Low-complexity intelligent metasurface broadband beam forming method based on space-frequency Fourier transform

Abstract

The invention provides a low-complexity intelligent metasurface broadband beam forming method based on space-frequency Fourier transform, an intelligent metasurface is used for carrying out beam forming on a signal sent by a sending end and then sending the signal to the position where an expected target is located, the intelligent metasurface comprises a plurality of array elements, and the method comprises the following steps: aiming at the intelligent metasurface, carrying out beam forming on the signal, obtaining a beam forming gain obtained by discrete summation, and obtaining a gain complex definite integral based on the beam forming gain; coordinate system transformation is carried out on the gain complex definite integral under the Cartesian coordinate system to obtain a target gain complex definite integral under a long ellipsoid coordinate system with the midpoint of the sending end and the expected target as the original point; obtaining a one-dimensional target gain complex definite integral based on the target gain complex definite integral; obtaining an extra phase shift of intelligent metasurface broadband beam forming based on a one-dimensional target gain complex definite integral by using a spatial domain-frequency domain Fourier transform theory; and calculating the target broadband phase shift of the intelligent metasurface based on the additional phase shift and the standard narrowband beam forming phase shift.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform. Background Art

[0002] Reconfigurable Intelligent Surface (RIS) is regarded as one of the potential key technologies of the sixth generation mobile communication system. RIS is a new type of material composed of a large number of low-cost, programmable electromagnetic elements, usually with a two-dimensional planar array structure. These electromagnetic elements (i.e., array elements) can change the electromagnetic properties of the incident electromagnetic wave, including phase, amplitude, and polarization. The mainstream low-cost RIS focuses the incident electromagnetic wave on the expected reflection direction or even position by designing the phase shift of the array element, thereby realizing passive beamforming. Compared with the traditional active phased-array antenna (APAA), since RIS does not require RF links and additional energy consumption, it has broad application prospects in the fields of communication, perception, and integrated sensing and communication (ISAC) in high-frequency bands (such as millimeter wave, terahertz and other frequency bands).

[0003] With the demand for higher data rates and perception capabilities, information communication systems are developing towards higher frequency bands and larger bandwidths. However, current RIS beamforming research mainly considers the situation of narrowband systems. The existing RIS narrowband beamforming scheme faces serious beam dispersion (beam squint) effect when applied to high-frequency and broadband scenarios. This effect causes the direction of RIS beamforming to change with frequency, causing the beam gain to change dramatically within the working bandwidth. There is an obvious loss of waveform gain at frequencies far away from the narrowband beamforming design frequency, resulting in severe distortion of the signal waveform at a given direction or position. This negative effect greatly limits the effective working bandwidth of RIS, reducing the communication rate and perception distance resolution. At the same time, due to the characteristics of the large array of high-frequency RIS, the beam dispersion effect has a particularly significant impact on the beamforming of large-array RIS in the high-frequency band.

[0004] The goal of RIS broadband beamforming is to achieve gain flatness within a given working bandwidth at a given RIS beam focusing direction or focusing position. There are two existing RIS broadband beamforming solutions. The first is to add additional RF devices to the RIS, and the other is to optimize the phase deflection of the RIS array through an optimization method represented by the convex optimization method.

[0005] Specifically, the first scheme refers to the traditional active phased array digital beamforming scheme. This scheme adds an additional true delay structure to the array element of RIS, so that the traditional active digital beamforming scheme can be used to realize broadband beamforming. The advantage of this scheme is that no complex algorithm is required to optimize the phase offset of the array element. The disadvantage is that additional RF devices are required. At present, it is very difficult to implement and the cost is high. It is not suitable for large-scale RIS arrays. The other scheme adopts a convex optimization method to model the RIS broadband beamforming problem into an optimization problem. The non-convex optimization problem is converted into a convex problem through methods such as semi-definite relaxation (SDR), so that the phase deflection of all array elements is optimized through the convex optimization method to realize broadband beamforming. The advantage of this scheme is that no additional RF hardware is required to RIS, and the low cost advantage of RIS can be maintained. The disadvantage is that since the scale of RIS array is far greater than that of traditional active antenna arrays, the convex optimization method is extremely complex and is only applicable to small array RIS. It is difficult to solve the broadband beamforming problem of RIS with a large array scale in high frequency bands in real time or cannot be solved within a limited time. Due to the low cost and large array characteristics of the RIS structure, the above two methods cannot achieve broadband beamforming of large array RIS while maintaining the low cost advantage of RIS, and are poor in terms of cost, power consumption and feasibility. Summary of the invention

[0006] The present invention aims to solve one of the technical problems in the related art at least to a certain extent.

[0007] To this end, the first object of the present invention is to propose a low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform to improve the communication rate and perception distance resolution.

[0008] The second object of the present invention is to propose a low-complexity intelligent metasurface broadband beamforming system based on space-frequency Fourier transform.

[0009] A third objective of the present invention is to provide an electronic device.

[0010] A fourth object of the present invention is to provide a computer-readable storage medium.

[0011] To achieve the above-mentioned object, the first aspect of the present invention proposes a low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform, wherein the intelligent metasurface is used to beamform a signal sent by a transmitting end and then send it to the position of the expected target, the intelligent metasurface includes a plurality of array elements, and the broadband beamforming method includes the following steps:

[0012] For the smart metasurface, a beamforming gain obtained by discrete summation is obtained, and a gain complex definite integral is obtained based on the beamforming gain;

[0013] Performing coordinate transformation on the gain complex definite integral in the Cartesian coordinate system to obtain the target gain complex definite integral in the long ellipsoid coordinate system with the midpoint of the transmitting end and the expected target as the origin;

[0014] Obtaining a one-dimensional target gain complex definite integral based on the target gain complex definite integral;

[0015] Using the spatial-frequency Fourier transform theory, the additional phase shift of the smart metasurface broadband beamforming is obtained based on the one-dimensional target gain complex definite integral.

[0016] The target broadband phase shift of the smart metasurface is calculated based on the additional phase shift and the standard narrowband beamforming phase shift.

[0017] In the method of the first aspect of the present invention, obtaining the gain complex definite integral based on the beamforming gain includes: processing the beamforming gain by a Riemann approximation method to obtain the gain complex definite integral.

