Transmissive ris-assisted mimo multi-beam alignment system and method

Abstract

The application provides a transmission type RIS assisted MIMO multi-beam alignment system and method, comprising a base station, a transmission type RIS system and multiple targets; the base station comprises a MIMO multi-antenna system; the transmission type RIS system assists the MIMO multi-antenna system to jointly perform beamforming, the minimum value of the beam gains of the beam directions of the multiple targets is maximized as a performance index, and the width of the multiple beams is limited, so that the expected beam direction of the multiple targets is obtained. The application adopts the transmission type RIS assisted MIMO multi-antenna system, realizes active and passive beamforming, improves the precision of the beamforming, enhances the beam strength of the target direction and reduces the width of the main lobe, and has good compatibility when being used in combination with the original multi-antenna MIMO system.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and more specifically, to a transmissive RIS-assisted MIMO multi-beam alignment system and method. Background Technology

[0002] Multiple-in-multiple-out (MIMO) technology has broad application prospects in the field of wireless networks. With the rapid development of smart devices and the Internet of Things (IoT), the demand for high-speed, high-bandwidth, and low-latency data transmission, as well as high resolution and high precision in sensing and positioning, is constantly increasing. In this regard, beamforming technology within MIMO plays a crucial role. Beamforming is a technique that generates directional beams by adjusting the phase and amplitude of antennas, allowing signal energy to be transmitted more concentratedly in a specific direction or area, thereby improving the strength and accuracy of communication signals. MIMO technology can utilize multiple antennas to transmit signals simultaneously, thus concentrating signal energy to the desired direction or area without increasing transmission power, thereby improving the beamforming effect. Simultaneously, MIMO technology can reduce multipath interference in beamforming, improving signal reliability and stability. In next-generation wireless networks, MIMO technology has become an indispensable component and has been widely applied in various fields such as connected vehicles, smart homes, and smart manufacturing. The application of massive MIMO arrays will further enhance the performance of MIMO technology, enabling it to play a more important role in a wider range of application scenarios. Therefore, the prospects for multi-antenna MIMO technology are very bright, and it will play an increasingly important role in future wireless networks. In next-generation wireless networks, high-frequency signals are typically used to improve communication rates and the resolution of sensing and positioning systems.

[0003] Chinese invention patent document CN105610478A discloses a method and apparatus for beam alignment of multiple subarrays in millimeter-wave MIMO communication. The method includes: the transceiver analyzing the codebooks corresponding to each subarray and spatially dividing the space accordingly; each subarray extracting codewords from its own codebook to form a corresponding subcodebook; the union of the extracted subcodebooks covering the original space; based on the extracted subcodebooks, the transmitter using multiple beams to transmit signals; and for the transmitter's transmission combination, the receiver simultaneously using multiple beams to receive signals based on the extracted subcodebooks. Using information acquired during the training phase, the main direction of the channel is calculated, and beam selection is further implemented.

[0004] Regarding the aforementioned technologies, the inventors believe that high-frequency signal transmission suffers from higher path loss, necessitating denser base station network deployment. This further increases the overall network deployment cost. To address this issue, it is crucial to research how to improve the performance of multi-antenna base station systems while controlling costs. This presents a significant practical challenge, as wireless network deployment costs are often very high in real-world applications. Therefore, a new technology is needed to balance network performance and deployment costs. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a transmissive RIS-assisted MIMO multi-beam alignment system and method.

[0006] According to the present invention, a transmissive RIS-assisted MIMO multi-beam alignment system includes a base station, a transmissive RIS system, and multiple targets;

[0007] The base station includes a MIMO multi-antenna system;

[0008] The transmissive RIS system assists the MIMO multi-antenna system in jointly performing beamforming. By using the minimum value among the beam gains that maximize the beam direction of multiple targets as the performance index and limiting the multi-beam width, the desired beam direction of multiple targets is obtained.

[0009] Preferably, the transmissive RIS system includes a transmissive RIS panel and an FPGA intelligent controller;

[0010] The transmissive RIS panel includes multiple transmissive units;

[0011] The base station generates control signals for the MIMO multi-antenna array, and performs active beamforming by controlling the amplitude and phase of the MIMO multi-antenna array through digital beamforming.

[0012] The FPGA intelligent controller generates control signals for the transmission units. The transmission-type RIS panel controls the amplitude and phase of each transmission unit through the FPGA intelligent controller to perform passive beamforming.

[0013] Preferably, the base station includes a radar base station;

[0014] The radar base station generates an active beamforming control signal and obtains the multi-antenna signal after active beamforming by the base station through the base station baseband signal.

