5G multi-frequency antenna and signal adjusting method

Abstract

This invention relates to the field of multi-frequency antenna technology, specifically a 5G multi-frequency antenna and signal conditioning method, including a reconfigurable metasurface antenna array, an intelligent signal conditioning module, and an environmental sensing and control module. By dynamically adjusting the electromagnetic characteristics of the metasurface units and employing an adaptive signal processing algorithm based on reinforcement learning, dynamic frequency band coverage, intelligent signal conditioning, and high energy efficiency can be achieved, making it widely applicable in the 5G communication field. It effectively addresses the shortcomings of existing technologies in terms of multi-frequency band coverage, signal conditioning capabilities, hardware complexity, and energy efficiency.

Description

Technical Field

[0001] This invention relates to the field of multi-frequency antenna technology, and more specifically, to a 5G multi-frequency antenna and a signal conditioning method. Background Technology

[0002] With the rapid development of 5G communication technology, the performance requirements for antennas are becoming increasingly stringent. Traditional multi-frequency antennas are typically designed to cover fixed frequency bands (such as Sub-6GHz or millimeter waves), making it difficult to dynamically adapt to the needs of different 5G frequency bands. Furthermore, existing signal conditioning techniques mainly rely on fixed algorithms, which cannot optimize signal processing strategies in real time according to environmental changes. In complex urban environments, signal interference sources and user distribution change dynamically, making it difficult for traditional methods to adjust beamforming and interference suppression strategies in real time, leading to a decline in communication quality. Therefore, to address the aforementioned problems, this paper proposes a 5G multi-frequency antenna and signal conditioning method that improves and solves the problems in terms of multi-band coverage and signal conditioning capabilities. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a 5G multi-frequency antenna and signal conditioning method to solve the problems existing in the background technology.

[0004] The above-mentioned technical objective of the present invention is achieved through the following technical solution: a 5G multi-frequency antenna, comprising a multi-frequency antenna body; the multi-frequency antenna body is provided with: a reconfigurable metasurface antenna array, an intelligent signal conditioning module for adjusting the reconfigurable metasurface antenna array according to the received signal strength, and an environmental sensing and control module for adjusting the reconfigurable metasurface antenna array according to environmental sensing data; the reconfigurable metasurface antenna array is detachably connected to the multi-frequency antenna body; the intelligent signal conditioning module and the environmental sensing and control module are respectively detachably connected to the multi-frequency antenna body; the intelligent signal conditioning module and the environmental sensing and control module are respectively electrically connected to the reconfigurable metasurface antenna array.

[0005] Optionally, the reconfigurable metasurface antenna array includes a plurality of metasurface elements; each of the metasurface elements is detachably connected to the multi-frequency antenna body; the metasurface elements are equidistantly arranged on the multi-frequency antenna body; and the metasurface elements are electrically connected to the intelligent signal conditioning module and the environmental sensing and control module, respectively.

[0006] Optionally, the metasurface unit includes a metal patch, a dielectric substrate, and a tunable element for changing the resonant frequency and phase response; the dielectric substrate is detachably connected to the multi-frequency antenna body; the metal patch is disposed on the upper surface of the dielectric substrate; and the tunable element is embedded in the dielectric substrate.

[0007] Optionally, the intelligent signal conditioning module includes: a signal receiving unit for receiving and initially processing signals transmitted by the reconfigurable metasurface antenna array; an intelligent signal processing unit for processing the data transmitted by the signal receiving unit and outputting an adjustment strategy; and a signal transmitting unit for amplifying the power and adjusting the phase of the signal according to the adjustment strategy output by the intelligent signal processing unit. The receiving end of the signal receiving unit is electrically connected to the reconfigurable metasurface antenna array, and the transmitting end of the signal receiving unit is electrically connected to the receiving end of the intelligent signal processing unit. The transmitting end of the intelligent signal processing unit is electrically connected to the signal transmitting unit. The signal transmitting unit is electrically connected to the reconfigurable metasurface antenna array.