[0018] In the method of the first aspect of the present invention, obtaining a one-dimensional target gain complex definite integral based on the target gain complex definite integral includes: simplifying the target gain complex definite integral through a geometric relationship to obtain a one-dimensional target gain complex definite integral.

[0019] In the method of the first aspect of the present invention, the use of spatial-frequency domain Fourier transform theory to obtain the additional phase shift of the intelligent metasurface broadband beamforming based on the one-dimensional target gain complex definite integral includes: based on the spatial-frequency domain Fourier transform theory, using the steady-state phase method to obtain the additional phase shift of the intelligent metasurface broadband beamforming based on the amplitude modulation function in the one-dimensional target gain complex definite integral.

[0020] To achieve the above-mentioned object, the second aspect of the present invention proposes a low-complexity intelligent metasurface broadband beamforming system based on space-frequency Fourier transform, wherein the intelligent metasurface is used to beamform the signal sent by the transmitting end and then send it to the position of the expected target, the intelligent metasurface includes multiple array elements, and the broadband beamforming system includes:

[0021] A fitting module, for obtaining a beamforming gain obtained by discrete summation for the smart metasurface, and obtaining a gain complex definite integral based on the beamforming gain;

[0022] A conversion module, used for performing coordinate system transformation on the gain complex definite integral in the Cartesian coordinate system to obtain the target gain complex definite integral in the long ellipsoid coordinate system with the midpoint of the transmitting end and the expected target as the origin;

[0023] A dimensionality reduction module, used for obtaining a one-dimensional target gain complex definite integral based on the target gain complex definite integral;

[0024] An additional phase shift calculation module is used to obtain the additional phase shift of the intelligent metasurface broadband beamforming based on the one-dimensional target gain complex definite integral by using the spatial-frequency Fourier transform theory;

[0025] The target broadband phase shift calculation module is used to calculate the target broadband phase shift of the smart metasurface based on the additional phase shift and the standard narrowband beamforming phase shift.

[0026] In the system of the second aspect of the present invention, in the fitting module, obtaining the gain complex definite integral based on the beamforming gain includes: processing the beamforming gain by a Riemann approximation method to obtain the gain complex definite integral.

[0027] In the system of the second aspect of the present invention, the dimension reduction module is specifically used to simplify the target gain complex definite integral through geometric relationships to obtain a one-dimensional target gain complex definite integral.

[0028] In the system of the second aspect of the present invention, the additional phase shift calculation module is specifically used to: based on the spatial-frequency domain Fourier transform theory, use the steady phase method to obtain the additional phase shift of the intelligent metasurface broadband beamforming based on the amplitude modulation function in the one-dimensional target gain complex definite integral.

[0029] To achieve the above-mentioned purpose, the third aspect of the present invention proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method proposed in the first aspect of the present invention.

[0030] To achieve the above-mentioned purpose, the fourth aspect of the present invention proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the method proposed in the first aspect of the present invention.

[0031] The present invention provides a low-complexity intelligent metasurface broadband beamforming method, system, electronic device and storage medium based on space-frequency Fourier transform. The intelligent metasurface is used to beamform a signal sent by a transmitter and then send it to the position of an expected target. The intelligent metasurface includes multiple array elements. The method obtains a beamforming gain obtained by discrete summation for the intelligent metasurface, and obtains a gain complex integral based on the beamforming gain; performs a coordinate system transformation on the gain complex integral in a Cartesian coordinate system to obtain a target gain complex integral in a long ellipsoid coordinate system with the midpoint of the transmitter and the expected target as the origin; obtains a one-dimensional target gain complex integral based on the target gain complex integral; utilizes the space-frequency domain Fourier transform theory to obtain an additional phase shift of the intelligent metasurface broadband beamforming based on the one-dimensional target gain complex integral; and calculates the target broadband phase shift of the intelligent metasurface based on the additional phase shift and the standard narrowband beamforming phase shift. In this case, the present invention does not require any additional hardware, maintaining the low-cost advantage of the smart metasurface; compared with the traditional software improvement method using optimization algorithms such as convex optimization, the present invention obtains a continuous gain complex integral through discrete summation of beamforming gains, and then obtains an additional phase shift after dimensionality reduction to obtain the required target broadband phase shift. It does not require any iterative process, improves computational efficiency, has a very significant advantage in algorithm complexity, and improves communication rate and perception distance resolution.

[0032] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0034] Figure 1 A system model diagram provided by an embodiment of the present invention;

[0035] Figure 2 A schematic flow chart of a low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform provided in an embodiment of the present invention;

[0036] Figure 3 A schematic diagram of a specific process of a low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform provided in an embodiment of the present invention;

[0037] Figure 4 An explanatory diagram of the coordinate system transformation provided by an embodiment of the present invention;

[0038] Figure 5 A geometric explanation diagram for the integral transformation process provided by an embodiment of the present invention;

[0039] Figure 6 The embodiment of the present invention provides Figure 1 A diagram of phase shift results of a smart metasurface beamforming;

[0040] Figure 7 The embodiment of the present invention provides Figure 1 Another smart metasurface beamforming phase shift result diagram;

[0041] Figure 8 A comparison diagram of frequency domain gains at different expected target positions provided by an embodiment of the present invention;

[0042] Fig. 9 A comparison diagram of frequency domain gains of different smart metasurface array sizes provided by an embodiment of the present invention;

[0043] Fig.10 Performance diagram of different smart metasurface phase shift quantization accuracies provided by the embodiments of the present invention;

[0044] Fig.11 A comparison chart of communication rates provided by an embodiment of the present invention;

[0045] Fig.12 A comparison chart of the perceived performance provided by the embodiments of the present invention;

[0046] Fig.13 A block diagram of a low-complexity intelligent metasurface broadband beamforming system based on space-frequency Fourier transform provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0048] The following describes a low-complexity intelligent metasurface broadband beamforming method and system based on space-frequency Fourier transform according to an embodiment of the present invention with reference to the accompanying drawings.

[0049] In the present invention, the smart metasurface is used to beamform the signal sent by the transmitter to the location of the intended target, and the smart metasurface includes multiple array elements. Each array element of the smart metasurface only needs to have the function of changing the phase of the incident signal (i.e., the phase of the signal sent by the transmitter).

