CN114567527B - Reconfigurable intelligent surface auxiliary superposition guide fusion learning channel estimation method - Google Patents
Reconfigurable intelligent surface auxiliary superposition guide fusion learning channel estimation method Download PDFInfo
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- H—ELECTRICITY
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- Y02D30/00—Reducing energy consumption in communication networks
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Abstract
The invention discloses a reconfigurable intelligent surface auxiliary superposition guidance fusion learning channel estimation method, wherein a receiver receives a wireless signal of a transmitter adopting a superposition guidance scheme to form a receiving signal; extracting the characteristics of the channel state to obtain 'CSI initial characteristics', and inputting the 'CSI initial characteristics' into a trained 'enhanced channel estimation network EN-CENet' to obtain 'enhanced CSI'; extracting equalization features of the received signal to obtain an initial equalization feature; eliminating the guide to obtain pre-equalization data, and inputting the pre-equalization data into a trained enhanced symbol detection network EN-SDNet to obtain detection symbols; the received signal, the enhanced CSI and the detection symbol are input into a trained updated fusion network Up-FUSNet, so as to obtain the enhanced detection symbol. The invention can improve the channel estimation precision and the symbol detection performance, and simultaneously solves the problem that the propagation path is blocked by RIS, thereby greatly improving the channel estimation performance and the symbol detection performance.
Description
Technical Field
The invention relates to the technical field of channel estimation assisted by a reconfigurable intelligent surface in wireless communication, in particular to a channel estimation method for learning by superposition, guidance and fusion assisted by the reconfigurable intelligent surface.
Background
The fifth (5G,fifth generation) and sixth (6G,sixth generation) generation networks being developed will be the basis for achieving this goal of future internet of things connections. The internet of things has attracted much attention, for example, an intelligent building system connected with an automatic robot can control and manage different intelligent devices through a network, so as to save energy and improve convenience of resident life. Other applications include smart medicine, smart driving, smart home, etc. In the internet of things system, channel estimation plays a key role. On the one hand, in an internet of things system such as an industrial internet of things, the channel condition is very complex, and a situation that the channel time changes or the shielding probability increases may be encountered. On the other hand, with good channel estimation, the internet of things device can transmit with affordable transmission power using appropriate modulation and coding methods.
Channel estimation of an internet of things system is critical to efficient receiver operation. Aiming at the Internet of things system, a one-dimensional time domain wiener filtering technology is combined with frequency domain maximum likelihood estimation with simple calculation, and a pilot frequency-based hybrid channel estimation method is provided. Channel estimation based on least squares and minimum mean square error in the internet of things system has also been studied. An improved computational efficiency linear minimum mean square error estimator for an internet of things system has also been proposed. However, the transmission guide of the channel estimation methods inevitably occupies frequency spectrum resources, and huge resource waste is caused for the internet of things system. In this regard, it has been proposed to transmit the pilot and the data in a superimposed manner, and no additional time-frequency resource is required to transmit the pilot, thereby alleviating the problems of low spectral efficiency and energy consumption. Thus, the method of using superposition guidance is a promising solution.
Although the above-mentioned method of channel estimation through superposition guidance solves the problem of spectral efficiency, the problem of energy consumption of the internet of things system is not well alleviated. The goal of the internet of things system is to extend the battery life of the user to 10 years, which makes it particularly important to solve the energy consumption problem. If the guidance and the data are transmitted separately, the energy consumption is inevitably increased, and the battery life of the terminal of the internet of things is finally reduced. Therefore, in order to reduce the energy consumption of the internet of things system, further development of channel estimation based on superposition guidance is required. This motivates us to develop pilot-based superposition channel estimation in this patent.
