Digital phase control electromagnetic surface beam forming method and system

Abstract

The invention discloses a digital phase control electromagnetic surface beam forming method and system, and the method comprises the steps: carrying out the dynamic spectrum sensing processing of an electromagnetic environment in a space range where a beam of a digital phase control electromagnetic surface needs to act or avoid, and obtaining the real-time spectrum occupancy state information; based on the real-time spectrum occupancy state information, combining historical spectrum data of the digital phase control electromagnetic surface, executing spectrum prediction and interference suppression processing, and generating optimized spectrum data; calculating a phase weight and a polarization state adjustment value of the digital phase-controlled electromagnetic surface according to the optimized spectrum data by adopting a self-adaptive optimization algorithm; and applying the phase weight and the polarization state adjustment value to the digital phase-controlled electromagnetic surface, and performing a beam forming operation. According to the invention, the anti-interference capability and the beam directivity of the digital phased antenna can be improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of antennas, in particular to a digital phased electromagnetic surface beam forming method and system. BACKGROUND

[0002] In the field of digital phased electromagnetic surface beam forming, current technologies mostly use fixed parameters or periodic adjustment to control the beam. The existing method usually first divides the available frequency band through static spectrum detection, and then determines the phase and amplitude weight of the array unit based on a preset algorithm to realize beam pointing to the target area. This approach relies on the pre-set parameter model and lacks dynamic adaptability to real-time changing electromagnetic environment; at the same time, traditional interference suppression methods mostly use single filtering or fixed coding strategy, which is difficult to cope with complex and variable interference signals.

[0003] In summary, the actual electromagnetic environment is complex and variable, and the signal interference is random and dynamic. The existing technology lacks dynamic spectrum sensing capability for the electromagnetic environment, resulting in that in the scene of complex electromagnetic environment and dense interference, the beam forming is difficult to adapt to the environmental changes, and there are problems of weak anti-interference ability and inaccurate beam pointing, which cannot meet the communication demand of high precision and high stability in modern communication. SUMMARY

[0004] The embodiments of the present application provide a digital phased electromagnetic surface beam forming method and system, which can effectively adapt to environmental changes, improve the anti-interference ability and beam pointing of the digital phased antenna, and effectively meet the communication demand of high precision and high stability in modern communication.

[0005] An embodiment of the present application provides a digital phased electromagnetic surface beam forming method based on dynamic spectrum sensing, comprising:

[0006] Performing dynamic spectrum sensing processing on the electromagnetic environment in the space range where the beam of the digital phased electromagnetic surface needs to act or avoid, to obtain real-time spectrum occupation state information;

[0007] Based on the real-time spectrum occupation state information, combining the historical spectrum data of the digital phased electromagnetic surface, performing spectrum prediction and interference suppression processing to generate optimized spectrum data;

[0008] Using an adaptive optimization algorithm, calculating the phase weight and polarization state adjustment value of the digital phased electromagnetic surface according to the optimized spectrum data;

[0009] Applying the phase weight and the polarization state adjustment value to the digital phased electromagnetic surface to perform beam forming operation.

[0010] As an improvement of the above scheme, the electromagnetic environment in the spatial range where the beam of the digital phased electromagnetic surface needs to act or avoid is dynamically spectrum-sensed to obtain real-time spectrum occupation state information, including the following sub-steps:

[0011] The spatial range where the beam of the digital phased electromagnetic surface needs to act or avoid is adaptively scanned to obtain a scanned spectrum data set;

[0012] The scanned spectrum data set is subjected to spectrum conflict detection processing to identify interference conflict frequency band information;

[0013] The interference conflict frequency band information is subjected to spectrum occupation analysis processing to generate real-time spectrum occupation state information.

[0014] As an improvement of the above scheme, based on the real-time spectrum occupation state information, combined with the historical spectrum data of the digital phased electromagnetic surface, spectrum prediction and interference suppression processing is performed to generate optimized spectrum data, including the following sub-steps:

[0015] The real-time spectrum occupation state information and the historical spectrum data of the digital phased electromagnetic surface are subjected to time-series spectrum prediction processing to obtain spectrum change trend information;

[0016] The spectrum change trend information is subjected to interference feature extraction processing to determine dynamic interference feature parameters;

[0017] Based on the dynamic interference feature parameters, online interference suppression processing is performed to generate preliminary optimized spectrum data;

[0018] The preliminary optimized spectrum data is subjected to multi-band joint coding processing to generate optimized spectrum data.

[0019] As an improvement of the above scheme, the adaptive optimization algorithm is used to calculate the phase weight and polarization state adjustment value of the digital phased electromagnetic surface according to the optimized spectrum data, including the following sub-steps:

[0020] Based on the optimized spectrum data, a multi-objective optimization problem of beam forming is defined, wherein the optimization objectives include maximum main lobe gain maximization and minimum interference suppression minimization;

[0021] An adaptive optimization algorithm is used to solve the multi-objective optimization problem to calculate the phase weight and polarization state adjustment value of the digital phased electromagnetic surface.

[0022] As an improvement of the above scheme, the phase weight and the polarization state adjustment value are applied to the digital phased electromagnetic surface to perform beam forming operation, including the following sub-steps:

[0023] configuring the phase weight and polarization state adjustment value into an array of the digital phased electromagnetic surface to generate a set of beam control parameters;

[0024] performing beam synthesis processing based on the set of beam control parameters to generate a target beam signal;

[0025] performing real-time feedback correction processing on the target beam signal to output a final beamforming signal.

