Efficient and energy-saving type 5G base station intelligent cooperative antenna system and method

Abstract

The invention relates to the technical field of 5G communication, and discloses an efficient and energy-saving type 5G base station intelligent cooperative antenna system and method. The environment sensing module is used for monitoring user distribution and dynamic change in real time; the channel state information updating module is used for receiving the user state data; the beamforming optimization module is used for receiving the updated channel state information; a dynamic beam forming module; the energy recovery and management module is used for capturing surrounding radio-frequency signals and converting the surrounding radio-frequency signals into electric energy; and the system adaptability adjusting and optimizing module is used for automatically adjusting a beam forming strategy in real time according to user distribution and environment change. Through close combination of the environment sensing module and the channel state information updating module, user distribution and dynamic change are monitored in real time, so that efficient resource allocation is realized, the adaptive capacity of a base station to user requirements is greatly improved, and the user experience is improved. Compared with the prior art, the defect that the service quality of a traditional method in a user dense area is reduced is overcome.

Description

Technical Field

[0001] This invention relates to the field of 5G communication technology, and in particular to a high-efficiency and energy-saving intelligent collaborative antenna system and method for 5G base stations. Background Technology

[0002] In the development of modern communication networks, users' demands for networks are becoming increasingly diversified. Especially in 5G base stations, uneven user distribution and environmental changes often lead to fluctuations in signal quality. Most existing technologies employ static channel management and a single beamforming method. This often fails to meet users' high-quality communication needs in a timely manner, particularly in densely populated areas.

[0003] Existing environmental monitoring technologies typically rely on simple signal strength measurements. This method is not precise enough and cannot effectively acquire user mobility status and specific distribution information. As a result, base stations lack real-time, accurate data support when optimizing resource allocation, leading to resource waste and persistent signal coverage problems.

[0004] Traditional beamforming schemes are often fixed in form, failing to consider changes in user needs and location. This "one-size-fits-all" approach is ill-suited to highly mobile user groups. Arbitrarily adjusting port signals and beam shapes results in a disconnect between signal coverage areas and actual user needs, impacting overall network service quality.

[0005] Currently, energy management primarily relies on traditional batteries, which cannot effectively recover energy from the environment. This results in consistently high energy consumption for 5G base stations during operation, further increasing maintenance costs. This not only affects the economics of base stations but is also environmentally unfriendly, limiting the development of broader 5G network applications. Summary of the Invention

[0006] The purpose of this invention is to provide a highly efficient and energy-saving intelligent collaborative antenna system and method for 5G base stations, which solves the problems of insufficient signal coverage and resource allocation of base stations under dynamic user distribution and environmental changes.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a high-efficiency and energy-saving 5G base station intelligent collaborative antenna system, comprising; The environment perception module is used to monitor user distribution and dynamic changes in real time to generate user status data; The channel state information update module receives the user state data and updates the channel state information in real time using a machine learning algorithm. The beamforming optimization module receives updated channel state information and uses compressed sensing technology to optimize beam pointing and shape; The dynamic beamforming module adjusts the antenna's beamforming based on the optimized beam direction and shape. The energy recovery and management module captures ambient radio frequency signals and converts them into electrical energy to reduce system energy consumption; The system adaptive tuning module automatically adjusts the beamforming strategy in real time based on user distribution and environmental changes.

[0008] Preferably, the environmental sensing module monitors user conditions using millimeter-wave radar, and the steps include: The signal is transmitted via millimeter waves, and reflected signals from the environment are received to determine the user's relative position. Record the coordinates and movement status of each user, and generate a user distribution dataset.

[0009] Preferably, the environment perception module further includes a Sub-6GHz receiver for acquiring user information and sending the user distribution data to the channel state information update module through a transmission interface.

[0010] Preferably, the channel state information update module processes user distribution data using a machine learning algorithm, and the steps include: An initial estimate of the channel state is generated based on user state data; The channel state is iteratively updated by training a model.

[0011] Preferably, the machine learning algorithm uses a deep learning model to predict the channel state in real time, so as to generate dynamic channel state information and output it to the beamforming optimization module.

