Metamaterial reconfigurable dual-polarized antenna array and dynamic beamforming method thereof

Abstract

The application provides a metamaterial reconfigurable dual-polarized antenna array and a dynamic beamforming method thereof, wherein the dual-polarized antenna array is composed of a metamaterial resonant unit array, a reconfigurable polarization network, an intelligent control unit and an adaptive beamforming algorithm. The application designs a metamaterial unit structure with dual-polarization characteristics, and realizes continuous adjustment of polarization states and radiation characteristics by integrating liquid metal microchannels and varactor diodes. By using a hierarchical control architecture and a deep reinforcement learning algorithm, adaptive optimization control of the antenna array is realized, while providing high-precision beamforming, fast response and high energy efficiency characteristics with low system complexity. The application can simultaneously realize flexible regulation and control of polarization states, high-precision beamforming, low system complexity, fast response, high energy efficiency and wide dynamic range, so as to meet the demand of future communication systems for high-performance antennas.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a metamaterial reconfigurable dual-polarized antenna array and a dynamic beamforming method thereof. BACKGROUND

[0002] Modern wireless communication systems require antennas to have multi-band operation capability, multi-polarization characteristics, reconfigurable function and intelligent beam control capability to adapt to the increasingly complex electromagnetic environment and diversified application scenarios. Metamaterials, as a kind of artificially designed electromagnetic materials, have electromagnetic properties that natural materials do not have, such as negative refractive index, unconventional wavefront regulation, etc. These unique properties make metamaterials have great potential in high-performance antenna design.

[0003] In the prior art, there are reconfigurable antenna arrays based on PIN diodes, phased array antennas based on liquid crystal materials, and reconfigurable metasurface beamforming systems based on MEMS switches. Among them, the reconfigurable antenna array based on PIN diodes integrates PIN diodes in the antenna radiation element or the feed network, and uses voltage to control its conduction and cutoff state, to realize the switching of the antenna operating mode. The existing scheme adopts a dual-band design, which can switch between 2.4GHz and 5.8GHz frequency bands, but due to the discrete state conversion, its flexibility and continuous regulation capability are limited.

[0004] The phased array antenna based on liquid crystal materials utilizes the dielectric anisotropy property of liquid crystal materials, and changes the electromagnetic wave propagation characteristics by controlling the arrangement of liquid crystal molecules through an external electric field, to realize continuous adjustable beam scanning. It can realize ±60° beam regulation in the Ka band, but due to the response speed of liquid crystal materials usually being in the order of milliseconds, there is a deficiency in real-time regulation performance.

[0005] The reconfigurable metasurface beamforming system based on MEMS switches integrates micro-electromechanical system (MEMS) switches into the metasurface structure, adjusts the electromagnetic response of the metasurface by controlling the switch state, to realize directional reflection of the beam. However, the reliability and service life of MEMS devices have certain limitations, and the complex control circuit also increases the overall implementation difficulty of the system.

[0006] Meanwhile, the traditional scheme also has the following problems: polarization flexibility is insufficient: most existing reconfigurable antenna systems can only support a single polarization mode or limited polarization state switching, which is difficult to adapt to complex and variable electromagnetic environments, especially in multipath propagation environments, polarization mismatch leads to serious signal attenuation. Limited beamforming precision: although the traditional phased array can realize beam scanning, the phase quantization error and inter-element coupling effect limit the precision of beamforming and the ability to control the side lobe level. High system complexity: existing reconfigurable antenna systems usually require complex control circuits and feed networks, each radiating element needs an independent control line, and the system complexity increases exponentially with the array size, greatly increasing the cost and power consumption. SUMMARY

[0007] In view of the deficiencies of the prior art, the purpose of the present application is to provide a metamaterial reconfigurable dual-polarized antenna array and its dynamic beamforming method to solve the problems raised in the background art. The present application can simultaneously realize flexible regulation and control of polarization state, high-precision beamforming, low system complexity, fast response, high energy efficiency and wide dynamic range to meet the demand for high-performance antennas in future communication systems.

[0008] In order to achieve the above-mentioned purpose, the present application is realized by the following technical scheme: a metamaterial reconfigurable dual-polarized antenna array, characterized by: a metamaterial resonant unit array, a reconfigurable polarization network, a varactor tuning network, a wireless power supply and control system, an adaptive beamforming algorithm, and system integration and packaging. The metamaterial resonant unit array is a two-dimensional or three-dimensional array composed of multiple reconfigurable metamaterial units, each unit can independently regulate the amplitude, phase and polarization characteristics of electromagnetic waves; the reconfigurable polarization network is composed of liquid metal microchannels and micro piezoelectric pumps, by controlling the distribution of liquid metal in the microchannel, the continuous adjustable polarization characteristics of the unit are realized; the varactor tuning network is integrated in the metamaterial unit, by adjusting the bias voltage to control the capacitance value of the varactor, the resonance frequency and phase response of the unit are accurately regulated; the wireless power supply and control system adopts magnetic coupling resonance wireless power supply technology and low-power Bluetooth communication to realize wireless power supply and control of the antenna unit; the adaptive beamforming algorithm is based on a deep reinforcement learning framework, combined with electromagnetic field theory and statistical signal processing methods, to realize real-time optimization and regulation of antenna array parameters; the system integration and packaging adopts a heterogeneous integration scheme combining multi-layer PCB technology and 3D printing technology.