[0026] Another embodiment of the present application provides a digital phased electromagnetic surface beamforming system based on dynamic spectrum sensing, comprising:

[0027] A sensing module is configured to perform dynamic spectrum sensing processing on an electromagnetic environment in a space range where a beam of a digital phased electromagnetic surface needs to act or avoid, to obtain real-time spectrum occupation state information;

[0028] A generating module is configured to perform spectrum prediction and interference suppression processing based on the real-time spectrum occupation state information and in combination with historical spectrum data of the digital phased electromagnetic surface, to generate optimized spectrum data;

[0029] A computing module is configured to calculate phase weight and polarization state adjustment values of the digital phased electromagnetic surface according to the optimized spectrum data by using an adaptive optimization algorithm;

[0030] An executing module is configured to apply the phase weight and the polarization state adjustment values to the digital phased electromagnetic surface to perform beamforming operation.

[0031] As an improvement of the above-mentioned scheme, the sensing module is specifically configured to:

[0032] perform adaptive spatial scanning processing on a space range where a beam of a digital phased electromagnetic surface needs to act or avoid, to obtain a scanning spectrum data set;

[0033] perform spectrum conflict detection processing on the scanning spectrum data set to identify interference conflict frequency band information;

[0034] perform spectrum occupation analysis processing on the interference conflict frequency band information to generate real-time spectrum occupation state information.

[0035] As an improvement of the above-mentioned scheme, the generating module is specifically configured to:

[0036] perform time-series spectrum prediction processing on the real-time spectrum occupation state information and the historical spectrum data of the digital phased electromagnetic surface to obtain spectrum change trend information;

[0037] perform interference feature extraction processing on the spectrum change trend information to determine dynamic interference feature parameters;

[0038] Performing online interference suppression processing based on the dynamic interference characteristic parameters to generate preliminary optimized spectrum data;

[0039] Performing multi-band joint coding processing on the preliminary optimized spectrum data to generate optimized spectrum data.

[0040] As an improvement of the above scheme, the computing module is specifically configured to:

[0041] Defining a multi-objective optimization problem of beamforming based on the optimized spectrum data, wherein the optimization objectives include maximum main lobe gain and minimum interference suppression;

[0042] Solving the multi-objective optimization problem by using an adaptive optimization algorithm to calculate phase weight and polarization state adjustment value of the digital phased electromagnetic surface.

[0043] As an improvement of the above scheme, the executing module is specifically configured to:

[0044] Configuring the phase weight and polarization state adjustment value to an array of the digital phased electromagnetic surface to generate a set of beam control parameters;

[0045] Performing beam synthesis processing based on the set of beam control parameters to generate a target beam signal;

[0046] Performing real-time feedback correction processing on the target beam signal to output a final beamforming signal.

[0047] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0048] Firstly, the real-time spectrum occupation state information is obtained by performing dynamic spectrum sensing on the electromagnetic environment in the space of the digital phased electromagnetic surface beam action or avoidance. This dynamic sensing method can timely capture the changes of the electromagnetic environment, and is more suitable for complex and variable actual scenes compared with the traditional static detection. Then, the real-time spectrum occupation state information is combined with historical spectrum data to perform spectrum prediction and interference suppression processing. The time dimension information of the data is fully utilized to effectively predict the spectrum change trend and suppress interference, so as to generate optimized spectrum data which is more in line with actual needs. Then, the adaptive optimization algorithm is used to calculate the phase weight and polarization state adjustment value of the digital phased electromagnetic surface according to the optimized spectrum data. The adaptive algorithm can dynamically adjust the calculation process according to the data, so that the calculation result is more accurate. Finally, the calculated phase weight and polarization state adjustment value are applied to the digital phased electromagnetic surface to realize beam forming operation. In summary, compared with the problems of weak anti-interference ability, inaccurate beam pointing and low communication efficiency in the background technology, the embodiment of the present application provides real-time data basis through dynamic sensing, enhances the adaptability to the environment through data combination and processing, guarantees the parameter calculation accuracy through adaptive algorithm, and finally realizes accurate beam forming. Therefore, the embodiment of the present application realizes dynamic adaptation to complex electromagnetic environment, improves the anti-interference ability, guarantees the accuracy of beam pointing, and further improves the communication efficiency and stability. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a flowchart of a digital phased electromagnetic surface beam forming method based on dynamic spectrum sensing provided by an embodiment of the present application.

[0050] Figure 2 is a structural diagram of a digital phased electromagnetic surface beam forming system based on dynamic spectrum sensing provided by an embodiment of the present application. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0052] Reference Figure 1 is a flowchart of a digital phased electromagnetic surface beam forming method based on dynamic spectrum sensing provided by an embodiment of the present application. The digital phased electromagnetic surface beam forming method based on dynamic spectrum sensing comprises the following steps:

[0053] S10, performing dynamic spectrum sensing processing on the electromagnetic environment in the space range where the beam of the digital phased electromagnetic surface needs to act or avoid, to obtain real-time spectrum occupation state information;

[0054] S11, based on the real-time spectrum occupation state information, combining the historical spectrum data of the digital phased electromagnetic surface, performing spectrum prediction and interference suppression processing to generate optimized spectrum data;

[0055] S12, using an adaptive optimization algorithm, calculating the phase weight and polarization state adjustment value of the digital phased electromagnetic surface according to the optimized spectrum data;

[0056] S13, applying the phase weight and the polarization state adjustment value to the digital phased electromagnetic surface to perform beamforming operation.