[0008] Optionally, the environmental sensing and control module includes several environmental sensors for identifying and acquiring external information and a dynamic adjustment unit for dynamically adjusting based on the acquired external information; the several environmental sensors are respectively disposed on the multi-frequency antenna body; the input terminal of the dynamic adjustment unit is electrically connected to the several environmental sensors respectively, and the output terminal of the dynamic adjustment unit is electrically connected to the reconfigurable metasurface antenna array.

[0009] A signal conditioning method for a 5G multi-frequency antenna based on the above includes,

[0010] Step A, Receiving Signals: The signal is received through the reconfigurable metasurface antenna array and transmitted to the intelligent signal conditioning module;

[0011] Step B, Signal Preprocessing: The signal receiving unit preprocesses the received signal to obtain noise-removed signal data;

[0012] Step C, Signal Feature Extraction: The intelligent signal processing unit extracts features from the noise-removed signal data to obtain the corresponding feature vectors;

[0013] Step D: Construct a signal conditioning model, input the feature vector, and obtain the conditioning strategy: The intelligent processing unit constructs a signal conditioning model and inputs the corresponding feature vector into the constructed signal conditioning model to obtain the corresponding adjustment strategy;

[0014] Step E: Signal modulation and enhancement based on the modulation strategy: The signal transmission unit processes and enhances the signal based on the derived modulation strategy before transmitting it.

[0015] Optionally, in step C, the intelligent signal processing unit performs feature extraction on the noise-removed signal data to obtain the corresponding feature vector. The specific implementation process is as follows:

[0016] C1, The received multi-band signal is represented as Where s i(t) represents the signal in the i-th frequency band;

[0017] C2. Normalize the received signal. Where μ y The mean of the signal, σ y It is expressed as the standard deviation of the signal;

[0018] C3. Perform a short-time Fourier transform on the normalized signal to extract time-frequency features. in, Represented as wavelet basis functions, where a is the scaling parameter and b is the translation parameter;

[0019] C4. Convert the extracted time-frequency features into feature vectors X, which will be used as the model input in step D.

[0020] Optionally, in step D, constructing a signal conditioning model involves inputting the feature vector to derive a conditioning strategy: the intelligent processing unit constructs a signal conditioning model and inputs the corresponding feature vector into the constructed model to derive the corresponding conditioning strategy. The specific implementation process is as follows:

[0021] D1. Define the state space S of the signal conditioning model, including the current signal strength, the location of the interference source, and user distribution information, denoted as s. t = {x, I, U}, where I is the interference source location matrix and U is the user identification matrix;

[0022] D2. Define the action space A of the signal conditioning model, including the configuration of the metasurface units, signal separation parameters, and beamforming parameters, denoted as a. t ={C,P,B}, where C is the metasurface unit configuration matrix, P is the signal separation parameter matrix, and B is the beamforming parameter matrix;

[0023] D3. Design the reward function R for the signal conditioning model to evaluate the signal processing effect, denoted as R(s). t ,a t )=α·SINR+β·EnergyEfficiency+γ·UserSatisfaction, where SINR is the signal-to-noise ratio, EnergyEfficiency is the energy efficiency ratio, UserSatisfaction is the user satisfaction, and α, β, and γ are the weight coefficients of each item;

[0024] D4. Optimize the signal conditioning model using a deep reinforcement learning algorithm. Through interaction with the environment, derive the optimal signal processing strategy, expressed as follows: Where γ is the discount factor and T is the time step;

[0025] D5. Input the acquired feature vector X of the corresponding signal into the adjusted signal conditioning model to obtain the conditioning strategy a. t .

[0026] Optionally, in step E, signal modulation and enhancement based on the adjustment strategy: the signal transmission unit processes and enhances the signal based on the derived adjustment strategy before transmitting it. The specific implementation process is as follows:

[0027] E1. Adjustment strategy a derived from step D t The received signal is separated and represented as Where f i Represented as a signal separation function, θ i For separation parameters;

[0028] E2. Enhance the separated signal to improve its signal-to-noise ratio and anti-interference capability, as shown in the following diagram. Where g i Represented as a signal enhancement function, φ i Represented as enhancement parameters;

[0029] E3. The enhanced signal is transmitted through the signal transmission unit.