[0012] Preferably, the beamforming optimization module utilizes compressed sensing technology to optimize the beam pointing and shape, and reconstructs the channel matrix using the following optimization formula: ; In the formula, Denotes the objective variable of the optimization problem. This represents a mathematical operator, indicating the parameter value that minimizes the objective function. This usually indicates the received signal. This represents the input signal, typically the independent variable or characteristic matrix. Optimized parameters.

[0013] Preferably, the beam parameters in the dynamic beamforming module are adjusted according to the following formula; ; In the formula, Indicates the first Phase adjustment value of each unit, This represents a complete circular angle, or 360 degrees. Indicates the total number of units. Indicates the first The unit number.

[0014] Preferably, the energy recovery and management module captures environmental radio frequency signals and converts them into electrical energy using the following formula: ; In the formula, Indicates input power. This represents the energy recovery efficiency coefficient. This indicates the received radio frequency.

[0015] Preferably, the system adaptive tuning module monitors user traffic in real time to automatically adjust channel parameters.

[0016] A method for a high-efficiency and energy-saving intelligent cooperative antenna for 5G base stations includes the following steps: Collect user status data and monitor user distribution in real time through the environmental awareness module; Update the channel state information; process the user state data through the channel state information update module. Optimize beamforming by using the updated channel state information through the beamforming optimization module; Adjust beamforming by adjusting beam parameters according to the dynamic beamforming module to improve signal coverage; Energy recovery is achieved by capturing and converting radio frequency signals in the environment into electrical energy through an energy management module; Perform system adaptive tuning and automatically adjust antenna beam parameters according to changes in user traffic.

[0017] In summary, the present invention has at least one of the following beneficial technical effects: 1. This invention achieves efficient resource allocation by tightly integrating an environmental perception module and a channel state information update module to monitor user distribution and dynamic changes in real time. This significantly improves the base station's adaptability to user demands and, compared to the inflexible channel management schemes in existing technologies, addresses the shortcomings of traditional methods in terms of service quality degradation in densely populated user areas.

[0018] 2. This invention utilizes a beamforming optimization module and a dynamic beamforming module to intelligently adjust the signal direction and coverage. Through this precise control, the system can dynamically respond to changes in different environments and optimize signal transmission performance. Compared to existing technologies with fixed beam shapes, this solves the problems of insufficient signal coverage and interference.

[0019] 3. This invention introduces an energy recovery and management module, which captures and utilizes radio frequency signals to convert them into electrical energy, significantly reducing system energy consumption. This technological effect enables base stations to achieve energy savings while maintaining high performance, solving the problems of excessive energy consumption and high operating costs compared to traditional power supply methods in existing technologies. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the system framework of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0021] The following is in conjunction with the appendix Figure 1 The present invention will be further described in detail below.

[0022] like Figure 1 As shown, this high-efficiency and energy-saving 5G base station intelligent collaborative antenna system includes: The environment perception module is used to monitor user distribution and dynamic changes in real time to generate user status data; Specifically, the overall structure of the environmental perception module in this embodiment includes a millimeter-wave radar unit and a Sub-6GHz receiver unit. The millimeter-wave radar unit is responsible for detecting the user's location, while the Sub-6GHz receiver unit is used to acquire other user information. The two parts are connected via a data bus to form a data transmission path.

[0023] First, millimeter-wave radar transmits millimeter-wave signals via a transmitting antenna. Specifically, the transmitting antenna beams the millimeter-wave signal into the surrounding environment. Upon receiving the reflected signal, the receiving antenna uses time-delay measurement technology to determine the relative position of each user. Based on the Doppler effect, the user's movement state can be analyzed, thereby obtaining the user's speed and direction. The core formula for this process is: ; In the formula, d represents the target distance, c is the speed of light, and t is the round-trip time of the signal. By sending and receiving signals multiple times, information about changes in the user's location can be obtained.