In addition, due to the complex scene of the internet of things, for example, in the industrial internet of things, the propagation path is blocked, which is also a common situation. Therefore, how to improve the robustness of the communication link is an urgent problem to be solved. Reconfigurable intelligent surfaces (RIS, reconfigurable Intelligent Surface) offer an attractive option for blocked propagation paths. RIS is a panel of man-made electromagnetic material consisting of a large number of low cost passive scattering elements that controls the wireless environment by adjusting the amplitude or phase shift of the reflected signal. Unlike conventional forward amplifying relays, these passive devices consume little energy. Recently, the inclusion of RIS into the internet of things has been considered a revolutionary means of converting any passive wireless communication environment into an active wireless communication environment. Furthermore, RIS improves system throughput by at least 40% and improves system coverage by 1/3. Therefore, the RIS is an ideal way for solving the problem of the blocking of the propagation path of the Internet of things in the Internet of things system. However, to our knowledge, this solution has not been well studied in existing work.
Disclosure of Invention
Compared with the existing channel estimation method, the method considers the energy service life problem and the situation that the propagation path is blocked, and effectively improves the system performance by means of a channel estimation network, a symbol detection network and a fusion network.
The technical scheme of the invention is as follows:
a reconfigurable intelligent surface auxiliary superposition guidance fusion learning channel estimation method comprises the following steps:
s1, a receiver receives a wireless signal of which the transmitter adopts a superposition guiding scheme to form a receiving signal y with the length of N;
the wireless signal undergoes RIS reflection;
s2, according to the received signal y, extracting the characteristics of the channel state information (CSI, channel State Information) to obtain the CSI initial characteristics with the length of N "
The CSI feature extraction method comprises traditional LS, MMSE linear channel estimation and a Bayesian-based, markov-based Monte Carlo-based, nonlinear filter-based maximum likelihood and other nonlinear channel estimation methods;
s3 to "CSI initial feature"
Inputting to trained enhanced channel estimation network EN-CENet to obtain enhanced CSI with length N>
S4 according to "enhanced CSI"
Extracting equalization features of the received signal y to obtain an initial equalization feature with the length N>
The equalization feature extraction method comprises the feature extraction of traditional Zero-Forcing (ZF) equalization, MMSE equalization, LMS equalization, RLS equalization and the like;
s5 according to the 'balanced initial feature'
Eliminate the "pilot" x p Obtain "pre-equalization data">
S6, pre-equalizing data "
Inputting into trained 'enhanced symbol detection network EN-Net' to obtain detection symbol with length N>
S7 receiving signal y, "enhanced CSI"
And detection symbol->
Inputting the data into a trained updating fusion network Up-FUSNet to obtain an enhanced detection symbol with the length of N>
According to some embodiments of the invention, step S1 further comprises:
the S11 receiver receives the radio signal generated by the transmitter via the RIS using the superposition pilot scheme, and forms a received signal y with a length N, which is expressed as follows:
y＝h⊙x+n；
wherein n represents zero mean variance of
Is a circular symmetric complex Gaussian noise of (1), x represents a transmitting signal, y represents a Hadamard product, h represents a complex channel frequency response formed by RIS, and is a direct link h TR And a reflection link H TRR The composition is as follows:
h＝h TR +H TRR Φ；
wherein ,Φ＝[Φ 1 ,Φ 2 ,…,Φ M ]representing the phase shift vector, expressed as follows:
wherein ,
representing the phase shift of the mth RIS subsurface, the total of M RIS subsurface, beta m Representing the amplitude of RIS subsurface, satisfying beta according to engineering experience design m ＝1；
Wherein the reflecting link H TRR ＝[h TRR,1 ,h TRR,2 ,…,h TRR,m ,…,h TRR,M ]Reflection link h of mth RIS subsurface TRR,m The expression is as follows:
h TRR,m ＝h TR,m ⊙h RR,m ；
wherein ,hTR,m Representing the channel frequency response of the mth RIS sub-surface transmitter to the RIS link, h RR,m The channel frequency response of the RIS-to-receiver link for the mth RIS subsurface is represented.