[0015] The transmission RIS system generates a passive beamforming control signal. The transmission RIS system performs passive beamforming on the active beamforming signal of the multi-antenna array and controls the transmission RIS coefficient matrix G to establish the amplitude and phase of all elements, so that the transmission signal is aligned with multiple targets.

[0016] Preferably, the transmissive RIS system is a passive transmissive RIS system;

[0017] When the transmissive RIS system assists the MIMO multi-antenna system in beamforming, an optimization problem is constructed with the objective function of maximizing the minimum value of the beam gain in the target direction, and the maximum transmit power and the transmission capability of the RIS unit as constraints.

[0018] The optimization problem of maximizing the beam gain in the target direction is a non-convex optimization problem. Based on the positive semidefinite relaxation and continuous convex approximation algorithm, the solution is obtained by alternately optimizing all optimization variables.

[0019] Preferably, the MIMO multi-antenna system has M transmit antennas, and there are O target directions to be sensed, numbered i∈{1,2,…,0}. The transmission RIS panel has N transmission elements; the signal y received by the i-th target... i Represented as

[0020]

[0021] Among them, the first term γ i a T (θ i GHws represents the expected signal received for the i-th target; the second term n i γ represents the noise interference experienced by the i-th target; i Let θ be the channel fading coefficient from the base station to the i-th target; i a(θ) represents the position of the i-th target relative to the base station angle; i () represents the guidance vector received for a target at angle i; (·) T The transpose operator represents a matrix or vector; G represents the matrix used for beamforming the RIS auxiliary signal; H represents the near-field channel between the radar antenna array and the RIS; w represents the base station beamforming vector; and s represents the transmitted discrete baseband signal. This means that the formula holds true for any i-th objective;

[0022] a(θ i ) represents

[0023]

[0024] Where e represents the natural constant; d represents the antenna spacing; f c The signal carrier frequency is represented by c; the speed of light is represented by j; and the imaginary unit is represented by j. Represents a vector of dimension N×1;

[0025]

[0026] Where, α n This represents the amplitude of the nth element during beamforming of the transmission RIS-assisted signal; β n The nth element represents the phase of the transmitted RIS auxiliary signal beamforming, g is the vector representation of the RIS coefficient matrix G, and diag(·) represents the operator that transforms the vector into a diagonal matrix;

[0027] Angle θ i The signal-to-noise ratio (SNR) of the target received signal (θ) i ) represents

[0028]

[0029] in, This represents the power of the noise experienced by the i-th target, maximizing the beam at θ. i The signal-to-noise ratio in the direction ensures beam strength while maximizing beam attenuation within a preset angle range. The original optimization problem P0 is represented as follows:

[0030] P0:

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] Among them, P max This represents the maximum transmit power of the radar base station, the first constraint. For the constraint of maximum transmit power; θ 3db This indicates the required 3dB bandwidth angle, the second constraint. and the third constraint Ensure the main lobe of the beam meets the width requirement; the fourth constraint The fifth constraint is the amplitude constraint for RIS. Phase constraint for RIS;

[0037] Due to the minimization problem, by introducing an auxiliary variable t, the original optimization problem P0 is re-expressed as:

[0038] P0:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] When the optimization problem is a non-convex optimization problem with respect to variables w and G, alternating optimization is used to solve it.

[0046] Preferably, the base station actively establishes beamforming when transmitting sensing signals, and establishes MIMO multi-antenna beamformings for users or targets at different angles to maximize beam gain and ensure beamwidth.

[0047] Solve for the optimal beamforming vector w of the base station:

[0048] When solving w alternately, the first optimization problem P1 is transformed into

[0049] P1:

[0050]

[0051]

[0052]

[0053]

[0054] Here, w appears in SNR(θ) in a quadratic form. i In the process, through the semidefinite relaxation SDR transformation, let W = ww H ,(·) H The conjugate transpose operator for a matrix or vector is SNR(θ). i Represented as

[0055]

[0056] Where tr(·) represents the trace operator for matrix optimization, W is the variable to be optimized in the first optimization problem, and S 1,i For the defined intermediate quantity, it is represented as After SDR conversion, the constraints on the objective function and beamwidth become convex constraints; (·) * The conjugate operator for matrices or vectors;

[0057] To ensure that the solution obtains W decomposed into ww HThe rank of W needs to be 1, and the rank-1 constraint is equivalent to:

[0058]

[0059] Where ||·||2 represents the operator for calculating the largest eigenvalue;

[0060] Calculating the largest eigenvalue involves non-convex operations and performing a first-order Taylor expansion.

