Satellite beamforming method and electronic device

Abstract

The application discloses a satellite beamforming method and electronic equipment. The method comprises the following steps: obtaining an initial phase matrix of a binary reflectarray on a satellite; based on the initial phase matrix, performing multiple simulation beam projections to a far-field direction diagram domain to obtain an ideal phase matrix of the binary reflectarray; based on the ideal phase matrix, performing error diffusion on the binary reflectarray to determine a discrete phase matrix of the binary reflectarray; and based on the discrete phase matrix, constructing a control signal of the binary reflectarray to control the binary reflectarray to synthesize an electromagnetic wave beam. The application solves the technical problem of poor accuracy of beamforming in the related art.

Description

Technical Field

[0001] This invention relates to the field of beamforming, and more specifically, to a satellite beamforming method and electronic device. Background Technology

[0002] Reconfigurable smart surfaces, as an emerging electromagnetic wave manipulation technology, can dynamically alter the propagation characteristics of electromagnetic waves by distributing a large number of low-cost passive radiating elements on their surface, thus demonstrating great potential in improving the coverage and spectral efficiency of wireless communication links. With the rapid development of low-Earth orbit satellite communication systems, spaceborne platforms have imposed stricter constraints on payload weight, volume, and power consumption, prompting research into the application of low-cost reconfigurable smart surfaces based on 1-bit quantization control in satellite communications. However, current beamforming strategies for 1-bit reconfigurable smart surfaces result in significant sidelobe interference, leading to poor beamforming accuracy.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a satellite beamforming method and electronic device to at least solve the technical problem of poor beamforming accuracy in related technologies.

[0005] According to one aspect of the present invention, a satellite beamforming method is provided, comprising: obtaining an initial phase matrix of a binary reflector array on a satellite, wherein the binary reflector array is used to synthesize an electromagnetic wave beam, and different phase matrices generate electromagnetic wave beams with different beam shapes; based on the initial phase matrix, performing multiple simulated beam projections into a far-field directional map domain to obtain an ideal phase matrix of the binary reflector array; based on the ideal phase matrix, performing error diffusion on the binary reflector array to determine a discrete phase matrix of the binary reflector array; and based on the discrete phase matrix, constructing a control signal for the binary reflector array to control the binary reflector array to synthesize an electromagnetic wave beam.

[0006] Furthermore, the binary reflective array includes multiple binary reflective units; based on the ideal phase matrix, error diffusion is performed on the binary reflective array to determine the discrete phase matrix of the binary reflective array, including: obtaining the error diffusion matrix of the binary reflective array; based on the ideal phase matrix, determining the ideal phase values ​​corresponding to the multiple binary reflective units respectively; based on the preset traversal order, the error diffusion matrix and the ideal phase values, error diffusion is performed on the multiple binary reflective units respectively to determine the discrete phase matrix.

[0007] Furthermore, based on the preset traversal order, error diffusion matrix, and ideal phase value, error diffusion is performed on multiple binary reflection units to determine the discrete phase matrix, including: determining the current traversal unit from multiple binary reflection units based on the preset traversal order; determining the phase adjustment base of the current traversal unit based on the ideal phase value; and performing error diffusion on the current traversal unit based on the phase adjustment base and the error diffusion matrix to determine the discrete phase matrix.

[0008] Further, based on the ideal phase value, the phase adjustment base of the current traversal unit is determined, including: obtaining the error diffusion state of the current traversal unit, which is used to characterize whether the current traversal unit receives diffusion errors from other binary reflection units besides the current traversal unit; if the current traversal unit does not receive diffusion errors from other binary reflection units, the ideal phase value corresponding to the current traversal unit is determined as the phase adjustment base, wherein other binary reflection units are binary reflection units besides the current traversal unit; if the current traversal unit receives diffusion errors from other binary reflection units, the diffusion errors and the ideal phase value are synthesized to obtain the phase adjustment base.

[0009] Furthermore, based on the phase adjustment base and the error diffusion matrix, error diffusion is performed on the current traversal unit to determine the discrete phase matrix, including: performing a nearest-neighbor quantization operation on the current traversal unit based on the phase adjustment base to obtain the discrete phase value corresponding to the current traversal unit; determining the error value of the current traversal unit based on the phase adjustment base and the discrete phase value; and performing error diffusion on the error value based on the error diffusion matrix to determine the discrete phase matrix.

[0010] Further, based on the error diffusion matrix, the error value is diffused to determine the discrete phase matrix, including: based on a preset traversal order, determining at least one neighboring reflection unit corresponding to the current traversal unit, wherein the at least one neighboring reflection unit is located after the current traversal unit in the preset traversal order; based on the error diffusion matrix, determining the diffusion weight corresponding to at least one neighboring reflection unit respectively; and based on the diffusion weight, diffusing the error value to at least one neighboring reflection unit to determine the discrete phase matrix.

[0011] Furthermore, the method also includes: constructing a discrete phase matrix based on the discrete phase values ​​corresponding to multiple binary reflection units.

[0012] Furthermore, based on the initial phase matrix, multiple simulated beam projections are performed into the far-field radiation pattern domain to obtain the ideal phase matrix of the binary reflector array. This includes: inputting the initial phase matrix into the array radiation pattern model, processing the initial phase matrix using the array radiation pattern model to obtain a far-field radiation pattern complex matrix, where the complex matrix includes phase and amplitude; replacing the amplitude in the far-field radiation pattern complex matrix with a preset amplitude to obtain a corrected radiation pattern complex matrix; performing near-field inverse mapping on the corrected radiation pattern complex matrix to obtain the intermediate phase matrix of the binary reflector array; determining the degree of deviation between the far-field radiation pattern corresponding to the intermediate phase matrix and the preset radiation pattern, and determining the intermediate phase matrix as the ideal phase matrix if the deviation is less than a preset value.

[0013] Furthermore, the method also includes: when the deviation is greater than or equal to a preset value, repeatedly executing the steps of inputting the intermediate phase matrix into the array pattern model, processing the intermediate phase matrix using the array pattern model to obtain a new far-field pattern complex matrix, replacing the amplitude in the new far-field pattern complex matrix with a preset amplitude to obtain a new corrected pattern complex matrix, performing near-field inverse mapping on the new corrected pattern complex matrix to obtain a new intermediate phase matrix, and determining the degree of deviation between the far-field pattern corresponding to the new intermediate phase matrix and the preset pattern, until the deviation is less than the preset value, or the number of repetitions reaches the preset number.

[0014] According to another aspect of the present invention, a satellite beamforming apparatus is also provided, comprising: an acquisition module for acquiring an initial phase matrix of a binary reflector array on a satellite, wherein the binary reflector array is used to synthesize an electromagnetic wave beam, and different phase matrices generate electromagnetic wave beams with different beam shapes; a simulation module for performing multiple simulated beam projections into a far-field directional map domain based on the initial phase matrix to obtain an ideal phase matrix of the binary reflector array; a diffusion module for performing error diffusion on the binary reflector array based on the ideal phase matrix to determine a discrete phase matrix of the binary reflector array; and a construction module for constructing control signals for the binary reflector array based on the discrete phase matrix to control the binary reflector array to synthesize an electromagnetic wave beam.