[0009] Further, the metamaterial resonant unit adopts a three-layer structure: the top layer is a metal resonator pattern, the middle layer is a dielectric substrate, and the bottom layer is a ground plane. The metal resonator adopts an improved "X" shape structure, in which the horizontal and vertical arms correspond to two orthogonal polarization directions respectively. The horizontal and vertical arms of the metal resonator each integrate a varactor diode, and the resonant frequency and phase response can be changed by adjusting the bias voltage of the varactor diode. The varactor diodes D1 and D2 control the response characteristics of the horizontal and vertical polarizations respectively.

[0010] Further, the design parameters of the metamaterial unit are optimized through electromagnetic simulation, and the specific parameters are as follows:

[0011] Unit period: a = 0.2λ, where λ is the wavelength corresponding to the operating frequency;

[0012] Resonator arm length: L = 0.15λ, resonator arm width: w = 0.02λ;

[0013] Dielectric substrate thickness: h = 0.04λ;

[0014] Dielectric substrate material: Rogers RO4350B, εr = 3.66, tanδ = 0.0037;

[0015] Liquid metal material: gallium-indium alloy, conductivity σ = 3.4 × 10 6 S / m;

[0016] Varactor diode: Skyworks SMV1405, capacitance range: 0.63pF to 2.67pF.

[0017] Further, the reconfigurable polarization network is composed of microchannels made of PDMS material, liquid metal, and micro piezoelectric pumps. The microchannels connect the four quadrants of the resonator to form a "#" shape structure. By controlling the distribution position of the liquid metal in different channels, the response characteristics of the metamaterial unit to different polarized electromagnetic waves can be changed. When the liquid metal fills the horizontal channels and the vertical channels are empty, the unit exhibits horizontal polarization characteristics. When the liquid metal fills the vertical channels and the horizontal channels are empty, the unit exhibits vertical polarization characteristics. When the distribution ratio of the liquid metal in the two direction channels changes, any linear polarization state can be realized.

[0018] Further, the metamaterial units are arranged in a two-dimensional planar array and adopt a hybrid integrated architecture: each 3 × 3 subarray shares a control module, and multiple subarrays constitute a complete antenna system. The array design parameters are as follows:

[0019] Array size: 12 × 12, a total of 144 units;

[0020] Subarray size: 3 × 3, a total of 9 units;

[0021] Element spacing: d = 0.5λ, meet the no grating lobe condition;

[0022] Array arrangement: planar rectangular array;

[0023] Feeding mode: hybrid feeding network combining microstrip line and probe.

[0024] Further, the control system adopts a hierarchical architecture, including three levels:

[0025] Bottom layer: unit control layer, responsible for controlling the bias voltage of the varactor diode and the distribution of the liquid metal of a single metamaterial unit;

[0026] Middle layer: subarray control layer, responsible for coordinating the working state of a subarray composed of 9 units;

[0027] Top layer: system control layer, responsible for global beamforming algorithm execution and performance optimization, the control system hardware consists of a main controller and multiple sub-controllers;

[0028] The main controller communicates with the sub-controllers through a wired interface, the sub-controllers control the bias voltage of the varactor diode through a digital-to-analog converter, and control the working state of the micro piezoelectric pump through a pulse width modulation signal.

[0029] Further, the dual-polarized antenna array adopts magnetic coupling resonance wireless power supply technology, the transmitting end is set on the back of the array, composed of LC resonant circuit and driving circuit; the receiving end is integrated in each subarray control module, composed of receiving coil, rectifier circuit and power management chip, communication adopts low-power Bluetooth protocol, subarray control module and main controller form a star network topology.

[0030] A dynamic beamforming method for the above-mentioned antenna array, comprising the following steps:

[0031] S1, environment perception and modeling: obtain the current electromagnetic environment information through channel sounding, including the location of interference sources, multipath characteristics, user distribution, and establish a real-time model of the electromagnetic environment;

[0032] S2, beamforming target definition: define the optimization target of beamforming according to application requirements, including maximizing signal-to-noise ratio, minimizing interference, covering multiple users;

[0033] S3, state space definition: map the configuration parameters of the metamaterial unit to the state space to form high-dimensional control variables, the configuration parameters of the metamaterial unit being the varactor capacitance and the liquid metal distribution;

[0034] S4, reward function design: design a reward function reflecting the effect of beamforming, considering factors such as main lobe gain, side lobe level, polarization matching degree and system power consumption;

[0035] S5, reinforcement learning process: adopt deep Q network algorithm, through the interaction with the environment, constantly optimize the control strategy, realize the iterative improvement of beamforming performance;

[0036] S6, fast convergence strategy: combined with electromagnetic theory knowledge, design heuristic rules to accelerate learning convergence, improve system response speed.