[0057] Compared with the prior art, the embodiment of the present application has the following beneficial effects:

[0058] Firstly, by performing dynamic spectrum sensing on the electromagnetic environment in the space where the beam of the digital phased electromagnetic surface needs to act or avoid, real-time spectrum occupation state information is obtained. This dynamic sensing method can timely capture the changes of the electromagnetic environment, and is more suitable for complex and variable actual scenes compared with traditional static detection. Then, the real-time spectrum occupation state information is combined with the historical spectrum data to perform spectrum prediction and interference suppression processing, so as to generate optimized spectrum data which is more in line with actual needs. Then, an adaptive optimization algorithm is used to calculate the phase weight and polarization state adjustment value of the digital phased electromagnetic surface according to the optimized spectrum data. The adaptive algorithm can dynamically adjust the calculation process according to the data, so that the calculation result is more accurate. Finally, the calculated phase weight and polarization state adjustment value are applied to the digital phased electromagnetic surface to realize beamforming operation. In summary, compared with the problems of weak anti-interference ability, inaccurate beam pointing and low communication efficiency in the background technology, the embodiment of the present application provides real-time data basis through dynamic sensing, enhances the adaptability to the environment through data combination and processing, guarantees the parameter calculation accuracy through adaptive algorithm, and finally realizes accurate beamforming. Therefore, the embodiment of the present application realizes dynamic adaptation to complex electromagnetic environment, improves the anti-interference ability, guarantees the accuracy of beam pointing, and further improves the communication efficiency and stability.

[0059] As one of the examples, the dynamic spectrum sensing processing on the electromagnetic environment in the space range where the beam of the digital phased electromagnetic surface needs to act or avoid to obtain real-time spectrum occupation state information includes the following sub-steps:

[0060] Performing adaptive spatial scanning processing on the space range where the beam of the digital phased electromagnetic surface needs to act or avoid to obtain a scanning spectrum data set;

[0061] performing spectrum conflict detection processing on the scanning spectrum dataset to identify interference conflict frequency band information;

[0062] performing spectrum occupation analysis processing on the interference conflict frequency band information to generate real-time spectrum occupation state information.

[0063] In this embodiment, for the electromagnetic environment in the space of digital phased electromagnetic surface wave beam action or avoidance, a hierarchical progressive spectrum sensing strategy is adopted. First, through adaptive spatial scanning processing, the scanning method is dynamically adjusted according to the change of spatial electromagnetic characteristics, the target space range is comprehensively and targetedly spectrum data collected, and the scanning spectrum dataset containing rich spatial electromagnetic information is obtained; then, based on the dataset, spectrum conflict detection processing is performed, and data feature analysis technology is used to accurately identify the frequency band information of the existing interference conflict and lock the frequency band where the interference source is located; finally, the interference conflict frequency band information is subjected to in-depth spectrum occupation analysis processing, and the use state, signal strength and other factors of the frequency band are comprehensively considered, and finally accurate real-time spectrum occupation state information is generated. The whole process from data collection, interference positioning to state analysis is closely related, and the fine perception of electromagnetic environment spectrum information is realized. In summary, through the technical combination of adaptive spatial scanning, spectrum conflict detection and spectrum occupation analysis, compared with the traditional single spectrum sensing mode, the detection accuracy and perception efficiency of the interference frequency band in the complex electromagnetic environment are significantly improved. Not only can the spectrum information of the target space be quickly and comprehensively obtained, but also the interference conflict frequency band can be accurately locked, which provides a more reliable and accurate data basis for subsequent spectrum prediction, interference suppression and beam forming, effectively enhancing the adaptability and anti-interference ability of digital phased electromagnetic surface in complex electromagnetic environment.

[0064] Among them, the specific working process of the embodiment is exemplarily:

[0065] In the actual implementation of the technical solution of the claim, the specific working process is as follows:

[0066] In the adaptive spatial scanning process to obtain the scanning spectrum dataset stage, after the system starts, the environmental feature library is constructed based on the application scene and historical data of the digital phased electromagnetic surface, and the initial scanning parameters are determined, including the preliminary spatial scanning range, the frequency starting value and the scanning direction. By controlling the phase of each array element of the phased array antenna technology of the digital phased electromagnetic surface, the space range to be acted on or avoided by the beam is scanned. In this process, the "adaptive reinforcement learning-fuzzy logic" scanning algorithm is introduced. The reinforcement learning model sets a reward mechanism, taking the amount of effective data obtained by scanning as the positive reward, and the energy consumption as the negative reward. The environmental state is the electromagnetic environmental features of the current scanning area, the remaining scanning area, etc. The Q-learning algorithm is used to update the Q value: Q(s, a) <- Q(s, a) + a [f + gmax a′ Q(s', a') - Q(s, a)], where Q(s, a) is the value of taking action a in state s, a is the learning rate, which is 0.3, r is the reward obtained after executing action a, g is the discount factor, which is 0.8, s' is the next state, and a' is the action in the next state. At the same time, combined with fuzzy logic, the environmental complexity (obtained by fuzzy processing of electromagnetic interference intensity, regional terrain, etc.) and the remaining scanning time are taken as inputs, and the scanning parameter adjustment amount such as scanning speed and angle step is taken as output. Through continuous learning and adjustment, high-precision scanning is preferentially performed on areas with high interference possibility. During scanning, electromagnetic signals in various directions are received, and after preprocessing steps such as signal amplification, filtering, and analog-to-digital conversion, data such as signal intensity and phase at different frequencies, angles, and time points are collected, forming a detailed scanning spectrum dataset.

[0067] After obtaining the scanning spectrum dataset, the frequency spectrum conflict detection processing is entered to identify the interference conflict frequency band information stage. First, the signal spectrum in the dataset is divided according to the time-frequency two-dimensional grid to form multiple frequency spectrum sub-blocks. The "adversarial generative network-migration learning" fusion spectrum conflict detection method is used. The generative adversarial network consists of a generator G and a discriminator D. The generator generates simulated spectrum data according to noise data, and the discriminator judges whether the input data is real spectrum data or generated data. During training, the goal of the generator is to generate data that can deceive the discriminator, and the goal of the discriminator is to accurately distinguish between real and generated data, and the learning ability of the model for spectrum data features is improved through the adversarial training of the two. At the same time, transfer learning is introduced, and the model parameters trained in other similar spectrum detection tasks are used as initialization to speed up the training speed and improve the detection accuracy. For each frequency spectrum sub-block, input it into the trained model, and output the probability value P conflict of the frequency band corresponding to the sub-block belonging to the conflict frequency band. conflictWhen the value of the ratio is greater than a preset probability threshold (θ), the frequency band is determined as an interference conflict frequency band, and detailed information of the conflict frequency band, such as a frequency range, a signal strength change curve, a conflict start time and an end time, is recorded.