[0015] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0016] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0017] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0018] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0019] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0020] In this embodiment of the invention, the initial phase matrix of a binary reflector array on a satellite is obtained. Based on the initial phase matrix, multiple simulated beam projections are performed into the far-field directional pattern domain to obtain the ideal phase matrix of the binary reflector array. Based on the ideal phase matrix, error diffusion is performed on the binary reflector array to determine its discrete phase matrix. Based on the discrete phase matrix, a control signal for the binary reflector array is constructed to control the synthesis of electromagnetic wave beams by the binary reflector array. By performing multiple simulated beam projections into the far-field directional pattern domain using the initial phase matrix to obtain the ideal phase matrix, a high-precision continuous phase distribution reference can be established. Subsequently, by introducing an error diffusion mechanism to process the ideal phase matrix, the truncation error generated during quantization can be dispersed to adjacent cells, effectively suppressing the interference of parasitic quantization lobes on the directional pattern. Finally, the control signal is constructed based on the processed discrete phase matrix, ensuring that the beam synthesized by the binary reflector array has high accuracy, thereby solving the technical problem of poor beamforming accuracy in related technologies. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0022] Figure 1 This is a flowchart of a satellite beamforming method according to an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of an optional error diffusion process according to an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of an optional binary reflection unit traversal process according to an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of a process for determining an optional phase adjustment base according to an embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram of an optional satellite beamforming process according to an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of a satellite beamforming device according to an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] According to an embodiment of the present invention, an embodiment of a satellite beamforming method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0031] Figure 1 This is a flowchart of a satellite beamforming method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0032] Step S102: Obtain the initial phase matrix of the binary reflector array on the satellite. The binary reflector array is used to synthesize electromagnetic wave beams. Different phase matrices produce electromagnetic wave beams with different beam shapes.

[0033] The aforementioned binary reflector array can refer to a planar or curved antenna structure composed of a large number of independently controllable electromagnetic reflector units. This binary reflector array is deployed on a satellite platform, where each binary reflector unit can independently adjust the reflected phase of the incident electromagnetic wave through an internal electronic switch. By coordinating the phase states of all binary reflector units on the array, and utilizing the principle of coherent superposition of electromagnetic fields in far-field space, precise control over the energy distribution and propagation direction of spatial electromagnetic waves can be achieved. In this embodiment, the binary reflector array can be a spaceborne 1-bit RIS (1-bit Reconfigurable Intelligent Surface).

[0034] The aforementioned initial phase matrix can refer to a two-dimensional data set containing the initial phase control values ​​of all reflective elements in the array, generated at the beginning of the beamforming task performed by the binary reflective array, based on the preset target beam pointing or specific beam shape requirements, and calculated according to the array factor formula or linear phase shift model. This initial phase matrix can serve as the initial input state for the beamforming algorithm, defining the original phase configuration of the binary reflective array before subsequent iterative adjustments or quantization processing.

[0035] The aforementioned electromagnetic wave beam can refer to an electromagnetic radiation field formed by the phase modulation and coherent superposition of electromagnetic fields in the far-field space, radiated or reflected by independent binary reflector units in a binary reflector array. The energy of this electromagnetic wave beam is mainly concentrated in the main lobe direction, and side lobes and null structures are formed in adjacent regions, which are used to realize the directional transmission or reception of signals in satellite communication.

[0036] The aforementioned beamform refers to the geometric characteristics and physical parameter combination of the amplitude distribution of an electromagnetic wave beam in the spatial angular domain. Specifically, it may include, but is not limited to, the pointing angle of the main lobe, the main lobe width, the side lobe level, beam symmetry, and null depth. This beamform can be determined by the amplitude weighting coefficients and phase distribution of each binary reflector element in a binary reflector array. Different phase matrices can generate different beamforms by changing the phase difference between the binary reflector elements to meet corresponding communication coverage or anti-interference requirements.

[0037] In one alternative embodiment, considering that the binary reflector array can achieve spatial synthesis of electromagnetic beams by controlling the phase distribution of each binary reflector unit, and that different phase matrices can correspond to different beam directions and shapes.

[0038] Based on this, the satellite beamforming system (hereinafter referred to as the beamforming system) can acquire the physical structure parameters and initial state of the binary reflector array deployed on the satellite platform. Then, the beamforming system can initialize an initial phase matrix corresponding to the number and arrangement of the binary reflector elements of the binary reflector array. Each element in the initial phase matrix can represent the phase control value of the corresponding binary reflector element at the initial moment, thereby providing initial input data for subsequent beamforming iterative algorithms to initiate the beamforming process.

[0039] For example, the beamforming system can calculate the geometric phase delay of each binary reflector relative to a reference point using the array factor formula based on the preset target beam pointing angle and array geometry, and generate a continuous phase distribution by combining it with the required beam shape constraints. Subsequently, the beamforming system can organize this continuous phase distribution into a two-dimensional data set containing the initial phase control values ​​of all binary reflectors, as the initial phase matrix.

[0040] In another alternative embodiment, the beamforming system can be based on a linear phase-shifting model, setting the beamforming target of the binary reflector array as plane wave synthesis in a specific direction. The beamforming system can calculate the linear phase gradient required by each binary reflector element to achieve the plane wavefront, and can map the calculated continuous phase values ​​into a two-dimensional data set as the initial phase matrix.

[0041] In another optional embodiment, the beamforming system can set the target pattern amplitude and phase distribution in the far-field space according to the preset target beam shape requirements, and transform the target field distribution in the spatial domain to the aperture domain through inverse Fourier transform, extracting the complex field distribution phase of each binary reflector position in the aperture domain. The beamforming system can organize the extracted complex field distribution phase into a two-dimensional data set containing the initial phase control values ​​of all binary reflectors, as the initial phase matrix.

[0042] Step S104: Based on the initial phase matrix, perform multiple simulated beam projections into the far-field directional domain to obtain the ideal phase matrix of the binary reflector array.

[0043] The aforementioned far-field radiation pattern domain can refer to the data space describing the distribution characteristics of electromagnetic wave radiation intensity or field strength amplitude as a function of spatial angle within a spatial region sufficiently far from the array center of a binary reflector array. This far-field radiation pattern domain can be mapped from the complex field distribution on the aperture surface of the binary reflector array to a complex matrix in the spatial angle domain using azimuth and elevation angles as coordinate axes through Fourier transform or array factor model.

[0044] The aforementioned ideal phase matrix can refer to the set of complex reflection coefficient phase distributions obtained through multiple iterations in a continuous phase space using a fast alternating projection algorithm. Theoretically, this set can closely approximate the target pattern constraints and is free from 1-bit quantization constraints. This ideal phase matrix can contain the optimal continuous phase values ​​of each binary reflection unit in the binary reflection array, thus serving as the input reference for subsequent error diffusion quantization algorithms.

[0045] In one alternative embodiment, it is considered that by simulating beam projection multiple times, an iterative adjustment mechanism can be established between the far-field pattern domain and the array aperture domain, thereby gradually approaching the optimal phase distribution that meets the expected beam shape and sidelobe suppression requirements.

[0046] Based on this, the shaping system can input the initial phase matrix into the array pattern model to generate a far-field pattern complex matrix. Next, the shaping system can replace the amplitude components in the far-field pattern complex matrix with a preset target amplitude to form a modified pattern complex matrix. Subsequently, the shaping system can perform near-field inverse mapping on the modified pattern complex matrix to generate an intermediate phase matrix containing more recent phase information. Finally, the shaping system can evaluate the deviation between the far-field pattern corresponding to the intermediate phase matrix and the preset pattern. If the deviation is below a preset threshold, the shaping system can stop iterating and determine the current intermediate phase matrix as the ideal phase matrix.