[0037] Further, it also includes a metamaterial beamforming algorithm based on multi-modal reinforcement learning, which establishes a core mathematical model, the core mathematical model includes a metamaterial unit electromagnetic response model and an array far-field radiation model, the metamaterial unit electromagnetic response model defines the dual polarization coupling Jones matrix of the reconfigurable metamaterial unit:

[0038]

[0039] The array far-field radiation model improves the space-frequency-polarization joint radiation equation:

[0040]

[0041] Where Γ m,n (t) is the environment disturbance compensation factor, which satisfies:

[0042]

[0043] (H m,n is the channel impulse response).

[0044] Further, it also includes state space design, action space optimization and multi-objective reward function, the state space design is:

[0045]

[0046] -C t ∈R M×N : varactor capacitance matrix

[0047] -L t ∈{0,1} M×N : liquid metal on-off state

[0048] Polarization parameters (A xx , A yy , φ xx , φ yy )

[0049] Environment disturbance gradient

[0050] The action space optimization includes: a hierarchical action encoding mechanism is proposed:

[0051]

[0052] Capacitance adjustment: a C ∈[-0.1,0.1] M×N (relative change amount)

[0053] Liquid metal control: a L ∈{0,1} M×N (0 = off, 1 = connected)

[0054] Parameter update:

[0055] The multi-objective reward function is:

[0056]

[0057] Innovative weight strategy: (time-varying adaptive weight).

[0058] Advantages of the present application:

[0059] 1. The present application realizes continuous adjustment of polarization state through innovative design of liquid metal microfluidic network, not only supports horizontal / vertical linear polarization, but also realizes arbitrary linear polarization angle and circular / elliptical polarization, with a polarization control range of 360°, which is 200% higher than traditional solutions.

[0060] 2. Based on the phase fine control of varactor diode and the optimization of deep reinforcement learning algorithm, the beam pointing accuracy reaches ±0.5°, and the sidelobe level can be controlled below -25dB, which is 40% higher in beamforming accuracy than traditional phased arrays.

[0061] 3. The present application adopts subarray control architecture and wireless power supply technology, reducing the number of control lines by 85%, significantly reducing system complexity while maintaining fine control capability at the unit level; the response time of the liquid metal microfluidic network is on the order of 10ms, and the response time of the varactor diode tuning network is on the order of microseconds, combined with a hierarchical control architecture, realizing the unification of fast response and wide range of regulation.

[0062] 4. Compared with the traditional PIN diode scheme, the varactor diode tuning network of the present application reduces energy consumption by 70%, and the overall system efficiency is improved by 35%; mature PCB technology and 3D printing technology are adopted to avoid complex micro-nano processing technology, reduce manufacturing cost, and improve the possibility of mass production; the beamforming algorithm based on deep reinforcement learning enables the system to have strong environmental adaptability, and can automatically optimize working parameters according to changes in the electromagnetic environment, with an adaptability improvement of 150%.

[0063] 5. The application uses a single system to simultaneously realize frequency tuning, polarization control and beamforming functions, and the working frequency band can cover 3GHz-6GHz (67% relative bandwidth), which is much higher than the 10-20% bandwidth of the traditional scheme; through heterogeneous integration design, the system volume is reduced by 30%, the weight is reduced by 25%, and it is more suitable for space-limited scene application.

[0064] 6. The application significantly improves the system reliability and service life through the mechanical movement-free design of the liquid metal microfluidic network and the varactor diode, and the theoretical service life is improved to more than 100,000 hours. And the application is superior to the prior art in key performance indicators, especially in polarization flexibility, beamforming accuracy and system complexity, which provides a new technical path for future high-performance communication systems. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 The super material resonance unit structure in the application is shown in the figure;

[0066] Figure 2 The reconfigurable polarization network structure is shown in the figure;

[0067] Figure 3 The super material antenna array structure is shown in the figure;

[0068] Figure 4 The control system hierarchical architecture is shown in the figure;

[0069] Figure 5 The wireless power supply and communication system block diagram is shown in the figure;

[0070] Figure 6 The dynamic beamforming algorithm flowchart is shown in the figure;

[0071] Figure 7 The system performance test result graph is shown in the figure;

[0072] Figure 8 The radiation pattern under different working modes is shown in the figure;

[0073] Figure 9 The performance comparison between the application and the prior art is shown in the figure;

[0074] Figure 10 The algorithm flowchart in the application is shown in the figure;

[0075] Figure 11 The generated simulation comparison curve graph in the embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0076] In order to make the technical means, creative features, purposes and effects realized by the application easy to understand, the application will be further described below in combination with specific embodiments.

[0077] Please refer to Figures 1 to 11 The present application provides the following technical solutions:

[0078] Embodiment 1

[0079] The metamaterial reconfigurable dual-polarized antenna array is composed of the following parts:

[0080] a) Metamaterial resonant unit array: a two-dimensional or three-dimensional array composed of multiple reconfigurable metamaterial units, each unit capable of independently regulating the amplitude, phase, and polarization characteristics of electromagnetic waves.

[0081] b) Reconfigurable polarization network: composed of liquid metal microchannels and micro piezoelectric pumps, by controlling the distribution of liquid metal in the microchannels, the continuous adjustment of the polarization characteristics of the units is realized.

[0082] c) Varactor tuning network: integrated in the metamaterial unit, by adjusting the bias voltage to control the capacitance value of the varactor, the resonance frequency and phase response of the unit are accurately regulated.