[0068] For the identified interference conflict frequency band information, spectrum occupation analysis processing is performed to generate real-time spectrum occupation state information. For signals in the interference conflict frequency band, a technology combining deep learning feature extraction and sparse representation is used to obtain key parameters such as a modulation type, a bandwidth, a transmission period and a power change curve of the signals. A spectrum occupation degree calculation method of “multi-scale space-time correlation-dynamic weight” is proposed. Based on multi-scale analysis, the time and frequency dimensions are divided into subintervals of different scales, and the energy distribution and change trend of signals in each subinterval are calculated. The space-time correlation degree between subintervals is calculated where C ijkl is the space-time correlation degree between the jth frequency subband of the ith time scale and the lth frequency subband of the kth time scale, x ijm is the mth data point in the corresponding subinterval, is the data mean value of the corresponding subinterval, and n is the number of data points. According to the correlation degree, the dynamic weight W ij of each subinterval is determined, and the spectrum occupation degree O is calculated, where O occupy represents the spectrum occupation degree, B ij is the bandwidth occupied by the interference signal in the ith time segment and the jth frequency subband, B total is the total bandwidth of the frequency band. According to the calculated spectrum occupation degree, combined with the preset five-level occupation threshold (0-20% is idle, 21-40% is lightly occupied, 41-60% is moderately occupied, 61-80% is heavily occupied, and 81-100% is completely occupied), the corresponding occupation state is divided. At the same time, considering the change trend of the interference signal in the time and space dimensions, such as the power diffusion speed, the frequency band hopping rule and other factors, the real-time spectrum occupation state information containing detailed parameters of the interference frequency band, the occupation rate, the occupation state, the time stamp and the trend prediction is finally generated, providing a comprehensive and accurate data basis for subsequent spectrum processing.

[0069] As one of the examples, based on the real-time spectrum occupation state information, combined with the historical spectrum data of the digital phased electromagnetic surface, spectrum prediction and interference suppression processing are performed to generate optimized spectrum data, including the following sub-steps:

[0070] Time sequence spectrum prediction processing is performed on the real-time spectrum occupation state information and the historical spectrum data of the digital phased electromagnetic surface to obtain spectrum change trend information;

[0071] Perform interference feature extraction processing on the spectrum change trend information to determine a dynamic interference feature parameter;

[0072] Perform online interference suppression processing based on the dynamic interference feature parameter to generate preliminary optimized spectrum data;

[0073] Perform multi-band joint coding processing on the preliminary optimized spectrum data to generate optimized spectrum data.

[0074] In this embodiment, through the cooperative processing of time series data mining and dynamic interference suppression, the optimization efficiency of spectrum resources is improved. First, the real-time spectrum occupation state information and the historical spectrum data are fused and analyzed in the time sequence dimension, the time correlation of the spectrum data is modeled using a time series prediction algorithm (such as LSTM), and the spectrum change trend information reflecting the spectrum occupation trend is output, realizing the prediction of the future state of the electromagnetic environment; secondly, for the trend information obtained by prediction, the time-frequency domain feature parameters (such as the interference intensity change rate, the frequency band occupation time, etc.) of the dynamic interference are extracted through feature engineering method, and the interference feature model is constructed; then, based on the dynamic interference feature parameter, adaptive filtering, beam zeroing and other online interference suppression technologies are used to suppress the predicted interference frequency band in real time, and preliminary optimized spectrum data is generated; finally, the multi-band joint coding processing is implemented on the preliminary optimized spectrum data, and the interference isolation between different frequency bands is realized through orthogonal coding, frequency hopping sequence design and other ways to form the final optimized spectrum data. Therefore, by introducing time series spectrum prediction and dynamic interference feature extraction, this embodiment breaks through the limitations of traditional fixed anti-interference strategies, can predict the interference evolution trend according to historical data and real-time state and actively adjust the suppression strategy, so that the interference suppression process is upgraded from "passive response" to "active prediction". At the same time, the multi-band joint coding technology enhances the anti-interference robustness of signals in different frequency bands through the structural reorganization of spectrum resources, significantly improves the utilization efficiency of spectrum resources and the reliability of communication links compared with single frequency band processing. In addition, through the data-driven dynamic processing mechanism, the digital phased electromagnetic surface can adapt to the time-varying characteristics of the complex electromagnetic environment, providing a more pure and stable spectrum data basis for the accurate calculation of subsequent beam forming parameters.

[0075] Among them, the specific working process of this embodiment is exemplified as:

[0076] First, the system integrates the real-time spectrum occupation state information and the historical spectrum data according to the time sequence, and adopts an improved time series prediction algorithm. The algorithm is based on a hybrid memory recursive model, combines the advantages of long short-term memory network (LSTM) and gated recurrent unit (GRU), and introduces a dynamic weight adjustment mechanism. The model input is a multi-dimensional data sequence X = [x1, x2,..., xn] containing frequency, occupation rate and timestamp, and the output is the spectrum change trend information reflecting the spectrum occupation trend. tFor the input of each time step, the historical information is selectively retained or forgotten through the gating structure.

[0077] The weight adjustment formula is where w i is the weight of the i-th time step data, MSE i is the mean square error of the prediction of this time step, and γ is the adjustment parameter. This formula makes the time step data with smaller prediction error obtain higher weight, and in the training process, the model dynamically adjusts the influence degree of each time step data on the current prediction according to the historical prediction error. After repeated training and optimization, the model outputs the frequency spectrum change trend information of each frequency band in the future period of time, including the frequency occupation rate fluctuation range, the probability of occurrence of potential interference frequency band and other key contents.