[0047] For example, the shaping system can forcibly impose main lobe pointing and low sidelobe amplitude constraints in the far-field radiation pattern domain, and forcibly retain amplitude and update phase in the aperture domain. The shaping system can perform multiple iterative projections between the far-field radiation pattern domain and the aperture domain until the phase distribution converges, thereby obtaining the ideal phase matrix of the binary reflector array.

[0048] In another alternative embodiment, the shaping system can construct an adjustment model with the objective function of minimizing the mean square error between the target radiation pattern and the array response. The shaping system can impose constant amplitude and continuous phase constraints in the aperture domain, and perform multiple iterative adjustments through gradient descent or alternating direction multiplier methods to solve for the phase distribution that minimizes the objective function, thereby obtaining the ideal phase matrix of the binary reflector array.

[0049] Step S106: Based on the ideal phase matrix, perform error diffusion on the binary reflective array to determine the discrete phase matrix of the binary reflective array.

[0050] The aforementioned discrete phase matrix can refer to a set of binary phase control values ​​that map continuous phase values ​​in an ideal phase matrix to only two discrete states (corresponding to 0-degree and 180-degree phases, i.e., 1-bit quantization states) through an error diffusion quantization algorithm. Each element in this discrete phase matrix represents the final determined discrete phase configuration of the corresponding binary reflector in the binary reflector array, which can be directly executed by the hardware circuit. The generation process of this discrete phase matrix breaks the periodic error distribution caused by direct quantization through a spatial domain error diffusion mechanism, thereby achieving phase value discretization while preserving low sidelobe performance.

[0051] In an alternative embodiment, considering that quantization errors are generated when converting continuous phase values ​​into 1-bit discrete phase states, the quantization error can be dispersed to adjacent cells in the spatial domain through an error diffusion mechanism, thereby disrupting the periodic distribution of the error and suppressing parasitic quantization lobes in the far-field pattern.

[0052] Based on this, the shaping system can determine the currently processed binary reflective unit according to a preset traversal order. Next, the shaping system can obtain the ideal phase value of this binary reflective unit as the phase adjustment base. If the binary reflective unit receives diffusion errors transmitted from previous units, these diffusion errors are accumulated into the phase adjustment base. Then, the shaping system can perform nearest-neighbor quantization on the accumulated phase adjustment base to obtain discrete phase values, and can determine the difference between the ideal phase value and the discrete phase value to obtain the quantization error. Finally, the shaping system can allocate this quantization error to unprocessed units located after the traversal order and spatially adjacent, according to predefined error diffusion matrix weights, thereby completing the generation of the discrete phase matrix for the entire array.

[0053] For example, the shaping system can traverse each binary reflector unit corresponding to the ideal phase matrix in a serpentine order from left to right and top to bottom. The system can extract the ideal phase value of the currently traversed binary reflector unit and sum it with the accumulated quantization error received from the previous processing unit as the phase adjustment base. Subsequently, the system can perform 1-bit nearest-neighbor quantization on this phase adjustment base to obtain discrete phase values, and determine the difference between the phase adjustment base and the discrete phase values ​​as the quantization error. Then, the system can multiply this quantization error by the corresponding weights according to the weight coefficients in the predefined error diffusion kernel matrix and accumulate it into the phase adjustment base of the unprocessed units located after the traversal order and spatially adjacent to it, until all binary reflector units have been traversed and a discrete phase matrix has been generated.

[0054] In another alternative embodiment, the shaping system can define a centrosymmetric two-dimensional error spread filter, traversing the binary reflector units from left to right and top to bottom. For the currently traversed binary reflector unit, the shaping system can add the ideal phase value corresponding to the binary reflector unit to the spread errors from the adjacent units on the left and top sides to obtain the phase to be quantized. Subsequently, the shaping system can perform 1-bit quantization on the phase to be quantized to obtain a binary phase value, and can determine the error between the phase to be quantized and the binary phase value. Then, the shaping system can use a preset error spread matrix to distribute the error according to weights to the adjacent unprocessed units on the right and bottom sides, and repeat the above process until the entire binary reflector array is processed, forming a discrete phase matrix containing the discrete phase values ​​after error correction.

[0055] In another alternative embodiment, the shaping system can pre-scramble the ideal phase matrix using a dithering matrix to break spatial periodicity. Subsequently, the shaping system can process each binary reflective unit sequentially according to the grating scanning order, and can add the error feedback from processed neighboring units to the pre-scrambled ideal phase value to obtain a synthesized phase. The shaping system can perform a threshold comparison operation on the synthesized phase to generate a 1-bit discrete phase and can determine the deviation between the synthesized phase and the discrete phase. Then, the shaping system can distribute this deviation to subsequent neighboring units according to a preset error diffusion coefficient. Through this error diffusion process with pre-scrambling and fixed weights, the final discrete phase matrix of the binary reflective array is generated.

[0056] Step S108: Based on the discrete phase matrix, construct the control signal for the binary reflection array to control the binary reflection array to synthesize an electromagnetic wave beam.

[0057] The aforementioned control signal can refer to the electrical signal that converts the binary phase data in the discrete phase matrix into a level signal or a digital pulse sequence, and sends it to the electronic switches inside each binary reflector unit of the binary reflector array via the onboard FPGA (Field-Programmable Gate Array) and driving circuit. This control signal can be used to precisely control each binary reflector unit to be in the on or off state, thereby achieving preset 1-bit phase modulation at the physical level.

[0058] In one alternative embodiment, considering that converting the discrete phase matrix into an electrical signal can directly drive the hardware execution unit in the binary reflector array, the theoretical beamforming algorithm is physicalized into actual electromagnetic wave radiation control.

[0059] Based on this, the shaping system can traverse each element in the discrete phase matrix to obtain the target phase state of the corresponding binary reflector. Next, the shaping system can convert the discrete phase value corresponding to each element into binary control commands. Specifically, the shaping system can map the discrete phase to a high-level or low-level signal to control the electronic switch of the corresponding binary reflector. Finally, the shaping system can summarize these binary control commands and generate a control signal sequence, which is then sent to the driving circuit of the binary reflector array. This allows each binary reflector to respond according to the state set by the discrete phase matrix, thereby synthesizing an electromagnetic wave beam with a predetermined direction and shape.

[0060] For example, the shaping system can directly map each discrete phase value in the discrete phase matrix to the on / off control state of the corresponding diode. That is, when the discrete phase value is 0 degrees, the shaping system can generate a low-level control signal to turn on the diode, while when the discrete phase value is 180 degrees, the shaping system can generate a high-level control signal to turn off the diode. Subsequently, the shaping system can convert these control signals into digital instruction sequences suitable for transmission by the spaceborne FPGA, and can send them to the diode driving circuit of the RIS array through the bus interface, thereby driving each binary reflection unit to generate the required reflection phase distribution to synthesize the target electromagnetic wave beam.

[0061] In another alternative embodiment, the shaping system can convert the discrete phase values ​​in the discrete phase matrix into corresponding digital control words, and can use a lookup table to map these digital control words into current drive pulses of a specific amplitude. The shaping system can convert these current drive pulses into analog drive voltages using a digital-to-analog converter, and can apply these analog drive voltages to the control terminals of the RF switching devices corresponding to each binary reflector element of the binary reflector array. Based on this, the shaping system can change the phase of the reflected signal by adjusting the on-resistance or capacitance state of the switching devices, thereby controlling the binary reflector array to synthesize an electromagnetic wave beam with a predetermined beam shape.