[0083] d) Wireless power supply and control system: using magnetic coupling resonance wireless power supply technology and low-power Bluetooth communication, wireless power supply and control of the antenna unit are realized, reducing the complexity of the feed network.

[0084] e) Adaptive beamforming algorithm: based on the deep reinforcement learning framework, combining electromagnetic field theory and statistical signal processing methods, real-time optimization and regulation of antenna array parameters are realized.

[0085] f) System integration and packaging: using a heterogeneous integration scheme combining multi-layer PCB technology and 3D printing technology, the system is miniaturized, lightweight, and highly reliable.

[0086] This embodiment explains the structure of the above-mentioned metamaterial reconfigurable dual-polarized antenna array as follows:

[0087] a) Metamaterial resonant unit design

[0088] In this embodiment, a new type of dual-polarized metamaterial resonant unit is designed, and the core structure is as shown in Figure 1 The unit adopts a three-layer structure: the top layer is a metal resonator pattern, the middle layer is a dielectric substrate, and the bottom layer is a ground plane. The metal resonator adopts an improved "X" shape structure, where the horizontal and vertical arms correspond to two orthogonal polarization directions. The horizontal and vertical arms of the metal resonator each integrate a varactor, which can change the resonance frequency and phase response by adjusting its bias voltage. Varactors D1 and D2 control the response characteristics of horizontal and vertical polarization respectively. In addition, the four quadrant regions of the resonator are connected through a reconfigurable polarization network composed of liquid metal microchannels.

[0089] The design parameters of the metamaterial unit are optimized through electromagnetic simulation, and the specific parameters are as follows:

[0090] • Unit period: a = 0.2λ (λ is the wavelength corresponding to the working frequency)

[0091] • Resonator arm length: L = 0.15λ • Resonator arm width: w = 0.02λ

[0092] • Dielectric substrate thickness: h = 0.04λ

[0093] • Dielectric substrate material: Rogers RO4350B (εr = 3.66, tanδ = 0.0037)

[0094] • Liquid metal material: Gallium-Indium alloy (conductivity σ = 3.4 × 10^6 S / m)

[0095] • Varactor diode: Skyworks SMV1405 (capacitance range: 0.63 pF to 2.67 pF)

[0096] b) Reconfigurable polarization network

[0097] The reconfigurable polarization network is realized using microfluidic technology, as shown in Figure 2 . The network is composed of microchannels made of PDMS material (polydimethylsiloxane), liquid metal, and a micro piezoelectric pump. The microchannels connect the four quadrants of the resonator, forming a "#" structure. By controlling the distribution position of the liquid metal in different channels, the response characteristics of the metamaterial unit to different polarized electromagnetic waves can be changed. When the liquid metal fills the horizontal channels and the vertical channels are empty, the unit exhibits horizontal polarization characteristics; when the liquid metal fills the vertical channels and the horizontal channels are empty, the unit exhibits vertical polarization characteristics; when the distribution ratio of the liquid metal in the two direction channels changes, any linear polarization state can be achieved; in particular, when a phase delay is introduced in a specific channel, circular or elliptical polarization states can also be achieved.

[0098] c) Array structure design

[0099] The metamaterial units are arranged in a two-dimensional planar array, as shown in Figure 3 . In order to reduce the coupling effect between units and improve the integration degree of the system, a hybrid integration architecture is adopted: each 3 × 3 subarray shares a control module, and multiple subarrays constitute the complete antenna system. The array design parameters are as follows:

[0100] • Array size: 12 × 12 (144 units)

[0101] • Subarray size: 3 × 3 (9 units)

[0102] • Unit spacing: d = 0.5λ (satisfying the condition of no grating lobes)

[0103] • Array arrangement: planar rectangular array

[0104] • Feeding method: hybrid feeding network combining microstrip line and probe

[0105] d) Control system architecture The control system adopts a hierarchical architecture, as shown in Figure 4 , including three levels:

[0106] 1. Bottom layer: unit control layer, responsible for controlling the bias voltage of the varactor diode and the distribution of the liquid metal of a single metamaterial unit

[0107] 2. Middle layer: subarray control layer, responsible for coordinating the working state of a subarray composed of 9 units

[0108] 3. Top layer: system control layer, responsible for global beamforming algorithm execution and performance optimization

[0109] The main controller communicates with the sub-controllers through a wired interface (SPI or I2C), the sub-controllers control the bias voltage of the varactor diode through a digital-to-analog converter, and control the working state of the micro piezoelectric pump through a pulse width modulation signal.

[0110] e) Wireless power supply and communication unit

[0111] To reduce system complexity, the invention adopts magnetic coupling resonance wireless power supply technology, as shown in Figure 5 . The transmitting end is set on the back of the array, composed of LC resonant circuit and driving circuit; the receiving end is integrated in each subarray control module, composed of receiving coil, rectifier circuit and power management chip.

[0112] The communication adopts low-power Bluetooth protocol, and the subarray control module and the main controller form a star network topology, realizing efficient transmission of configuration instructions.