[0024] Subsequently, the environmental perception module records the coordinates and movement status of each user, generating a user distribution dataset. The dataset is recorded as follows: during each signal reflection acquisition, the data acquisition device stores the acquired user status information in the following format: ; In the formula, For user distribution datasets, Indicates the first The coordinates of each user Indicates the first Speed ​​information for each user This represents the total number of users in the dataset.

[0025] Furthermore, the Sub-6GHz receiver will be used to acquire additional user information, such as user equipment type and signal strength. This process is performed via interfaces such as SSID. The acquired user information is filtered and integrated by the data processing unit, ultimately forming an information data packet that can be used for channel state updates.

[0026] At this stage, the user distribution data undergoes preprocessing to remove noise and outliers. This process is accomplished using algorithms such as the median filter, as shown in the following formula: It is the median ; In the formula, This represents the filtered dataset obtained after preprocessing. Indicates the first element in the filtered dataset. Each element.

[0027] After this processing, the user data will be continuously transmitted to the channel status information update module via the data bus, providing a high-quality data source for the real-time update of subsequent channel status information.

[0028] The channel state information update module receives the user state data and updates the channel state information in real time using a machine learning algorithm. Specifically, in this embodiment, the channel state information update module first receives user state data from the environment perception module through the data receiving unit. This user state data includes the user's location information and its dynamic changes, and is connected via a data bus, making data transmission and reception more efficient.

[0029] After receiving user state data, the module establishes a channel state prediction model. This model uses a machine learning algorithm, specifically including an initial channel estimation phase and a model training phase. First, for the input user state data, an initial estimate of the channel state is generated using the least squares estimation method, with the following formula: ; In the formula, This indicates the initial channel state. Indicates the amount of received signal. Indicates the amount of transmitted signal. Indicates the channel state. This indicates a minimize operation. The norm of the square.

[0030] Subsequently, a suitable machine learning algorithm is selected for model training. For example, a deep learning model is used for real-time prediction, trained using known historical data and real-time user status data. The model's loss function can be defined as: ; In the formula, Represents the loss function. Indicates the number of samples. This represents the channel state predicted by the model. Indicates the actual channel state. This represents the summation operation. This represents the squared error.

[0031] After model training is complete, the channel state information update module can output optimized channel state information to the beamforming optimization module in real time. The data processing unit generates channel state information based on the algorithm's output, making the subsequent beamforming process more accurate.

[0032] Real-time updates of channel state information enable the entire system to efficiently adapt to changes in user distribution, improving network service quality. This module, through close integration with the environment awareness module and beamforming optimization module, ensures smooth data flow.

[0033] The beamforming optimization module receives updated channel state information and uses compressed sensing technology to optimize beam pointing and shape; Specifically, in this embodiment, the beamforming optimization module plays a crucial role in the high-efficiency, energy-saving 5G base station intelligent cooperative antenna system. Its main task is to optimize the pointing and shape of the antenna beam based on real-time channel state information. This module integrates dynamic channel state information from the channel state information update module and utilizes compressed sensing technology and channel matrix reconstruction methods to achieve beam optimization.

[0034] The beamforming optimization module includes a channel input unit, an optimization processing unit, and a beam output unit. The channel input unit is connected to the channel state information update module to ensure real-time reception of the latest channel state information. The main function of the channel input unit is to obtain the channel matrix. ; In the formula, Represents the channel matrix. Indicates the first The transmitting antenna and the first Channel status between receiving users Indicates the number of transmitting antennas. This indicates the number of users who received the message.

[0035] First, the beamforming optimization module processes the input channel information into beamforming optimization targets. Compressed sensing technology is used to reconstruct the channel matrix to optimize the beam pointing and shape. The reconstructed target can be expressed by the following formula: ; In the formula, Denotes the objective variable of the optimization problem. This represents a mathematical operator, indicating the parameter value that minimizes the objective function. This usually indicates the received signal. This represents the input signal, typically the independent variable or characteristic matrix. Indicates the parameters to be optimized.