[0078] Then, the system uses a multi-scale feature fusion algorithm to extract interference features from the obtained spectrum change trend information. First, the trend information is divided into multiple scale sub-regions according to the time and frequency dimensions, and time domain features (such as signal intensity change slope, fluctuation period), frequency domain features (such as harmonic component proportion, bandwidth change rate) and time-frequency domain joint features (such as energy distribution entropy after short-time Fourier transform) are extracted in each sub-region to form a feature vector set F = [f1, f2,..., f m ]. Then an adaptive threshold discrimination mechanism is introduced, and the threshold T is calculated by the formula T = μ + σ · k, where μ is the mean of the feature values, σ is the standard deviation, and k is a coefficient dynamically adjusted according to the current electromagnetic environment complexity. The environment complexity is calculated by the information entropy evaluation of the feature vector set, where p i is the probability of the i-th feature value. The relationship between k and H is a and b are adjustment parameters. When the feature value of a sub-region exceeds the corresponding dynamic threshold, it is determined that there is an interference feature in the region, and dynamic interference feature parameters such as interference center frequency, interference intensity change curve and interference frequency band duration prediction value are extracted.

[0079] Then, based on the extracted dynamic interference feature parameters, an interference suppression strategy combining intelligent reconstruction and spectrum avoidance is adopted. For narrowband interference, an improved compressive sensing algorithm is used to solve the optimization problem min xSignal reconstruction is achieved using ||x||1 st||y-Φx||2≤∈, where x is the sparse representation of the original signal, y is the observed signal, Φ is the measurement matrix, and ∈ represents the noise margin. For broadband interference, a Generative Adversarial Network (GAN) is used to generate a signal complementary to the characteristics of the interfering frequency band, using the normal frequency band signal as a reference, to cancel it out. Simultaneously, based on spectrum change trend information, services in severely interfered frequency bands are dynamically migrated to idle or low-occupancy frequency bands. The service migration strategy is optimized using a reinforcement learning algorithm, aiming to minimize service interruption time and performance loss. The reward function is set as R=-t. interrupt +w·s performance , where t interrupt It is the business interruption time, s performance ω is the post-migration business performance indicator, and ω is the weighting coefficient. Through continuous trial and error and reward feedback, the optimal migration scheme is determined, ultimately forming preliminary optimized spectrum data, effectively reducing the impact of interference on system performance.

[0080] Finally, for the initially optimized spectrum data, a multi-band joint coding method combining layered encryption and cooperative modulation is adopted. First, the spectrum is divided into multiple frequency bands according to frequency, and different encryption algorithms are used for each band group to encrypt the data, improving the security of spectrum data transmission. Regarding modulation, modulation parameters are dynamically allocated based on factors such as channel quality and interference conditions for each frequency band. An optimization formula M for the modulation parameters is introduced. p = α×CQ+β×(1-IR), where M p Here, CQ represents the modulation parameters (e.g., modulation order), and CQ is the channel quality assessment value, calculated by measuring the signal-to-noise ratio (SNR) and bit error rate (BER) of the signal, using the following formula: IR is the interference rate, calculated as the proportion of interference signal energy to total signal energy; α and β are weighting coefficients, and This formula adaptively adjusts the modulation scheme of each frequency band, enabling efficient transmission and collaborative operation of multi-band data. Ultimately, it generates optimized spectrum data, providing a precise data foundation for subsequent adjustment of the phase weight and polarization state of the digital phased electromagnetic surface.

[0081] As one example, the method employs an adaptive optimization algorithm to calculate the phase weight and polarization state adjustment value of the digital phased electromagnetic surface based on the optimized spectrum data, including the following sub-steps:

[0082] Based on the optimized spectrum data, a multi-objective optimization problem for beamforming is defined, wherein the optimization objectives include maximizing the main lobe gain and minimizing interference suppression;

[0083] An adaptive optimization algorithm is used to solve the multi-objective optimization problem, and the phase weight and polarization state adjustment value of the digital phased electromagnetic surface are calculated.

[0084] In the embodiment, first, based on the optimized spectrum data, the core requirement of beamforming is transformed into a multi-objective optimization problem containing the maximum main lobe gain and the minimum interference suppression, the performance optimization direction of beamforming is clearly defined through mathematical modeling, and the subsequent calculation is more targeted; then, an adaptive optimization algorithm is used to solve the constructed multi-objective optimization problem, and the adaptive optimization algorithm can dynamically adjust the parameters according to the calculation process. Under the condition of meeting the spectrum constraint, the phase weight and polarization state adjustment value of the digital phased electromagnetic surface are calculated quickly and accurately, the optimization calculation of the key parameters of beamforming is realized, and the whole process from problem definition to parameter solving forms a complete and efficient calculation process. In summary, the embodiment defines a multi-objective optimization problem, and the two key performance indicators of main lobe gain and interference suppression in the beamforming process are included in the unified optimization framework, which changes the limitation of traditional single-objective optimization that cannot consider multiple performance requirements. At the same time, by using the dynamic adjustment ability of the adaptive optimization algorithm, compared with the optimization algorithm with fixed parameters, the calculation efficiency and solution accuracy are greatly improved, and the phase weight and polarization state adjustment value meeting the complex electromagnetic environment requirement can be quickly obtained. This makes the digital phased electromagnetic surface not only enhance the main lobe gain of signal transmission, but also effectively suppress interference when performing beamforming, significantly improves the performance of beamforming and the overall communication quality of the system, and provides strong support for realizing high-precision and high-stability communication.