[0062] In another alternative embodiment, the beamforming system can encode and compress the discrete phase matrix to reduce data transmission volume. The beamforming system can then transmit the compressed data to a ground station or onboard processing unit via a satellite communication link. The ground station or onboard processing unit can decode the data and reconstruct it into array control commands. Subsequently, the ground station or onboard processing unit can send these array control commands to the baseband controller of the binary reflector array. The baseband controller can generate corresponding timing control pulse signals according to the commands and transmit these timing control pulse signals to the modulation and demodulation circuits of each binary reflector element via a serial peripheral interface. Finally, the modulation and demodulation circuits can drive the radio frequency switches to generate the desired discrete phase reflection effect to synthesize an electromagnetic wave beam.

[0063] In this embodiment of the invention, the initial phase matrix of a binary reflector array on a satellite is obtained. Based on the initial phase matrix, multiple simulated beam projections are performed into the far-field directional pattern domain to obtain the ideal phase matrix of the binary reflector array. Based on the ideal phase matrix, error diffusion is performed on the binary reflector array to determine its discrete phase matrix. Based on the discrete phase matrix, a control signal for the binary reflector array is constructed to control the synthesis of electromagnetic wave beams by the binary reflector array. By performing multiple simulated beam projections into the far-field directional pattern domain using the initial phase matrix to obtain the ideal phase matrix, a high-precision continuous phase distribution reference can be established. Subsequently, by introducing an error diffusion mechanism to process the ideal phase matrix, the truncation error generated during quantization can be dispersed to adjacent cells, effectively suppressing the interference of parasitic quantization lobes on the directional pattern. Finally, the control signal is constructed based on the processed discrete phase matrix, ensuring that the beam synthesized by the binary reflector array has high accuracy, thereby solving the technical problem of poor beamforming accuracy in related technologies.

[0064] Furthermore, the binary reflective array includes multiple binary reflective units; based on the ideal phase matrix, error diffusion is performed on the binary reflective array to determine the discrete phase matrix of the binary reflective array, including: obtaining the error diffusion matrix of the binary reflective array; based on the ideal phase matrix, determining the ideal phase values ​​corresponding to the multiple binary reflective units respectively; based on the preset traversal order, the error diffusion matrix and the ideal phase values, error diffusion is performed on the multiple binary reflective units respectively to determine the discrete phase matrix.

[0065] The aforementioned binary reflection unit can refer to the basic functional modules constituting a binary reflection array. Each binary reflection unit can contain a switchable electromagnetic reflection element and a corresponding feeding structure. The electromagnetic reflection element can quickly switch between two discrete states (corresponding to 0 degrees and 180 degrees phase), thereby independently controlling the reflection phase of the electromagnetic wave passing through the binary reflection unit. These binary reflection units can be arranged in space according to a specific geometric pattern to form an overall binary reflection array.

[0066] The aforementioned error diffusion matrix can refer to a predefined two-dimensional weighted coefficient array, which can be used to quantize the error generated by the current binary reflector unit when quantizing continuous phase values ​​into discrete states, and can distribute this error to adjacent unprocessed units in space according to a specific ratio. This error diffusion matrix defines the spatial topology and weighting of error propagation; therefore, it can be used to redistribute errors in the spatial domain, thereby disrupting the periodicity of quantization errors and suppressing quantization lobes in the far-field radiation pattern.

[0067] The aforementioned ideal phase value can refer to the optimal phase control parameter of the binary reflector unit in the continuous phase space, extracted from the ideal phase matrix. This ideal phase value can be a real or complex angle, representing the theoretically required phase offset of the binary reflector unit to achieve the target beam shape without 1-bit discrete quantization constraints. Thus, it can serve as the initial input reference for the error diffusion quantization process.

[0068] The aforementioned preset traversal order refers to a rule sequence that determines the order in which binary reflective elements are processed when performing the error diffusion quantization algorithm on all binary reflective elements of the binary reflective array. This preset traversal order can adopt a raster scanning method from left to right, from top to bottom, or a serpentine scanning method to ensure that when processing the current binary reflective element, the subsequent elements adjacent to it have not yet been quantized. This ensures that the error can be accurately diffused to the unprocessed neighborhood, maintaining the consistency of the algorithm logic.

[0069] In an alternative embodiment, considering that the error diffusion mechanism can disperse the local phase truncation error generated by a single quantization operation to the spatial neighborhood, thereby disrupting the periodic distribution pattern of the error on the array and suppressing the high-level parasitic quantization lobe generated by the coherent superposition of periodic errors in the far-field pattern.

[0070] Based on this, the shaping system can acquire a predefined error spread matrix containing specific weighting coefficients to establish the spatial rules of error propagation. Next, the shaping system can traverse the ideal phase matrix to extract the ideal continuous phase value corresponding to each binary reflector unit as initial input. Then, the shaping system can process each binary reflector unit sequentially according to a preset left-to-right or serpentine traversal order. When processing any binary reflector unit, the shaping system can add the ideal phase value of that binary reflector unit to the accumulated quantization error received from previously processed units to obtain a phase adjustment base. Subsequently, the shaping system can perform 1-bit nearest-neighbor quantization on this phase adjustment base to obtain discrete phase values ​​and determine the quantization error. Finally, the shaping system can accumulate the quantization error generated by the binary reflector unit into the phase adjustment bases of unprocessed units located after the traversal order and spatially adjacent, according to the weighting coefficients at the corresponding positions in the error spread matrix, until all units have been traversed, thereby generating a discrete phase matrix containing the error-corrected discrete phase values.

[0071] In another alternative embodiment, the shaping system can initialize and clear an error accumulation matrix with the same size as the ideal phase matrix. Subsequently, the shaping system can traverse the binary reflective array according to a preset traversal order. For the current binary reflective element, the shaping system can sum the ideal phase value with the accumulated value at the corresponding position in the error accumulation matrix, and perform 1-bit quantization on the summation result to generate a discrete phase value. Based on this, the shaping system can determine the error between the summation result and the discrete phase value, and write the error into the position corresponding to the subsequent neighboring element in the error accumulation matrix according to the weights of the error spread matrix. After completing the traversal, the shaping system can assemble the discrete phase values ​​generated by each element into a discrete phase matrix.

[0072] In another alternative embodiment, the shaping system can construct an adjacency table corresponding to the topology of the binary reflective array. Subsequently, the shaping system can process each binary reflective unit according to a preset traversal order and extract the ideal phase value of the current binary reflective unit. The shaping system can add the error contribution from the preceding unit, retrieved from the adjacency table, to this ideal phase value to obtain a corrected phase value. The shaping system can perform a threshold decision on the corrected phase value to obtain a discrete phase value, and can determine the decision error based on the corrected phase value and the discrete phase value. The shaping system can inject the decision error proportionally into the error buffer of the subsequent adjacent unit specified in the adjacency table, based on the pre-stored non-zero weights in the error diffusion matrix. The shaping system can iteratively execute the above process until all binary reflective units have been processed, collecting all discrete phase values ​​to form a discrete phase matrix.

[0073] For ease of understanding, Figure 2 This is a schematic diagram of an optional error diffusion process according to an embodiment of the present invention, such as... Figure 2 As shown, the shaping system can obtain the error spread matrix of the binary reflective array. Then, based on the ideal phase matrix, the shaping system can determine the ideal phase values ​​corresponding to each of the multiple binary reflective elements. Next, based on a preset traversal order, the error spread matrix, and the ideal phase values, the shaping system can perform error spread on the multiple binary reflective elements respectively until the traversal is complete, obtaining the discrete phase matrix.