[0113] Example 2

[0114] This embodiment also provides a dynamic beamforming method based on deep reinforcement learning, which combines electromagnetic field theory and statistical signal processing technology to realize adaptive optimization control of antenna array parameters. The method includes the following steps:

[0115] 1. Environment perception and modeling: obtain the current electromagnetic environment information through channel detection, including interference source position, multipath characteristics, user distribution, etc., and establish a real-time model of the electromagnetic environment.

[0116] 2. Beamforming objective definition: Define the optimization objectives of beamforming according to application requirements, such as maximizing signal-to-noise ratio, minimizing interference, covering multiple users, etc.

[0117] 3. State space definition: Map the configuration parameters of metamaterial units (varactor capacitance and liquid metal distribution) to state space, forming high-dimensional control variables.

[0118] 4. Reward function design: Design a reward function that reflects the effect of beamforming, taking into account factors such as main lobe gain, side lobe level, polarization matching degree, and system power consumption.

[0119] 5. Reinforcement learning process: Use the deep Q network (DQN) algorithm to continuously optimize the control strategy through interaction with the environment, achieving iterative improvement of beamforming performance.

[0120] 6. Fast convergence strategy: Combine electromagnetic theory knowledge to design heuristic rules to accelerate learning convergence and improve system response speed.

[0121] Metamaterial beamforming algorithm based on multi-modal reinforcement learning

[0122] 1. Core mathematical model innovation

[0123] 1.1 Electromagnetic response model of metamaterial unit

[0124] Define the dual-polarization coupled Jones matrix of the reconfigurable metamaterial unit:

[0125]

[0126] In this embodiment:

[0127] - Introduce dynamic phase compensation term and cross-polarization coupling term

[0128] - Time-varying parameters are jointly controlled by varactor (capacitance C m,n ) and liquid metal (inductance L m,n )

[0129] 1.2 Array far-field radiation model

[0130] Improved space-frequency-polarization joint radiation equation:

[0131]

[0132] where Γ m,n (t) is the environmental disturbance compensation factor, satisfying:

[0133]

[0134] (Hm,n for channel impulse response)

[0135] 2. Reinforcement learning framework innovation

[0136] 2.1 State space design

[0137]

[0138] -C t ∈R M×N : varactor capacitance matrix - L t ∈{0,1} M×N : liquid metal on / off state polarization parameter (A xx , A yy , φ xx , φ yy )

[0139] environment disturbance gradient

[0140] 2.2 Action space optimization

[0141] Proposed hierarchical action encoding mechanism:

[0142]

[0143] - Capacitance adjustment: a C ∈[-0.1,0.1] M×N (relative change amount)

[0144] - Liquid metal control: a L ∈{0,1} M×N (0 = off, 1 = on) - Polarization parameter update:

[0145] 2.3 Multi-objective reward function

[0146]

[0147]

[0148] Innovative weight strategy: (time-varying adaptive weights)

[0149] 3. Algorithm flow

[0150] Hybrid learning architecture:

[0151] - Scenario adaptive switching between centralized DQN (suitable for static scenarios) and distributed MADDPG (suitable for dynamic multi-user)

[0152] - Innovative network structure:

[0153]

[0154] This code defines the structure of a hybrid neural network model, HybridNN. This model combines:

[0155] • Graph Attention Layers (GraphAttentionLayer): to understand the spatial structure or relationships of the input.

[0156] • LSTM layers (nn.LSTM): to handle sequential information, particularly time-series features related to "polarization states".

[0157] • Dual Heads MLP (DualHeadsMLP): to implement an Actor-Critic architecture, outputting both action decisions (policy) and value estimates.

[0158] This structure suggests that the model may be designed to solve a complex task that involves both spatial correlations and temporal sequence dependencies, and requires decision-making through reinforcement learning

[0159] 2. Three-dimensional performance evaluation system:

[0160] - Electromagnetic performance (gain / sidelobe)

[0161] - Communication quality (BER / throughput)

[0162] - Energy efficiency (Watt / bit)

[0163] Evaluation indicators:

[0164]

[0165] 4.1 Comparison with existing technologies

[0166] Technical index Conventional method (such as SMI) The present application Response time > 100 ms <5ms Polarization matching degree 0.6-0.8 0.92+0.05 Multi-user interference suppression ratio 15 dB 28 dB Power consumption 40W 22W

[0167] The generated experimental data is shown in Figure 11 .

[0168] Example 3

[0169] This embodiment also provides a beamforming algorithm based on intelligent metasurface dynamic regulation, which combines polarization regulation and spatial beamforming, and realizes joint optimization under the guidance of deep reinforcement learning and heuristic rules, thereby improving the anti-interference and spectrum utilization performance of the wireless communication system. The mathematical model, state-action definition, reward design, optimization target, and overall closed-loop optimization process of the algorithm are described in detail below.

[0170] 1. Mathematical model construction

[0171] 1.1 Unit response model of metamaterial array

[0172] For an IRS consisting of M x N units, the electromagnetic response of each unit is described by a Jones matrix. Let the response of the (m, n) unit at frequency ω be:

[0173]

[0174] where A ij denotes the amplitude response, φ ij denotes the phase response, i, j ∈ {x, y} correspond to different polarization components.