[0085] Among them, the specific working process of the embodiment is exemplarily as follows:

[0086] Firstly, the system constructs a multi-objective optimization problem based on the optimized spectrum data, and takes the maximum main lobe gain and the minimum interference suppression as the core targets. The mathematical model of the problem is as follows: w，p min [α·(-G(w,p))+β·I(w,p)], where w is the phase weight vector, and p is the polarization state adjustment vector. The main lobe gain function G(w,p) is defined as the normalized array gain in the expected direction θ d , that is, where a(θ,p) is the array manifold vector considering the polarization state. The interference suppression function I(w,p) is defined as the sum of the array gains in all interference directions , that is, The weighting coefficients α and β are dynamically adjusted by and , where S d and S i represent the expected signal strength and the interference signal strength respectively, and γ is the adjustment parameter. The constraint conditions include the phase weight amplitude constraint |w n | = 1, the polarization state constraint 0 ≤ p n ≤ π / 2, and the total transmit power constraint

[0087] Then, the system solves the optimization problem by using the improved adaptive differential evolution algorithm, wherein, in the dynamic parameter adjustment mechanism, the scaling factor F and the crossover probability CR are respectively updated by and , wherein F min = 0.1, F max = 0.9, CR min = 0.2, CR max = 0.9, λ = 2 is the attenuation coefficient, g is the current iteration number, and G max is the maximum iteration number. The multi-strategy co-evolution simultaneously uses three mutation strategies, and dynamically allocates selection probability by , wherein success j represents the success rate of strategy j in the last T iterations, and η = 2 is the adjustment parameter. The constraint processing mechanism uses the improved repair strategy, and for the individual that violates the constraint, the phase weight amplitude is repaired by , the polarization state is repaired by p n = max(0, min(p n , π / 2)), and the power constraint is repaired by . The algorithm optimizes by iteration, dynamically adjusts the parameters and strategies, and finally outputs the phase weight w * and the polarization state p * corresponding to the optimal individual, so as to realize the balance between the maximum main lobe gain and the minimum interference suppression.

[0088] As one of the examples, the application of the phase weight and the polarization state adjustment value to the digital phase-controlled electromagnetic surface to perform the beamforming operation includes the following sub-steps:

[0089] The phase weight and the polarization state adjustment value are configured into an array of the digital phase-controlled electromagnetic surface to generate a set of beam control parameters;

[0090] The beam synthesis processing is performed based on the set of beam control parameters to generate a target beam signal;

[0091] The target beam signal is subjected to real-time feedback correction processing to output a final beamforming signal.

[0092] In this embodiment, firstly, the calculated phase weight and polarization state adjustment value are accurately configured to the array unit of the digital phase-controlled electromagnetic surface, the complete beam control parameter set is generated through parameter integration, and the basic control instruction is provided for beam forming; secondly, based on the beam control parameter set, the beam synthesis processing is performed by using the cooperative working mechanism between the array units, the electromagnetic radiation signals of each unit are superimposed and phase adjusted, and the target beam signal meeting the expected direction and characteristics is generated; finally, the real-time feedback correction mechanism is introduced, the actual transmission characteristics of the target beam signal are monitored, compared and analyzed with the preset ideal parameters, the target beam signal is dynamically adjusted, and the final beam forming signal meeting the complex electromagnetic environment requirement is output, so that the accuracy and stability of beam forming are ensured. From the above analysis, it can be seen that, by using the three-level processing architecture, the problems of extensive application of parameters and lack of dynamic adjustment in the traditional beam forming process are solved. The parameter configuration link realizes accurate distribution of beam forming parameters, and the accuracy of beam control is ensured; the beam synthesis processing enables the array units to work cooperatively, and the target beam signal is efficiently generated; and the real-time feedback correction mechanism gives the system adaptive ability, can dynamically optimize the beam characteristics according to the change of the actual transmission environment, and effectively improves the anti-interference ability and transmission stability of the beam forming signal. The combination of the three can significantly enhance the adaptability of the digital phase-controlled electromagnetic surface in the complex electromagnetic environment, improve the precision and reliability of the beam forming, and provide technical support for realizing stable and efficient wireless communication.

[0093] Among them, the specific working process of the embodiment is exemplified as follows:

[0094] Firstly, when the phase weight w = [w1, w2, …, wn] and the polarization state adjustment value p = [p1, p2, …, pn] are obtained, N ] T and the polarization state adjustment value p = [p1, p2, …, pn], N T After that, the system will convert these parameters into control signals that can directly control the array of the digital phase-controlled electromagnetic surface. For the phase weight, since it is essentially a complex number, the amplitude angle ∠w n is converted into the control angle of the phase shifter of the phased array, assuming that the control accuracy of the phase shifter is then the actual control code value is For the polarization state adjustment value p n , which represents the polarization angle of the antenna unit, by mapping p n to the driving voltage range [V min , V max ] of the polarization controller, the calculation formula is Finally, the phase control code value and the polarization control voltage value of all antenna units are integrated to form a beam control parameter set containing 2N parameters which provides accurate control instructions for subsequent beam synthesis. ​

[0095] Then, according to the generated beam control parameter set C, the digital phased electromagnetic surface array adjusts the signal of each unit according to the corresponding control parameter. For each antenna unit, its output signal s n (t) can be expressed as where A n is the signal amplitude (assumed to be a fixed value at this stage), f0 is the carrier frequency, is the phase offset determined by the phase control code value P(p n ) is the polarization direction function related to the polarization angle p n The radiation field strength of the entire array can be calculated by the pattern multiplication theorem in antenna array theory, that is, where is the pattern function of the nth antenna unit. By cooperatively controlling the amplitude, phase and polarization of the signals of each unit, the array radiation field produces in-phase superposition in the desired direction θ d and is weakened in other directions, thereby synthesizing a target beam signal with specific directivity and polarization characteristics

