Channel estimation method for IRS auxiliary environment backscatter communication system based on LMMSE (Line Minimum Mean Square Error)

Abstract

The invention relates to an LMMSE-based IRS auxiliary environment backscatter communication system channel estimation method, which belongs to the technical field of Internet of Things, and comprises the following steps: S1, initializing an LMMSE-based IRS auxiliary environment backscatter communication system parameter; s2, the radio frequency source sends a pilot signal, and a signal model received by the reader-writer is constructed; and S3, an LMMSE estimator is adopted, through phase shift matrix orthogonalization design, a mean square error is minimized, estimation precision is improved, and thus the optimal phase shift of the system is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of Internet of Things and relates to a channel estimation method for an IRS-assisted environmental backscatter communication system based on LMMSE. Background Art

[0002] With the rapid development of the Internet of Things (IoT), intelligent sensing, and green communication technologies, ambient backscatter communication (ABC) has shown great potential in large-scale IoT applications due to its extremely low power consumption, low cost, and passive communication capabilities. For example, it has significantly contributed to the intelligentization of social life in smart home systems, intelligent traffic management, and remote medical monitoring. However, because ABC systems rely on external RF sources for communication, their channel environments are complex and susceptible to environmental factors, making channel estimation a key bottleneck affecting system performance. Furthermore, the signals in ABC systems are typically weak, and the cascading effects of the backscatter channel make traditional wireless channel estimation methods difficult to directly apply.

[0003] In IRS-assisted backscatter communication systems in multi-antenna environments, channel estimation is a key technology for ensuring reliable communication. While multiple antennas can improve communication performance, they also increase the complexity of channel estimation. Existing algorithms do not fully consider the complexity of the reflection channel and the high-dimensional characteristics of the system. Although the least squares method is computationally simple, its accuracy is insufficient at low signal-to-noise ratios. The LMMSE method, by leveraging the statistical characteristics of the channel and noise, has strong anti-interference capabilities and stable estimation performance in complex environments, making it highly valuable for application. However, because the LMMSE method involves complex matrix operations, its computational complexity increases dramatically with system scale in multi-antenna and large-scale IRS scenarios, and is limited by hardware computing power and power consumption. Therefore, how to effectively reduce the computational complexity of the LMMSE channel estimation while maintaining its high accuracy has become a difficult problem that needs to be overcome in the research of IRS-assisted backscatter communication systems in multi-antenna environments.

[0004] Based on this, in order to solve the problems of high computational complexity, difficult hardware adaptation and high precision in channel estimation in the IRS-assisted environmental backscatter communication system, the present invention designs a channel estimation method for the IRS-assisted environmental backscatter communication system based on LMMSE. Specifically, first, a channel model of the IRS-assisted multi-antenna environmental backscatter communication system is established, covering the direct link, the tag reflection link and the secondary reflection link passing through the IRS. Secondly, based on the statistical characteristics of the channel and noise and the pilot signal sent by the radio frequency, a signal model of the reader is constructed. Subsequently, combined with the reflection characteristics of the IRS, the LMMSE estimator is adopted to further improve the estimation accuracy and reduce the complexity of the hardware implementation by optimizing the phase shift matrix. Finally, the channel estimation performance of the proposed method is analyzed through simulation experiments to verify its effectiveness under different conditions. Summary of the Invention

[0005] In view of this, the object of the present invention is to provide a channel estimation method for an IRS-assisted environment backscatter communication system based on LMMSE, the method comprising:

[0006] S1: Initialize the parameters of a LMMSE-based IRS-assisted environmental backscatter communication system channel estimation;

[0007] S2: Based on a LMMSE-based IRS-assisted environment backscatter communication system model, a signal model received by the reader is constructed;

[0008] S3: Using the LMMSE estimator, through the orthogonal design of the phase shift matrix, the mean square error is minimized and the estimation accuracy is improved, thereby obtaining the optimal phase shift of the system.