[0050] In the present invention, the smart metasurface is a reflective or transmissive smart metasurface. For the convenience of description, the method embodiments and system embodiments of the present invention are described by taking the reflective smart metasurface as an example.

[0051] Figure 1 The system model diagram provided by the embodiment of the present invention. The intelligent super surface of the present invention is a passive device. Figure 1 As shown in FIG. 1 , the smart metasurface system includes a transmitter TX, a reflective smart metasurface and an expected target DT, wherein the transmitter is generally a horn antenna.

[0052] In the present invention, the position of the transmitting end is arbitrary, and the position of the receiving end (i.e., the intended target) is arbitrary. The beam center of the transmitting end needs to point to the geometric center of the smart metasurface to achieve the best broadband beamforming effect. In this embodiment, for the convenience of explanation, Figure 1 As shown, the smart metasurface is on the xOy plane, and the transmitter TX is placed in the axial direction (boresight) of the smart metasurface, that is, in the direction perpendicular to the smart metasurface (that is, the z-axis direction).

[0053] In the present invention, the shape and size of the smart metasurface are arbitrary, but the shape and size will affect the effect of the smart metasurface broadband beamforming. Figure 1 As shown, the present invention takes a circular planar intelligent super surface as an example for relevant description.

[0054] In the intelligent metasurface system (hereinafter referred to as the system) of the present invention, the center frequency of the system is f c , the bandwidth of the system is B. Figure 1 As shown, the smart hypersurface is a circular plane smart hypersurface with a radius of R, and the center of the hypersurface (i.e., the center of the array) is located at the origin O of the (x, y, z) coordinate system. The smart hypersurface is located in the xOy plane and has a set number N RIS The smart metasurface array elements are evenly and equally spaced in the x-axis and y-axis directions, and the interval between the array elements is defined as Δ. The coordinate origin of the system O(0,0,0) is defined as the array center of RIS. The set of two-dimensional coordinates of all RIS array elements is defined as gather Contains N RIS coordinate elements, and these coordinate elements satisfy x 2 +y 2 ≤R 2 And z = 0, that is Each element of the smart metasurface is regarded as a phase shifter in the airspace, and the smart metasurface is regarded as a RIS A spatial phase shifter network composed of phase shifters.

[0055] In the above-mentioned intelligent metasurface system, the transmitter sends a broadband signal (also called a radio frequency signal), and the intelligent metasurface beamforms the broadband signal sent by the transmitter and reflects the broadband signal to the location of the intended target DT. The intended target can be regarded as a spectrum probe located in space. For the convenience of explanation, it is assumed that the transmitter is located directly above the center of the RIS array and the distance is l TX , that is, the coordinates of the sender are P TX =(0,0,l TX ). Assume that the distance between the expected target DT (i.e., the receiving end) and the center of the smart metasurface array is l DT , and the angle between the straight line connecting the position of the expected target and the origin of the coordinates and the normal line of the RIS center is γ, that is, the coordinates of the position of the expected target are P DT =(0,l DT sinγ,l DT cosγ).

[0056] Let the coordinates P of the transmitter TX and a RIS array element be x,y =The distance between (x, y, 0) and TX (x,y), its expression is as follows:

[0057]

[0058] Define the coordinates P of the expected target DT and a RIS array element x,y =The distance between (x, y, 0) and DT (x,y), its expression is as follows:

[0059]

[0060] In addition, in order to facilitate the problem statement, let l TX = l TX (0,0), l DT = l DT (0,0).

[0061] The core indicators of RIS broadband beamforming are the flatness and gain of RIS frequency-domain beamforming gain at a given position (near-field situation) or a given direction (far-field situation), and the definition of |g(f)| 2 is the frequency domain gain of RIS broadband beamforming, and the expression of RIS beamforming gain is as follows:

[0062]

[0063] Among them, f∈[f c -B / 2,f c +B / 2] is the signal frequency, w(x,y) represents the RIS for the incident signal to the array element The beamforming weight of the RF signal on , h(x,y,f) is expressed as follows:

[0064]

[0065] Among them, h TX (x,y,f) and h DT (x, y, f) are the reflection channels between the transmitter and the intended target and the smart metasurface element located at (x, y), c is the speed of light. TX (x,y) and l DT (x,y) are the distances from the transmitter and the expected target to the RIS array element (x,y) respectively.

[0066] Since each array element of the smart metasurface can only change the phase of the incident signal, the beamforming weight w(x,y) of the smart metasurface can be defined by the corresponding phase shift φ(x,y), {φ(x,y)}, That is, the weight w(x,y) can be expressed as:

[0067]

[0068] The phase shift φ corresponding to standard narrowband beamforming std (x,y) can be expressed as:

[0069]

[0070] Where l(x,y)=l TX (x,y)+l DT (x,y).

[0071] For the intelligent metasurface system, an embodiment of the present invention provides a low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform to improve the communication rate and perception distance resolution.

[0072] Figure 2 A schematic flow chart of a low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform provided in an embodiment of the present invention.

[0073] like Figure 2 As shown, the low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform includes the following steps:

[0074] Step S101, for the smart metasurface, obtain the beamforming gain obtained by discrete summation, and obtain the gain complex definite integral based on the beamforming gain.

[0075] In step S101, the beamforming gain obtained by discrete summation is the RIS broadband beamforming frequency domain gain |g(f)| at the expected target DT. 2 .

[0076] In step S101, a gain complex definite integral is obtained based on the beamforming gain, including: processing the beamforming gain by a Riemann approximation method to obtain a gain complex definite integral.

[0077] Step S102, performing coordinate transformation on the gain complex definite integral in the Cartesian coordinate system to obtain the target gain complex definite integral in the prolate ellipsoidal coordinate system with the midpoint between the transmitting end and the expected target as the origin.

[0078] Step S103, obtaining a one-dimensional target gain complex definite integral based on the target gain complex definite integral.

[0079] In step S103, a one-dimensional target gain complex definite integral is obtained based on the target gain complex definite integral, including: simplifying the target gain complex definite integral through a geometric relationship to obtain a one-dimensional target gain complex definite integral.

[0080] Step S104, using the spatial-frequency domain Fourier transform theory, an additional phase shift of the intelligent metasurface broadband beamforming is obtained based on the one-dimensional target gain complex definite integral.

