A distributed communication transmission system and method based on RIS

Abstract

The application discloses a distributed communication transmission system and method based on RIS, and relates to the field of RIS communication.The central control unit of the application can analyze communication data and environmental feedback in real time, generate optimized control instructions, and dynamically adjust the phase, amplitude and polarization of signals through the programmable control unit of RIS.In addition, the adaptive algorithm and machine learning algorithm used by the application further improve the intelligent level of beam forming and tracking, ensuring the robustness and adaptability of the system in complex environments.

Description

Technical Field

[0001] This invention relates to the field of RIS communication, specifically a RIS-based distributed communication transmission system and method. Background Technology

[0002] In the field of wireless communication, distributed communication transmission systems play a crucial role, utilizing advanced technologies to achieve long-distance data transmission and communication. Existing technologies typically include a transmitter, a receiver, and a fixed or adjustable antenna array to guide the propagation of electromagnetic waves. These systems perform well in many applications, such as mobile communication, satellite communication, and military communication. However, with the increasing demands for communication, especially in high-speed data transmission and wide-area coverage, existing systems face some limitations.

[0003] Against the backdrop of rapid development in wireless communication technology, significant progress has been made in the design and implementation of distributed communication transmission systems. In recent years, in particular, Reconfigurable Intelligent Surface (RIS) technology, as an emerging solution, has attracted widespread attention from academia and industry. RIS technology provides a flexible means of controlling the propagation of wireless signals by using a large number of low-cost, programmable reflective or transmissive elements. The core of RIS technology lies in its ability to adjust the phase, amplitude, and even polarization of incident electromagnetic waves, thereby intelligently reconstructing the propagation environment of wireless signals. This technology utilizes the principle of electromagnetic wave-matter interaction, dynamically changing the electromagnetic properties of the intelligent surface through electronic control to adapt to different communication needs. Compared with traditional fixed-beam or mechanically adjusted antennas, RIS technology offers greater flexibility and lower energy consumption, while reducing system complexity and cost.

[0004] Furthermore, the main shortcomings of existing technologies lie in the insufficient flexibility of beamforming and tracking. Traditional fixed-beam or mechanically adjusted antennas cannot quickly adapt to dynamically changing communication environments, such as changes in the orientation of mobile user equipment or the appearance of obstacles in the signal propagation path. In addition, existing systems also have deficiencies in real-time data processing and environmental feedback response, causing beamforming and tracking algorithms to fail to fully utilize current environmental information to optimize signal transmission. These problems limit the performance and reliability of the system in complex environments, especially in scenarios requiring rapid response and high data transmission rates. Summary of the Invention

[0005] This invention discloses a RIS-based distributed communication transmission system, which significantly improves the efficiency and accuracy of beamforming and tracking by integrating advanced data processing modules and intelligent algorithm processing modules.

[0006] A RIS-based distributed communication transmission system includes:

[0007] The transmitting end is used to transmit electromagnetic signals at a predetermined frequency;

[0008] The receiving end is used to receive and process electromagnetic signals reflected from the reconfigurable smart surface;

[0009] At least one reconfigurable smart surface for dynamically adjusting the phase, amplitude, and polarization of the incident electromagnetic signal;

[0010] The input of the reconfigurable smart surface is connected to the output of the transmitter via an RF cable, and the output of the reconfigurable smart surface is connected to the input of the receiver via an antenna; the reconfigurable smart surface includes a programmable control unit and a central control unit, and the central control unit is connected to at least one programmable control unit;

[0011] The central control unit is characterized in that it generates control commands through intelligent algorithms based on real-time communication data and environmental feedback data to perform beamforming and beam tracking.

[0012] Furthermore, the programmable control unit includes:

[0013] At least one phase adjuster is used to receive instructions from the central control unit and adjust the phase of the electromagnetic signal in real time to control the direction of the beam.

[0014] At least one amplitude modulator is used to adjust the signal strength according to instructions from the central control unit, and to optimize the beam shape and coverage.

[0015] At least one polarization regulator is used to adjust the polarization state of the electromagnetic signal according to the signal reception requirements of the communication equipment.