[0074] Furthermore, based on the preset traversal order, error diffusion matrix, and ideal phase value, error diffusion is performed on multiple binary reflection units to determine the discrete phase matrix, including: determining the current traversal unit from multiple binary reflection units based on the preset traversal order; determining the phase adjustment base of the current traversal unit based on the ideal phase value; and performing error diffusion on the current traversal unit based on the phase adjustment base and the error diffusion matrix to determine the discrete phase matrix.

[0075] The aforementioned currently traversed unit can refer to a binary reflection unit that is currently locked by the algorithm pointer or iteration loop and is ready for phase quantization processing during the execution of the error diffusion quantization algorithm on the binary reflection array according to the preset traversal order.

[0076] The aforementioned phase adjustment base value refers to the reference phase value used to calculate the final discrete phase value of the current traversal unit before performing a 1-bit quantization operation. This phase adjustment base value can be obtained by adding the accumulated error value from previously quantized units through the error diffusion matrix to the initial ideal phase value of the current traversal unit in the ideal phase matrix. This phase adjustment base value represents the effective phase input value possessed by the current traversal unit at the quantization decision moment, after considering the spatial error diffusion effect, and can determine whether the current traversal unit is ultimately mapped to 0 degrees or 180 degrees.

[0077] In one alternative embodiment, considering that large-scale array-level quantization can be completed in linear time through sequential traversal and local error accumulation mechanisms, a beamforming process with low computational latency can be achieved while avoiding the exponential computational complexity brought about by global discrete adjustment algorithms.

[0078] Based on this, the shaping system can sequentially select the currently processed binary reflection unit as the current traversal unit according to a preset left-to-right or serpentine traversal order. Next, the shaping system can read the ideal phase value corresponding to the current traversal unit as the initial phase reference and accumulate the quantization error transmitted from previously processed units through the error diffusion matrix to generate a phase adjustment base. Subsequently, the shaping system can perform a 1-bit quantization operation on the phase adjustment base to obtain discrete phase values, and determine the quantization error based on the phase adjustment technique and the discrete phase values. Finally, the shaping system can distribute the quantization error generated by the current traversal unit to the unprocessed units located after the traversal order and spatially adjacent, according to the weighting coefficients defined in the error diffusion matrix, thereby gradually constructing a complete discrete phase matrix.

[0079] In another optional embodiment, the shaping system can sequentially select binary reflective elements in the binary reflective array as the current traversal element according to the raster scanning order. The shaping system can initialize an error accumulation matrix of the same size as the binary reflective array and add the ideal phase value of the currently traversed element to the corresponding value in the error accumulation matrix to obtain the phase adjustment base. Subsequently, the shaping system can perform a threshold comparison operation on the phase adjustment base to generate binary discrete phase values ​​and determine the error between the phase adjustment base and the discrete phase values. Then, the shaping system can use the energy transfer rules defined by the error diffusion matrix to distribute the error to the adjacent unprocessed elements on the right and bottom sides with different weights, and update the corresponding positions in the error accumulation matrix with the allocated error values. After completing the error diffusion of all binary reflective elements, the shaping system can output a discrete phase matrix.

[0080] In another alternative embodiment, the shaping system can select binary reflective units in the binary reflective array as the current traversal unit based on the access order defined by the Hilbert space-filling curve. The shaping system can add the ideal phase value of the current traversal unit to the error feedback accumulated from historical processing units and mapped to the position of the current traversal unit to obtain the phase adjustment basis. Subsequently, the shaping system can perform 1-bit quantization on the phase adjustment basis to obtain the discrete phase value of the current traversal unit. The shaping system can determine the phase difference before and after quantization as the quantization error, and can distribute this quantization error to multiple subsequent neighboring unprocessed units in the traversal sequence according to the non-zero weight terms in the error diffusion matrix. Through this error diffusion mechanism based on the space-filling curve, a uniformly distributed discrete phase matrix is ​​finally obtained.

[0081] For ease of understanding, Figure 3 This is a schematic diagram illustrating an optional binary reflection unit traversal process according to an embodiment of the present invention, as shown below. Figure 3 As shown, the shaping system can determine the current traversal unit from multiple binary reflection units based on a preset traversal order. Subsequently, the shaping system can determine the phase adjustment base of the current traversal unit based on the ideal phase value. Finally, the shaping system can perform error propagation on the current traversal unit based on the phase adjustment base and the error propagation matrix.

[0082] Further, based on the ideal phase value, the phase adjustment base of the current traversal unit is determined, including: obtaining the error diffusion state of the current traversal unit, which is used to characterize whether the current traversal unit receives diffusion errors from other binary reflection units besides the current traversal unit; if the current traversal unit does not receive diffusion errors from other binary reflection units, the ideal phase value corresponding to the current traversal unit is determined as the phase adjustment base, wherein other binary reflection units are binary reflection units besides the current traversal unit; if the current traversal unit receives diffusion errors from other binary reflection units, the diffusion errors and the ideal phase value are synthesized to obtain the phase adjustment base.

[0083] The aforementioned error diffusion state can refer to a logical or numerical marker characterizing whether the current traversal unit has accumulated phase corrections from previously processed units through the error diffusion mechanism during algorithm execution. This error diffusion state reflects the initial input composition of the current traversal unit before receiving new errors, i.e., whether the phase adjustment base of the current traversal unit is directly composed of the ideal phase value or is composed of the ideal phase value and historical accumulated errors.

[0084] The aforementioned diffusion error can refer to the quantization truncation error generated when previously quantized adjacent binary reflective units map continuous phase values ​​to discrete phase values. After being weighted by the corresponding weights in the error diffusion matrix, this diffusion error is passed to and accumulated in the phase input of the currently traversed unit, and is used to perform spatial domain compensation correction on the ideal phase value of the current unit before quantization decision.

[0085] In one alternative embodiment, considering that during error diffusion quantization, the phase decision of the current traversal unit depends not only on the ideal continuous phase value corresponding to the current traversal unit, but also on the quantization error transmitted from the previous processing unit, by acquiring and synthesizing these diffusion errors in real time, the non-periodic distribution of quantization errors in space can be ensured, thereby effectively suppressing parasitic quantization lobes.

[0086] Based on this, the shaping system can detect the error propagation state of the current traversal cell to determine whether the current traversal cell has accumulated quantization errors from previous processing cells. If the current traversal cell has not received any propagation errors, the shaping system can directly determine the ideal phase value of the current traversal cell as the phase adjustment base. However, if the current traversal cell has received propagation errors from other binary reflection cells, the shaping system can algebraically add the received propagation error value to the ideal phase value of the current traversal cell to synthesize the phase adjustment base, thereby providing an input reference containing error compensation information for subsequent 1-bit quantization operations.

[0087] For ease of understanding, Figure 4 This is a schematic diagram illustrating an optional phase adjustment base determination process according to an embodiment of the present invention, such as... Figure 4 As shown, the shaping system can acquire the error diffusion state of the currently traversed cell and determine whether the currently traversed cell has received diffusion errors from other binary reflective cells. If the currently traversed cell has not received diffusion errors from other binary reflective cells, the shaping system can determine the ideal phase value corresponding to the currently traversed cell as the phase adjustment base. If the currently traversed cell has received diffusion errors from other binary reflective cells, the shaping system can synthesize the diffusion errors and the ideal phase value to obtain the phase adjustment base.