[0081] In step S104, the spatial-frequency domain Fourier transform theory is used to obtain an additional phase shift of the intelligent metasurface broadband beamforming based on the one-dimensional target gain complex definite integral, including: based on the spatial-frequency domain Fourier transform theory, a phase-stabilizing method is used to obtain an additional phase shift of the intelligent metasurface broadband beamforming based on the amplitude modulation function in the one-dimensional target gain complex definite integral. It is easy to understand that the phase shift is the phase offset.

[0082] Step S105, calculating the target broadband phase shift of the smart metasurface based on the additional phase shift and the standard narrowband beamforming phase shift.

[0083] In step S105, the additional phase shift obtained in step 4) is superimposed on the standard narrowband beamforming phase shift to obtain the target broadband phase shift.

[0084] In some embodiments, Figure 3 A schematic diagram of a specific process of a low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform provided in an embodiment of the present invention.

[0085] like Figure 3 As shown, the low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform specifically includes:

[0086] Step 1): Approximate the discrete RIS as a continuous RIS.

[0087] Specifically, the Riemann approximation method can be used to convert the discrete summed beamforming gain |g(f)| 2 It is approximated as a complex definite integral (also called gain complex definite integral), thereby making the discrete smart hypersurface continuous. The Riemann approximation method uses integral approximation processing.

[0088] The specific process is as follows: When the number of smart metasurface array elements is large enough, g(f) can be approximated from the sum form to the integral form. Assume that there is a smart metasurface with a continuous aperture and a radius of R. The coordinates of the array elements that make up the continuous aperture RIS are defined as:

[0089]

[0090] The continuous aperture RIS is composed of countless reflective array elements, the area of ​​each reflective array element is infinitely small, and the array interval is infinitely small, that is, Δ→0. Therefore, the beamforming weight w(x,y) of the continuous aperture smart metasurface can be expressed as:

[0091]

[0092] in, And the sum form of g(f) can be approximated as the integral form of g a (f) is defined as:

[0093]

[0094] The discrete sum form g(f) is the continuous integral form g a (f) Riemann approximation, the approximation error is in the number of array elements N RIS It can be ignored if it is large enough.

[0095] In the process of designing broadband beamforming, the method of the present invention uses a standard narrowband beamforming phase shift φ std (x,y), an additional phase shift φ is designed des (x,y) and superimposed on the standard narrowband beamforming phase shift to ensure flatness and gain while improving communication rate and perceived distance resolution.

[0096] The final designed φ(x,y) is the narrowband optimal phase shift φ std (x,y) and compensation phase shift φ des (x, y) superposition, that is, the target broadband phase shift of the present invention satisfies φ(x, y) = φ std (x,y)+φ des (x,y).

[0097] g in the form of continuous integral a (f) can be further expressed as follows:

[0098]

[0099] in, It is the amplitude coefficient related only to the frequency f, which is the spatial angular frequency in radians per meter (rad / m), l(x,y)=l TX (x,y)+l DT (x, y) is the distance and the signal travels from the transmitter to the desired target location through the array element (x, y). The amplitude coefficient η(f) has little effect on the result. We ignore η(f) and only focus on the integral part. We approximate the RIS beamforming gain |g(f)| by integration. 2 , we can get the complex definite integral (i.e. the gain complex definite integral in the Cartesian coordinate system). The expression of the complex definite integral is as follows:

[0100]

[0101] Among them, φ des (x,y) represents the two-dimensional additional phase offset. w represents the RIS for the incident light to the array element. The beamforming weight of the RF signal on the beamforming weight, l(x,y) is the sum of the distances that the signal travels from the transmitter and is reflected by the array element (x,y) to the desired target location, that is, l(x,y) = l TX (x,y)+l DT (x,y).

[0102] Through step 1), the discrete summation form of the original problem can be replaced by mathematical integration, so that the original problem can be approximately solved by the integration tool. Since the number of array elements of the ultra-large-scale intelligent metasurface is large, the approximate error of the integral compared to the original discrete form is within an acceptable range. At the same time, as the scale of the RIS array increases, the error of the approximation will be smaller.

[0103] Step 2): Coordinate system transformation.

[0104] Specifically, Figure 4 As shown in the figure, the Cartesian coordinate system (x, y, z) with O as the origin is changed to P through the coordinate system transformation. TX and P DT The long ellipsoid coordinate system with the midpoint as the origin

[0105] Let O1 be P TX and P DT The midpoint of O1 is (x c ,y c ,z c ), let a be O1 to P TX Length. Replace the original (x, y, z) coordinate system, where σ∈[1,∞], τ∈[-1,1], Let γ c is the positive z-axis and the vector The following mapping relationship can be obtained:

[0106]

[0107] Let l represent the distance from any point on the smart hypersurface to P TX and P DT The distance and, let l = 2aσ∈[l min ,l max ], where l min and l max They are respectively any point in the smart hypersurface to P Tx and P DT After the coordinate system transformation, in essence, the integral expression of the RIS frequency domain response is replaced, and the two-dimensional additional phase shift φ des (x,y) reduces the dimension to one dimension, that is, φ des (l) According to the points conversion rule, The corresponding target gain complex definite integral in the prolate ellipsoid coordinate system It can be written as follows:

[0108]

[0109] Among them, φ des (l) is the one-dimensional additional phase offset, |J(l,θ)| is the absolute value of the determinant of the Jacobian matrix at a given l, l TX (l,θ) and l DT (l, θ) are the distances from the transmitter and the expected target to the RIS array element in the prolate ellipsoid coordinate system. θ1(l) and θ2(l) are the angles θ corresponding to the intersection of the prolate ellipsoid and the boundary of the circle where the smart hypersurface is located under a given l, as shown in Figure 5 As shown,

[0110] By means of the coordinate system transformation in step 2), the originally complex two-dimensional integral mapping relationship of the RIS frequency-domain beamforming gain is simplified into another mapping relationship, so as to facilitate further simplifying the two-dimensional integral expression into a one-dimensional integral expression in step 3).

[0111] Step 3): Simplify the two-dimensional integral expression of the frequency domain gain into a one-dimensional integral expression.