[0016] Furthermore, the central control unit includes:

[0017] The data processing module is used to analyze real-time communication data and environmental feedback data;

[0018] The intelligent algorithm processing module is used to execute beamforming and beam tracking algorithms;

[0019] The instruction generation module is used to generate control instructions based on the output of the intelligent algorithm.

[0020] The intelligent algorithm processing module automatically adjusts beam parameters based on changes in the communication environment using an adaptive algorithm, and optimizes beamforming and tracking processes using a machine learning algorithm.

[0021] Furthermore, the intelligent algorithm processing module includes:

[0022] The beamforming unit is used to calculate and allocate the weights of the programmable control unit to form the desired beam direction;

[0023] The RSSI measurement unit is used to estimate the optimal beam pointing angle based on the RSSI value and a known signal propagation model.

[0024] The beam adjustment unit is used to dynamically adjust the beam direction according to the target's real-time position and motion parameters;

[0025] The optimization unit is used to dynamically adjust the parameters of the beamforming and tracking algorithms based on historical beamforming and tracking data and current communication environment parameters, according to the prediction results of the machine learning model.

[0026] Furthermore, in the beamforming unit, the weights of the programmable control unit are specifically expressed as follows:

[0027]

[0028] Wherein, w i Let N represent the weight of the i-th programmable control unit, N represent the total number of programmable control units in the array, j represent the number of programmable control units, and h represent the weight of the i-th programmable control unit. j This represents the complex weight corresponding to the target direction, where ∈ indicates a positive number, used to ensure numerical stability.

[0029] Furthermore, in the RSSI measurement unit, the estimation of the optimal beam pointing angle based on the RSSI value and the known signal propagation model is specifically expressed as follows:

[0030]

[0031] Wherein, the θ opt The w represents the optimal beam pointing angle. i (θ) represents the weight of the i-th unit pointing at angle θ, where θ represents the pointing angle, and |s i | 2 This represents the power of the signal received by the i-th unit.

[0032] A RIS-based distributed communication transmission method, implemented based on any of the aforementioned RIS-based distributed communication transmission systems, includes the following steps:

[0033] S1. The transmitter outputs an electromagnetic signal at a predetermined frequency and transmits the electromagnetic signal to the reconfigurable smart surface via an RF cable;

[0034] S2. The reconfigurable smart surface receives signals from the transmitter and dynamically adjusts the phase, amplitude, and polarization of the signals according to the instructions of the central control unit through the built-in programmable control unit;

[0035] S3. The central control unit analyzes real-time data and environmental feedback, generates control commands through intelligent algorithms, and the reconfigurable intelligent surface performs beamforming and beam tracking through control commands to optimize signal direction;

[0036] S4. The reconfigurable smart surface treatment and optimization signal is transmitted to the receiver via the antenna, and the receiver receives and processes the signal.

[0037] Furthermore, step S3 specifically includes the following sub-steps:

[0038] S301. Calculate and allocate the weights of the programmable control unit to form the desired beam direction;

[0039] S302. Estimate the optimal beam pointing angle based on the RSSI value and the known signal propagation model;

[0040] S303. Dynamically adjust the beam direction based on the target's real-time position and motion parameters;

[0041] S304. Based on historical beamforming and tracking data and current communication environment parameters, dynamically adjust the parameters of the beamforming and tracking algorithms according to the prediction results of the machine learning model.

[0042] Furthermore, in step S301, the calculation of the weights of the programmable control unit is specifically expressed as follows:

[0043]

[0044] Wherein, w i Let N represent the weight of the i-th programmable control unit, N represent the total number of programmable control units in the array, j represent the number of programmable control units, and h represent the weight of the i-th programmable control unit. j This represents the complex weight corresponding to the target direction, where ∈ indicates a positive number, used to ensure numerical stability.

[0045] Furthermore, step S302 is specifically expressed as follows:

[0046]

[0047] Wherein, the θ opt The w represents the optimal beam pointing angle. i (θ) represents the weight of the i-th unit pointing at angle θ, where θ represents the pointing angle, and |s i | 2 This represents the power of the signal received by the i-th unit.