[0061]

[0062] Among them, u max (W (m) () represents the eigenvector corresponding to the largest eigenvalue of the first optimization variable W in the m-th iteration; To define the symbol, here (·) lb Defined as the lower bound of a numerical value;

[0063] The first optimization problem P1 is transformed into the first optimization problem P1.1, denoted as:

[0064] P1.1:

[0065]

[0066]

[0067]

[0068]

[0069] Where γ0 represents the penalty factor and κ represents the exponential growth factor;

[0070] The optimal solution W is obtained by solving convex optimization problems using software, and the optimal w is obtained by matrix factorization.

[0071] Preferably, based on the passive beamforming establishment of the transmission RIS-assisted MIMO multi-antenna, the amplitude and phase of each unit of the RIS are established for user or target orientation at different angles to maximize beam gain and ensure beam width;

[0072] Solve for the RIS-assisted transmit beamforming matrix G:

[0073] When solving G alternately, the second optimization problem P2 is transformed into

[0074] P2:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080] Wherein, G appears in SNR(θ) in quadratic form. i In this process, through the positive semidefinite relaxation SDR transformation, by utilizing the properties of the G diagonal matrix, namely a T (θ i G = g T ·diag(a T (θ i The signal-to-noise ratio is re-expressed by constructing a quadratic form as follows:

[0081]

[0082] Where R = g T ·g * R is the variable to be optimized in the second optimization problem, and S 2,i An intermediate variable that is not defined can be represented as After SDR conversion, the signal-to-noise ratio constraint becomes a convex constraint.

[0083] To ensure that the solution R can be decomposed into g T ·g * The rank of R needs to be 1, and the rank-1 constraint is equivalent to:

[0084]

[0085] Perform a first-order Taylor expansion

[0086]

[0087] Among them, u max (R (m) The eigenvector represents the eigenvalue of the optimization variable R at the m-th iteration; the second optimization problem P2 is transformed into problem P2.1, denoted as...

[0088] P2.1:

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] The optimal R is obtained by solving convex optimization problems using software, and the optimal g is obtained by matrix factorization.

[0096] According to the present invention, a transmissive RIS-assisted MIMO multi-beam alignment method is provided, which uses a transmissive RIS-assisted MIMO multi-beam system. The transmissive RIS system assists the MIMO multi-antenna system in jointly performing beamforming. The desired beam direction of the multi-target is obtained by using the minimum value among the beam gains of maximizing the beam direction of the multi-target as the performance index and limiting the multi-beam width.

[0097] Preferably, the method includes the following steps:

[0098] Active beamforming steps: The base station generates control signals for the MIMO multi-antenna array, and controls the amplitude and phase of the MIMO multi-antenna array through digital beamforming to perform active beamforming;

[0099] Passive beamforming steps: The FPGA intelligent controller generates control signals for the transmission units. The transmissive RIS panel controls the amplitude and phase of each transmission unit through the FPGA intelligent controller to perform passive beamforming.

[0100] Preferably, in the active beamforming step, the radar base station generates an active beamforming control signal and obtains the multi-antenna signal after active beamforming by the base station through the base station baseband signal.

[0101] In the passive beamforming step, the transmission RIS system generates a passive beamforming control signal. The transmission RIS system performs passive beamforming on the signal of active beamforming of the multi-antenna array, and controls the transmission RIS coefficient matrix G to establish the amplitude and phase of all elements, so that the transmission signal is aligned with multiple targets.

[0102] Compared with the prior art, the present invention has the following beneficial effects:

[0103] 1. This invention employs a transmission RIS-assisted MIMO multi-antenna system, which realizes active and passive beamforming, improves beamforming accuracy, enhances beam strength in the target direction, and reduces the main lobe width. It has good compatibility when used in conjunction with the original multi-antenna MIMO system.

[0104] 2. This invention utilizes a transmissive RIS to achieve signal transmission, thus avoiding the signal self-interference and signal attenuation problems caused by a reflected RIS;

[0105] 3. The architecture proposed in this invention is simple, including RIS and intelligent controller. Its hardware deployment cost is low and its additional power consumption is small, thus it is a new type of green auxiliary sensing system. Attached Figure Description

[0106] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0107] Figure 1 For a transmissive RIS-assisted MIMO multi-antenna multi-beam aiming system;

[0108] Figure 2 Let θ = [-π / 6, π / 6], θ 3dB =π / 16, M=18, N=36 Comparison chart with and without RIS auxiliary beam;

[0109] Figure 3 is θ=[-π / 4, 0, π / 3], θ 3dB =π / 16, M=18, N=36 Comparison diagram with and without RIS auxiliary beam. Detailed Implementation

[0110] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0111] This invention discloses a transmissive RIS-assisted MIMO multi-antenna multi-beam alignment system, such as... Figure 1 As shown, this includes a MIMO multi-antenna system, a transmissive RIS panel, an FPGA intelligent controller, and the desired beam pattern for multiple targets. Furthermore, it includes minimizing the beam gain in maximizing the beam direction for multiple targets and limiting the beamwidth. The transmissive RIS-assisted MIMO multi-antenna system achieves the ideal beam pattern through joint beamforming design of the RIS surface and the MIMO multi-antenna system during signal transmission. The transmissive RIS system assists the MIMO multi-antenna system in joint beamforming, achieving the desired beam direction for multiple targets by maximizing the minimum beam gain in the beam direction for multiple targets as the performance metric and limiting the multi-beamwidth.