[0030] Optionally, the signal separation function in step E1 is a signal separation model based on a deep neural network, and its expression is f. i (y(t); θ i ) = NN i (y(t); θ i ), where NN i Represented as a deep neural network, θ i Represented as network parameters.

[0031] In summary, the present invention has the following beneficial effects:

[0032] 1. The multi-frequency antenna body adopts reconfigurable metasurface technology, which can dynamically adjust the antenna's operating frequency band to adapt to the needs of different 5G frequency bands; in wide-area coverage scenarios, the antenna can switch to the Sub-6GHz frequency band; in high-density hotspot areas, the antenna can switch to the millimeter-wave frequency band; the dynamic switching capability significantly improves the antenna's flexibility and resource utilization.

[0033] 2. During the operation of the antenna, the antenna can intelligently adjust the signal. It adopts an adaptive signal processing algorithm based on reinforcement learning, which can optimize the signal processing strategy in real time according to environmental changes. In complex urban environments, the system can adjust beamforming and interference suppression strategies in real time, significantly improving communication quality and user experience.

[0034] 3. Through intelligent signal conditioning and environmental perception, the system's energy consumption is reduced and the energy efficiency ratio is improved; the antenna system can dynamically adjust the transmission power according to user distribution and signal strength, reducing unnecessary energy consumption. Attached Figure Description

[0035] Figure 1 This is a schematic flowchart of the signal conditioning method of the present invention. Detailed Implementation

[0036] To make the objectives, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein.

[0037] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances. The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature.

[0038] In this invention, unless otherwise expressly specified and limited, "above" or "below" a second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of a second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" of a second feature includes the first feature being directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature. The terms "vertical," "horizontal," "left," "right," "above," "below," and similar expressions are for illustrative purposes only and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed or operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0039] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0040] This invention provides a 5G multi-frequency antenna and a signal conditioning method, such as... Figure 1 As shown, the antenna includes a multi-frequency antenna body; the multi-frequency antenna body is equipped with: a reconfigurable metasurface antenna array, an intelligent signal conditioning module for adjusting the reconfigurable metasurface antenna array according to the received signal strength, and an environmental sensing and control module for adjusting the reconfigurable metasurface antenna array according to environmental sensing data; the reconfigurable metasurface antenna array is detachably connected to the multi-frequency antenna body; the intelligent signal conditioning module and the environmental sensing and control module are respectively detachably connected to the multi-frequency antenna body; the intelligent signal conditioning module and the environmental sensing and control module are respectively electrically connected to the reconfigurable metasurface antenna array.

[0041] In the specific implementation process, when receiving signals, the reconfigurable metasurface antenna array is used to adjust different frequency bands to receive signals of different frequency bands. After receiving the signal, it is analyzed by the intelligent signal conditioning module, and the signal is enhanced and then transmitted to achieve better communication quality. In daily operation, the operating frequency of the reconfigurable metasurface antenna array is dynamically adjusted by the environmental perception and control module to achieve better energy-saving effect.

[0042] Furthermore, the reconfigurable metasurface antenna array includes a plurality of metasurface elements; each of the metasurface elements is detachably connected to the multi-frequency antenna body; the metasurface elements are equidistantly arranged on the multi-frequency antenna body; and the metasurface elements are electrically connected to the intelligent signal conditioning module and the environmental sensing and control module, respectively.

[0043] In Embodiment 1, the size of each metasurface unit is λ / 4, where λ is the operating wavelength. During the arrangement process, several metasurface units are arranged at equal intervals to form a rectangular grid structure with a unit spacing of λ / 2 to ensure the radiation characteristics of the antenna array.