[0048] The beneficial effects of the invention are:

[0049] The central control unit of this invention analyzes communication data and environmental feedback in real time to generate optimized control commands, and dynamically adjusts the phase, amplitude, and polarization of the signal through the RIS programmable control unit. Furthermore, the adaptive and machine learning algorithms employed in this invention further enhance the intelligence level of beamforming and tracking, ensuring the system's robustness and adaptability in complex environments. Attached Figure Description

[0050] Figure 1 This is a system architecture diagram of a RIS-based distributed communication transmission system provided in an embodiment of the present invention. Detailed Implementation

[0051] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention; that is, the described embodiments are only a part of the embodiments of the invention, and not all of them. The components of the embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0053] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention. It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0054] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0055] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0056] A RIS-based distributed communication transmission system includes:

[0057] The transmitting end is used to transmit electromagnetic signals at a predetermined frequency;

[0058] The receiving end is used to receive and process electromagnetic signals reflected from the reconfigurable smart surface;

[0059] At least one reconfigurable smart surface for dynamically adjusting the phase, amplitude, and polarization of the incident electromagnetic signal;

[0060] The input of the reconfigurable smart surface is connected to the output of the transmitter via an RF cable, and the output of the reconfigurable smart surface is connected to the input of the receiver via an antenna; the reconfigurable smart surface includes a programmable control unit and a central control unit, and the central control unit is connected to at least one programmable control unit;

[0061] The central control unit is characterized in that it generates control commands through intelligent algorithms based on real-time communication data and environmental feedback data to perform beamforming and beam tracking.

[0062] Reconfigurable Intelligent Surface (RIS) is a novel technology for future mobile communication systems. Typically a two-dimensional array surface composed of large-scale, subwavelength, programmable units, it can actively and flexibly control the phase, amplitude, polarization, and / or frequency of electromagnetic signals, intelligently reconfiguring the wireless transmission environment. This overcomes the uncontrollable characteristics of traditional wireless channels and significantly improves the performance of mobile communication networks. Unlike traditional massive MIMO technology, RIS achieves directional reflection / refraction / transmission of electromagnetic signals in three-dimensional space, as well as signal enhancement or suppression, featuring high performance, low cost, low power consumption, and easy deployment. As a novel and highly promising fundamental key technology, RIS provides an interface between the physical electromagnetic world and the digital information world, constructing a new paradigm for intelligent programmable wireless environments. The reconfigurable intelligent surface described in this embodiment uses RIS series devices in the Sub-6 GHz and millimeter-wave bands, with 1-2 bit unit independent phase control functions, enabling sub-microsecond flexible modulation of linearly polarized or orthogonally bipolarized signals. It boasts excellent performance, low cost, low power consumption, and a thin and light planar design. RIS phased array base station antennas in the Sub-6 GHz and millimeter-wave bands offer radiation performance comparable to traditional technologies while reducing costs by over 90% and power consumption by over 60%, effectively lowering construction and maintenance costs for communication base stations. A typical RIS architecture based on phased electromagnetic surface technology consists of a RIS cell array and a wave control system. Each RIS cell integrates control devices such as pin diodes, providing flexible phase modulation or signal amplification and regeneration capabilities for incident electromagnetic wave signals. An array of RIS cells of a certain scale is formed, and the wave control system flexibly regulates the operating state of the integrated control devices in each cell, thereby achieving intelligent wireless channel reconfiguration. RIS cell array fabrication is typically based on multilayer microwave composite substrate technology, while the wave control system employs a large-scale parallel high-speed integrated control circuit with an FPGA as the main control chip.

[0063] Specifically, RIS is a subwavelength-sized artificial two-dimensional material, typically composed of metals, dielectrics, and tunable elements, and can be equivalently characterized as an RLC circuit. Adjusting the physical properties of the electromagnetic units, such as capacitive reactance, impedance, or inductive reactance, alters the radiation characteristics of the RIS, achieving unconventional physical phenomena such as irregular reflection, negative refraction, absorption, focusing, and polarization conversion, thereby dynamically controlling electromagnetic waves. For example, installing a RIS phased array on the booster station side of a wind farm can serve as a VPN public network signal transmitter, transmitting 5G / 4G signal beams to the RIS target receiver on the wind turbine. The RIS target receiver then uses a Wi-Fi router to cover all areas of the wind turbine, including the nacelle, turbine layer, and transformer substation. Furthermore, due to the uncertainty and uncontrollability of the electromagnetic environment, which can lead to the leakage of confidential information and complex interference, deploying RIS devices near eavesdropping users allows the reflected signals to be tuned to cancel the direct link signal received from the base station and the eavesdropper, effectively reducing information leakage.

