N) occurs ForI.e.
Wherein, N is all possible number,C indicates number of combinations formula.NvIt indicates on k-th of subcarrier Dimension is NrNt× 1 channel vector projects to the vector component h in angle domainvThe number of nonzero element in [k].
Further, channel is projected in virtual angle domain and obtains channel vector component hv[k], hv[k] as follows It calculates:
Wherein, AtExpression dimension is Nt×NtTransmitting front response vector composition transmitting dictionary matrix,Indicate square Battle array AtConjugation；Indicate Kronecker product；ArExpression dimension is Nr×NrReceive front response vector composition reception dictionary Matrix；H [k] indicates that the dimension on k-th of subcarrier is Nr×NtPhysical channel matrix, vec (H [k]) representing matrix H's [k] Vector quantization.
Wherein, AtIt indicates as follows:
Wherein,(p ∈ { 1,2 ..., Nt) expression dimension be Nt× 1 transmitting front response vector, InWherein, NtThe horizontal axis antenna of=P × Q, P expression transmitting antenna front Number, Q indicate the longitudinal axis antenna number of transmitting antenna front；
ArIt indicates as follows:
Wherein,(q ∈ { 1,2 ..., NrExpression dimension be Nr× 1 reception front response vector, In,Wherein, NrThe horizontal axis antenna of=I × J, I expression receiving antenna front Number, J indicate the longitudinal axis antenna number of receiving antenna front；
Further, optimal number estimated matrix in the step 4Use Minimum Mean Square Error of Channel Estimation Criterion,It can be calculated as follows:
Wherein, k ∈ { 1,2 ..., K } indicates that k-th of subcarrier, K indicate total number of sub-carriers；Indicate k-th of son N on carrier waveRFr×NvOptimal digital estimated matrix, M indicates channel access times, i.e. channel estimation number in coherence time.ηb Indicate distortion factor related with analog-digital converter (ADC) quantizing bit number b；Ω [k] indicates that the dimension on k-th of subcarrier is MNRFr×NvCalculation matrix；ΩHThe conjugate transposition of [k] representing matrix Ω [k]；Indicate the large-scale fading coefficient of channel；Expression dimension is Nv×NvUnit matrix；Indicate that equivalent noise vector is each The variance of element,Indicate additive white Gaussian noise (AWGN) variance；P indicates transmitting pilot power.
Further, channel estimation number M is indicated with following formula:
Expression rounds up.
The calculation matrix Ω [k] is indicated with following formula:
Ω [k]=Φ [k] Ψ Pv[k],
Wherein, dimension NRFr×NrNtPilot tone correlation matrixsm[k](m ∈ { 1,2 ..., M }) indicate the m time it is trained when dimension be NRFr× 1 transmitting pilot tone vector.
Wherein, Ψ indicates that dimension is NrNt×NrNtSpace conversion matrix, Ψ are indicated as follows:
Wherein, AtExpression dimension is Nt×NtTransmitting front response vector composition transmitting dictionary matrix, ArIndicate dimension For Nr×NrReceive front response vector composition reception dictionary matrix.
Wherein, Pv[k] indicates that dimension is NrNt×NvSelection matrix.
Further, in step 5Pass through the full connection deep learning network of feed-forward as follows It obtains:
(1) nonzero element number N known for onevCondition of sparse channel, channel matrix H [k] indicate k-th of subcarrier on Dimension be Nr×NtPhysical channel matrix, under each signal-to-noise ratio, receiving end calculates all possibility according to sparse features information PvCovariance matrix C [the k]=Ω of [k] compositionH[k] Ω [k], wherein Ω [k]=Φ [k] Ψ Pv[k].Meanwhile according to right It is required that method described in 4, calculates and each PvThe covariance matrix C [k] of [k] composition is correspondingIt designs newly Digital estimated matrixAnd by all possible C [k] and corresponding It stores in database.
(2) data are extracted from database, are divided into training data and two groups of test data.Training data progress complex value is torn open Divide operation, it will training set C [k] and W 'D[k] is split as real-part matrix CR[k]、With imaginary-part matrix CI[k]、 Two parts.
(3) then by CR[k]、And CI[k]、Matrix vector operation is carried out, obtaining dimension is Nv 2× 1 column vector cR[k]、And cI[k]、By cR[k]、As the defeated of real part deep learning network Enter and training objective, by cI[k]、Input and training objective as imaginary part deep learning network.