[0044] In the operating mode, the resonant frequency of the metasurface unit is adjusted to 3.5GHz in the Sub-6GHz band to achieve wide-area coverage; in the millimeter-wave band, the resonant frequency of the metasurface unit is adjusted to 28GHz to achieve high-density hotspot coverage; by adjusting the phase distribution of the metasurface unit, dynamic beamforming is achieved, improving signal coverage and anti-interference capability.

[0045] Furthermore, the metasurface unit includes a metal patch, a dielectric substrate, and a tunable element for changing the resonant frequency and phase response; the dielectric substrate is detachably connected to the multi-frequency antenna body; the metal patch is disposed on the upper surface of the dielectric substrate; and the tunable element is embedded in the dielectric substrate.

[0046] In Embodiment 2, the tunable element is a varactor diode, whose capacitance value is adjusted by an external control voltage, thereby changing the resonant frequency and phase response of the metasurface unit to receive signals of different frequencies and frequency bands.

[0047] Furthermore, the intelligent signal conditioning module includes: a signal receiving unit for receiving signals transmitted by the reconfigurable metasurface antenna array and performing preliminary processing before transmission; an intelligent signal processing unit for processing the data transmitted by the signal receiving unit and outputting an adjustment strategy; and a signal transmitting unit for amplifying the power and adjusting the phase of the signal according to the adjustment strategy output by the intelligent signal processing unit. The receiving end of the signal receiving unit is electrically connected to the reconfigurable metasurface antenna array, and the transmitting end of the signal receiving unit is electrically connected to the receiving end of the intelligent signal processing unit. The transmitting end of the intelligent signal processing unit is electrically connected to the signal transmitting unit. The signal transmitting unit is electrically connected to the reconfigurable metasurface antenna array.

[0048] In Embodiment 3, the signal receiving unit performs preliminary filtering and amplification on the received signal, and then performs normalization processing, wherein the filtering processing is expressed as follows: The received signal is bandpass filtered to remove out-of-band noise, where h(t) represents the impulse response of the bandpass filter. After filtering, the filtered signal is further amplified, denoted as y. amplified (t)=G·y filtered (t), where G represents the amplification gain; the signal is preprocessed to facilitate subsequent analysis and adjustment.

[0049] Furthermore, the environmental perception and control module includes several environmental sensors for identifying and acquiring external information and a dynamic adjustment unit for dynamically adjusting based on the acquired external information; the environmental sensors are respectively disposed on the multi-frequency antenna body; the input terminal of the dynamic adjustment unit is electrically connected to the several environmental sensors, and the output terminal of the dynamic adjustment unit is electrically connected to the reconfigurable metasurface antenna array.

[0050] In embodiment four, the environmental sensor includes a signal strength sensor for real-time measurement of the strength of the received signal, denoted as... An interference source localization sensor that uses the Doppler effect to locate the interference source is denoted as I = (x i y i , z i The user distribution sensor, which determines the user's location range by receiving signal strength indications from user equipment, is represented as U = {(x...} u y u , z uIt also obtains network load through external network feedback: the number of users and data traffic on the current network; interference source information: the location and intensity of interference sources from other base stations or devices; and user location: the precise location of user devices obtained through GPS or base station triangulation.

[0051] Based on the acquired data, the dynamic adjustment unit performs dynamic adjustments, including the following: Metasurface unit configuration adjustment: The phase distribution of the metasurface units is calculated based on the location of the interference source and the user distribution range. Where H u Let D be the channel matrix for user u. u Represented as the desired beamforming direction, dynamic beamforming is achieved by adjusting the phase distribution of the metasurface units, avoiding interference sources and covering the target user;

[0052] Based on signal strength P r and interference source strength P i Calculate the signal-to-interference-plus-noise ratio, expressed as Where P n Expressed as noise power, based on the calculated signal-to-interference-plus-noise ratio (SIR), the signal separation parameter P and beamforming parameter B are dynamically adjusted, P = f sep (SINR), B=f beam (SINR), where f sep and f beam This is a preset adjustment function, which can be automatically generated by the system or manually adjusted.

[0053] The transmit power P is dynamically adjusted based on user distribution U and network load. t , represented as Where P max P represents the maximum transmit power. req This is expressed as the power required by the user.