[0064] As a preferred technical solution of the above embodiments, the specific integration relationship of the RIS communication system for wind power generation is as follows:

[0065] Link 1: External network communication signal — (RJ45) phased array directional transmitter (RIS beam) — phased array directional receiver (RIS beam) — network switch (RJ45) — phased array large array transmitter (RIS beam) — phased array omnidirectional target receiver (RJ45) — nacelle top router (can support multiple services) (RJ45 / wifi) — wind turbine layer router (can support multiple services) (RJ45 / wifi) — transformer top router (can support multiple services) (RJ45 / wifi); The Ethernet interface of the communication module is connected to the router with a network cable and then connected to another router with a network cable to extend the signal coverage. The specific number of services supported depends on the bandwidth occupied by the services.

[0066] Link 2: External network communication signal — (RJ45) phased array directional transmitter (RIS beam) — phased array directional receiver (RIS beam) — network switch (RJ45) — optical transceiver (fiber optic signal) — ring network optical transceiver (fiber optic signal) — transformer top router (can support multiple services) (RJ45 / wifi) — wind turbine layer router (can support multiple services) (RJ45 / wifi) — transformer top router (can support multiple services) (RJ45 / wifi); The Ethernet interface of the communication module is connected to the router with a network cable and then connected to another router with a network cable to extend the signal coverage. The specific number of services supported depends on the bandwidth occupied by the services.

[0067] In addition, the real-time communication data and environmental feedback data described in the above embodiments may, for example, include signal strength index (RSSI), signal-to-noise ratio (SNR), and channel state information (CSI). These example indicators can reflect signal propagation loss, communication quality, and channel characteristics. They may also include user location and speed information, as well as device orientation and attitude, providing the system with dynamic behavior of user devices, enabling the beam to track users in real time. Environmental feedback data may include environmental feature data, such as buildings, terrain, and vegetation, as well as multipath effects and obstruction, helping the system understand and predict physical obstacles that may be encountered during signal propagation. Historical communication data provides training data for machine learning models, optimizing beamforming algorithms. Real-time network status, including network load and spectrum usage, is also included, ensuring the system can effectively allocate resources. Device feedback information, such as battery level and processing power, as well as interference sources and weather conditions, further provides the basis for the system to adjust its operating parameters. By integrating these multi-dimensional data, the central control unit can generate control commands to dynamically adjust the reflective characteristics of the reconfigurable smart surface to adapt to the constantly changing communication environment and user needs.

[0068] Furthermore, the programmable control unit includes:

[0069] At least one phase adjuster is used to receive instructions from the central control unit and adjust the phase of the electromagnetic signal in real time to control the direction of the beam.

[0070] At least one amplitude modulator is used to adjust the signal strength according to instructions from the central control unit, and to optimize the beam shape and coverage.

[0071] At least one polarization regulator is used to adjust the polarization state of the electromagnetic signal according to the signal reception requirements of the communication equipment.

[0072] Furthermore, the central control unit includes:

[0073] The data processing module is used to analyze real-time communication data and environmental feedback data;

[0074] The intelligent algorithm processing module is used to execute beamforming and beam tracking algorithms;

[0075] The instruction generation module is used to generate control instructions based on the output of the intelligent algorithm.

[0076] The intelligent algorithm processing module automatically adjusts beam parameters based on changes in the communication environment using an adaptive algorithm, and optimizes beamforming and tracking processes using a machine learning algorithm.

[0077] Furthermore, the intelligent algorithm processing module includes:

[0078] The beamforming unit is used to calculate and allocate the weights of the programmable control unit to form the desired beam direction;

[0079] The RSSI measurement unit is used to estimate the optimal beam pointing angle based on the RSSI value and a known signal propagation model.

[0080] The beam adjustment unit is used to dynamically adjust the beam direction according to the target's real-time position and motion parameters;

[0081] The optimization unit is used to dynamically adjust the parameters of the beamforming and tracking algorithms based on historical beamforming and tracking data and current communication environment parameters, according to the prediction results of the machine learning model.