[0088] Furthermore, based on the phase adjustment base and the error diffusion matrix, error diffusion is performed on the current traversal unit to determine the discrete phase matrix, including: performing a nearest-neighbor quantization operation on the current traversal unit based on the phase adjustment base to obtain the discrete phase value corresponding to the current traversal unit; determining the error value of the current traversal unit based on the phase adjustment base and the discrete phase value; and performing error diffusion on the error value based on the error diffusion matrix to determine the discrete phase matrix.

[0089] The aforementioned discrete phase value refers to the final phase control quantity, which contains only one of two discrete states, obtained by mapping the phase adjustment base of the current traversal unit through a 1-bit quantization function. This discrete phase value is the core data constituting the corresponding element in the discrete phase matrix.

[0090] The aforementioned error value can refer to the difference between the phase adjustment base of the current traversal cell and the finally determined discrete phase value, i.e., the quantization truncation error between the continuous phase and the discrete quantization result. This error value can reflect the accuracy loss introduced by the quantization process and can be passed to subsequent unprocessed adjacent cells through weighted calculation of the error diffusion matrix to compensate for the beam performance loss caused by discretization in the spatial domain.

[0091] In an alternative embodiment, considering that by performing a proximity quantization operation to map a continuous phase to a discrete phase, executable hardware control instructions can be generated, and by calculating the quantization error and spreading the quantization error to adjacent cells, far-field parasitic quantization lobes caused by periodic phase errors can be eliminated.

[0092] Based on this, the shaping system can perform a 1-bit nearest-neighbor quantization operation on the phase adjustment basis to obtain the discrete phase value corresponding to the current traversal unit. Next, the shaping system can subtract the discrete phase value from the phase adjustment basis to calculate the error value of the current traversal unit. Finally, the shaping system can distribute this error value to the unprocessed units located after the traversal sequence and spatially adjacent, according to the preset weighting coefficients in the error diffusion matrix. This allows these error values ​​to be accumulated in the subsequent processing of adjacent units, ultimately generating a complete discrete phase matrix.

[0093] Further, based on the error diffusion matrix, the error value is diffused to determine the discrete phase matrix, including: based on a preset traversal order, determining at least one neighboring reflection unit corresponding to the current traversal unit, wherein the at least one neighboring reflection unit is located after the current traversal unit in the preset traversal order; based on the error diffusion matrix, determining the diffusion weight corresponding to at least one neighboring reflection unit respectively; and based on the diffusion weight, diffusing the error value to at least one neighboring reflection unit to determine the discrete phase matrix.

[0094] The aforementioned neighboring reflective elements refer to other binary reflective elements in the binary reflective array that are located after the currently traversed element according to a preset traversal order, and that are spatially adjacent to the currently traversed element or within the influence range defined by the error diffusion matrix. These neighboring reflective elements have not yet undergone quantization operations, and therefore can receive and accumulate the quantization error transmitted from the currently traversed element for subsequent phase correction.

[0095] The aforementioned diffusion weight can refer to the coefficient value in the error diffusion matrix corresponding to the spatial relative position of the current traversal cell and the neighboring reflection cells. This diffusion weight determines what proportion of the quantization error generated by the current traversal cell is transmitted and accumulated into the phase adjustment basis of each neighboring reflection cell, thereby achieving a non-uniform distribution of error in the spatial domain to disrupt the periodicity of the error.

[0096] In one alternative embodiment, it is considered that by establishing a directional propagation mechanism for the error, the periodic distribution of the quantization error in space can be broken, thereby suppressing parasitic quantization lobes and reducing sidelobe levels.

[0097] Based on this, the shaping system can determine the position of the current processing unit in the array according to a preset traversal order, and identify at least one neighboring reflective unit that is in a subsequent position in the preset traversal order. Next, the shaping system can read the weighting coefficients in the error diffusion matrix that match the relative spatial geometry of the currently traversed unit and its neighboring units, thereby determining the error diffusion proportion borne by each neighboring reflective unit. Finally, the shaping system can multiply the quantization error value generated by the currently traversed unit by the corresponding diffusion weight, and accumulate the weighted error amount into the phase adjustment base of the neighboring reflective units. As the traversal process progresses, the error value is gradually dispersed throughout the entire binary reflective array, ultimately forming a discrete phase matrix.

[0098] Furthermore, the method also includes: constructing a discrete phase matrix based on the discrete phase values ​​corresponding to multiple binary reflection units.

[0099] In one alternative embodiment, considering that the discrete phase values ​​of the individual binary reflection units are spatially aggregated, a complete array control data set can be formed, thereby providing a unified data structure for subsequent hardware drivers.

[0100] Based on this, the shaping system can initialize a two-dimensional matrix data structure that matches the physical layout size of the binary reflector array. Then, the shaping system can sequentially fill the corresponding coordinate positions in the two-dimensional matrix with the discrete phase value determined after error diffusion processing for each binary reflector unit according to a preset traversal order, until the discrete phase values ​​of all binary reflector units are filled, and finally construct a discrete phase matrix containing the discrete phase distribution information of the entire binary reflector array.

[0101] Furthermore, based on the initial phase matrix, multiple simulated beam projections are performed into the far-field radiation pattern domain to obtain the ideal phase matrix of the binary reflector array. This includes: inputting the initial phase matrix into the array radiation pattern model, processing the initial phase matrix using the array radiation pattern model to obtain a far-field radiation pattern complex matrix, where the complex matrix includes phase and amplitude; replacing the amplitude in the far-field radiation pattern complex matrix with a preset amplitude to obtain a corrected radiation pattern complex matrix; performing near-field inverse mapping on the corrected radiation pattern complex matrix to obtain the intermediate phase matrix of the binary reflector array; determining the degree of deviation between the far-field radiation pattern corresponding to the intermediate phase matrix and the preset radiation pattern, and determining the intermediate phase matrix as the ideal phase matrix if the deviation is less than a preset value.

[0102] The aforementioned array pattern model can refer to a model based on electromagnetic field theory, which can be used to describe the conversion relationship between the spatial distribution, phase distribution, and far-field radiation characteristics of each binary reflector unit in a binary reflector array. This array pattern model can be implemented through Fourier transform or array factor formula, converting the complex field distribution (including amplitude and phase) on the array aperture into radiation pattern data in the far-field space.

[0103] The aforementioned far-field pattern complex matrix can refer to a dataset output from the array pattern model, used to describe the distribution of radiation field intensity of a binary reflector array in various angular directions in the far-field space. Each element in this far-field pattern complex matrix is ​​a complex number, where the real part or modulus represents the radiation amplitude in that direction, and the imaginary part or argument represents the phase information in that direction.

[0104] The aforementioned preset amplitude can refer to the target amplitude distribution pattern that is pre-set according to the communication task requirements and is expected to be achieved in the far-field radiation pattern, which is used as a reference for correcting the far-field radiation characteristics during the iteration process.

[0105] The aforementioned modified radiation pattern complex matrix can refer to a new complex matrix formed by replacing the amplitude information in the far-field radiation pattern complex matrix with a preset amplitude, while retaining the phase information in the original far-field radiation pattern complex matrix. This modified radiation pattern complex matrix is ​​forced to meet the target performance requirements in the amplitude domain, while retaining the phase characteristics under the current iteration step in the phase domain. It can be used as intermediate data for inverse mapping to the near field in the alternating projection algorithm to gradually approximate a better solution that satisfies both amplitude and phase constraints.