(4) fully-connected network of two deep learnings is constructed, network structure is identical.It is all the full connection of two layers of feed-forward Neural network, first layer neuron number are N, whereinBiasing (bias) connection is set in first layer simultaneously, and The transfer function of first layer is set as softmax function；First layer is exported and is connect with second layer neuron, neuron number is N, and biasing (bias) connection is equally set in the second layer.
Softmax function is defined as follows:
Wherein, the output of each layer of fully-connected network such as following formula:
Wherein, W and b indicates the parameter of full Connection Neural Network, yiAnd biIndicate i-th of element of y and b, xjIndicate x's J-th of element, WI, jIndicate that position is the element of (i, j) in W.
(5) such full Connection Neural Network is trained, and passes through the fractionation of the complex value of test data and matrix arrow Quantization tests the deep learning network as input, to obtain the stabilizing network structure under each signal-to-noise ratio.
(6) in coherence time, for the condition of sparse channel under each signal-to-noise ratio, by the channel association side of the sparse information containing channel Poor Matrix C [k] inputs network, can obtain corresponding digital estimator
Further, the channel estimation value in step 5It obtains as follows:
Wherein, ()HThe conjugate transposition operation of representing matrix；()-1The inversion operation of representing matrix.
Compared with prior art, the invention has the advantages that and the utility model has the advantages that
1) broad-band channel should be estimated, the present invention is modulated using OFDM, to carry out frequency domain letter to each narrow-band sub-carriers Road estimation, the present invention is using millimeter wave channel sparse characteristic as prior information, by the selection matrix of condition of sparse channel and its corresponding number Word estimator is trained the full connection deep neural network of design as input, obtains the depth suitable for different signal-to-noise ratio Neural network is used for millimeter wave surface communications channel estimation；2) program is using full connection phase shifter network, by configuring each shifting Phase device uniform phase is distributed to design isotropic analog transceiver；The channel sparse features information of acquisition is known as priori Know, designs optimal digital estimator, it will be both as the training data of full connection deep learning network.For under each signal-to-noise ratio Condition of sparse channel, the sparse information of channel is inputted into network, corresponding digital estimator can be obtained, to obtain channel estimation As a result；3) the condition of sparse channel estimator that the present invention provides can reduce low precision analog-digital converter nonlinear quantization bring and miss Difference, and using deep learning network implementations to reduce channel estimation complexity.In the mixing using low precision analog-digital converter In framework multiaerial system, channel estimation methods that inventive can be optimal on approximation theory.
Detailed description of the invention
Fig. 1 is present system block diagram；
Fig. 2 is the millimeter wave antenna front of 8*4, under the analog-digital converter quantization of 4 bit of receiving end, channel estimation normalization The curve graph that mean square error (NMSE) changes with signal-to-noise ratio (SNR)；
Specific embodiment
Technical solution provided by the invention is described in detail below with reference to specific embodiment, it should be understood that following specific Embodiment is only illustrative of the invention and is not intended to limit the scope of the invention.
The invention proposes a kind of sparse front channel estimation methods of millimeter wave based on deep learning network, with millimeter wave Channel sparse characteristic is prior information, using the selection matrix of condition of sparse channel and its corresponding digital estimator as input to design Full connection deep neural network be trained, obtain the deep neural network suitable for different signal-to-noise ratio, be used for millimeter wave battle array Face communications channel estimation.
It is isotropic to design by configuring each phase shifter uniform phase distribution first using full connection phase shifter network Analog transceiver；Then using the channel sparse information of acquisition as priori knowledge, optimal digital estimator is designed, the two is made For the training data for connecting deep learning network entirely.For the condition of sparse channel under each signal-to-noise ratio, the sparse information of channel is inputted Network can obtain corresponding digital estimator, to obtain channel estimation results.The condition of sparse channel estimator that the present invention provides Can reduce low precision analog-digital converter (ADC) nonlinear quantization bring error, and using deep learning network implementations to Reduce channel estimation complexity.In the hybrid structure multiaerial system using low precision analog-digital converter, inventive energy energy Optimal channel estimation methods on enough approximation theories.