[0082] Furthermore, in the beamforming unit, the weights of the programmable control unit are specifically expressed as follows:

[0083]

[0084] Wherein, w i Let N represent the weight of the i-th programmable control unit, N represent the total number of programmable control units in the array, j represent the number of programmable control units, and h represent the weight of the i-th programmable control unit. j This represents the complex weight corresponding to the target direction, where ∈ indicates a positive number, used to ensure numerical stability.

[0085] Furthermore, in the RSSI measurement unit, the estimation of the optimal beam pointing angle based on the RSSI value and the known signal propagation model is specifically expressed as follows:

[0086]

[0087] Wherein, the θ opt The w represents the optimal beam pointing angle. i (θ) represents the weight of the i-th unit pointing at an angle θ, where θ represents the pointing angle, and |s i | 2 This represents the power of the signal received by the i-th unit.

[0088] Preferably, in the beam adjustment unit, the adjustment process is implemented using a Kalman filter, specifically through state updates: Among them, the This represents the state estimation at time k, the stated This represents the state estimate at time k-1, where u k Indicates control input, the w k Let F represent the process noise, B represent the state transition matrix, and G represent the control matrix.

[0089] Furthermore, the optimization unit specifically includes:

[0090] The data acquisition subunit is used to collect data generated during beamforming and tracking.

[0091] The feature extraction subunit is used to extract features from the data as input for model training.

[0092] The model training subunit is used to train the model based on the extracted features;

[0093] The output decision subunit is used to dynamically adjust the parameters of the beamforming and tracking algorithms based on the model's output.

[0094] Specifically, the beam adjustment unit described in the above embodiments dynamically adjusts the beam direction based on the user's real-time location and motion parameters to ensure directional signal transmission and coverage. The optimization unit, supported by the intelligent algorithm processing module, combines historical beamforming and tracking data with current communication environment parameters, using prediction results from a machine learning model to dynamically adjust the parameters of the beamforming and tracking algorithm, thereby achieving more accurate and efficient beam management.

[0095] Furthermore, the intelligent algorithm processing module utilizes the analysis results to automatically adjust beam parameters through an adaptive algorithm to adapt to the current communication environment. At the same time, the machine learning algorithm uses historical data to optimize the beamforming and tracking process, improving the system's accuracy in predicting changes.

[0096] As a preferred embodiment of the above embodiments, a RIS-based distributed communication transmission method is proposed. This method is implemented based on a RIS-based distributed communication transmission system as described in any of the above embodiments, and includes the following steps:

[0097] S1. The transmitter outputs an electromagnetic signal at a predetermined frequency and transmits the electromagnetic signal to the reconfigurable smart surface via an RF cable;

[0098] S2. The reconfigurable smart surface receives signals from the transmitter and dynamically adjusts the phase, amplitude, and polarization of the signals according to the instructions of the central control unit through the built-in programmable control unit;

[0099] S3. The central control unit analyzes real-time data and environmental feedback, generates control commands through intelligent algorithms, and the reconfigurable intelligent surface performs beamforming and beam tracking through control commands to optimize signal direction;

[0100] S4. The reconfigurable smart surface treatment and optimization signal is transmitted to the receiver via the antenna, and the receiver receives and processes the signal.

[0101] Furthermore, step S3 specifically includes the following sub-steps:

[0102] S301. Calculate and allocate the weights of the programmable control unit to form the desired beam direction;

[0103] S302. Estimate the optimal beam pointing angle based on the RSSI value and the known signal propagation model;

[0104] S303. Dynamically adjust the beam direction based on the target's real-time position and motion parameters;

[0105] S304. Based on historical beamforming and tracking data and current communication environment parameters, dynamically adjust the parameters of the beamforming and tracking algorithms according to the prediction results of the machine learning model.