[0009] The present invention is aimed at the IRS-assisted multi-antenna environment backscatter communication system, introduces the LMMSE method in channel estimation, makes full use of the statistical characteristics of the channel and noise to optimize the estimation process, and significantly improves the anti-interference ability and estimation stability in complex channel environments. By constructing a channel model in a multi-antenna scenario and combining the IRS phase shift matrix optimization strategy (such as discrete Fourier transform matrix and Hadamard matrix construction method), the hardware implementation complexity is reduced while ensuring the estimation accuracy. Simulation results show that compared with the traditional least squares method, the algorithm of the method of the present invention can still effectively reduce the mean square error under low signal-to-noise ratio conditions, especially in large-scale IRS configurations and multi-antenna systems, it can improve the channel estimation performance through spatial diversity gain, and provide a better channel estimation solution for low-power, high-reliability Internet of Things communications. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0011] Figure 1 This is a channel estimation network model for an IRS-assisted environmental backscatter communication system based on LMMSE according to the present invention;

[0012] Figure 2 This is a flow chart of the channel estimation method of the IRS-assisted environmental backscatter communication system of the LMMSE of the present invention;

[0013] Figure 3 This is the relationship between the cascade mean square error and the number N of IRS reflection units under different methods described in the present invention. DETAILED DESCRIPTION

[0014] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0015] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0016] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0017] The present invention provides a LMMSE-based IRS-assisted backscatter communication system channel estimation method, considering an LMMSE-based IRS-assisted backscatter communication system model. Figure 1 As shown in Figure 1, an LMMSE-based IRS-assisted backscatter communication system includes: a radio frequency base station equipped with a single antenna, a reader equipped with M antennas, an IRS with K reflective elements, and a tag equipped with a single antenna. The tag modulates its data onto the incident RF signal by adjusting the impedance characteristics of its own circuit and then backscatters it. However, the communication link between the tag and the reader is limited by environmental obstructions. To address this, an IRS is introduced to optimize the signal propagation path and enhance signal coverage. Ultimately, the reader receives both the direct signal from the RF source and the modulated signal reflected by the IRS and carrying the tag information, thereby decoding and recovering the information.

[0018] like Figure 2 As shown, this method specifically includes the following steps:

[0019] S1: Initialize the parameters of a LMMSE-based IRS-assisted environmental backscatter communication system channel estimation;

[0020] The parameters of the LMMSE-based IRS-assisted environmental backscatter communication system in step S1 include: the number of system RF source antennas L, the number of reader antennas M, the tag reflection coefficient λ∈[0,1], the number of IRS reflection units K, and the number of pilots T.

[0021] S2: Based on a LMMSE-based IRS-assisted environment backscatter communication system model, a signal model received by the reader is constructed;

[0022] Specifically, S2 includes the following steps:

[0023] In the tth time slot, the RF source sends a QPSK signal x(t) to the tag and the reader. The tag adjusts its circuit impedance to transmit the binary data c t ∈{0,1} is modulated onto the received signal and reflected to the IRS and reader. Assuming that timing synchronization technology is used to synchronize the direct signal and the reflected signal at the reader, the two signals received by the reader from the RF source and the secondary reflection through the tag and IRS can be expressed as:

[0024] y t =h SR x t +λh ST H IR H diag(Φ t )h TI c t x t +n t

[0025] Where λ∈[0,1] is the label reflection coefficient, c t ∈{0,1} is the binary data carried by the tag itself, is the additive white Gaussian noise at the reader, is the channel gain from the RF source to the reader, is the channel gain from the RF source to the tag, is the channel gain vector from label to IRS, is the channel gain vector from IRS to reader, is the reflection coefficient vector of the IRS in the tth time slot.

[0026] Define the cascade channel as:

[0027] V=h ST H IR H diag(h TI )

[0028] in, and:

[0029]

[0030] Assume that during the pilot training phase, the tag is always in full reflection state, i.e. c t =1,λ=1. Therefore, the formula y t =

[0031] h SR x t +λh ST H IR H diag(Φ t )h TI c t x t +n t It can be further simplified as:

[0032] y t =(h SR +VΦ t )x t +n t =x t [I M Φ t,1 I M …Φ t,K I M ]μ+n t

[0033] in

[0034] In the entire pilot training cycle, the T pilot symbols sent by the RF source are defined as x = [x1, x2, ..., x T ] T , so the signal received by the reader can be expressed as:

[0035]

[0036] Stack all T data samples together and define: The IRS phase shift matrix is ​​Θ.