[0106] The aforementioned intermediate phase matrix can refer to the complex matrix obtained by performing a near-field inverse mapping (i.e., inverse Fourier transform or array factor inverse transform) on the modified pattern complex matrix, reflecting the phase distribution of each binary reflector element on the aperture plane of the binary reflector array. This intermediate phase matrix can include the theoretical phase distribution generated on the array plane in the current iteration step to satisfy the modified far-field amplitude constraint.

[0107] The aforementioned far-field radiation pattern can refer to the distribution graph or data set of the actual radiated energy of a binary reflector array in the far-field space, obtained by forward calculation of the intermediate phase matrix through an array radiation pattern model. This pattern describes the distribution of energy as a function of angle. The far-field radiation pattern can intuitively reflect the radiation characteristics of the binary reflector array in the current iteration state, including the main lobe pointing direction, width, side lobe level, and null position.

[0108] The aforementioned preset radiation pattern can refer to the ideal far-field radiation performance standard that is predefined based on communication link budget and anti-interference requirements. This preset radiation pattern can serve as a benchmark for judging whether the current iteration result has converged or meets the design requirements.

[0109] The aforementioned deviation can refer to the quantified difference between the far-field pattern obtained in the current iteration and the preset pattern in terms of amplitude distribution, phase distribution, or overall performance indicators. This deviation can be used to measure how close the current iteration state is to the target performance. If the deviation is lower than the preset value, it indicates that the iteration process has converged, and the current intermediate phase matrix is ​​considered to be the ideal phase matrix that satisfies the constraints.

[0110] In one alternative embodiment, considering that an iterative adjustment mechanism can be used to obtain a better continuous phase distribution at the aperture of the binary reflector array while satisfying the far-field beam shape constraints, thereby providing a high-precision reference for subsequent discrete quantization.

[0111] Based on this, the shaping system can input the initial phase matrix into the array pattern model to generate a far-field pattern complex matrix containing phase and amplitude information. Next, the shaping system can replace the amplitude components in the far-field pattern complex matrix with preset amplitudes to form a corrected pattern complex matrix. Subsequently, the shaping system can perform near-field inverse mapping on the corrected pattern complex matrix to generate an intermediate phase matrix containing more recent phase information. Finally, the shaping system can evaluate the deviation between the far-field pattern corresponding to the intermediate phase matrix and the preset pattern. If the deviation is less than a preset value, the shaping system can stop iterating and determine the current intermediate phase matrix as the ideal phase matrix.

[0112] Furthermore, the method also includes: when the deviation is greater than or equal to a preset value, repeatedly executing the steps of inputting the intermediate phase matrix into the array pattern model, processing the intermediate phase matrix using the array pattern model to obtain a new far-field pattern complex matrix, replacing the amplitude in the new far-field pattern complex matrix with a preset amplitude to obtain a new corrected pattern complex matrix, performing near-field inverse mapping on the new corrected pattern complex matrix to obtain a new intermediate phase matrix, and determining the degree of deviation between the far-field pattern corresponding to the new intermediate phase matrix and the preset pattern, until the deviation is less than the preset value, or the number of repetitions reaches the preset number.

[0113] In one alternative embodiment, considering that the iterative adjustment mechanism can gradually approximate a better continuous phase solution that satisfies the far-field pattern constraint, the obtained ideal phase matrix can be ensured to have sufficient accuracy when the number of iterations reaches the upper limit or the error converges.

[0114] Based on this, when the shaping system determines that the deviation between the far-field pattern corresponding to the intermediate phase matrix and the preset pattern is greater than or equal to a preset value, the shaping system can re-input the current intermediate phase matrix into the array pattern model to generate a new far-field pattern complex matrix. Then, the shaping system can replace the amplitude components in the new far-field pattern complex matrix with preset amplitudes to form a new corrected pattern complex matrix, and perform near-field inverse mapping on the new corrected pattern complex matrix to update and obtain a new intermediate phase matrix. On this basis, the shaping system can recalculate the deviation between the far-field pattern corresponding to the new intermediate phase matrix and the preset pattern, repeating the above process until the deviation is less than a preset value or the number of iterations reaches a preset number, finally outputting an ideal phase matrix that meets the accuracy requirements.

[0115] For ease of understanding, Figure 5 This is a schematic diagram of an optional satellite beamforming process according to an embodiment of the present invention, such as... Figure 5 As shown, the shaping system can determine the ideal phase matrix of the 1-bit RIS using the alternating projection method and initialize the error spread filter weights. Subsequently, the shaping system can read the continuous phases of the currently traversed cells in a serpentine sequence and perform 1-bit nearest-neighbor quantization. Based on this, the shaping system can determine the phase quantization error of the currently traversed cell and can accumulate the error proportionally to the surrounding unprocessed adjacent cells. At this point, the shaping system can determine whether all binary reflection cells have been traversed. If all binary reflection cells have been traversed, a discrete phase matrix can be output and injected into the driving circuit. If not, the traversal can continue in a serpentine sequence.

[0116] According to an embodiment of the present invention, a satellite beamforming device is provided. It should be noted that this device can be used to perform the above-described satellite beamforming method. The specific implementation process and application scenarios are the same as those in the above embodiment, and will not be repeated here. Figure 6 This is a schematic diagram of a satellite beamforming device according to an embodiment of the present invention, such as... Figure 6 As shown, the device includes:

[0117] The acquisition module 602 is used to acquire the initial phase matrix of the binary reflector array on the satellite, wherein the binary reflector array is used to synthesize electromagnetic wave beams, and different phase matrices produce electromagnetic wave beams with different beam shapes.

[0118] The simulation module 604 is used to perform multiple simulated beam projections into the far-field directional map domain based on the initial phase matrix to obtain the ideal phase matrix of the binary reflector array.

[0119] The diffusion module 606 is used to perform error diffusion on the binary reflective array based on the ideal phase matrix in order to determine the discrete phase matrix of the binary reflective array.

[0120] Module 608 is used to construct control signals for a binary reflection array based on a discrete phase matrix, so as to control the binary reflection array to synthesize an electromagnetic wave beam.

[0121] Furthermore, the binary reflective array includes multiple binary reflective units; the diffusion module is also used to: obtain the error diffusion matrix of the binary reflective array; determine the ideal phase value corresponding to each of the multiple binary reflective units based on the ideal phase matrix; and perform error diffusion on the multiple binary reflective units based on the preset traversal order, the error diffusion matrix and the ideal phase value to determine the discrete phase matrix.

[0122] Furthermore, the diffusion module is also used to: determine the current traversal unit from multiple binary reflection units based on a preset traversal order; determine the phase adjustment base of the current traversal unit based on the ideal phase value; and perform error diffusion on the current traversal unit based on the phase adjustment base and the error diffusion matrix to determine the discrete phase matrix.

[0123] Furthermore, the diffusion module is also used to: obtain the error diffusion state of the current traversed unit, the error diffusion state being used to characterize whether the current traversed unit receives diffusion errors from other binary reflection units besides the current traversed unit; if the current traversed unit does not receive diffusion errors from other binary reflection units, determine the ideal phase value corresponding to the current traversed unit as the phase adjustment base, where other binary reflection units are binary reflection units besides the current traversed unit; if the current traversed unit receives diffusion errors from other binary reflection units, synthesize the diffusion error and the ideal phase value to obtain the phase adjustment base.

[0124] Furthermore, the diffusion module is also used to: perform nearest-neighbor quantization on the current traversal unit based on the phase adjustment base to obtain the discrete phase value corresponding to the current traversal unit; determine the error value of the current traversal unit based on the phase adjustment base and the discrete phase value; and perform error diffusion on the error value based on the error diffusion matrix to determine the discrete phase matrix.