[0096] Finally, during the transmission of the target beam signal, the actual parameters of the beam signal, including beam pointing deviation Δθ, gain attenuation ΔG and polarization distortion ΔP, are collected in real time by monitoring equipment deployed in the target area or the periphery. An adaptive correction algorithm based on the least mean square error (LMS) is used to adjust the beam control parameters in real time according to the deviation. Taking the beam pointing deviation as an example, assuming that the desired pointing direction is θ d and the actual measured pointing direction is θ real , the phase adjustment amount can be calculated by the formula , where k θ is the pointing-phase adjustment coefficient, which is determined by the array geometric parameters and the wavelength. For gain attenuation and polarization distortion, corresponding adjustment models are established, such as gain adjustment coefficient k G and polarization adjustment coefficient k P , to update the phase control code value and polarization control voltage in the beam control parameter set C. Through continuous iterative adjustment, the actual parameters of the beam signal gradually approach the theoretical design values, and finally the final beam forming signal that meets the performance requirements is output

[0097] Referring to Figure 2 , it is a structural schematic diagram of a digital phased electromagnetic surface beam forming system based on dynamic spectrum sensing provided by an embodiment of the present application. The digital phased electromagnetic surface beam forming system based on dynamic spectrum sensing comprises:

[0098] The perception module 10 is used for performing dynamic spectrum sensing processing on an electromagnetic environment in a space range in which a beam of the digital phased electromagnetic surface needs to act or avoid, to obtain real-time spectrum occupation state information.

[0099] The generation module 11 is used for performing spectrum prediction and interference suppression processing based on the real-time spectrum occupation state information and in combination with historical spectrum data of the digital phased electromagnetic surface, to generate optimized spectrum data.

[0100] The calculation module 12 is used for calculating phase weights and polarization state adjustment values of the digital phased electromagnetic surface according to the optimized spectrum data by using an adaptive optimization algorithm.

[0101] The execution module 13 is used for applying the phase weights and the polarization state adjustment values to the digital phased electromagnetic surface to perform beamforming operation.

[0102] Compared with the prior art, the embodiment of the present application has the following beneficial effects:

[0103] Firstly, real-time spectrum occupation state information is obtained by performing dynamic spectrum sensing on an electromagnetic environment in a space range in which a beam of the digital phased electromagnetic surface needs to act or avoid. This dynamic sensing manner can timely capture changes in the electromagnetic environment, and is more suitable for complex and variable actual scenes than traditional static detection. Then, the real-time spectrum occupation state information is combined with historical spectrum data to perform spectrum prediction and interference suppression processing, so as to fully utilize time dimension information of the data, effectively predict a spectrum change trend and suppress interference, and thus generate optimized spectrum data that is more in line with actual requirements. Then, an adaptive optimization algorithm is used to calculate phase weights and polarization state adjustment values of the digital phased electromagnetic surface according to the optimized spectrum data. The adaptive algorithm can dynamically adjust a calculation process according to the data, so that the calculation result is more accurate. Finally, the calculated phase weights and polarization state adjustment values are applied to the digital phased electromagnetic surface to realize beamforming operation. In summary, compared with the problems of weak anti-interference ability, inaccurate beam pointing and low communication efficiency in the background technology, the embodiment of the present application provides real-time data basis by dynamic sensing, enhances adaptability to the environment by data combination and processing, guarantees parameter calculation accuracy by the adaptive algorithm, and finally realizes accurate beamforming. Therefore, the embodiment of the present application realizes dynamic adaptation to a complex electromagnetic environment, improves anti-interference ability, guarantees accuracy of beam pointing, and thus improves communication efficiency and stability.

[0104] As an improvement of the above-mentioned scheme, the perception module is specifically used for:

[0105] performing adaptive spatial domain scanning processing on a space range in which a beam of the digital phased electromagnetic surface needs to act or avoid, to obtain a scanning spectrum data set;

[0106] The scanning spectrum data set is subjected to spectrum conflict detection processing to identify interference conflict frequency band information.

[0107] The interference conflict frequency band information is subjected to spectrum occupation analysis processing to generate real-time spectrum occupation state information.

[0108] As an improvement of the above scheme, the generation module is specifically configured to:

[0109] The real-time spectrum occupation state information and the historical spectrum data of the digital phased electromagnetic surface are subjected to time-series spectrum prediction processing to obtain spectrum change trend information.

[0110] The spectrum change trend information is subjected to interference feature extraction processing to determine dynamic interference feature parameters.

[0111] Based on the dynamic interference feature parameters, online interference suppression processing is performed to generate preliminary optimized spectrum data.

[0112] The preliminary optimized spectrum data is subjected to multi-band joint coding processing to generate optimized spectrum data.

[0113] As an improvement of the above scheme, the calculation module is specifically configured to:

[0114] Based on the optimized spectrum data, a multi-objective optimization problem of beamforming is defined, wherein the optimization objectives include maximum main lobe gain and minimum interference suppression.

[0115] An adaptive optimization algorithm is used to solve the multi-objective optimization problem to calculate the phase weight and polarization state adjustment value of the digital phased electromagnetic surface.

[0116] As an improvement of the above scheme, the execution module is specifically configured to:

[0117] The phase weight and polarization state adjustment value are configured to the array of the digital phased electromagnetic surface to generate a set of beam control parameters.

[0118] Based on the set of beam control parameters, beam synthesis processing is performed to generate a target beam signal.

[0119] The target beam signal is subjected to real-time feedback correction processing to output a final beamforming signal.

[0120] It should be noted that the system embodiments described above are only illustrative, wherein the units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e. can be located in one place, or can be distributed to multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs. In addition, the connection relationship between the modules in the system embodiment provided by the present application indicates that there is a communication connection between them, which can be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement it without creative labor.

[0121] The above is the preferred embodiment of the present application. It should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements are also considered within the scope of protection of the present application.