[0112] In this system, the base station controls the amplitude and phase of the multi-antenna array through digital beamforming, while the RIS controls the amplitude and phase of each transmission unit through an intelligent controller, thus achieving a combined passive and active beamforming design.

[0113] Transmissive RIS-assisted MIMO multi-antenna system: The base station and transmissive RIS are jointly beamforming designed. The control signals for the multi-antenna array are generated at the base station, while the control signals for the transmissive RIS unit are generated by the intelligent controller.

[0114] Generating active beamforming control signals for radar base stations: Considering the base station baseband signal as s, the multi-antenna signal after active beamforming design of the base station is ws, which includes the amplitude and phase design in active beamforming.

[0115] The transmission RIS generates passive beamforming control signals: The transmission RIS performs further passive beamforming on the active beamforming signals of the multi-antenna array. The transmission RIS coefficient matrix is ​​represented as G, which includes the amplitude and phase design of all element units, so that the transmission signal can be further aligned with multiple targets.

[0116] Beamforming scheme in a transmissive RIS transceiver system: The performance index is to maximize the minimum beam gain in multiple target directions, while limiting the multi-beam width. By constructing an optimization problem with the objective function of maximizing the minimum beam gain in the target directions, and constrained by the maximum transmit power and the transmission capability of the RIS unit, this invention achieves the design of a novel transmissive RIS-assisted MIMO multi-antenna multi-beam alignment system.

[0117] Beamforming scheme in a transmissive RIS-assisted MIMO multi-antenna multi-beam alignment system: The transmissive RIS system required for this design is passive and does not require an additional RF link to process the incident signal. It achieves beamforming in a passive manner, achieving better performance at a lower cost.

[0118] Beamforming scheme design for a transmission-type RIS-assisted MIMO multi-antenna system: The design objective is to maximize the beam gain in the target direction, but this optimization problem is non-convex, making it difficult to directly obtain the global optimum. This invention proposes a method based on semi-definite relaxation and continuous convex approximation algorithms, and obtains a high-quality suboptimal solution by alternately optimizing all optimization variables.

[0119] To address the demands of next-generation wireless networks for reduced beam gain performance and lower costs, this invention presents a novel design for a transmissive RIS-assisted MIMO multi-antenna multi-beam targeting system. This invention provides a joint design scheme based on a transmissive RIS-assisted MIMO multi-antenna system to achieve more efficient wireless communication or sensing systems. The system includes a base station antenna array and a transmissive RIS, both requiring beamforming design. The beamforming design of the base station antenna array achieves active transmit beamforming by controlling the amplitude and phase of each antenna to improve the accuracy and effectiveness of the transmitted signal. The beamforming design of the transmissive RIS achieves passive transmit beamforming by controlling the amplitude and phase of each transmissive element to improve signal accuracy and amplitude.

[0120] Consider a MIMO multi-antenna system with M transmit antennas, where s represents the transmit discrete baseband signal (which can be a communication or radar signal). There are O target directions to be sensed, numbered i∈{1,2,…,O}, θ i Let represent the position angle of the i-th target. For the i-th target, the received signal can be represented as:

[0121]

[0122] Among them, the first term α i a T (θ i GHws represents the expected signal received for the i-th target; the second term n i Let n represent the noise interference experienced by the i-th target. i With a mean of 0 and a variance of A complex Gaussian random variable; α i Let θ be the channel fading coefficient from the base station to the i-th target; i γ represents the position of the i-th target relative to the base station angle; i Let a(θ) be the channel fading coefficient from the base station to the i-th target; i () represents the guidance vector received for a target at angle i; (·) T The transpose operator represents a matrix or vector; G represents the matrix used for beamforming the RIS auxiliary signal; H represents the near-field channel between the radar antenna array and the RIS; w represents the base station beamforming vector; and s represents the transmitted discrete baseband signal. This means that the formula holds true for any i-th objective.

[0123] a(θ i The steering vector received for a target at angle i can be represented as:

[0124]

[0125] Where e represents the natural constant; d represents the antenna spacing; f c The signal carrier frequency is represented by c; the speed of light is represented by j; and the imaginary unit is represented by j. This represents a vector with dimension N×1.