As shown in Figure 1, under each signal-to-noise ratio, by compressed sensing technology acquisition channel priori sparse information, and according to All possible condition of sparse channel design the optimal digital estimator under corresponding least mean-square error, connect entirely as deep learning The training input of network.By the way that deep learning network is trained and is tested, the network that can be used under each signal-to-noise ratio is obtained, For channel estimation.Under hybrid structure, transmitting terminal pilot signal transmitted prelists by the simulation of the phase shifter composition connected entirely Code device reaches receiver via channel, by simulated estimation device, low precision analog-digital converter quantizer, under each signal-to-noise ratio The sparse information of channel is inputted network, corresponding digital estimator can be obtained, to obtain channel estimation knot by condition of sparse channel Fruit.The condition of sparse channel estimator that the present invention provides can reduce low precision analog-digital converter nonlinear quantization bring error, and Using deep learning network implementations to reduce channel estimation complexity.More using the hybrid structure of low precision analog-digital converter In antenna system, channel estimation methods that inventive can be optimal on approximation theory.
Channel estimation methods proposed by the present invention include the following steps:
Step 1: base station design simulates precoding and pilot signal transmitted, simulates precoding FAmIt designs according to the following formula:
Wherein, []ijThe element that i-th row of representing matrix, jth arrange；FAmExpression dimension is Nt×NRFtSimulation precoding Matrix, NtIndicate transmitting antenna number, NRFtIndicate transmitting terminal rf chain number；Indicate the i-th row, the jth of simulation pre-coding matrix The phase of column element；
Step 2: receiving end optimization design simulated estimation device WAm, WAmIt calculates according to the following formula:
Wherein, WAmExpression dimension is Nr×NRFrSimulated estimation matrix, NrIndicate receiving antenna number, NRFrIndicate receiving end Rf chain number；Indicate the i-th row of simulated estimation matrix, the phase of jth column element；
Step 3: receiving end obtains channel by compressed sensing technology, such as classical orthogonal matching pursuit (OMP) technology Sparse features information, i.e. nonzero element position in condition of sparse channel matrix, and in this, as the prior information of subsequent channel estimation.
Step 4: receiving end is for all possible selection matrix P corresponding to condition of sparse channel characteristic informationvThe collection of [k] Conjunction is stored, and according to each selection matrix PvThe corresponding digital estimated matrix of [k] design
Step 5: above-mentioned selection matrix PvThe set of [k] and digital estimated matrix correspondinglyAs complete The training set of Connection Neural Network obtains the feed-forward net connected entirely being suitable under each signal-to-noise ratio by neural metwork training Network.In coherence time, receiving end is according to the corresponding selection matrix P of condition of sparse channelv[k], application network structure obtain network output Digital estimated matrixIt is detected with this data estimated matrix to signal is received, to obtain channel estimation value
Further, selection matrix P in the step 4v[k] is indicated with following formula:
Wherein, eπ(i)(π (i) ∈ { 1,2 ..., NrNt) expression dimension be NrNt× 1 a element of π (i) be 1, remaining The vector that element is 0.NvIndicate that dimension is N on k-th of subcarrierrNt× 1 channel vector projects to the vector in angle domain point Measure hvThe number of nonzero element in [k].
For the nonzero channel element in channel matrix, the selection matrix P of channelvThe form of [k] has N kind possibility, for All possible set { Pv1[k], Pv2[k] ..., PvN[k] }, each Pvi[k] is equal a possibility that (i=1 ..., N) occurs ForI.e.
Wherein, N is all possible number,C indicates number of combinations formula.NvIt indicates to tie up on k-th of subcarrier Degree is NrNt× 1 channel vector projects to the vector component h in angle domainvThe number of nonzero element in [k].
Further, channel is projected in virtual angle domain and obtains channel vector component hv[k], hv[k] as follows It calculates:
Wherein, AtExpression dimension is Nt×NtTransmitting front response vector composition transmitting dictionary matrix,Representing matrix AtConjugation；Indicate Kronecker product；ArExpression dimension is Nr×NrReceive front response vector composition reception dictionary square Battle array；H [k] indicates that the dimension on k-th of subcarrier is Nr×NtPhysical channel matrix, the arrow of vec (H [k]) representing matrix H [k] Quantization.
Wherein, AtIt indicates as follows:
Wherein,(p ∈ { 1,2 ..., Nt) expression dimension be Nt× 1 transmitting front response vector, InWherein, NtThe horizontal axis antenna of=P × Q, P expression transmitting antenna front Number, Q indicate the longitudinal axis antenna number of transmitting antenna front.
ArIt indicates as follows:
Wherein,(q ∈ { 1,2 ..., NrExpression dimension be Nr× 1 reception front response vector, In,Wherein, NrThe horizontal axis antenna of=I × J, I expression receiving antenna front Number, J indicate the longitudinal axis antenna number of receiving antenna front.