[0112] Specifically, through coordinate system transformation and geometric relationship, the two-dimensional integral expression of the frequency domain gain (that is, the expression of the complex definite integral of the target gain) is transformed into ) is simplified to a one-dimensional integral expression (i.e., the expression of the one-dimensional target gain complex definite integral ).

[0113] With O1 as the center of the sphere and P TX and P DT As l increases, the intersection of the long ellipsoid with two foci and the circular surface where the smart hypersurface is located will have the following states, from no intersection to intersection at one point P E (l min ), recorded as the initial intersection point, the set of intersection points forms an ellipse (the center of the ellipse is P E (l)), until the ellipse is cut into an elliptical arc by the circle where the smart hypersurface is located, and then intersects at a point. According to the change of the state of the intersection point, the two-dimensional integral can be transformed into a one-dimensional integral. The solution process requires the initial intersection point P E (l min ) Whether to classify within the circular surface where the smart hypersurface is located.

[0114] For the intersection point P E (l min ) In the case of the circular surface where the smart hypersurface is located, the following one-dimensional integral expression can be obtained:

[0115]

[0116] The above formula reflects the space-frequency Fourier transform relationship, where l mid is the critical value of l when the set of intersection points changes from an ellipse to an elliptical arc, l mid > min , A(l) is the The amplitude modulation function is expressed as follows:

[0117]

[0118] When l mid <l<l max When θ1(l) and θ2(l) are calculated according to Figure 5 The geometric relationships shown are obtained through numerical calculations.

[0119] For the initial intersection point P E (l min ) falls outside the circle, we can get the following one-dimensional integral expression:

[0120]

[0121] Among them, the expression of the amplitude modulation function A(l) is as follows:

[0122]

[0123] Similarly, l1(l) and θ2(l) can be obtained by numerical calculation according to the geometric relationship. For the amplitude modulation function A(l), the amount of calculation and the final effect can be weighed by adjusting the accuracy of the numerical integration.

[0124] Step 4): Design the RIS additional phase shift using the space-frequency Fourier transform relationship and the phase stabilization method.

[0125] Specifically, based on the spatial-frequency domain Fourier transform theory, the Fourier transform of the spatial nonlinear continuous frequency modulation wave is flat within a given bandwidth in the frequency domain. The phase-stabilization method is used to obtain the additional phase shift φ of the intelligent metasurface broadband beamforming according to the amplitude modulation function term in the one-dimensional integral expression of the frequency domain gain. des (x,y).

[0126] According to the steady-phase method, the frequency domain gain integral in Fourier transform form satisfies the following approximate relationship:

[0127]

[0128] Among them, φ des ″(l) is the additional phase shift φ of the smart metasurface des The second derivative of (l), ω l is a des ′(l min )=-πB / c grows to φ des ′(l max )=πB / c, and φ des ″(l)>0, However, l The growth of is nonlinear and depends on the modulation function A(l). Since, φ des ″(l)>0,φ des ″(l) satisfies the following relationship:

[0129] φ des ″(l)=κA 2 (l);

[0130] Where κ is a positive constant. After obtaining the above relationship, since A(l) can be obtained by numerical calculation in step 3), φ des ″(l) can also be obtained. Due to the boundary condition of the integral l min and l max It can also be obtained through geometric relations, so according to φ des ″(l) Take the definite integral and get φ des (l), and then find the definite integral according to the boundary conditions to obtain φdes (l). So far, the additional phase shift is obtained by the phase stabilization method. Then, through the geometric relationship, φ des (l) Mapping back to φ des (x,y), we can get the additional phase shift in two-dimensional form.

[0131] In step 4), the amplitude modulation function A(l) in the frequency domain gain integral expression and the additional phase shift φ of the smart metasurface broadband beamforming are established by the phase stabilization method. des (x, y), so that according to the goal of broadband beamforming frequency domain gain (obtaining a flat and large gain within a given bandwidth), the additional phase shift φ can be obtained according to the amplitude modulation function A(l). des (x,y).

[0132] Step 5): Add the narrowband phase shift and the additional phase shift to obtain a broadband phase shift result.

[0133] Specifically, the additional phase shift φ obtained in step 4) is des (x,y) Phase shift φ corresponding to standard narrowband beamforming std (x, y) are superimposed to obtain the final broadband phase shift of the smart metasurface (also called broadband phase shift result or target broadband phase shift).

[0134] Figure 4 An explanatory diagram of the coordinate system transformation provided in an embodiment of the present invention. Figure 4 The Cartesian coordinate system with O as the origin is transformed into Change to P TX and P DT The long ellipsoid coordinate system with the midpoint as the origin in, Figure 4 (a) is to translate the origin of the Cartesian coordinate system (x, y, z) to P TX and P DT The midpoint O1(x c ,y c ,z c ), and rotate γ based on O1 c Angle (i.e. the direction of the positive semi-axis of the z-axis and the vector The angle between the two angles) is obtained, and the coordinate system after translation and rotation with the origin at O1 is obtained. Figure 4 (b) is to change the coordinate system After the coordinate system is transformed and mapped into a long ellipsoid coordinate system Figure 4 The coordinate system transformation relationship shown is consistent with the integral substitution in the embodiment of the present invention, and is a geometric interpretation of the integral substitution step.

[0135] Figure 5A geometric explanation diagram for the integral transformation process provided by an embodiment of the present invention. Figure 5 In the figure, the blue line indicates the circle where the smart hypersurface is located, and the brown line indicates the circle with O1 as the center and P as the center. TX and P DT As l increases, the intersection of the long ellipsoid with two foci and the plane where the smart hypersurface is located (i.e., z = 0 plane) is obtained. According to the affine principle, the intersection lines represented by the brown lines are all ellipses, denoted by P. E (l) The center of the ellipse, the center of the ellipse P E (l) changes with the change of l; E The intersection of the ellipse centered at (l) and the plane z = 0 is denoted by P I (l,θ) represents; E The ellipse with (l) as the center and the circle where the intelligent hypersurface is located have intersections in some cases and do not have intersections in some cases. If there are intersections, the intersections are recorded as P I (l,θ1(l)) and P I (l,θ2(l)). According to the positional relationship between TX, DT and the smart metasurface, when l = l min (i.e. any point within the circle where the intelligent hypersurface is located to P TX and P DT The minimum value of the distance and the distance), the ellipse center P E (l min ) may fall inside or outside the circle where the smart metasurface is located. For these two cases, different integral calculation methods are required. Figure 5 (a) shows the ellipse center P E (l min ) falls within the circle of the smart hypersurface. In this case, P E (l min ) centered on the ellipse and the circle where the intelligent hypersurface is located do not have an intersection at first. As l increases, an intersection point P I (l,θ1(l)) and P I (l,θ2(l)), therefore, it is necessary to perform piecewise integration based on whether there is an intersection point. The intersection point P I (l,θ1(l)) and P I (l,θ2(l)) can be obtained by numerical calculation by computer; Figure 5 (b) shows the ellipse center P E (l min ) falls outside the circle of the smart hypersurface. In this case, P E (l min ) as the center and the circle where the smart hypersurface is located in the initial situation (i.e. l ​​= l min ), there is an intersection point, so no piecewise integration is required.