[0036] Subsequently, the optimization processing unit uses the channel matrix based on the optimization results to generate the final beamforming parameters. Specifically, the channel utilization efficiency for each user is first evaluated using the channel capacity formula: ; In the formula, Indicates channel capacity, Indicates bandwidth. Indicates the power of the received signal. Represents the channel state matrix The norm square, Represents the noise power spectral density. Represents a binary logarithm.

[0037] After optimization, the beam parameters are output to the dynamic beamforming module via the beam output unit to adjust the antenna beamforming. The output beam parameters include information such as beam direction, shape, and gain, which directly affect the signal coverage and quality.

[0038] Through this processing and optimization process, the beamforming optimization module can effectively improve the signal directivity and coverage of the base station, and achieve the ability to dynamically respond to user needs.

[0039] The dynamic beamforming module adjusts the antenna's beamforming based on the optimized beam direction and shape. Specifically, in this embodiment, the dynamic beamforming module is responsible for dynamically adjusting the antenna's beam shape and direction based on real-time channel state information to optimize signal coverage and quality. This module, through close integration with the beamforming optimization module, obtains the latest beam parameters, ensuring the system's flexibility and efficiency in responding to user needs.

[0040] The dynamic beamforming module includes a beam parameter receiving unit, a beamforming control unit, and a beam output unit. The beam parameter receiving unit is connected to the beamforming optimization module to ensure that the latest beamforming parameters are received in real time. These parameters include beam direction, gain, and shape information.

[0041] First, the module's beam parameter receiving unit receives beamforming parameters from the beamforming optimization module. These parameters, after being parsed, are transmitted to the beamforming control unit for further processing and adjustment. The specific processing includes quantizing the received beam parameters to achieve digital beam control. The beam direction adjustment can be expressed by the following formula: ; In the formula, Indicates the first Phase adjustment value of each unit, This represents a complete circular angle, or 360 degrees. Indicates the total number of units. Indicates the first The unit number.

[0042] Secondly, the beamforming control unit generates and adjusts the final beam shape based on the received parameters. The beam shape adjustment can be represented by a phase compensation method, with the following formula: ; In the formula, This represents the generated beam signal, indicating the time... Beam output at any given moment Indicates the first The amplitude of each antenna element represents the amplitude of the beam. Indicates the first The direction of each antenna element Indicates the first The phase compensation amount for each antenna element is used to adjust the phase of the beam. This indicates the number of antenna elements, that is, the total number of antennas in the beamforming system. Represented as a complex exponential form, it represents the beam at... Phase adjustment on each antenna element.

[0043] The core technology of active beamforming lies in monitoring signal quality through a real-time feedback mechanism. This process first monitors the signal strength and channel status reported by users, updating the user distribution and signal quality in the system in real time. Further beam parameter adjustments are then made based on the feedback information.

[0044] Finally, the processed beam signal is output to the antenna array through the beam output unit to achieve coverage of the target user. In this way, the dynamic beamforming module can ensure that the beam is adjusted quickly and effectively when user distribution, channel conditions, or environmental changes occur, thereby improving signal transmission efficiency and quality.

[0045] The energy recovery and management module captures ambient radio frequency signals and converts them into electrical energy to reduce system energy consumption; Specifically, in this embodiment, the energy recovery and management module reduces the base station's energy consumption by collecting and managing energy from radio frequency signals. This module includes a radio frequency energy harvesting unit, an energy conversion and storage unit, and an energy management and distribution unit, ensuring efficient energy capture and utilization.

[0046] The energy recovery and management module first includes a radio frequency (RF) energy harvesting unit, which is responsible for capturing RF signals present in the surrounding environment. This module receives signals via an RF energy harvesting antenna, primarily from signals between other communication devices or base stations. The core of this process lies in the energy capture efficiency, which can be expressed by the following formula: ; In the formula, Indicates input power. This represents the energy recovery efficiency coefficient. This indicates the received radio frequency.

[0047] The captured radio frequency (RF) signal is processed by the energy conversion and storage unit, which converts the RF signal into direct current (DC) energy. Specifically, the module uses a rectifier circuit to convert the RF signal into DC power and stores the energy using an energy storage device (such as a supercapacitor or lithium battery). This process involves optimizing energy conversion efficiency by controlling the operating state of the rectifier circuit to improve energy storage efficiency.