[0126] H represents the near-field channel between the radar antenna array and the transmission RIS. G represents the matrix when the transmission RIS is used to assist the signal. Where, α n This represents the amplitude of the nth element during beamforming of the transmission RIS-assisted signal; β n α represents the phase of the nth element during beamforming of the RIS-assisted signal, g is the vector representation of the RIS coefficient matrix G, and diag(·) represents the operator for converting a vector into a diagonal matrix; n and β n Let represent the amplitude and phase of the i-th element when transmitting the RIS auxiliary signal, respectively. Therefore, the angle is θ. i The signal-to-interference plus noise ratio (SNR) of the signal received by the target can be expressed as:

[0127]

[0128] in, This represents the power of the noise experienced by the i-th target. To improve beam quality, it is necessary to maximize the beam strength at θ. i The signal-to-noise ratio in the direction ensures beam strength while also guaranteeing sufficient attenuation within the required angular range. Therefore, the entire optimization problem can be expressed as follows: [Formula omitted for brevity]. Thus, the original optimization problem P0 is...

[0129] P0:

[0130]

[0131]

[0132]

[0133]

[0134]

[0135] Among them, P max This represents the maximum transmit power of the radar base station, the first constraint. For the constraint of maximum transmit power; θ 3db This indicates the required 3dB bandwidth angle, the second constraint. and the third constraint Ensure the main lobe of the beam meets the width requirement; the fourth constraint The fifth constraint is the amplitude constraint for RIS. Phase constraint for RIS. P max The first constraint represents the maximum transmit power of the radar base station; θ represents the maximum transmit power constraint. 3dB The first constraint represents the required 3dB bandwidth angle. The second and third constraints ensure that the main lobe of the beam meets the width requirement. The fourth and fifth constraints are the amplitude and phase constraints of the RIS, respectively. Since this is a maximum-minimum problem, by introducing an auxiliary variable t, the original problem can be rewritten as follows:

[0136] P0:

[0137]

[0138]

[0139]

[0140]

[0141]

[0142]

[0143] The entire optimization problem is a non-convex optimization problem with two variables, w and G. This invention uses an alternating optimization method to solve it.

[0144] Example 1:

[0145] This invention presents an active beam design for transmitting sensing signals, which designs a base station MIMO multi-antenna beam for users or targets at different angles, thereby maximizing beam gain and ensuring beamwidth.

[0146] (1) Solve for the optimal beamforming vector w of the base station

[0147] When solving for w alternately, the entire optimization problem can be transformed into, i.e., the first optimization problem P1 is transformed into

[0148] P1:

[0149]

[0150]

[0151]

[0152]

[0153] Because w appears in SNR(θ) in quadratic form i Therefore, we transform it using the semidefinite relaxation (SDR) method, letting W = ww H ,(·) H The conjugate transpose operator represents a matrix or vector, so SNR(θ) i ) can be reformulated as

[0154]

[0155] Where tr(·) represents the trace operator for matrix optimization, W is the variable to be optimized in the first optimization problem, and S 1,i For the defined intermediate quantity, it is represented as After SDR conversion, the constraints on the objective function and beamwidth become convex constraints; (·) * This represents the conjugate operator for matrices or vectors. However, to ensure that the solved W can be decomposed into ww H The rank of W needs to be 1. The rank-1 constraint can be equivalent to: Here, ||·||2 represents the operator for calculating the largest eigenvalue. However, calculating the largest eigenvalue is also a non-convex operation, which can be performed using a first-order Taylor expansion.

[0156]

[0157] Among them, u max (W (m) ) represents the eigenvector corresponding to the largest eigenvalue of the optimization variable W in the m-th iteration. To define the symbol, here (·) lb This is defined as the lower bound of the numerical value. Therefore, the optimization problem P1 can be further transformed into problem P1.1, expressed as...

[0158] P1.1:

[0159]

[0160]

[0161]

[0162]

[0163] Here, γ0 represents the penalty factor, and κ represents the exponential growth factor. After transformation, the entire problem becomes a standard semi-definite program (SDP) convex problem, which can be solved using convex optimization software (such as CVX) to obtain a high-quality optimal solution W, and the optimal w can be obtained through matrix factorization.

[0164] Example 2:

[0165] This invention provides a passive beamforming design for transmission RIS-assisted MIMO multi-antenna systems. The amplitude and phase of each RIS unit are designed for user or target orientation at different angles to maximize beam gain and ensure beamwidth.