[0054] A signal conditioning method for a 5G multi-frequency antenna based on the above includes,

[0055] Step A, Receiving Signals: The signal is received through the reconfigurable metasurface antenna array and transmitted to the intelligent signal conditioning module;

[0056] Step B, Signal Preprocessing: The signal receiving unit preprocesses the received signal to obtain noise-removed signal data;

[0057] Step C, Signal Feature Extraction: The intelligent signal processing unit extracts features from the noise-removed signal data to obtain the corresponding feature vectors;

[0058] Step D: Construct a signal conditioning model, input the feature vector, and obtain the conditioning strategy: The intelligent processing unit constructs a signal conditioning model and inputs the corresponding feature vector into the constructed signal conditioning model to obtain the corresponding adjustment strategy;

[0059] Step E: Signal modulation and enhancement based on the modulation strategy: The signal transmission unit processes and enhances the signal based on the derived modulation strategy before transmitting it.

[0060] Optionally, in step C, the intelligent signal processing unit performs feature extraction on the noise-removed signal data to obtain the corresponding feature vector. The specific implementation process is as follows:

[0061] C1, The received multi-band signal is represented as Where s i (t) represents the signal in the i-th frequency band;

[0062] C2. Normalize the received signal. Where μ y The mean of the signal, σ y It is expressed as the standard deviation of the signal;

[0063] C3. Perform a short-time Fourier transform on the normalized signal to extract time-frequency features. in, Represented as wavelet basis functions, where a is the scaling parameter and b is the translation parameter;

[0064] C4. Convert the extracted time-frequency features into feature vectors X, which will be used as the model input in step D.

[0065] Optionally, in step D, constructing a signal conditioning model involves inputting the feature vector to derive a conditioning strategy: the intelligent processing unit constructs a signal conditioning model and inputs the corresponding feature vector into the constructed model to derive the corresponding conditioning strategy. The specific implementation process is as follows:

[0066] D1. Define the state space S of the signal conditioning model, including the current signal strength, the location of the interference source, and user distribution information, denoted as s. t = {x, I, U}, where I is the interference source location matrix and U is the user identification matrix;

[0067] D2. Define the action space A of the signal conditioning model, including the configuration of the metasurface units, signal separation parameters, and beamforming parameters, denoted as a. t ={C,P,B}, where C is the metasurface unit configuration matrix, P is the signal separation parameter matrix, and B is the beamforming parameter matrix;

[0068] D3. Design the reward function R for the signal conditioning model to evaluate the signal processing effect, denoted as R(s). t ,a t )=α·SINR+β·EnergyEfficiency+γ·UserSatisfaction, where SINR is the signal-to-noise ratio, EnergyEfficiency is the energy efficiency ratio, UserSatisfaction is the user satisfaction, and α, β, and γ are the weight coefficients of each item;

[0069] D4. Optimize the signal conditioning model using a deep reinforcement learning algorithm. Through interaction with the environment, derive the optimal signal processing strategy, expressed as follows: Where γ is the discount factor and T is the time step;

[0070] D5. Input the acquired feature vector X of the corresponding signal into the adjusted signal conditioning model to obtain the conditioning strategy a. t .

[0071] Optionally, in step E, signal modulation and enhancement based on the adjustment strategy: the signal transmission unit processes and enhances the signal based on the derived adjustment strategy before transmitting it. The specific implementation process is as follows:

[0072] E1. Adjustment strategy a derived from step D t The received signal is separated and represented as θ i ), where f i Represented as a signal separation function, θ i For separation parameters;

[0073] E2. Enhance the separated signal to improve its signal-to-noise ratio and anti-interference capability, as shown in the following diagram. Where g i Represented as a signal enhancement function, φ i Represented as enhancement parameters;

[0074] E3. The enhanced signal is transmitted through the signal transmission unit.

[0075] Optionally, the signal separation function in step E1 is a signal separation model based on a deep neural network, and its expression is f. i (y(t); θ i ) = NN i (y(t); θ i ), where NN i Represented as a deep neural network, θ i Represented as network parameters.