[0048] During energy management and allocation, the energy management and allocation unit monitors the energy storage status and adjusts the system's energy usage. This unit uses control algorithms to dynamically allocate stored energy among various functional modules of the base station to ensure energy supply under different operating conditions. This module can output the current system energy status, such as: ; In the formula, Indicates available energy. Represents the total stored energy. This indicates the current energy consumption of the system.

[0049] The various components are connected by circuits to form a complete energy recovery and management system. The radio frequency energy harvesting unit is connected to the energy conversion and storage unit to jointly complete energy capture and storage. The energy management and distribution unit receives energy status information from the storage unit to optimize the energy distribution strategy, thereby achieving dynamic response to the system's energy needs.

[0050] This energy recovery and management module offers significant energy savings, reducing base station operating costs and improving resource utilization efficiency. By effectively converting radio frequency energy into electrical energy and implementing intelligent energy management, this module ensures the continuous and efficient operation of the base station under various working environments.

[0051] The system adaptive tuning module automatically adjusts the beamforming strategy in real time based on user distribution and environmental changes; Specifically, in this embodiment, the system adaptive tuning module is responsible for optimizing system parameters to improve overall performance based on changes in the dynamic environment and user needs. This module achieves flexible system tuning under different operating conditions through real-time data acquisition and analysis. The following is a detailed implementation of this module.

[0052] The system adaptive tuning module includes a data acquisition unit, a tuning decision unit, and a parameter optimization output unit. The data acquisition unit connects to each submodule and acquires system operating status and user demand information in real time. The data acquisition unit is responsible for monitoring environmental changes, user distribution, and signal quality, and acquires key data through sensors and network monitoring interfaces.

[0053] First, the module analyzes user data using a statistical model. The model calculates the user channel state and power requirements to obtain the current operating state of the system. The system state is assumed to be represented by the following formula: ; In the formula, For system status, For user demand vectors, For channel quality, A function that describes the system state. This is determined by both of these factors. The optimization decision-making department receives input data from the data acquisition department and uses decision-making algorithms to evaluate the effectiveness of the current system and the necessity for adjustments.

[0054] For example, when a decline in channel quality is detected in real time, the tuning decision-making department triggers parameter adjustments based on the following conditions: ; In the formula, To adjust the range, This is the sensitivity coefficient. This is the preset minimum threshold for channel quality. Based on the evaluation results, the decision-making unit formulates optimization strategies, including adjusting transmit power, modifying beam direction, or optimizing scheduling strategies.

[0055] After decision-making, the parameter optimization output unit updates the system parameters based on the output of the tuning decision unit. During this process, the module comprehensively considers channel state information, load conditions, and user requirements, and outputs new system parameter settings. The output parameters update the controller via control signals, as shown in the following example: ; In the formula, For the new transmission power, This represents the current transmission power.

[0056] The components in the system adaptive tuning module are physically connected to form a coherent feedback loop, ensuring the real-time nature and accuracy of data acquisition and decision feedback. The data acquisition unit is responsible for information input, the tuning decision unit provides the adjustment logic, and the parameter optimization output unit updates the control signals.

[0057] The high-efficiency and energy-saving 5G base station intelligent collaborative antenna method described below can be referred to in correspondence with the high-efficiency and energy-saving 5G base station intelligent collaborative antenna system described above.

[0058] Please see the appendix Figure 2 The present invention also provides a method for a high-efficiency and energy-saving intelligent cooperative antenna for 5G base stations, comprising the following steps: Collect user status data and monitor user distribution in real time through the environmental awareness module; Update the channel state information; process the user state data through the channel state information update module. Optimize beamforming by using the updated channel state information through the beamforming optimization module; Adjust beamforming by adjusting beam parameters according to the dynamic beamforming module to improve signal coverage; Energy recovery is achieved by capturing and converting radio frequency signals in the environment into electrical energy through an energy management module; Perform system adaptive tuning and automatically adjust antenna beam parameters according to changes in user traffic.