Further, optimal number estimated matrix in the step 4Use Minimum Mean Square Error of Channel Estimation Criterion,It can be calculated as follows:
Wherein, k ∈ { 1,2 ..., K } indicates that k-th of subcarrier, K indicate total number of sub-carriers；Indicate k-th of son N on carrier waveRFr×NvOptimal digital estimated matrix, M indicates channel access times, i.e. channel estimation number in coherence time.ηb Indicate distortion factor related with analog-digital converter (ADC) quantizing bit number b；Ω [k] indicates that the dimension on k-th of subcarrier is MNRFr×NvCalculation matrix；ΩHThe conjugate transposition of [k] representing matrix Ω [k]；Indicate the large-scale fading coefficient of channel；Expression dimension is Nv×NvUnit matrix；Indicate that equivalent noise vector is each The variance of element,Indicate additive white Gaussian noise (AWGN) variance；P indicates transmitting pilot power.
Further, channel estimation number M is indicated with following formula:
Expression rounds up.
The calculation matrix Ω [k] is indicated with following formula:
Ω [k]=Φ [k] Ψ Pv[k],
Wherein, dimension NRFr×NrNtPilot tone correlation matrixsm[k] (m ∈ 1, 2 ..., M }) indicate the m time it is trained when dimension be NRFr× 1 transmitting pilot tone vector.
Wherein, Ψ indicates that dimension is NrNt×NrNtSpace conversion matrix, Ψ are indicated as follows:
Wherein, AtExpression dimension is Nt×NtTransmitting front response vector composition transmitting dictionary matrix, ArIndicate dimension For Nr×NrReceive front response vector composition reception dictionary matrix.
Wherein, Pv[k] indicates that dimension is NrNt×NvSelection matrix.
Further, in step 5Pass through the full connection deep learning network of feed-forward as follows It obtains:
(1) nonzero element number N known for onevCondition of sparse channel, channel matrix H [k] indicate k-th of subcarrier on Dimension be Nr×NtPhysical channel matrix, under each signal-to-noise ratio, receiving end calculates all possibility according to sparse features information PvCovariance matrix C [the k]=Ω of [k] compositionH[k] Ω [k], wherein Ω [k]=Φ [k] Ψ Pv[k].Meanwhile according to right It is required that method described in 4, calculates and each PvThe covariance matrix C [k] of [k] composition is correspondingIt designs newly Digital estimated matrixAnd by all possible C [k] and corresponding It stores in database.
(2) data are extracted from database, are divided into training data and two groups of test data.Training data progress complex value is torn open Divide operation, it will training set C [k] and W 'D[k] is split as real-part matrix CR[k]、With imaginary-part matrix CI[k]、 Two parts.
(3) then by CR[k]、And CI[k]、Matrix vector operation is carried out, obtaining dimension is Nv 2× 1 column vector cR[k]、And cI[k]、By cR[k]、As the defeated of real part deep learning network Enter and training objective, by cI[k]、Input and training objective as imaginary part deep learning network.
(4) fully-connected network of two deep learnings is constructed, network structure is identical.It is all the full connection of two layers of feed-forward Neural network, first layer neuron number are N, whereinBiasing (bias) connection is set in first layer simultaneously, and The transfer function of first layer is set as softmax function；First layer is exported and is connect with second layer neuron, neuron number is N, and biasing (bias) connection is equally set in the second layer.
Softmax function is defined as follows:
Wherein, the output of each layer of fully-connected network such as following formula:
Wherein, W and b indicates the parameter of full Connection Neural Network, yiAnd biIndicate i-th of element of y and b, xjIndicate x's J-th of element, WI, jIndicate that position is the element of (i, j) in W.
(5) such full Connection Neural Network is trained, and passes through the fractionation of the complex value of test data and matrix arrow Quantization tests the deep learning network as input, to obtain the stabilizing network structure under each signal-to-noise ratio.
(6) in coherence time, for the condition of sparse channel under each signal-to-noise ratio, by the channel association side of the sparse information containing channel Poor Matrix C [k] inputs network, can obtain corresponding digital estimator
Further, the channel estimation value in step 5It obtains as follows:
Wherein, ()HThe conjugate transposition operation of representing matrix；()-1The inversion operation of representing matrix.
As shown in Fig. 2, channel estimation methods proposed by the present invention are extremely close to theoretical optimal minimum under condition of sparse channel Mean square error (MMSE) estimator, especially at low signal-to-noise ratio (SNR).Under condition of sparse channel, the present invention proposes to use depth Learning network further improves the precision of channel estimation and reduces complexity.