According to some embodiments of the invention, step S2 further comprises:
s21 constructs a superimposed transmit signal x, expressed as follows:
wherein ρ represents a power scaling factor, E represents a transmit power, x P Indicating guidance, x d Representing modulation symbols, the transmitted signal x is represented by the pilot x P And modulation symbol x d Superposition;
the S22 receiver receives the radio signal generated by the RIS, and forms a received signal y, which is expressed as follows:
y＝h⊙x+n；
s23, extracting the characteristics of the channel state information (CSI, channel State Information) to obtain a CSI initial characteristic with the length of N "
The CSI feature extraction method comprises traditional LS, MMSE linear channel estimation and a Bayesian-based, markov Monte Carlo-based, nonlinear filter-based nonlinear channel estimation methods such as maximum likelihood, and the like, and is represented by taking LS estimation as an example as follows:
wherein ,(·)T Representing the transpose.
According to some embodiments of the invention, step S3 further comprises:
1 single hidden layer, 1 input layer and 1 output layer, the number of neurons in each layer is 2N;
normalizing the output of the hidden layer, limiting the output value to a range [0,1], and outputting the normalized hidden layer to an activation function;
the hidden layer activation function is:
the input layer and the output layer adopt linear activation functions;
the loss function is set as a mean square error function;
utilizing training sets
Training the network, and storing a network model and parameters thereof after error convergence;
the training input of the network is
Training label is->
The training label is obtained by modeling a channel model according to actual scene measurement, and finally, the network outputs enhanced CSI (channel state information)>
According to some embodiments of the invention, step S4 further comprises:
s41 according to "enhanced CSI"
Extracting equalization features of the received signal y to obtain an initial equalization feature with the length N>
The equalization feature extraction method includes feature extraction such as traditional Zero-Forcing (ZF) equalization, MMSE equalization, LMS equalization, RLS equalization, etc., and is represented by the following example of ZF equalization:
wherein ,GEQ The equalization matrix is represented as follows:
wherein the equalization matrix G EQ In which the elements are "enhanced CSI" by reading "
Obtained from the values of (2);
wherein "enhanced CSI"
The vector form of (a) is: />
According to some embodiments of the invention, step S5 further comprises:
s51 will "equalize initial characteristics"
Eliminate the "pilot" x p Obtain "pre-equalization data">
The expression is as follows:
according to some embodiments of the invention, step S6 further comprises:
p (p is more than or equal to 2) hidden layers, 1 input layer and 1 output layer;
the number of neurons of the input layer and the output layer is set to be 2N, and the number of neurons of each hidden layer is set to be qN;
the activation function of the first hidden layer is set as a LeakyReLU, the activation functions of the other n-1 hidden layers are set as ReLU, and the input layer and the output layer adopt linear activation functions;
collecting training sets
Training the network, and storing a network model and parameters thereof after error convergence;
parameter tuning and setting are carried out on the number n (n is more than or equal to 2) of the hidden layers and the number qN of the hidden layer nodes according to engineering experience;
the training input of the network is
Training label x d Training label is modulation symbol, and output of the final network is detection symbol +.>
According to some embodiments of the invention, step S7 further comprises:
the update fusion network Up-FUSNet has a single hidden layer, 1 input layer and 1 output layer;
the number of neurons of the hidden layer of the update fusion network Up-FUSNet is u 1 N, the number of neurons of the input layer and the output layer is 2N;
the hidden layer adopts an activation function ReLU, and the input layer and the output layer both adopt linear activation functions;
the loss function of the network is set as a mean square error function;
collecting training sets
Training the network, and storing a network model and parameters thereof after error convergence;
the hidden layer neuron number u 1 N, parameter tuning and setting are carried out according to engineering experience;
the training input of the network is
From the received signal y, "enhanced CSI">
And detection symbol->
The training label is modulation symbol x d The output of the final network is "enhanced detection symbol">
The invention constructs three neural networks: the simulation result shows that the Error Rate of the updating fusion network reaches the Error Rate of an ideal channel after MMSE equalization.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
fig. 2 is a schematic diagram of CSI feature extraction flow.
Detailed Description
The present invention will be described in detail with reference to the following examples and drawings, but it should be understood that the examples and drawings are only for illustrative purposes and are not intended to limit the scope of the present invention in any way. All reasonable variations and combinations that are included within the scope of the inventive concept fall within the scope of the present invention.