[0106] Specifically, the technical principle and process of the above embodiment are as follows: collecting and analyzing real-time communication data and environmental feedback data, including but not limited to signal strength, signal-to-noise ratio, channel state information, user location, speed, device orientation, environmental characteristics, multipath effects, interference source information, and meteorological conditions. This data provides the system with a comprehensive view of the communication environment; furthermore,

[0107] Furthermore, in step S301, the calculation of the weights of the programmable control unit is specifically expressed as follows:

[0108]

[0109] Wherein, w i Let N represent the weight of the i-th programmable control unit, N represent the total number of programmable control units in the array, j represent the number of programmable control units, and h represent the weight of the i-th programmable control unit. j This represents the complex weight corresponding to the target direction, where ∈ indicates a positive number, used to ensure numerical stability.

[0110] Furthermore, step S302 is specifically expressed as follows:

[0111]

[0112] Wherein, the θ opt The w represents the optimal beam pointing angle. i (θ) represents the weight of the i-th unit pointing at angle θ, where θ represents the pointing angle, and |s i | 2 This represents the power of the signal received by the i-th unit.

[0113] Furthermore, a three-node communication system is proposed, consisting of a transmitter, a receiver, and a RIS (Reflection System) with large-scale reflection units. Due to obstruction or other unfavorable propagation conditions, the direct signal path between the transmitter and receiver is ignored, such as... Figure 1 As shown, according to the calculation, the signal y received by the receiver is:

[0114]

[0115] The equivalent cascaded channel hΦH between the receiver and transmitter is the product of the channel h between the RIS and the receiver, the adjustable phase shift diagonal matrix Φ of the RIS, and the channel H between the transmitter and the RIS. g represents the direct link between the receiver and the transmitter, s represents the signal transmitted by the transmitter, and n represents Gaussian white noise. When using RIS-assisted communication, for an ideal RIS with no energy loss, the signal reflected by the RIS unit can be expressed as the product of the incident signal and the phase shift coefficient of the reflected signal. Due to the quasi-passive nature of the RIS, the thermal noise introduced by the radiation process is considered negligible. By designing the phase shift matrix of the RIS, the reflected signals of the RIS can be superimposed in phase at the user end, maximizing the signal-to-noise ratio received at the user end, thereby improving the transmission rate of the system. Similarly, this model can be easily extended to scenarios with multiple antennas, multiple base stations, multiple RIS, and multiple users. Thanks to the channel freedom provided by the RIS, future designs of customized RIS phase shift matrices are needed to further improve transmission performance in various scenarios.

[0116] Specifically, the system first generates an electromagnetic signal at the transmitter, which is transmitted to the RIS (Radio Frequency Identification System) via an RF cable. The RIS, as the core of the system, consists of multiple programmable control units (ECUs) capable of dynamically adjusting the phase, amplitude, and polarization of the incident signal. The ECUs receive instructions from the central control unit, which integrates a data processing module, an intelligent algorithm processing module, and an instruction generation module. This unit is responsible for analyzing real-time communication data and environmental feedback data, executing beamforming and tracking algorithms, and generating control instructions. Using adaptive algorithms, the central control unit automatically adjusts beam parameters according to changes in the communication environment. Simultaneously, machine learning algorithms optimize the beamforming and tracking process based on historical beamforming and tracking data and current communication environment parameters. The beamforming unit calculates and allocates weights to the ECUs to form the desired beam direction, while the RSSI (Received Signal Strength Indication) measurement unit estimates the optimal beam pointing angle based on the Received Signal Strength Indication (RSSI) value. The beam adjustment unit dynamically adjusts the beam direction based on the target's real-time position and motion parameters, while the optimization unit dynamically adjusts the algorithm parameters based on the prediction results of the machine learning model. Finally, the signal processed and optimized by the RIS is transmitted to the receiver via an antenna, completing the entire communication transmission process. This seamless process not only improves the efficiency and accuracy of signal transmission, but also significantly enhances the system's adaptability and robustness to complex communication environments through intelligent beam management and dynamic adjustment.