The technical means disclosed in the embodiments of the present invention is not limited only to technological means disclosed in above embodiment, further includes Technical solution consisting of any combination of the above technical features.It should be pointed out that for those skilled in the art For, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also considered as Protection scope of the present invention.

Claims (7)

1. a kind of sparse front channel estimation methods of millimeter wave based on deep learning network, which is characterized in that the method packet Include following steps:
Step 1: base station design simulates precoding and pilot signal transmitted, simulates precoding FAmIt designs according to the following formula:
Wherein, []ijThe element that i-th row of representing matrix, jth arrange；FAmExpression dimension is Nt×NRFtSimulation pre-coding matrix, NtIndicate transmitting antenna number, NRFtIndicate transmitting terminal rf chain number；Indicate the i-th row, the jth column member of simulation pre-coding matrix The phase of element；
Step 2: receiving end optimization design simulated estimation device WAm,WAmIt calculates according to the following formula:
Wherein, WAmExpression dimension is Nr×NRFrSimulated estimation matrix, NrIndicate receiving antenna number, NRFrIndicate receiving end radio frequency Number of links；Indicate the i-th row of simulated estimation matrix, the phase of jth column element；
Step 3: receiving end obtains the sparse features information of channel by compressed sensing technology, i.e., non-in condition of sparse channel matrix Neutral element position, and in this, as the prior information of subsequent channel estimation；
Step 4: receiving end is for all possible selection matrix P corresponding to condition of sparse channel characteristic informationvThe set of [k] carries out Storage, and according to each selection matrix PvThe corresponding digital estimated matrix of [k] design
Step 5: above-mentioned selection matrix PvThe set of [k] and digital estimated matrix correspondinglyAs full connection The training set of neural network obtains the feed-forward network connected entirely being suitable under each signal-to-noise ratio by neural metwork training, Receiving end is according to the corresponding selection matrix P of condition of sparse channelv[k], application network structure obtain the digital estimated matrix of network outputIt is detected with this data estimated matrix to signal is received, to obtain channel estimation value
2. the millimeter wave surface channel estimation methods according to claim 1 based on deep learning network, which is characterized in that In the step 4, the sparse features information of channel, corresponding selection matrix P are obtained using compressed sensing technologyvUnder [k] is used Face formula indicates:
Wherein, eπ(i)(π(i)∈{1,2,…,NrNt) expression dimension be NrNt× 1 a element of π (i) is 1, remaining element is 0 vector, NvIndicate that dimension is N on k-th of subcarrierrNt× 1 channel vector projects to the vector component h in angle domainv The number of nonzero element in [k],
For the nonzero channel element in channel matrix, the selection matrix P of channelvThe form of [k] have N kind may, for it is all can Set { the P of energyv1[k],Pv2[k],…,PvN[k] }, each PviA possibility that [k] (i=1 ..., N) occurs beI.e.
Wherein, N is all possible number,C indicates number of combinations formula.NvIndicate that dimension is on k-th of subcarrier NrNt× 1 channel vector projects to the vector component h in angle domainvThe number of nonzero element in [k].
3. the sparse front channel estimation methods of the millimeter wave according to claim 2 based on deep learning network, feature It is, channel is projected in virtual angle domain and obtains channel vector component hv[k], hv[k] is calculated as follows:
Wherein, AtExpression dimension is Nt×NtTransmitting front response vector composition transmitting dictionary matrix,Representing matrix At's Conjugation；Indicate Kronecker product；ArExpression dimension is Nr×NrReceive front response vector composition reception dictionary matrix；H [k] indicates that the dimension on k-th of subcarrier is Nr×NtPhysical channel matrix, the vector of vec (H [k]) representing matrix H [k] Change；
Wherein, AtIt indicates as follows:
Wherein,Expression dimension is Nt× 1 transmitting front response vector, whereinWherein, NtThe horizontal axis of=P × Q, P expression transmitting antenna front Antenna number, Q indicate the longitudinal axis antenna number of transmitting antenna front；
ArIt indicates as follows:
Wherein,Expression dimension is Nr× 1 reception front response vector, whereinWherein, NrThe horizontal axis day of=I × J, I expression receiving antenna front Line number, J indicate the longitudinal axis antenna number of receiving antenna front.