[0136] Figure 6 The embodiment of the present invention provides Figure 1 A diagram of the phase shift results of an intelligent metasurface beamforming. Figure 6 Shows Figure 1 When γ = 0°, the phase shift result of the smart metasurface beamforming obtained by the proposed method is, where Figure 6 (a) is the phase shift result of narrowband standard beamforming. Figure 6 (b) is the additional phase shift result obtained. Figure 6 (c) is the phase shift result of the superimposed smart metasurface broadband beamforming.

[0137] Figure 7 The embodiment of the present invention provides Figure 1 Another smart metasurface beamforming phase shift result diagram. Figure 7 Shows Figure 1 When γ = 15°, the phase shift result of the smart metasurface beamforming obtained by the proposed method is: Figure 7 (a) is the phase shift result of narrowband standard beamforming. Figure 7 (b) is the additional phase shift result obtained. Figure 7 (c) is the phase shift result of the superimposed smart metasurface broadband beamforming.

[0138] Figure 8 A comparison diagram of frequency domain gains at different expected target positions provided by an embodiment of the present invention. Figure 8 The frequency domain gain comparison diagram of the smart metasurface narrowband beamforming and the proposed broadband beamforming at different expected target locations is shown. The dotted line marks the operating bandwidth of the system, which is [f min ,f max ], the blue line is the frequency domain gain of the smart metasurface narrowband beamforming, and the red line is the frequency domain gain of the smart metasurface broadband beamforming proposed in this application. It can be seen that the gain of the intelligent metasurface generated by broadband beamforming is flat within the system bandwidth and decays rapidly outside the working bandwidth.

[0139] Fig. 9 A comparison chart of frequency domain gains under different sizes of smart metasurface arrays provided in an embodiment of the present invention. Fig. 9 The frequency domain gain comparison of narrowband beamforming and the proposed broadband beamforming under different smart metasurface array sizes is shown. The dotted line marks the operating bandwidth of the system, which is [f min ,f max ], the blue line is the frequency domain gain of the smart metasurface narrowband beamforming, and the red line is the frequency domain gain of the smart metasurface broadband beamforming proposed in this application. Fig. 9It can be seen that when the array scale increases, the proposed broadband beamforming method can obtain a flatter gain, and the attenuation outside the working bandwidth is steeper. This shows that the method of the present invention is still applicable when the scale of the smart metasurface array is large, and the effect is more significant.

[0140] Fig.10 Performance diagram of the smart metasurface with different phase shift quantization accuracies provided by the embodiments of the present invention. Fig.10 The performance of the proposed broadband beamforming method under different smart metasurface phase shift quantization accuracies is shown. The working bandwidth is [f min ,f max ], the blue line is the frequency domain gain of the smart metasurface narrowband beamforming, the red dashed curve is the frequency domain gain of the broadband beamforming at 1-bit quantization accuracy; the yellow dashed curve is the frequency domain gain of the broadband beamforming at 2-bit quantization accuracy, and the purple solid curve is the frequency domain gain of the continuous phase broadband beamforming. Fig.10 It can be seen that when 1-bit quantization accuracy is used, there is an overall gain loss of about 4 dB compared to the continuous phase shift case, but it still maintains the good characteristics of being flat within the designed bandwidth and rapidly decaying outside the band; when 2-bit quantization accuracy is used, there is almost no gain loss. This shows that the method of the present invention can be used for large-scale intelligent metasurfaces based on diodes with low cost and low quantization accuracy, and has significant practical application value.

[0141] Fig.11 A comparison chart of communication rates provided by embodiments of the present invention. Fig.11 A comparison of the communication rate between the smart metasurface narrowband beamforming and the proposed broadband beamforming method is shown. Broadband beamforming has a performance gain of about 10% in theoretical spectral efficiency.

[0142] Fig.12 A comparison chart of the perceived performance provided by the embodiments of the present invention. Fig.12 The figure shows the comparison of the perception performance of the smart metasurface narrowband beamforming and the proposed broadband beamforming method. The yellow line is the autocorrelation function of the linear frequency modulation wave, which shows a sharper characteristic; the blue line is the result of the linear frequency modulation wave signal after narrowband beamforming. Since narrowband beamforming narrows the effective bandwidth of the signal, the autocorrelation function of the linear frequency modulation wave is widened, resulting in a decrease in its distance resolution; in contrast, the orange line is the result of the linear frequency modulation wave signal after broadband beamforming, and its autocorrelation characteristics remain good, and the distance resolution remains high precision.

[0143] This embodiment adopts a near-field spherical wave signal model, and the far-field plane wave assumption can be considered as a special case of the near-field spherical wave assumption. Therefore, the proposed method is applicable to both near-field and far-field situations.

[0144] This embodiment takes the reflective smart metasurface as an example, and the method can also be applied to the transmissive smart metasurface.

[0145] Compared with the traditional intelligent metasurface broadband beamforming method represented by convex optimization, this method has significant advantages in terms of algorithm complexity. When the convex optimization method is used for intelligent metasurface broadband beamforming, its algorithm complexity is usually Under this complexity, traditional methods have great constraints on the optimization elements of smart metasurfaces, and the number of optimized elements usually cannot exceed 10. 2 The invented method does not require multiple iterations, and reduces the calculation dimension of the additional phase shift of the intelligent metasurface from two dimensions to one dimension during the calculation process. It also adopts the method of continuous solution and then discretization of the approximate mapping result, and can adjust the algorithm accuracy and algorithm calculation amount according to the needs. Therefore, the number of array elements can exceed 10 5 Considering that the beam dispersion effect mainly affects high-frequency band and large-array intelligent metasurface beamforming, the method of the present invention has a very significant advantage in applicability.