[0166] (2) Solve for the RIS-assisted transmit beamforming matrix G

[0167] When solving G alternately, the entire optimization problem can be transformed into

[0168] P2:

[0169]

[0170]

[0171]

[0172]

[0173]

[0174] Similarly, because G appears in SNR(θ) in quadratic form i Therefore, we transform it using the semidefinite relaxation SDR method. Since we cannot directly merge the quadratic forms of G, we need to utilize GG. t The property of a diagonal matrix, i.e., a T (θ i G = g T ·diag(a T (θ i The signal-to-noise ratio can be reformulated by constructing a quadratic form as follows:

[0175]

[0176] Where R = g T ·g * , After SDR conversion, the signal-to-noise ratio constraint becomes a convex constraint. However, to ensure that the solved R can be decomposed into g... T ·g *The rank of R needs to be 1. The rank-1 constraint can be equivalent to: Here, ||·||2 represents the operator for calculating the largest eigenvalue. However, calculating the largest eigenvalue is also a non-convex operation, which can be performed using a first-order Taylor expansion.

[0177]

[0178] Among them, u max (R (m) Let represent the eigenvector corresponding to the largest eigenvalue of the optimization variable R in the m-th iteration. Therefore, the optimization problem P2 can be further transformed into problem P2.1, expressed as:

[0179] P2.1:

[0180] st

[0181]

[0182]

[0183]

[0184]

[0185]

[0186] Here, γ0 represents the penalty factor. After transformation, the entire problem becomes a standard SDP convex problem, which can be solved using CVX, a convex optimization software, to obtain a high-quality optimal R, and the optimal g can be obtained through matrix factorization.

[0187] The two sub-problems are optimized alternately until they converge, resulting in the final system design.

[0188] Figure 1 The basic structural components of the invention are described. Figure 2 and Figure 3 The beam gain of the invention at different target angles was compared.

[0189] In recent years, reconfigurable metasurface (RIS) technology has become a highly anticipated technology in wireless communication and sensing systems, with its application scope gradually expanding and playing a significant role in the future development of wireless networks. As a novel wireless electronic device, RIS, with its flexible and controllable characteristics, significantly improves the performance of communication and sensing systems by controlling the reflection, refraction, and transmission of electromagnetic waves, while also reducing system deployment costs. For communication systems, the application of RIS can greatly improve the quality of communication channels and solve the problem of blind coverage, thereby achieving full-coverage communication. In multi-antenna systems, RIS can achieve beamforming by controlling signal reflection and transmission, improving channel capacity in multipath transmission and thus achieving efficient data transmission. Furthermore, by eliminating signal interference, RIS can optimize communication quality and signal coverage. For sensing systems, RIS can also improve system performance. RIS can achieve passive beamforming, accurately locating the target to be sensed or positioned, improving the accuracy and strength of the sensed signal. In addition, because RIS can receive multipath signals, it can achieve higher resolution accuracy, thereby improving the system's sensing performance. Therefore, RIS has broad application prospects in future wireless sensing networks. It is worth noting that, compared to traditional reflective RIS, transmissive RIS can effectively reduce self-interference and feed duct obstruction problems, improve aperture efficiency, and thus achieve a more efficient performance improvement in MIMO multi-antenna systems. Furthermore, the passive nature of RIS can significantly reduce system deployment costs, bringing substantial economic benefits to network construction and maintenance. In conclusion, RIS technology has broad application prospects and practical value, and will have a profound impact on the development of future wireless communication and sensing systems. Therefore, in the research and development process, its flexible and controllable characteristics should be fully utilized to leverage its important role in wireless communication and sensing systems.

[0190] This invention relates to a transmissive intelligent metasurface (RIS)-assisted MIMO multi-antenna base station system, which aims to achieve precise targeting of information beams in multiple target directions through the joint design of active and passive beamforming. The system consists of a MIMO antenna array, a transmissive RIS, an FPGA intelligent controller, and a desired target pattern.

[0191] In this system, the MIMO antenna and the RIS intelligent controller perform beamforming on the multi-antenna transmitting array and the transmissive RIS using amplitude and phase design information, respectively, to achieve accurate targeting and transmission of the information beam to the target location. Furthermore, to optimize system performance, this invention designs a joint active and passive beamforming scheme, maximizing the minimum beam gain among multiple target directions as the performance metric, while limiting the multi-beamwidth.

[0192] Compared to traditional MIMO multi-antenna systems, this invention uses a transmissive RIS-assisted MIMO multi-antenna system, which achieves better performance while requiring relatively low additional cost and power consumption, making it environmentally friendly and green. This system is expected to be applied to next-generation wireless networks.