4. the millimeter wave surface channel estimation methods according to claim 3 based on deep learning network, which is characterized in that Optimal number estimated matrix in the step 4Using Minimum Mean Square Error of Channel Estimation criterion,By as follows Formula calculates:
Wherein, k ∈ { 1,2 ..., K } indicates that k-th of subcarrier, K indicate total number of sub-carriers；Indicate k-th of subcarrier Upper NRFr×NvOptimal digital estimated matrix, M indicates channel access times, i.e. channel estimation number in coherence time；ηbIt indicates Distortion factor related with analog-digital converter (ADC) quantizing bit number b；Ω [k] indicates that the dimension on k-th of subcarrier is MNRFr ×NvCalculation matrix；ΩHThe conjugate transposition of [k] representing matrix Ω [k]；Indicate the large-scale fading coefficient of channel；Table Show that dimension is Nv×NvUnit matrix；Indicate that equivalent noise vector is each The variance of element,Indicate additive white Gaussian noise (AWGN) variance；P indicates transmitting pilot power.
5. the millimeter wave surface channel estimation methods according to claim 4 based on deep learning network, which is characterized in that Channel estimation number M is indicated with following formula:
Expression rounds up；
The calculation matrix Ω [k] is indicated with following formula:
Ω [k]=Φ [k] Ψ Pv[k],
Wherein, dimension NRFr×NrNtPilot tone correlation matrixsm[k](m∈{1, 2 ..., M) indicate the m time it is trained when dimension be NRFr× 1 transmitting pilot tone vector.
Wherein, Ψ indicates that dimension is NrNt×NrNtSpace conversion matrix, Ψ are indicated as follows:
Wherein, AtExpression dimension is Nt×NtTransmitting front response vector composition transmitting dictionary matrix, ArExpression dimension is Nr ×NrReceive front response vector composition reception dictionary matrix；
Wherein, Pv[k] indicates that dimension is NrNt×NvSelection matrix.
6. the millimeter wave surface channel estimation methods according to claim 1 based on deep learning network, which is characterized in that In step 5It is obtained by the fully-connected network of feed-forward, steps are as follows:
(1) nonzero element number N known for onevCondition of sparse channel, channel matrix H [k] indicate k-th of subcarrier on dimension Degree is Nr×NtPhysical channel matrix, under each signal-to-noise ratio, receiving end calculates all possible P according to sparse features informationv Covariance matrix C [the k]=Ω of [k] compositionH[k] Ω [k], wherein Ω [k]=Φ [k] Ψ Pv[k].Meanwhile according to claim Method described in 4 calculates and each PvThe covariance matrix C [k] of [k] composition is correspondingDesign new number Estimated matrixAnd by all possible C [k] and correspondingStorage Into database；
(2) data are extracted from database, are divided into training data and two groups of test data, and training data is subjected to complex value and splits behaviour Make, by training set C [k] and W 'D[k] is split as real-part matrix CR[k]、With imaginary-part matrix CI[k]、Two Point；
(3) then by CR[k]、And CI[k]、Matrix vector operation is carried out, obtaining dimension is Nv 2× 1 column Vector cR[k]、And cI[k]、By cR[k]、Input and instruction as real part deep learning network Practice target, by cI[k]、Input and training objective as imaginary part deep learning network；
(4) fully-connected network of two deep learnings is constructed, network structure is identical, is all the full connection nerve of two layers of feed-forward Network, first layer neuron number are N, whereinBiasing (bias) connection, and first are set in first layer simultaneously The transfer function of layer is set as softmax function；First layer is exported and is connect with second layer neuron, neuron number N, and And biasing (bias) connection is equally set in the second layer；
Softmax function is defined as follows:
Wherein, the output of each layer of fully-connected network such as following formula:
Wherein, W and b indicates the parameter of full Connection Neural Network, yiAnd biIndicate i-th of element of y and b, xjIndicate j-th of x Element, Wi,jIndicate that position is the element of (i, j) in W；
(5) such full Connection Neural Network is trained, and passes through the fractionation of the complex value of test data and matrix vector The deep learning network is tested as input, to obtain the stabilizing network structure under each signal-to-noise ratio；
(6) in coherence time, for the condition of sparse channel under each signal-to-noise ratio, by the channel covariancc square of the sparse information containing channel Battle array C [k] inputs network, can obtain corresponding digital estimator
7. the millimeter wave surface channel estimation methods according to claim 1 based on deep learning network, which is characterized in that Channel estimation value in step (5)It is calculated as follows:
Wherein, ()HThe conjugate transposition operation of representing matrix；()-1The inversion operation of representing matrix.
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