According to the technical scheme of the invention, the reconfigurable intelligent surface auxiliary superposition guidance fusion learning channel estimation method comprises a flow shown in a figure 1, and specifically comprises the following steps:
setting: the number of subcarriers n=6, the number of multipaths l=4, the number of ris subsurface m=4, ρ=0.15;
in step S2, the channel state information (CSI, channel State Information) is feature extracted according to the received signal y, as shown in fig. 2Obtaining a length-N CSI initial feature "
In step S3, "CSI initial characteristics"
Inputting to trained enhanced channel estimation network EN-CENet to obtain enhanced CSI with length N>
According to the original channel value h of the reference, the following is adopted:
the normalized mean square error NMSE at signal-to-noise ratio snr=0, 5,10, …,25dB can be found as follows:
NMSE_LS＝[1.48,1.38,1.29,1.28,1.26,1.26]；
NMSE_Net＝[0.04,0.03,0.02,0.016,0.014,0.013]；
it can be seen that after the channel estimation model is used, the NMSE value is reduced, and the channel estimation performance is improved;
in step S5, the "equalization initial feature" is applied "
Eliminate the "pilot" x p Obtain "pre-equalization data">
In step S7, the received signal y, the "enhanced CSI"
And detection symbol->
Inputting the data into a trained updating fusion network Up-FUSNet to obtain an enhanced detection symbol with the length of N>
Modulation symbols x according to reference d The following are provided:
the bit error rate BER at signal-to-noise ratio snr=0, 5,10, …,25dB can be found as follows:
BER＝[0.93,0.82,0.67,0.66,0.63,0.63]；
BER_Net＝[0.204,0.086,0.014,0.067,0.083,0]；
it can be seen that the BER is reduced and the symbol detection performance is improved after the updated converged network model is used.
The above examples are only preferred embodiments of the present invention, and the scope of the present invention is not limited to the above examples. All technical schemes belonging to the concept of the invention belong to the protection scope of the invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.
Claims (5)
1. The reconfigurable intelligent surface auxiliary superposition guidance fusion learning channel estimation method is characterized by comprising the following steps of:
s1, a receiver receives a wireless signal of which the transmitter adopts a superposition guiding scheme to form a receiving signal y with the length of N;
the wireless signal undergoes RIS reflection;
s2, according to the received signal y, extracting the characteristics of the channel state information to obtain the CSI initial characteristics with the length of N "
S3 to "CSI initial feature"
Inputting to trained enhanced channel estimation network EN-CENet to obtain enhanced CSI with length N>
Step S3 comprises the following sub-steps:
1 single hidden layer, 1 input layer and 1 output layer, the number of neurons in each layer is 2N;
normalizing the output of the hidden layer, limiting the output value to a range [0,1], and outputting the normalized hidden layer to an activation function;
the hidden layer activation function is:
the input layer and the output layer adopt linear activation functions;
the loss function is set as a mean square error function;
utilizing training sets
Training the network, and storing a network model and parameters thereof after error convergence;
the training input of the network is
Training label is->
The training label is obtained by modeling a channel model according to actual scene measurement, and finally, the network outputs enhanced CSI (channel state information)>
S4 according to "enhanced CSI"
Extracting equalization features of the received signal y to obtain an initial equalization feature with the length N>
S5 according to the 'balanced initial feature'
Eliminate the "pilot" x p Obtain "pre-equalization data">
S6, pre-equalizing data "
Inputting into trained 'enhanced symbol detection network EN-Net' to obtain detection symbol with length N>
Step S6 comprises the following sub-steps:
p hidden layers, p is more than or equal to 2,1 input layer and 1 output layer;
the number of neurons of the input layer and the output layer is set to be 2N, and the number of neurons of each hidden layer is set to be qN;
the activation function of the first hidden layer is set as a LeakyReLU, the activation functions of the other n-1 hidden layers are set as ReLU, and the input layer and the output layer adopt linear activation functions;