[0076] Optionally, the signal enhancement function in step E2 is a filter-based signal enhancement model, and its expression is: Where h i (t) represents the impulse response of the filter, φ i These are represented as filter parameters.

[0077] This invention discloses a 5G multi-frequency antenna and signal conditioning method. The multi-frequency antenna body adopts reconfigurable metasurface technology, which can dynamically adjust the antenna's operating frequency band to adapt to the needs of different 5G frequency bands. In wide-area coverage scenarios, the antenna can switch to the Sub-6GHz band; in high-density hotspot areas, the antenna can switch to the millimeter-wave band. The dynamic switching capability significantly improves the antenna's flexibility and resource utilization. During antenna operation, the antenna can intelligently adjust the signal, employing an adaptive signal processing algorithm based on reinforcement learning, which can optimize the signal processing strategy in real time according to environmental changes. In complex urban environments, the system can adjust beamforming and interference suppression strategies in real time, significantly improving communication quality and user experience. Through intelligent signal conditioning and environmental perception, the system's energy consumption is reduced, and the energy efficiency ratio is improved. The antenna system can dynamically adjust the transmission power according to user distribution and signal strength, reducing unnecessary energy consumption.

[0078] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A 5G multi-frequency antenna, characterized in that, The system includes a multi-frequency antenna body; the multi-frequency antenna body is equipped with: a reconfigurable metasurface antenna array, an intelligent signal conditioning module for adjusting the reconfigurable metasurface antenna array according to the received signal strength, and an environmental sensing and control module for adjusting the reconfigurable metasurface antenna array according to environmental sensing data; the multi-frequency antenna body can dynamically switch to the Sub-6GHz frequency band to achieve wide-area coverage, or switch to the millimeter-wave frequency band to achieve high-density hotspot area coverage; The reconfigurable metasurface antenna array is detachably connected to the multi-frequency antenna body; the intelligent signal conditioning module and the environmental sensing and control module are detachably connected to the multi-frequency antenna body; the intelligent signal conditioning module and the environmental sensing and control module are electrically connected to the reconfigurable metasurface antenna array. The reconfigurable metasurface antenna array includes a plurality of metasurface elements; each of the metasurface elements is detachably connected to the multi-frequency antenna body. A plurality of metasurface units are equidistantly arranged on the multi-frequency antenna body; the plurality of metasurface units are electrically connected to the intelligent signal conditioning module and the environmental sensing and control module respectively; the size of each metasurface unit is λ / 4, where λ is the operating wavelength; during the arrangement process, a plurality of metasurface units are arranged equidistantly to form a rectangular grid structure with a unit spacing of λ / 2 to ensure the radiation characteristics of the antenna array; The metasurface unit includes a metal patch, a dielectric substrate, and a tunable element for changing the resonant frequency and phase response. The dielectric substrate is detachably connected to the multi-frequency antenna body; The metal patch is disposed on the upper surface of the dielectric substrate; The tunable element is embedded within the dielectric substrate; The intelligent signal conditioning module includes: a signal receiving unit for receiving and pre-processing the signals transmitted by the reconfigurable metasurface antenna array; an intelligent signal processing unit for processing the data transmitted by the signal receiving unit and outputting an adjustment strategy; and a signal transmitting unit for amplifying the power and adjusting the phase of the signal according to the adjustment strategy output by the intelligent signal processing unit. The receiving end of the signal receiving unit is electrically connected to the reconfigurable metasurface antenna array, and the transmitting end of the signal receiving unit is electrically connected to the receiving end of the intelligent signal processing unit; the transmitting end of the intelligent signal processing unit is electrically connected to the signal transmitting unit; and the signal transmitting unit is electrically connected to the reconfigurable metasurface antenna array.