[0117] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A RIS-based distributed communication transmission system, comprising: The transmitting end is used to transmit electromagnetic signals at a predetermined frequency; The receiving end is used to receive and process electromagnetic signals reflected from the reconfigurable smart surface; At least one reconfigurable smart surface for dynamically adjusting the phase, amplitude, and polarization of the incident electromagnetic signal; The input of the reconfigurable smart surface is connected to the output of the transmitter via an RF cable, and the output of the reconfigurable smart surface is connected to the input of the receiver via an antenna; the reconfigurable smart surface includes a programmable control unit and a central control unit, and the central control unit is connected to at least one programmable control unit; The central control unit is characterized in that it generates control commands through intelligent algorithms based on real-time communication data and environmental feedback data to perform beamforming and beam tracking. The central control unit includes: The data processing module is used to analyze real-time communication data and environmental feedback data; The intelligent algorithm processing module is used to execute beamforming and beam tracking algorithms; The instruction generation module is used to generate control instructions based on the output of the intelligent algorithm. The intelligent algorithm processing module automatically adjusts beam parameters based on changes in the communication environment using an adaptive algorithm, and optimizes beamforming and tracking processes using a machine learning algorithm. The intelligent algorithm processing module includes: The beamforming unit is used to calculate and allocate the weights of the programmable control unit to form the desired beam direction; The RSSI measurement unit is used to estimate the optimal beam pointing angle based on the RSSI value and a known signal propagation model. The beam adjustment unit is used to dynamically adjust the beam direction according to the target's real-time position and motion parameters; The optimization unit is used to dynamically adjust the parameters of the beamforming and tracking algorithms based on historical beamforming and tracking data and current communication environment parameters, according to the prediction results of the machine learning model. In the beamforming unit, the weights of the programmable control unit are specifically represented as follows: ； Among them, the Let N represent the weight of the i-th programmable control unit, N represent the total number of programmable control units in the array, and j represent the number of programmable control units. This represents the complex weight corresponding to the target direction. Represents positive numbers, used to ensure numerical stability; In the RSSI measurement unit, the estimation of the optimal beam pointing angle based on the RSSI value and a known signal propagation model is specifically expressed as follows: ； Among them, the Indicates the optimal beam pointing angle, the Indicates the pointing angle is The weight of the i-th unit, the Indicates the pointing angle, the This represents the power of the signal received by the i-th unit.

2. The RIS-based distributed communication transmission system as described in claim 1, characterized in that, The programmable control unit includes: At least one phase adjuster is used to receive instructions from the central control unit and adjust the phase of the electromagnetic signal in real time to control the direction of the beam. At least one amplitude modulator is used to adjust the signal strength according to instructions from the central control unit, and to optimize the beam shape and coverage. At least one polarization regulator is used to adjust the polarization state of the electromagnetic signal according to the signal reception requirements of the communication equipment.

3. A RIS-based distributed communication transmission method, wherein the method is implemented based on a RIS-based distributed communication transmission system as described in any one of claims 1-2, characterized in that, Includes the following steps: S1. The transmitter outputs an electromagnetic signal at a predetermined frequency and transmits the electromagnetic signal to the reconfigurable smart surface via an RF cable; S2. The reconfigurable smart surface receives signals from the transmitter and dynamically adjusts the phase, amplitude, and polarization of the signals according to the instructions of the central control unit through the built-in programmable control unit; S3. The central control unit analyzes real-time data and environmental feedback, generates control commands through intelligent algorithms, and the reconfigurable intelligent surface performs beamforming and beam tracking through control commands to optimize the signal direction; S4. The reconfigurable smart surface treatment and optimization signal is transmitted to the receiving end via the antenna, and the receiving end receives and processes the signal; Step S3 specifically includes the following sub-steps: S301. Calculate and allocate the weights of the programmable control unit to form the desired beam direction; S302. Estimate the optimal beam pointing angle based on the RSSI value and the known signal propagation model; S303. Dynamically adjust the beam direction based on the target's real-time position and motion parameters; S304. Based on historical beamforming and tracking data and current communication environment parameters, dynamically adjust the parameters of the beamforming and tracking algorithms according to the prediction results of the machine learning model; In step S301, the weight of the programmable control unit is specifically expressed as follows: ； Among them, the Let N represent the weight of the i-th programmable control unit, N represent the total number of programmable control units in the array, and j represent the number of programmable control units. This represents a complex weight corresponding to the target direction, where the value is positive to ensure numerical stability. Step S302 is specifically represented as follows: ； Among them, the Indicates the optimal beam pointing angle, the Indicates the pointing angle is The weight of the i-th unit, the Indicates the pointing angle, the This represents the power of the signal received by the i-th unit.

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