[0125] Furthermore, the diffusion module is also used to: determine at least one neighboring reflection unit corresponding to the current traversal unit based on a preset traversal order, wherein the at least one neighboring reflection unit is located after the current traversal unit in the preset traversal order; determine the diffusion weight corresponding to at least one neighboring reflection unit based on the error diffusion matrix; and diffuse the error value to at least one neighboring reflection unit based on the diffusion weight to determine the discrete phase matrix.

[0126] Furthermore, the device also includes a matrix construction module for constructing a discrete phase matrix based on the discrete phase values ​​corresponding to multiple binary reflective units.

[0127] Furthermore, the simulation module is also used to: input the initial phase matrix into the array pattern model, process the initial phase matrix using the array pattern model to obtain a far-field pattern complex matrix, wherein the complex matrix includes phase and amplitude; replace the amplitude in the far-field pattern complex matrix with a preset amplitude to obtain a corrected pattern complex matrix; perform near-field inverse mapping on the corrected pattern complex matrix to obtain the intermediate phase matrix of the binary reflector array; determine the degree of deviation between the far-field pattern corresponding to the intermediate phase matrix and the preset pattern, and determine the intermediate phase matrix as the ideal phase matrix if the deviation is less than a preset value.

[0128] Furthermore, the device also includes: a loop control module, used to repeatedly execute the following steps when the deviation is greater than or equal to a preset value: inputting the intermediate phase matrix into the array pattern model, processing the intermediate phase matrix using the array pattern model to obtain a new far-field pattern complex matrix, replacing the amplitude in the new far-field pattern complex matrix with a preset amplitude to obtain a new corrected pattern complex matrix, performing near-field inverse mapping on the new corrected pattern complex matrix to obtain a new intermediate phase matrix, and determining the degree of deviation between the far-field pattern corresponding to the new intermediate phase matrix and the preset pattern, until the deviation is less than the preset value or the number of repetitions reaches the preset number.

[0129] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention during runtime.

[0130] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0131] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0132] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of the present invention.

[0133] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of the present invention.

[0134] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0136] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0137] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0138] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0139] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A satellite beamforming method, characterized in that, include: Obtain the initial phase matrix of the binary reflector array on the satellite, wherein the binary reflector array is used to synthesize electromagnetic wave beams, and different phase matrices produce electromagnetic wave beams with different beam shapes; Based on the initial phase matrix, multiple simulated beam projections are performed into the far-field directional map domain to obtain the ideal phase matrix of the binary reflection array. Based on the ideal phase matrix, error diffusion is performed on the binary reflection array to determine the discrete phase matrix of the binary reflection array; Based on the discrete phase matrix, a control signal for the binary reflection array is constructed to control the binary reflection array to synthesize an electromagnetic wave beam.

2. The satellite beamforming method according to claim 1, characterized in that, The binary reflection array includes multiple binary reflection units; Based on the ideal phase matrix, error propagation is performed on the binary reflector array to determine the discrete phase matrix of the binary reflector array, including: Obtain the error diffusion matrix of the binary reflection array; Based on the ideal phase matrix, the ideal phase values ​​corresponding to the plurality of binary reflection units are determined respectively; Based on the preset traversal order, the error diffusion matrix, and the ideal phase value, error diffusion is performed on the plurality of binary reflection units to determine the discrete phase matrix.

3. The satellite beamforming method according to claim 2, characterized in that, Based on a preset traversal order, the error diffusion matrix, and the ideal phase value, error diffusion is performed on the plurality of binary reflection units to determine the discrete phase matrix, including: Based on the preset traversal order, the current traversal unit is determined from the plurality of binary reflection units; Based on the ideal phase value, determine the phase adjustment base of the current traversal unit; Based on the phase adjustment base and the error diffusion matrix, the current traversal unit is subjected to error diffusion to determine the discrete phase matrix.

4. The satellite beamforming method according to claim 3, characterized in that, Based on the ideal phase value, the phase adjustment base of the current traversal unit is determined, including: Obtain the error diffusion state of the current traversal unit, the error diffusion state being used to characterize whether the current traversal unit receives diffusion errors from other binary reflection units besides the current traversal unit; If the current traversal unit does not receive diffusion errors from other binary reflection units, the ideal phase value corresponding to the current traversal unit is determined to be the phase adjustment base, wherein the other binary reflection units are binary reflection units other than the current traversal unit. When the current traversal unit receives the diffusion error from the other binary reflection unit, the diffusion error and the ideal phase value are synthesized to obtain the phase adjustment base.

5. The satellite beamforming method according to claim 3, characterized in that, Based on the phase adjustment base and the error diffusion matrix, error diffusion is performed on the current traversal unit to determine the discrete phase matrix, including: Based on the phase adjustment base, the nearest quantization operation is performed on the current traversal unit to obtain the discrete phase value corresponding to the current traversal unit; Based on the phase adjustment base and the discrete phase value, the error value of the current traversal unit is determined; Based on the error diffusion matrix, the error value is subjected to error diffusion to determine the discrete phase matrix.

6. The satellite beamforming method according to claim 5, characterized in that, Based on the error diffusion matrix, the error value is subjected to error diffusion to determine the discrete phase matrix, including: Based on the preset traversal order, at least one neighboring reflection unit corresponding to the current traversal unit is determined, wherein the at least one neighboring reflection unit is located after the current traversal unit in the preset traversal order; Based on the error diffusion matrix, the diffusion weights corresponding to the at least one neighboring reflection unit are determined respectively; Based on the diffusion weight, the error value is diffused to the at least one neighboring reflection unit to determine the discrete phase matrix.

7. The satellite beamforming method according to any one of claims 1 or 6, characterized in that, The method further includes: The discrete phase matrix is ​​constructed based on the discrete phase values ​​corresponding to multiple binary reflection units.

8. The satellite beamforming method according to claim 7, characterized in that, Based on the initial phase matrix, multiple simulated beam projections are performed into the far-field directional domain to obtain the ideal phase matrix of the binary reflector array, including: The initial phase matrix is ​​input into the array pattern model, and the array pattern model is used to process the initial phase matrix to obtain a far-field pattern complex matrix, wherein the complex matrix includes phase and amplitude; The amplitude in the far-field pattern complex matrix is ​​replaced with a preset amplitude to obtain the modified pattern complex matrix; Near-field inverse mapping is performed on the modified radiation pattern complex matrix to obtain the intermediate phase matrix of the binary reflection array; The degree of deviation between the far-field radiation pattern corresponding to the intermediate phase matrix and the preset radiation pattern is determined. If the degree of deviation is less than the preset value, the intermediate phase matrix is ​​determined to be the ideal phase matrix.

9. The satellite beamforming method according to claim 8, characterized in that, The method further includes: If the deviation is greater than or equal to the preset value, the following steps are repeated: inputting the intermediate phase matrix into the array pattern model, processing the intermediate phase matrix using the array pattern model to obtain a new far-field pattern complex matrix, replacing the amplitude in the new far-field pattern complex matrix with a preset amplitude to obtain a new corrected pattern complex matrix, performing near-field inverse mapping on the new corrected pattern complex matrix to obtain a new intermediate phase matrix, and determining the deviation between the far-field pattern corresponding to the new intermediate phase matrix and the preset pattern, until the deviation is less than the preset value or the number of repetitions reaches a preset number.

10. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 9.

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