[0146] In order to realize the above-mentioned embodiments, the present invention also proposes a low-complexity intelligent metasurface broadband beamforming system based on space-frequency Fourier transform, wherein the intelligent metasurface is used to beamform the signal sent by the transmitter and send it to the location of the intended target, and the intelligent metasurface includes multiple array elements.

[0147] Fig.13 A block diagram of a low-complexity intelligent metasurface broadband beamforming system based on space-frequency Fourier transform provided in an embodiment of the present invention.

[0148] like Fig.13 As shown, the low-complexity intelligent metasurface broadband beamforming system based on space-frequency Fourier transform includes a fitting module 11, a conversion module 12, a dimensionality reduction module 13, an additional phase shift calculation module 14 and a target broadband phase shift calculation module 15, wherein:

[0149] A fitting module 11 is used to obtain a beamforming gain obtained by discrete summation for the smart metasurface, and obtain a gain complex definite integral based on the beamforming gain;

[0150] A conversion module 12 is used to perform coordinate system transformation on the gain complex definite integral in the Cartesian coordinate system to obtain the target gain complex definite integral in the long ellipsoid coordinate system with the midpoint of the transmitting end and the expected target as the origin;

[0151] A dimension reduction module 13, used for obtaining a one-dimensional target gain complex definite integral based on the target gain complex definite integral;

[0152] An additional phase shift calculation module 14 is used to obtain an additional phase shift of the intelligent metasurface broadband beamforming based on a one-dimensional target gain complex definite integral by using a spatial-frequency domain Fourier transform theory;

[0153] The target broadband phase shift calculation module 15 is used to calculate the target broadband phase shift of the smart metasurface based on the additional phase shift and the standard narrowband beamforming phase shift.

[0154] Further, in a possible implementation manner of the embodiment of the present invention, in the fitting module 11, obtaining the gain complex definite integral based on the beamforming gain includes: processing the beamforming gain by a Riemann approximation method to obtain the gain complex definite integral.

[0155] Furthermore, in a possible implementation manner of the embodiment of the present invention, the dimension reduction module 13 is specifically used to simplify the target gain complex definite integral through a geometric relationship to obtain a one-dimensional target gain complex definite integral.

[0156] Furthermore, in a possible implementation of an embodiment of the present invention, the additional phase shift calculation module 14 is specifically used to: based on the spatial-frequency domain Fourier transform theory, adopt a steady-state phase method to obtain the additional phase shift of the intelligent metasurface broadband beamforming based on the amplitude modulation function in the complex definite integral of the one-dimensional target gain.

[0157] It should be noted that the aforementioned explanation of the embodiment of the low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform is also applicable to the low-complexity intelligent metasurface broadband beamforming system based on space-frequency Fourier transform of this embodiment, and will not be repeated here.

[0158] In an embodiment of the present invention, an intelligent metasurface is used to beamform a signal sent by a transmitting end and then send it to the location of an expected target. The intelligent metasurface includes multiple array elements. The method obtains a beamforming gain obtained by discrete summation for the intelligent metasurface, and obtains a gain complex definite integral based on the beamforming gain; performs a coordinate system transformation on the gain complex definite integral in a Cartesian coordinate system to obtain a target gain complex definite integral in a prolate ellipsoid coordinate system with a midpoint between the transmitting end and the expected target as the origin; obtains a one-dimensional target gain complex definite integral based on the target gain complex definite integral; utilizes the spatial-frequency domain Fourier transform theory to obtain an additional phase shift of broadband beamforming of the intelligent metasurface based on the one-dimensional target gain complex definite integral; and calculates the target broadband phase shift of the intelligent metasurface based on the additional phase shift and the standard narrowband beamforming phase shift. In this case, the present invention does not require any additional hardware, maintaining the low-cost advantage of the smart metasurface; compared with the traditional software improvement method using optimization algorithms such as convex optimization, the present invention obtains a continuous gain complex integral through discrete summation of beamforming gains, and then obtains an additional phase shift after dimensionality reduction to obtain the required target broadband phase shift. It does not require any iterative process, improves computational efficiency, has a very significant advantage in algorithm complexity, and improves communication rate and perception distance resolution.

[0159] The purpose of the present invention is to solve the problem of beam dispersion in intelligent metasurface beamforming. The method and system of the present invention can approximate the discrete summed beamforming gain as a complex definite integral through the Riemann approximation method, thereby making the discrete intelligent metasurface continuous; the Cartesian coordinate system is changed to a prolate ellipsoid coordinate system through coordinate system transformation; the two-dimensional integral expression of the frequency domain gain is simplified to a one-dimensional integral expression through coordinate system transformation and geometric relationship; based on the spatial-frequency domain Fourier transform theory, the Fourier transform of the spatial nonlinear continuous frequency modulation wave is used to be flat within a given bandwidth in the frequency domain, and the steady phase method is used to obtain the additional phase shift of the intelligent metasurface broadband beamforming according to the amplitude modulation function term in the frequency domain gain integral expression; the obtained additional phase shift is superimposed with the standard narrowband beamforming phase shift to obtain the final intelligent metasurface broadband phase shift. The method and system of the present invention can realize the intelligent metasurface broadband beamforming with extremely low complexity without increasing the hardware cost of the intelligent metasurface, so as to improve the communication rate and perception distance resolution of the broadband intelligent metasurface system.