collecting training sets
Training the network, and storing a network model and parameters thereof after error convergence;
parameter tuning and setting are carried out on the number n (n is more than or equal to 2) of the hidden layers and the number qN of the hidden layer nodes according to engineering experience;
the training input of the network is
Training label x d Training label is modulation symbol, and output of the final network is detection symbol +.>
S7 receiving signal y, "enhanced CSI"
And detection symbol->
Inputting the data into a trained updating fusion network Up-FUSNet to obtain an enhanced detection symbol with the length of N>
/>
Step S7 comprises the following sub-steps:
the update fusion network Up-FUSNet has a single hidden layer, 1 input layer and 1 output layer;
the number of neurons of the hidden layer of the update fusion network Up-FUSNet is u 1 N, the number of neurons of the input layer and the output layer is 2N;
the hidden layer adopts an activation function ReLU, and the input layer and the output layer both adopt linear activation functions;
the loss function of the network is set as a mean square error function;
collecting training sets
Training the network, and storing a network model and parameters thereof after error convergence;
the hidden layer neuron number u 1 N, parameter tuning and setting are carried out according to engineering experience;
the training input of the network is
From the received signal y, "enhanced CSI">
And detection symbol->
The training label is modulation symbol x d The output of the final network is "enhanced detection symbol">
2. The method for reconstructing intelligent surface assisted superposition guided fusion learning channel estimation according to claim 1, wherein step S1 comprises the following sub-steps:
the S11 receiver receives the radio signal generated by the transmitter via the RIS using the superposition pilot scheme, and forms a received signal y with a length N, which is expressed as follows:
y＝h⊙x+n；
wherein n represents zero mean variance of
Is a circular symmetric complex Gaussian noise of (1), x represents a transmitting signal, y represents a Hadamard product, h represents a complex channel frequency response formed by RIS, and is a direct link h TR And a reflection link H TRR The composition is as follows:
h＝h TR +H TRR Φ；
wherein ,Φ＝[Φ 1 ,Φ 2 ,…,Φ M ]representing the phase shift vector, expressed as follows:
wherein ,
representing the phase shift of the mth RIS subsurface, the total of M RIS subsurface, beta m Representing the amplitude of RIS subsurface, satisfying beta according to engineering experience design m ＝1；
Wherein the reflecting link H TRR ＝[h TRR,1 ,h TRR,2 ,…,h TRR,m ,…,h TRR,M ]Reflection link h of mth RIS subsurface TRR,m The expression is as follows:
h TRR，m ＝h TR，m ⊙h RR，m ；
wherein ,hTR,m Representing the channel frequency response of the mth RIS sub-surface transmitter to the RIS link, h RR,m The channel frequency response of the RIS-to-receiver link for the mth RIS subsurface is represented.
3. The method for learning channel estimation by reconfigurable intelligent surface assisted superposition guidance fusion according to claim 1, wherein in step S2, feature extraction is performed on channel state information CSI to obtain "CSI initial feature" with length N "
The CSI feature extraction method comprises traditional LS, MMSE linear channel estimation and a Bayesian-based, markov Monte Carlo-based, nonlinear filter-based maximum likelihood and other nonlinear channel estimation methods.
4. The method for reconfigurable intelligent surface aided superposition guided fusion learning channel estimation according to claim 1, wherein step S4 comprises the following sub-steps:
s41 according to "enhanced CSI"
Extracting equalization features of the received signal y to obtain an initial equalization feature with the length N>
/>
The equalization feature extraction method comprises the feature extraction of traditional zero-forcing ZF equalization, MMSE equalization, LMS equalization, RLS equalization and the like.
5. The method for reconfigurable intelligent surface aided superposition guided fusion learning channel estimation according to claim 1, wherein step S5 comprises the following sub-steps:
will "equalize initial characteristics"
Eliminate the "pilot" x p Obtain "pre-equalization data">
The expression is as follows:
ρ represents a power scaling factor and E represents a transmission power. Phi
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