[0059] The method in this embodiment can be used to execute the above method embodiments, and its principle and technical effect are similar, so it will not be described again here.

[0060] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-efficiency energy-saving 5G base station intelligent collaborative antenna system, characterized in that, Comprising; An environmental perception module for real-time monitoring of user distribution and dynamic changes to generate user state data; A channel state information update module that receives the user state data and uses machine learning algorithms to update channel state information in real time; A beamforming optimization module that receives the updated channel state information and optimizes beam pointing and shape using compressed sensing techniques; A dynamic beamforming module that adjusts the antenna's beamforming according to the optimized beam pointing and shape; An energy recovery and management module that captures ambient radio frequency signals and converts them into electrical energy to reduce system energy consumption; A system adaptive tuning module that automatically adjusts the beamforming strategy in real time according to user distribution and environmental changes. 2.The high-efficiency energy-saving 5G base station intelligent cooperative antenna system of claim 1, wherein, The environmental perception module monitors user conditions through a millimeter wave radar, the steps of which include: Sending signals through millimeter waves and receiving reflected signals in the environment to determine the relative positions of users; Record the coordinate position and movement state of each user and generate a user distribution dataset. 3.The high-efficiency energy-saving 5G base station intelligent cooperative antenna system of claim 1, wherein, The environmental perception module also includes a Sub-6GHz receiver for obtaining user information and sending the user distribution data to the channel state information update module through a transmission interface. 4.The high-efficiency energy-saving 5G base station intelligent cooperative antenna system of claim 1, wherein, The channel state information update module processes user distribution data through machine learning algorithms, the steps of which include: Generating an initial estimate of channel state based on user state data; Iteratively updating the channel state through a trained model.

5. The high-efficiency energy-saving 5G base station intelligent cooperative antenna system according to claim 4, characterized in that, The machine learning algorithm uses a deep learning model to make real-time predictions of the channel state to generate dynamic channel state information and output it to the beamforming optimization module. 6.The high-efficiency energy-saving 5G base station intelligent cooperative antenna system of claim 1, wherein, The beamforming optimization module optimizes beam pointing and shape using compressed sensing techniques, and reconstructs the channel matrix through the following optimization formula: ； wherein, denotes an objective variable of an optimization problem, denotes a mathematical operator, denotes a parameter value that minimizes the objective function, denotes a received signal, denotes an input signal, typically an independent variable or a feature matrix, an optimized parameter.

7. The high-efficiency energy-saving 5G base station intelligent cooperative antenna system of claim 1, wherein, The dynamic beamforming module adjusts beam parameters according to the following formula: ； In the formula, denotes the phase adjustment value of the unit, denotes the complete angle of the circle, i.e. 360 degrees, denotes the total number of units, denotes the number of the unit. 8.The high-efficiency energy-saving 5G base station intelligent cooperative antenna system of claim 1, wherein, The energy recovery and management module captures ambient radio frequency signals and converts them into electrical energy through the following formula: ； wherein represents the input power, represents the energy recovery efficiency coefficient, represents the received radio frequency. 9.The high-efficiency energy-saving 5G base station intelligent cooperative antenna system of claim 1, wherein, The system adaptive tuning module monitors user traffic in real time to automatically adjust channel parameters.

10. A method for a high-efficiency, energy-saving 5G base station intelligent cooperative antenna, applied to the high-efficiency, energy-saving 5G base station intelligent cooperative antenna system as described in any one of claims 1-9, characterized in that, Comprising the following steps: Collecting user state data by real-time monitoring of user distribution through the environmental perception module; Updating channel state information by processing the user state data through the channel state information update module; Optimizing beamforming by using the updated channel state information for beam optimization through the beamforming optimization module; Adjusting beamforming according to the dynamic beamforming module to improve signal coverage; Implementing energy recovery by capturing and converting ambient radio frequency signals into electrical energy through the energy management module; Performing system adaptive tuning by automatically adjusting antenna beam parameters according to changes in user traffic.

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