[0193] This invention also discloses a transmissive RIS-assisted MIMO multi-beam alignment method. The transmissive RIS-assisted MIMO multi-beam system is used, in which the transmissive RIS system assists the MIMO multi-antenna system in jointly performing beamforming. The performance index is determined by maximizing the minimum beam gain of the multi-target beam directions and limiting the multi-beam width to obtain the desired multi-target beam directions.

[0194] The method includes the following steps:

[0195] Active beamforming steps: The base station generates control signals for the MIMO multi-antenna array, and controls the amplitude and phase of the MIMO multi-antenna array through digital beamforming to perform active beamforming; the radar base station generates active beamforming control signals, and obtains the multi-antenna signals after active beamforming by the base station through the base station baseband signal.

[0196] Passive beamforming steps: The FPGA intelligent controller generates control signals for the transmission units. The transmission-type RIS panel controls the amplitude and phase of each transmission unit through the FPGA intelligent controller to perform passive beamforming. The transmission RIS system generates passive beamforming control signals. The transmission RIS system performs passive beamforming on the signals from the active beamforming of the multi-antenna array, controlling the amplitude and phase of all elements of the transmission RIS coefficient matrix G to align the transmission signal with multiple targets.

[0197] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0198] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A transmissive RIS-assisted MIMO multibeam alignment system, characterized in that, This includes base stations, transmissive RIS systems, and multiple targets; The base station includes a MIMO multi-antenna system; The transmissive RIS system assists the MIMO multi-antenna system in jointly performing beamforming. By using the minimum value among the beam gains that maximize the beam direction of multiple targets as the performance index and limiting the multi-beam width, the desired beam direction of multiple targets is obtained. The transmissive RIS system includes a transmissive RIS panel and an FPGA intelligent controller. The transmissive RIS panel includes multiple transmissive units; The base station generates control signals for the MIMO multi-antenna array, and performs active beamforming by controlling the amplitude and phase of the MIMO multi-antenna array through digital beamforming. The FPGA intelligent controller generates control signals for the transmission units. The transmission-type RIS panel controls the amplitude and phase of each transmission unit through the FPGA intelligent controller to perform passive beamforming. The MIMO multi-antenna system has a total of There are [number] transmitting antennas, totaling [number] antennas. The direction of each target needs to be sensed, numbered as follows: The transmissive RIS panel has a total of The first transmission unit; the first Signal received by the target Represented as Among them, the first item For the first The first target expects to receive the signal; the second item Indicates the first The target is subject to noise interference; For base station to the Channel fading coefficient of each target; Indicates the first The location of each target relative to the angle of the base station; This indicates that for an angle of The target receiving guidance vector; The transpose operator for matrices or vectors. The matrix representing beamforming of the transmission RIS-assisted signal; This represents the near-field channel between the radar antenna array and the transmission RIS; Represents the base station beamforming vector; Indicates the transmission of discrete baseband signals; Indicates about The formula for any th All objectives were achieved; Represented as in, Represents the natural constant; Indicates the antenna spacing; Indicates the signal carrier frequency; Represents the speed of light; Represents the imaginary unit. The dimension is ; in, Indicates the first step of beamforming the RIS-assisted signal during transmission. n The amplitude of each element; Indicates the first step of beamforming the RIS-assisted signal during transmission. n The phase of each element, RIS coefficient matrix The vector representation of , Operators that transform a vector into a diagonal matrix; Angle is The signal-to-noise ratio of the target received signal Represented as in, Indicates the first The power of the noise received by the target is maximized by the beam. The signal-to-noise ratio in the direction ensures beam strength while maximizing beam attenuation within a preset angle range. This addresses the original optimization problem. Represented as in, This represents the maximum transmit power of the radar base station, the first constraint. Constraints on maximum transmit power; This indicates the required 3dB bandwidth angle, the second constraint. and the third constraint Ensure the main lobe of the beam meets the width requirement; the fourth constraint The fifth constraint is the amplitude constraint for RIS. Phase constraint for RIS; Due to the minimization problem, by introducing auxiliary variables... The original optimization problem P0 can be rewritten as: ， When the optimization problem is about variables and When solving non-convex optimization problems with variables, alternating optimization is used.

2. The transmission-type RIS-assisted MIMO multibeam alignment system according to claim 1, characterized in that, The base station includes a radar base station; The radar base station generates an active beamforming control signal and obtains the multi-antenna signal after active beamforming by the base station through the base station baseband signal. The transmissive RIS system generates a passive beamforming control signal. The transmissive RIS system performs passive beamforming on the active beamforming signal from the multi-antenna array, controlling the transmissive RIS coefficient matrix. Establish the amplitude and phase of all elements to align the transmitted signal with multiple targets.