2. A 5G multi-frequency antenna according to claim 1, characterized in that, The environmental perception and control module includes several environmental sensors for identifying and acquiring external information and a dynamic adjustment unit for making dynamic adjustments based on the acquired external information. Several environmental sensors are respectively disposed on the multi-frequency antenna body; the input terminal of the dynamic adjustment unit is electrically connected to several environmental sensors respectively, and the output terminal of the dynamic adjustment unit is electrically connected to the reconfigurable metasurface antenna array.

3. A signal conditioning method for a 5G multi-frequency antenna according to any one of claims 1-2, characterized in that, include, Step A, Receiving Signals: The signal is received through the reconfigurable metasurface antenna array and transmitted to the intelligent signal conditioning module; Step B, Signal Preprocessing: The signal receiving unit preprocesses the received signal to obtain noise-removed signal data; Step C, Signal Feature Extraction: The intelligent signal processing unit extracts features from the noise-removed signal data to obtain the corresponding feature vectors; Step D: Construct a signal conditioning model, input the feature vector, and obtain the conditioning strategy: The intelligent processing unit constructs a signal conditioning model and inputs the corresponding feature vector into the constructed signal conditioning model to obtain the corresponding adjustment strategy; Step E: Signal modulation and enhancement based on the modulation strategy: The signal transmission unit processes and enhances the signal based on the derived modulation strategy before transmitting it; In step C, the intelligent signal processing unit extracts features from the noise-removed signal data to obtain the corresponding feature vectors. The specific implementation process is as follows. C1, The received multi-band signal is represented as ,in This is represented as the signal in the i-th frequency band; C2. Normalize the received signal. ,in It is represented as the mean of the signal. It is expressed as the standard deviation of the signal; C3. Perform a short-time Fourier transform on the normalized signal to extract time-frequency features. ,in, Represented as wavelet basis functions, where a is the scaling parameter and b is the translation parameter; C4. Convert the extracted time-frequency features into feature vectors X, which will be used as the model input in step D. In step D, constructing a signal conditioning model involves inputting feature vectors to derive a conditioning strategy: the intelligent processing unit constructs a signal conditioning model and inputs the corresponding feature vectors into the constructed model to obtain the corresponding conditioning strategy. The specific implementation process is as follows. D1. Define the state space S of the signal conditioning model, including the current signal strength, the location of the interference source, and user distribution information, represented as follows: ,in Let U be the location matrix of the interference sources, and U be the location matrix of each user. D2. Define the action space A of the signal conditioning model, including the configuration of the metasurface units, signal separation parameters, and beamforming parameters, expressed as follows: Where C is the metasurface unit configuration matrix, P is the signal separation parameter matrix, and B is the beamforming parameter matrix; D3. Design the reward function R for the signal conditioning model to evaluate the signal processing effect, expressed as: ,in The signal-to-interference-to-noise ratio (SNR) For energy efficiency ratio, For user satisfaction, α, β, and γ are the weighting coefficients for each item, respectively. D4. Optimize the signal conditioning model by using deep reinforcement learning algorithms and interacting with the environment to obtain the optimal signal processing strategy. D5. Input the acquired feature vector X of the corresponding signal into the adjusted signal conditioning model to obtain the conditioning strategy. ; The signal modulation and enhancement based on the adjustment strategy in step E is as follows: the signal transmission unit processes and enhances the signal based on the derived adjustment strategy before transmitting it. The specific implementation process is as follows. E1. Adjustment strategy derived from step D The received signal is separated and represented as ,in Represented as a signal separation function, For separation parameters; E2. Enhance the separated signal to improve its signal-to-noise ratio and anti-interference capability, as shown in the following diagram. ,in Represented as a signal enhancement function, Represented as enhancement parameters; E3. The enhanced signal is transmitted through the signal transmission unit; The signal enhancement function in step E2 is a filter-based signal enhancement model, and its expression is: , where h i (t) represents the impulse response of the filter. These are represented as filter parameters.

4. The signal conditioning method for a 5G multi-frequency antenna according to claim 3, characterized in that, The signal separation function in step E1 is a signal separation model based on a deep neural network, and its expression is: ,in Represented as a deep neural network, Represented as network parameters.

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