[0037] Therefore, the signal received by the reader can be organized into a standard linear form:

[0038]

[0039] in, is the Kronecker product, is the observation matrix, is the noise at the reader.

[0040] The LMMSE-based IRS-assisted environmental backscatter communication system channel estimation method described in step S3 is characterized in that: the IRS phase shift matrix orthogonalization design described in step S3, thereby transforming the optimization problem, specifically includes the following steps:

[0041] S31: First, derive the minimum mean square error as follows:

[0042] The goal of LMMSE estimation is to find a linear estimator W so that the estimated value Approximate the true channel μ with the minimum mean square error:

[0043]

[0044] in, is the cross-correlation matrix between the channel and the received signal, is the autocorrelation matrix of the received signal.

[0045] Since Y = Xμ + N, the channel μ and the noise N are independent of each other, and the noise mean is 0, so R can be calculated μY :

[0046]

[0047] Among them, R μ is the autocorrelation matrix of the channel.

[0048] Similarly, R Y It can be expressed as:

[0049]

[0050] Therefore, the LMMSE estimate is:

[0051]

[0052] Assume that the power is evenly distributed during the pilot training period and each pilot signal is a unit power signal, that is, Therefore, the mean square error of LMMSE is:

[0053]

[0054] Therefore, the optimization problem about phase shift can be formulated as:

[0055]

[0056] Where C1 means that the first column of the phase shift matrix is ​​set to 1 to estimate the direct channel h SR , C2 is the amplitude constraint of the IRS reflection unit, and C3 is the phase shift constraint of the IRS reflection unit. t,krepresents the magnitude of the reflection coefficient of the kth reflection unit of the IRS in the tth time slot, θ t,k represents the phase of the reflection coefficient of the kth reflection unit of the tth time slot IRS, [Θ] t,1 Indicates that the first column of the IRS phase shift matrix for the tth time slot is all 1.

[0057] S32: Then, the LS-based channel estimation method is used to design the reflection matrix Θ, that is, the channel is estimated by turning on the IRS reflection elements in sequence. Therefore, Θ is expressed as:

[0058]

[0059] Among them, 0 K represents a K-dimensional all-zero vector, 1 K represents a K-dimensional all-one vector. It can be seen that the design of Θ satisfies the constraints in P1, but it is not the optimal solution. In order to reduce the mean square error, the objective function needs to be further transformed because:

[0060]

[0061] Among them, if and only if Θ H The equation holds true when Θ is a diagonal matrix. And because the channel correlation matrix R μ is a constant, so the optimization problem can be transformed into:

[0062]

[0063] Among them, C4 is a diagonal matrix constraint. In addition, the upper bound of the objective function is:

[0064]

[0065] S33: Next, the method of the present invention is used to solve the phase shift optimization problem. Θ is constructed based on the discrete Fourier transform matrix. The first T rows and K+1 columns of the discrete Fourier transform matrix are used to construct Θ, where T≥K+1:

[0066]

[0067] Since Tr(F T,K+1 H F T,K+1 )=T(K+1), reaching the upper bound of the objective function, so Θ=F T,K+1 is the optimal solution to the phase shift optimization problem.

[0068] S34: Similarly, the Hadamard channel estimation method is used. The first T rows and K+1 columns of the Hadamard matrix are used to construct Θ, where T≥K+1. Therefore, Θ=H T,K+1 , according to the properties of the Hadamard matrix:

[0069] Tr(Θ H Θ)=Tr(H T,K+1 H H T,K+1 )=T(K+1)

[0070] The Hadamard matrix is ​​defined as follows:

[0071]

[0072] Where ξ is a positive integer. Therefore, Θ constructed based on the Hadamard matrix is ​​also the optimal solution to the phase shift optimization problem.

[0073] In summary, since Θ constructed by the LS-based channel estimation method does not fully utilize the full reflection capability of the IRS, the channel estimation accuracy is low, while the method of the present invention and Hadamard can provide higher estimation accuracy.