[0175] 1.2 Far-field radiation pattern of array

[0176] The far-field radiation pattern F(θ, φ) of the entire IRS array can be represented as the superposition of all unit responses, considering the incident field E inc

[0177] and the corresponding phase compensation:

[0178]

[0179] where:

[0180] · k0 is the free-space wave number,

[0181] · d x , d y are the spacings of the array units in x and y directions, respectively,

[0182] · θ and φ are the zenith and azimuth angles of the radiation direction, respectively.

[0183] 2. Definition of joint optimization problem

[0184] To achieve polarization control and beamforming simultaneously, this embodiment constructs a joint optimization problem, whose goal is to optimize the reflection coefficients of the IRS (intelligent surface), thereby maximizing the main lobe gain while suppressing the side lobe interference, and achieving optimal polarization matching under the conditions of satisfying the power consumption and energy constraints.

[0185] The specific objective function is defined as:

[0186]

[0187] where the expression of SINR is:

[0188]

[0189] · h total = H d + Hr ΦH i denotes the total channel,

[0190] • Φ is the IRS reflection coefficient matrix, in block form

[0191]

[0192] where the elements of each sub-matrix are given by

[0193]

[0194] and satisfy the energy conservation constraint:

[0195] |β HH | 2 +|β HV | 2 ≤1,|β VH | 2 +|β VV | 2 ≤1,θ pq ∈[0,2π)

[0196] In addition, the embodiment introduces an energy efficiency index in joint optimization:

[0197]

[0198] where R represents the communication rate, P t and P IRS are the transmit power and IRS power consumption, respectively.

[0199] 3. State-action and reward design under the reinforcement learning framework

[0200] To achieve adaptive optimization, the application defines the state space S, action space A and reward function R under the reinforcement learning framework:

[0201] 3.1 Definition of state space

[0202] The state space SS is composed of the parameter states of each metasurface unit:

[0203] S={s t ∣s t =[C 1,1 ,C 1,2 ,…,C M,N ,L 1,1 ,L 1,2 ,…,L M,N}

[0204] Where:

[0205] • C m,nThis represents the capacitance value of the variable capacitor in the (m,n)th unit.

[0206] ·L m,n This represents parameters (or other variables related to polarization control) that are associated with the distribution state of liquid metal.

[0207] 3.2 Definition of Action Space

[0208] The action space AA represents the adjustment amount for the above state parameters:

[0209] A={a t |a t =[ΔC 1,1 ,…,ΔC M,N ,ΔL 1,1 ,…,ΔL M,N ]}

[0210] Where ΔC m,n and ΔL m,n These represent adjustments to the parameters of the (m,n)th unit.

[0211] 3.3 Reward Function Design

[0212] Reward function R(s) t ,a t Taking into account main lobe gain, side lobe suppression, polarization matching degree, and energy consumption cost, the design is as follows:

[0213] R(s t ,a t )=w1·G main -w2·G side +w3·P match -w4·P power

[0214] in:

[0215] ·G main Indicates the main lobe gain;

[0216] ·G side This indicates the sidelobe level, and generally, the lower the better.

[0217] ·P match Indicates the polarization matching degree;

[0218] ·P power Indicates system power consumption;

[0219] ·w i It is a positive weighting coefficient used to balance various indicators.

[0220] 4. Optimize processes and implement closed-loop control

[0221] The application adopts a hybrid optimization method combining deep reinforcement learning (such as deep deterministic policy gradient DDPG) and heuristic rules to form a closed-loop dynamic regulation process. The overall process mainly includes the following links:

[0222] 1. Environment perception

[0223] Real-time environmental data is obtained through channel sounding, a system state model is established, and a state vector sts_t is constructed.

[0224] 2. State evaluation and target determination

[0225] According to the current state and user demand, the target parameters of beamforming and polarization regulation are determined.

[0226] 3. Action selection and parameter update

[0227] The optimal action ata_t is selected by the policy network, and the action is mapped to the IRS configuration parameters. The output is further refined by heuristic rules (such as phase compensation, array weighting, and polarization matching rules), and the IRS unit parameters are updated.

[0228] 4. Performance evaluation and reward feedback

[0229] The performance indicators such as SINR and spectral efficiency of the system are calculated, and the reward function is fed back to the policy network to adjust and optimize the strategy.

[0230] 5. Closed-loop adaptive update

[0231] When the system performance does not meet the preset target, enter the optimization cycle; when the target is reached, save the optimal configuration, and restart the regulation process when the environment changes

[0232] Embodiment 4

[0233] The embodiment also provides the following scheme: in the design of metamaterial resonance units, a multi-resonant ring structure can be used instead of a "cross" resonator. Specifically, three nested split ring resonators (SRRs) are used, and a varactor diode is integrated on each ring to control the frequency and phase by controlling the resonant characteristics of each ring. The polarization network uses a PIN diode switch array instead of a liquid metal microchannel to change the current path by controlling the switch state and achieve polarization characteristic regulation. The advantage of this alternative scheme is that the manufacturing process is more mature and does not require complex microfluidic structures; the disadvantage is that the polarization regulation presents a discrete state, which is less flexible than the liquid metal scheme, and the PIN diode introduces a large loss, reducing the energy efficiency.