Claims

1. A digital phased electromagnetic surface wave beamforming method based on dynamic spectrum sensing, characterized by, The method comprises the following steps: performing dynamic spectrum sensing processing on the electromagnetic environment in the spatial range where the beam of the digital phased electromagnetic surface needs to act or avoid, to obtain real-time spectrum occupation state information; based on the real-time spectrum occupation state information, combining the historical spectrum data of the digital phased electromagnetic surface, performing spectrum prediction and interference suppression processing to generate optimized spectrum data; using an adaptive optimization algorithm, calculating the phase weight and polarization state adjustment value of the digital phased electromagnetic surface according to the optimized spectrum data; applying the phase weight and the polarization state adjustment value to the digital phased electromagnetic surface to perform beam forming operation.

2. The dynamic spectrum-sensing based digital phased electromagnetic surface wave beamforming method of claim 1, wherein, The method of performing dynamic spectrum sensing processing on the electromagnetic environment in the spatial range where the beam of the digital phased electromagnetic surface needs to act or avoid, to obtain real-time spectrum occupation state information, comprises the following sub-steps: performing adaptive spatial scanning processing on the spatial range where the beam of the digital phased electromagnetic surface needs to act or avoid, to obtain a scanning spectrum data set; performing spectrum conflict detection processing on the scanning spectrum data set to identify interference conflict frequency band information; performing spectrum occupation analysis processing on the interference conflict frequency band information to generate real-time spectrum occupation state information.

3. The dynamic spectrum-sensing based digital phased electromagnetic surface wave beamforming method of claim 1, wherein, The method of performing spectrum prediction and interference suppression processing based on the real-time spectrum occupation state information, combining the historical spectrum data of the digital phased electromagnetic surface, to generate optimized spectrum data, comprises the following sub-steps: performing time sequence spectrum prediction processing on the real-time spectrum occupation state information and the historical spectrum data of the digital phased electromagnetic surface to obtain spectrum change trend information; performing interference feature extraction processing on the spectrum change trend information to determine dynamic interference feature parameters; performing online interference suppression processing based on the dynamic interference feature parameters to generate preliminary optimized spectrum data; performing multi-frequency band joint coding processing on the preliminary optimized spectrum data to generate optimized spectrum data.

4. The dynamic spectrum-sensing based digital phased electromagnetic surface wave beamforming method of claim 1, wherein, The method of using an adaptive optimization algorithm to calculate the phase weight and polarization state adjustment value of the digital phased electromagnetic surface according to the optimized spectrum data, comprises the following sub-steps: defining a multi-objective optimization problem of beam forming based on the optimized spectrum data, wherein the optimization objectives include maximum main lobe gain and minimum interference suppression; solving the multi-objective optimization problem using an adaptive optimization algorithm to calculate the phase weight and polarization state adjustment value of the digital phased electromagnetic surface.

5. The dynamic spectrum-sensing based digital phased electromagnetic surface wave beamforming method of claim 1, wherein, The method of applying the phase weight and the polarization state adjustment value to the digital phased electromagnetic surface to perform beam forming operation, comprises the following sub-steps: configuring the phase weight and polarization state adjustment value to the array of the digital phased electromagnetic surface to generate a set of beam control parameters; performing beam synthesis processing based on the set of beam control parameters to generate a target beam signal; performing real-time feedback correction processing on the target beam signal to output a final beam forming signal.

6. A digital phased electromagnetic surface wave beamforming system based on dynamic spectrum sensing, characterized by, The method comprises the following steps: a perception module for performing dynamic spectrum sensing processing on the electromagnetic environment in the spatial range where the beam of the digital phased electromagnetic surface needs to act or avoid, to obtain real-time spectrum occupation state information; The generating module is configured to perform spectrum prediction and interference suppression processing based on the real-time spectrum occupation state information and historical spectrum data of the digital phased electromagnetic surface, and generate optimized spectrum data; The calculating module is configured to calculate phase weights and polarization state adjustment values of the digital phased electromagnetic surface according to the optimized spectrum data by using an adaptive optimization algorithm; The executing module is configured to apply the phase weights and the polarization state adjustment values to the digital phased electromagnetic surface, and perform beamforming operation.

7. The dynamic spectrum-sensing based digital phased electromagnetic surface wave beamforming system of claim 6, wherein, The sensing module is specifically configured to: perform adaptive spatial scanning processing on a spatial range of beam action or avoidance of the digital phased electromagnetic surface, and obtain a scanning spectrum data set; perform spectrum conflict detection processing on the scanning spectrum data set, and identify interference conflict frequency band information; perform spectrum occupation analysis processing on the interference conflict frequency band information, and generate real-time spectrum occupation state information.

8. The dynamic spectrum-sensing based digital phased electromagnetic surface wave beamforming system of claim 6, wherein, The generating module is specifically configured to: perform time-series spectrum prediction processing on the real-time spectrum occupation state information and the historical spectrum data of the digital phased electromagnetic surface, and obtain spectrum change trend information; perform interference feature extraction processing on the spectrum change trend information, and determine dynamic interference feature parameters; perform online interference suppression processing based on the dynamic interference feature parameters, and generate preliminary optimized spectrum data; perform multi-frequency band joint coding processing on the preliminary optimized spectrum data, and generate optimized spectrum data.

9. The dynamic spectrum-sensing based digital phased electromagnetic surface wave beamforming system of claim 6, wherein, The calculating module is specifically configured to: define a multi-objective optimization problem of beamforming based on the optimized spectrum data, wherein optimization objectives include maximum main lobe gain maximization and minimum interference suppression minimization; solve the multi-objective optimization problem by using an adaptive optimization algorithm, and calculate phase weights and polarization state adjustment values of the digital phased electromagnetic surface.

10. The dynamic spectrum-sensing based digital phased electromagnetic surface wave beamforming system of claim 6, wherein, The executing module is specifically configured to: configure the phase weights and the polarization state adjustment values to an array of the digital phased electromagnetic surface, and generate a set of beam control parameters; perform beam synthesis processing based on the set of beam control parameters, and generate a target beam signal; perform real-time feedback correction processing on the target beam signal, and output a final beamforming signal.

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