[0160] The method and system of the present invention include the following benefits:

[0161] The low complexity of the method and system of the present invention is reflected in that, by using the formula derivation method, an approximate closed-form solution of the intelligent metasurface broadband beamforming is obtained. It is only necessary to calculate the boundary conditions and the amplitude modulation function A(l) through computer numerical calculation to quickly obtain the phase shift P of the intelligent metasurface broadband beamforming. DT, the whole process does not require iterative optimization, and the computational efficiency can be improved through parallel computing, so the calculation is very efficient;

[0162] The method and system of the present invention have significant advantages over the traditional intelligent metasurface broadband beamforming method represented by convex optimization, which is reflected in the algorithm complexity. When the convex optimization method is used for intelligent metasurface broadband beamforming, its algorithm complexity is usually Under this complexity, traditional methods have great constraints on the optimization elements of smart metasurfaces, and the number of optimized elements usually cannot exceed 10. 2 The method described in the invention does not require multiple iterations, and reduces the calculation dimension of the additional phase shift of the intelligent metasurface from two dimensions to one dimension during the calculation process. The method of continuous solution and then discrete approximate mapping results can adjust the algorithm accuracy and algorithm calculation amount according to needs. Therefore, the number of array elements can be more than 10 5 Considering that the beam dispersion effect mainly affects high-frequency band and large-array intelligent metasurface beamforming, the method described in the present invention has a very significant advantage in applicability.

[0163] The method and system of the present invention are characterized in that in the broadband beamforming of the intelligent metasurface, the spatial frequency Fourier transform method is used to establish the mapping relationship between the spatial phase offset of the intelligent metasurface and the beam focusing position, the additional phase shift of the intelligent metasurface is approximated by the steady-state phase method, and the final broadband phase shift of the intelligent metasurface is obtained by superimposing the additional phase shift and the narrow-band optimal phase shift.

[0164] The method and system of the present invention realize low-complexity intelligent metasurface broadband beamforming without adding any additional hardware. Compared with the traditional hardware improvement method of adding a delay module, the present invention does not require any additional hardware, maintaining the low-cost advantage of the intelligent metasurface; compared with the traditional software improvement method using optimization algorithms such as convex optimization, the present invention does not require any iterative process, and the algorithm complexity only depends on the size of the intelligent metasurface array and the discrete accuracy. The algorithm accuracy and operation time can be weighed independently according to the needs. It has a very significant advantage in algorithm complexity, can solve the problem of high-frequency band and large-scale intelligent metasurface beam dispersion, realize ultra-large-scale intelligent metasurface broadband beamforming, and help improve the communication rate and perception distance resolution of broadband information systems.

[0165] In order to implement the above embodiments, the present invention also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0166] In order to implement the above embodiments, the present invention further proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0167] In order to implement the above embodiments, the present invention further provides a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0168] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0169] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0170] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0171] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0172] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0173] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0174] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0175] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform, characterized in that: The smart metasurface is used to beamform a signal sent by a transmitting end and then send it to the location of an expected target. The smart metasurface includes a plurality of array elements. The broadband beamforming method includes the following steps: For the smart metasurface, a beamforming gain obtained by discrete summation is obtained, and a gain complex definite integral is obtained based on the beamforming gain; Performing coordinate transformation on the gain complex definite integral in the Cartesian coordinate system to obtain the target gain complex definite integral in the long ellipsoid coordinate system with the midpoint of the transmitting end and the expected target as the origin; Obtaining a one-dimensional target gain complex definite integral based on the target gain complex definite integral; Using the spatial-frequency Fourier transform theory, the additional phase shift of the smart metasurface broadband beamforming is obtained based on the one-dimensional target gain complex definite integral. The target broadband phase shift of the smart metasurface is calculated based on the additional phase shift and the standard narrowband beamforming phase shift.

2. According to claim 1, the low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform is characterized in that: The obtaining a gain complex definite integral based on the beamforming gain comprises: The beamforming gain is processed by a Riemann approximation method to obtain a gain complex definite integral.

3. The low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform according to claim 1 is characterized in that: The step of obtaining a one-dimensional target gain complex definite integral based on the target gain complex definite integral comprises: The target gain complex definite integral is simplified through geometric relations to obtain a one-dimensional target gain complex definite integral.

4. The low-complexity intelligent metasurface broadband beamforming method based on space-frequency Fourier transform according to claim 1 is characterized in that: The method utilizes the spatial-frequency Fourier transform theory to obtain the additional phase shift of the intelligent metasurface broadband beamforming based on the one-dimensional target gain complex definite integral, including: Based on the spatial-frequency Fourier transform theory, the additional phase shift of the smart metasurface broadband beamforming is obtained by the phase-stabilization method based on the amplitude modulation function in the complex definite integral of the one-dimensional target gain.

5. A low-complexity intelligent metasurface broadband beamforming system based on space-frequency Fourier transform, characterized in that: The smart metasurface is used to beamform a signal sent by a transmitting end and then send it to the location of an expected target. The smart metasurface includes a plurality of array elements. The broadband beamforming system includes: A fitting module, for obtaining a beamforming gain obtained by discrete summation for the smart metasurface, and obtaining a gain complex definite integral based on the beamforming gain; A conversion module, used for performing coordinate system transformation on the gain complex definite integral in the Cartesian coordinate system to obtain the target gain complex definite integral in the long ellipsoid coordinate system with the midpoint of the transmitting end and the expected target as the origin; A dimensionality reduction module, used for obtaining a one-dimensional target gain complex definite integral based on the target gain complex definite integral; An additional phase shift calculation module is used to obtain the additional phase shift of the intelligent metasurface broadband beamforming based on the one-dimensional target gain complex definite integral by using the spatial-frequency Fourier transform theory; The target broadband phase shift calculation module is used to calculate the target broadband phase shift of the smart metasurface based on the additional phase shift and the standard narrowband beamforming phase shift.

6. The low-complexity intelligent metasurface broadband beamforming system based on space-frequency Fourier transform according to claim 5, characterized in that: In the fitting module, obtaining a gain complex definite integral based on the beamforming gain includes: processing the beamforming gain by a Riemann approximation method to obtain a gain complex definite integral.

7. The low-complexity intelligent metasurface broadband beamforming system based on space-frequency Fourier transform according to claim 5, characterized in that: The dimension reduction module is specifically used to simplify the target gain complex definite integral through geometric relationships to obtain a one-dimensional target gain complex definite integral.

8. The low-complexity intelligent metasurface broadband beamforming system based on space-frequency Fourier transform according to claim 5, characterized in that: The additional phase shift calculation module is specifically used to obtain the additional phase shift of the intelligent metasurface broadband beamforming based on the amplitude modulation function in the one-dimensional target gain complex definite integral by using the phase-steady method based on the spatial-frequency domain Fourier transform theory.

9. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 4 when executed by a processor.

Images