[0074] The application effect of the present invention is described in detail below with reference to simulation.

[0075] 1) Simulation conditions:

[0076] The simulation experiment adopts the spatial correlation Rayleigh flat fading channel model. The channel correlation matrix R μ Based on the exponential decay law, its elements satisfy R μ (i,j)=ρ |i-j| , where ρ = 0.6 is the preset spatial correlation coefficient. The channel that introduces spatial correlation is μ corr =R μ 1 / 2 μ represents the channel length. The system configuration is as follows: L = 1 RF source antenna, M = 10 reader antennas, and the tag's reflection coefficient is set to λ = 1. The pilot signal is modulated using normalized QPSK. The signal-to-noise ratio is 0 dB. Channel estimation performance is evaluated using the mean square error of 1000 Monte Carlo experiments.

[0077] 2) Simulation results

[0078] In this embodiment, Figure 3The relationship between the cascaded mean square error and the number of IRS reflection units K under the different methods is given. As can be seen from the figure, as the number of IRS reflection units K increases, the cascaded mean square error of the method of the present invention decreases more than that of other methods. The mean square error of the traditional LS method remains basically unchanged as the number of IRS reflection units increases, while the mean square error of the method of the present invention decreases significantly as the number of IRS reflection units increases. This shows that the traditional LS method cannot fully utilize the additional reflection units to enhance channel observability when the number of IRS reflection units increases, while the method of the present invention can effectively utilize the reflection characteristics of the IRS, so that the channel estimation accuracy improves as the scale of the IRS increases, and the cascaded mean square error of the method of the present invention decreases more than the Hadamard-based channel estimation method.

[0079] In the above embodiments, references to "this embodiment" in the specification indicate that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily refer to the same embodiment.

[0080] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. The embodiments of the present invention are intended to encompass all such alternatives, modifications, and variations that fall within the broad scope of the appended claims.

[0081] The present invention can be used in a wide variety of general-purpose or special-purpose computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above.

[0082] The present invention may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A channel estimation method for an IRS-assisted ambient backscatter communication system based on LMMSE, characterized by: The following steps are involved: S1: Initialize the parameters of a LMMSE-based IRS-assisted environmental backscatter communication system channel estimation; S2: Based on a LMMSE-based IRS-assisted environment backscatter communication system model, a signal model received by the reader is constructed; S3: Using the LMMSE estimator, through the orthogonal design of the phase shift matrix, the mean square error is minimized and the estimation accuracy is improved, thereby obtaining the optimal phase shift of the system.

2. The method for channel estimation of an IRS-assisted environment backscatter communication system based on LMMSE according to claim 1, wherein: The parameters of the LMMSE-based IRS-assisted environmental backscatter communication system channel estimation in step S1 include: the number of system RF source antennas L, the number of reader antennas M, the number of IRS reflection units K, the tag reflection coefficient λ∈[0,1], and the frequency T.

3. The LMMSE-based IRS-assisted environmental backscatter communication system model according to claim 2, characterized in that: In step S2, the LMMSE-based IRS-assisted environment backscatter communication system model is used to build a signal model received by the reader. Specifically: In the tth time slot, the RF source sends a QPSK signal x(t) to the tag and the reader. The tag adjusts its circuit impedance to transmit the binary data c t ∈{0,1} is modulated onto the received signal and reflected to the IRS and reader. Assuming that timing synchronization technology is used to synchronize the direct signal and the reflected signal at the reader, the two signals received by the reader from the RF source and the secondary reflection through the tag and IRS can be expressed as: y t =h SR x t +λh ST H IR H diag(Φ t )h TI c t x t +n t Where λ∈[0,1] is the label reflection coefficient, c t ∈{0,1} is the binary data carried by the tag itself, is the additive white Gaussian noise at the reader, is the channel gain from the RF source to the reader, is the channel gain from the RF source to the tag, is the channel gain vector from label to IRS, is the channel gain vector from IRS to reader, is the reflection coefficient vector of the IRS in the tth time slot. Define the cascade channel as: V=h ST H IR H diag(h TI ) in, and: Assume that during the pilot training phase, the tag is always in full reflection state, i.e. c t =1,λ=1. Therefore, the formula y t =h SR x t +λh ST H IR H diag(Φ t )h TI c t x t +n t It can be further simplified as: y t =(h SR +VΦ t )x t +n t =x t [I M Φ t,1 I M … Φ t,K I M ]μ+n t in In the entire pilot training cycle, the T pilot symbols sent by the RF source are defined as x = [x1, x2, ..., x T ] T , so the signal received by the reader can be expressed as: Stack all T data samples together and define: The IRS phase shift matrix is ​​Θ. Therefore, the signal received by the reader can be organized into a standard linear form: in, is the Kronecker product, is the observation matrix, is the noise at the reader.