3. The transmission-type RIS-assisted MIMO multibeam alignment system according to claim 1, characterized in that, The transmissive RIS system is a passive transmissive RIS system; When the transmissive RIS system assists the MIMO multi-antenna system in beamforming, an optimization problem is constructed with the objective function of maximizing the minimum value of the beam gain in the target direction, and the maximum transmit power and the transmission capability of the RIS unit as constraints. The optimization problem of maximizing the beam gain in the target direction is a non-convex optimization problem. Based on the positive semidefinite relaxation and continuous convex approximation algorithm, the solution is obtained by alternately optimizing all optimization variables.

4. The transmission-type RIS-assisted MIMO multibeam alignment system according to claim 1, characterized in that, When the base station transmits sensing signals, it actively establishes MIMO multi-antenna beams for users or targets at different angles, maximizing beam gain and ensuring beamwidth. Solving for the optimal beamforming vector of the base station : Alternate solution At that time, the first optimization problem P1 is transformed into in, It appears in a secondary form In the middle, through the semidefinite relaxation SDR transformation, let , The conjugate transpose operator for a matrix or vector is then... Represented as in, Operators for finding the trace of a matrix. This is the variable to be optimized in the first optimization problem. For the defined intermediate quantity, it is represented as After SDR conversion, the constraints on the objective function and beamwidth become convex constraints. The conjugate operator for matrices or vectors; To ensure that the solution is obtained Decomposed into ,need The rank is 1, and the rank-1 constraint is equivalent to: in, Operator for calculating the largest eigenvalue; Calculating the largest eigenvalue involves non-convex operations and performing a first-order Taylor expansion. in, Represents the first optimization variable In the The eigenvector corresponding to the largest eigenvalue at the nth iteration; To define the symbols, here Defined as the lower bound of a numerical value; The first optimization problem P1 is transformed into the first problem P1.1, denoted as: ， in, Indicates the penalty factor. Indicates the exponential growth factor; The optimal solution is obtained by solving convex optimization problems using software. Optimal results can be obtained through matrix decomposition. .

5. The transmission-type RIS-assisted MIMO multibeam alignment system according to claim 1, characterized in that, Based on the passive beamforming establishment of RIS-assisted MIMO multi-antenna, the amplitude and phase of each unit of RIS are established for user or target orientation at different angles to maximize beam gain and ensure beam width. Solve the RIS-assisted transmit beamforming matrix : Alternate solution At that time, the second optimization problem P2 is transformed into ， in, It appears in a secondary form In the middle, through the semidefinite relaxation SDR transformation, by utilizing The properties of diagonal matrices, namely The signal-to-noise ratio is re-expressed by constructing a quadratic form as follows: in, , This is the variable to be optimized in the second optimization problem. For the defined intermediate variable, it is represented as After SDR conversion, the signal-to-noise ratio constraint becomes a convex constraint. To ensure that the solution is obtained Can be decomposed into ,need The rank is 1, and the rank-1 constraint is equivalent to: Perform a first-order Taylor expansion in, Represents optimization variables In the The eigenvector corresponding to the largest eigenvalue at the next iteration; the second optimization problem P2 is transformed into problem P2.1, expressed as ， The optimal solution is obtained by using convex optimization problem solving software. Optimal results can be obtained through matrix decomposition. .

6. A transmission-type RIS-assisted MIMO multibeam alignment method, characterized in that, The transmissive RIS-assisted MIMO multi-beam alignment system according to any one of claims 1-5 is used to assist the MIMO multi-antenna system in jointly performing beamforming. The desired beam direction of the multi-target is obtained by using the minimum value among the beam gains of maximizing the beam direction of the multi-target as the performance index and limiting the multi-beam width.

7. The transmission-type RIS-assisted MIMO multi-beam alignment method according to claim 6, characterized in that, The method includes the following steps: Active beamforming steps: The base station generates control signals for the MIMO multi-antenna array, and controls the amplitude and phase of the MIMO multi-antenna array through digital beamforming to perform active beamforming; Passive beamforming steps: The FPGA intelligent controller generates control signals for the transmission units. The transmissive RIS panel controls the amplitude and phase of each transmission unit through the FPGA intelligent controller to perform passive beamforming.

8. The transmission-type RIS-assisted MIMO multi-beam alignment method according to claim 7, characterized in that, In the active beamforming step, the radar base station generates an active beamforming control signal and obtains the multi-antenna signal after active beamforming by the base station through the base station baseband signal. In the passive beamforming step, the transmission RIS system generates a passive beamforming control signal. The transmission RIS system performs passive beamforming on the active beamforming signal of the multi-antenna array and controls the transmission RIS coefficient matrix. Establish the amplitude and phase of all elements to align the transmitted signal with multiple targets.

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