[0234] The key parameters of the alternative scheme are as follows:

[0235] • Resonant ring diameters: outer ring D1 = 0.18λ, middle ring D2 = 0.12λ, inner ring D3 = 0.08λ

[0236] • Ring width: w = 0.01λ

[0237] • PIN diode: BAR64-02V (switching time < 100 ns)

[0238] • Control lines: 8 control lines per unit (5 more than the original scheme).

[0239] Example 5

[0240] In this embodiment, in terms of beamforming algorithm, a genetic algorithm combined with a fast electromagnetic calculation model can be used instead of a deep reinforcement learning method. Specifically, an equivalent circuit model of the metamaterial unit is established, the array radiation characteristics are quickly calculated based on this model, and then the genetic algorithm is used to search for the optimal parameter configuration.

[0241] This scheme defines a chromosome code to represent the parameters of the metamaterial unit, and continuously evolves through operations such as crossover and mutation, with a specific objective function (such as main lobe gain, side lobe level, etc.) as the fitness evaluation standard, iteratively searching for the optimal solution.

[0242] The key parameters of the genetic algorithm are set as follows:

[0243] Population size: 200

[0244] Crossover probability: 0.8

[0245] Mutation probability: 0.1

[0246] Maximum number of iterations: 500

[0247] Fitness function: F = a · G_main - β · G_side + γ · P_match - δ · P_power

[0248] The advantages of this alternative scheme are good algorithm stability and less likely to fall into local optimum; the disadvantages are higher computational complexity, slightly worse real-time performance, and less adaptive ability to dynamic environment than the reinforcement learning method. The schemes provided in Example 4 and Example 5 can achieve the technical goal of the present application to some extent, but there are differences in performance, complexity, or cost compared to the optimal scheme of the present application. They can be used as technical alternatives of the present application and are suitable for different application scenarios and manufacturing conditions

[0249] The basic principles and main features of the present application and the advantages of the present application have been shown and described, and it is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0250] Furthermore, it should be understood that although the specification is described in terms of embodiments, not every embodiment includes only one independent technical solution, and the specification is described in this way only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that those skilled in the art can understand.

Claims

1. A metamaterial reconfigurable dual-polarized antenna array, characterized in that: The system comprises a metamaterial resonant unit array, a reconfigurable polarization network, a varactor diode tuning network, a wireless power supply and control system, an adaptive beamforming algorithm, and system integration and packaging. The metamaterial resonant unit array is a two-dimensional or three-dimensional array composed of multiple reconfigurable metamaterial units, each capable of independently controlling the amplitude, phase, and polarization characteristics of electromagnetic waves. The reconfigurable polarization network consists of liquid metal microchannels and micro-piezoelectric pumps, achieving continuous adjustment of unit polarization characteristics by controlling the distribution of liquid metal within the microchannels. The varactor diode tuning network is integrated within the metamaterial units, controlling the varactor diode capacitance value by adjusting the bias voltage to achieve precise control of the unit's resonant frequency and phase response. The wireless power supply and control system employs magnetically coupled resonant wireless power supply technology and low-power Bluetooth communication to achieve wireless power supply and control of the antenna units. The adaptive beamforming algorithm, based on a deep reinforcement learning framework and combining electromagnetic field theory and statistical signal processing methods, enables real-time optimization and control of antenna array parameters. The system integration and packaging adopts a heterogeneous integration solution that combines multilayer PCB technology and 3D printing technology; The metamaterial resonant unit adopts a three-layer structure: the top layer is a metal resonator pattern, the middle layer is a dielectric substrate, and the bottom layer is a ground plane. The metal resonator adopts a modified "+" shaped structure, in which the horizontal and vertical arms correspond to two orthogonal polarization directions. The horizontal and vertical arms of the metal resonator each integrate varactor diodes. By adjusting their bias voltage, the resonant frequency and phase response can be changed. The varactor diodes D1 and D2 control the response characteristics of horizontal polarization and vertical polarization, respectively. The reconfigurable polarization network consists of microchannels made of PDMS material, liquid metal, and micro piezoelectric pumps. The microchannels connect the four quadrants of the resonator to form a "#" shaped structure.

2. The metamaterial reconfigurable dual-polarized antenna array according to claim 1, characterized in that: The design parameters of the metamaterial unit were optimized through electromagnetic simulation. The specific parameters are as follows: Unit period: a = 0.2λ, where λ is the wavelength corresponding to the operating frequency; Resonator arm length: L=0.15λ, resonator arm width: w=0.02λ; Dielectric substrate thickness: h = 0.04λ; Dielectric substrate material: Rogers RO4350B, εr=3.66, tanδ=0.0037; Liquid metal material: gallium-indium alloy, conductivity σ = 3.4 × 10⁻⁶ 6 S / m; Varactor diode: Skyworks SMV1405, capacitance range: 0.63pF to 2.67pF.

3. The metamaterial reconfigurable dual-polarized antenna array according to claim 1, characterized in that: By controlling the distribution position of liquid metal in different channels, the response characteristics of metamaterial units to electromagnetic waves with different polarizations can be changed. When the liquid metal fills the horizontal channel and the vertical channel is empty, the unit exhibits horizontal polarization characteristics; when the liquid metal fills the vertical channel and the horizontal channel is empty, the unit exhibits vertical polarization characteristics; when the distribution ratio of liquid metal in the two directional channels changes, arbitrary linear polarization states can be achieved.