4. The LMMSE-based IRS-assisted environmental backscatter communication system according to claim 3, wherein the LMMSE estimator is used for estimation, and is characterized in that: Step S3, in which the IRS phase shift matrix is ​​orthogonalized to transform the optimization problem, specifically includes the following steps: First, the minimum mean square error is derived as follows: The goal of LMMSE estimation is to find a linear estimator W so that the estimated value Approximate the true channel μ with the minimum mean square error: in, is the cross-correlation matrix between the channel and the received signal, is the autocorrelation matrix of the received signal. Since Y = Xμ + N, the channel μ and the noise N are independent of each other, and the noise mean is 0, so R can be calculated μY : Among them, R μ is the autocorrelation matrix of the channel. Similarly, R Y It can be expressed as: Therefore, the LMMSE estimate is: Assume that the power is evenly distributed during the pilot training period and each pilot signal is a unit power signal, that is, Therefore, the mean square error of LMMSE is: Therefore, the optimization problem about phase shift can be formulated as: Where C1 means that the first column of the phase shift matrix is ​​set to 1 to estimate the direct channel h SR , C2 is the amplitude constraint of the IRS reflection unit, and C3 is the phase shift constraint of the IRS reflection unit. t,k represents the magnitude of the reflection coefficient of the kth reflection unit of the IRS in the tth time slot, θ t,k represents the phase of the reflection coefficient of the kth reflection unit of the tth time slot IRS, [Θ] t,1 Indicates that the first column of the IRS phase shift matrix for the tth time slot is all 1. Then, a least squares (LS)-based channel estimation method is used to design the reflection matrix Θ, that is, the channel is estimated by turning on the IRS reflective elements in sequence. Therefore, Θ is expressed as: Among them, 0 K represents a K-dimensional all-zero vector, 1 K represents a K-dimensional all-one vector. It can be seen that the design of Θ satisfies the constraints in P1, but it is not the optimal solution. In order to reduce the mean square error, the objective function needs to be further transformed because: Among them, if and only if Θ H The equation holds true when Θ is a diagonal matrix. And because the channel correlation matrix R μ is a constant, so the optimization problem can be transformed into: C4: I H Θ=diag(α1,α1,…,α K+1 ) Among them, C4 is a diagonal matrix constraint. In addition, the upper bound of the objective function is: Next, the method of the present invention is used to solve the phase shift optimization problem: The discrete Fourier transform matrix is ​​used to construct Θ. The first T rows and K+1 columns of the discrete Fourier transform matrix are used to construct Θ, where T ≥ K+1: Since Tr(F T,K+1 H F T,K+1 )=T(K+1), reaching the upper bound of the objective function, so Θ=F T,K+1 is the optimal solution to the phase shift optimization problem. Similarly, the Hadamard channel estimation method is used, and the first T rows and K+1 columns of the Hadamard matrix are used to construct Θ, where T≥K+1. Therefore, Θ=H T,K+1 , according to the properties of the Hadamard matrix: Tr(Θ H Θ)=Tr(H T,K+1 H H T,K+1 )=T(K+1) The Hadamard matrix is ​​defined as follows: Where ξ is a positive integer. Therefore, Θ constructed based on the Hadamard matrix is ​​also the optimal solution to the phase shift optimization problem. In summary, since Θ constructed by the LS-based channel estimation method does not fully utilize the full reflection capability of the IRS, the channel estimation accuracy is low, while the method of the present invention and Hadamard can provide higher estimation accuracy.

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