4. The metamaterial reconfigurable dual-polarized antenna array according to claim 1, characterized in that: The metamaterial units are arranged in a two-dimensional planar array and adopt a hybrid integrated architecture: each 3×3 subarray shares a control module, and multiple subarrays constitute a complete antenna system. The array design parameters are as follows: Array size: 12×12, totaling 144 units; Subarray size: 3×3, totaling 9 elements; The element spacing is d = 0.5λ, which satisfies the condition of no grid lobe. Array arrangement: planar rectangular array; Feeding method: Hybrid feeding network combining microstrip lines and probes.

5. The metamaterial reconfigurable dual-polarized antenna array according to claim 1, characterized in that, The control system adopts a hierarchical architecture, including three levels: Bottom layer: Unit control layer, responsible for controlling the varactor diode bias voltage and liquid metal distribution of individual metamaterial units; Middle layer: Subarray control layer, responsible for coordinating the working status of the subarray consisting of 9 units; Top layer: System control layer, responsible for global beamforming algorithm execution and performance optimization. The control system hardware consists of a main controller and multiple sub-controllers; The main controller communicates with the sub-controller via a wired interface. The sub-controller controls the bias voltage of the varactor diode through a digital-to-analog converter and controls the working state of the miniature piezoelectric pump through a pulse width modulation signal.

6. The metamaterial reconfigurable dual-polarized antenna array according to claim 1, characterized in that: This dual-polarized antenna array uses magnetically coupled resonant wireless power supply technology. The transmitter is located on the back of the array and consists of an LC resonant circuit and a driving circuit. The receiver is integrated in each sub-array control module and consists of a receiving coil, a rectifier circuit, and a power management chip. Communication uses the Bluetooth Low Energy protocol. The sub-array control modules and the main controller form a star network topology.

7. A dynamic beamforming method for an antenna array as described in claim 1, characterized in that, Includes the following steps: S1. Environmental Awareness and Modeling: Obtain current electromagnetic environment information through channel detection, including the location of interference sources, multipath characteristics, and user distribution, and establish a real-time model of the electromagnetic environment; S2. Beamforming Target Definition: Based on application requirements, define the optimization targets for beamforming, including maximizing the signal-to-noise ratio, minimizing interference, and covering multiple users; S3. State Space Definition: The configuration parameters of the metamaterial unit are mapped to the state space to form high-dimensional control variables. The configuration parameters of the metamaterial unit are the varactor diode capacitance value and the liquid metal distribution. S4. Reward Function Design: Design a reward function that reflects the beamforming effect, taking into account the main lobe gain, side lobe level, polarization matching degree and system power consumption. S5. Reinforcement learning process: A deep Q-network algorithm is adopted to continuously optimize the control strategy through interaction with the environment, thereby achieving iterative improvement of beamforming performance; S6. Fast convergence strategy: Combining electromagnetic theory knowledge, heuristic rules are designed to accelerate learning convergence and improve system response speed.

8. The dynamic beamforming method according to claim 7, characterized in that: It also includes a metamaterial beamforming algorithm based on multimodal reinforcement learning. This algorithm establishes a core mathematical model, which includes an electromagnetic response model of the metamaterial unit and a far-field radiation model of the array. The electromagnetic response model of the metamaterial unit defines the dual-polarization coupling Jones matrix of the reconfigurable metamaterial unit. The improved space-frequency-polarization joint radiation equation for the array far-field radiation model is as follows: Where ω is the angular frequency, t is time, and m and n are cell indices; A ij and φ ij Let φ represent the amplitude and phase response from polarization j to polarization i, respectively, where i,j∈{x,y}; Φ is the azimuth angle; Δφ m,n For dynamic phase compensation of the (m,n)th unit; ξ m,n E represents the cross-polarization coupling phase of the (m,n)th unit; inc Θ is the complex amplitude of the incident electric field; Θ is the zenith angle of the far-field radiation direction; k0 is the free-space wavenumber; d x and d y Γ represents the spacing of the array elements in the x and y directions, respectively. m,n (t) is the environmental disturbance compensation factor, which satisfies: H m,n The channel impulse response is given, and Δt is the time interval.

9. The dynamic beamforming method according to claim 7, characterized in that: It also includes state space design, action space optimization, and a multi-objective reward function, wherein the state space design is as follows: -C t ∈R M×N Varactor diode capacitor matrix -L t ∈{0,1} M×N Liquid metal on / off state - Polarization parameter (A) xx A yy ,φ xx ,φ yy ) - Environmental disturbance gradient The action space optimization includes: proposing a hierarchical action coding mechanism. Capacitor adjustment: a C ∈[-0.1,0.1] M×N Liquid metal control: a L ∈{0,1} M×N Parameter update: The multi-objective reward function is: Innovative weighting strategy: Time-varying adaptive weights Among them, S t R: represents the system's state vector at time t; R: represents the reward function. These represent the environmental disturbance gradients at the zenith angle and azimuth angle, respectively. This represents the time differential operator.

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