Method for improving data transmission coverage range of power supply station

Abstract

The invention provides a method for improving the data transmission coverage range of a power supply station, and relates to the technical field of wireless communication network optimization. The method comprises the following steps: constructing a multi-hop transmission path, and ensuring that data is smoothly transmitted to a destination node through a plurality of relay nodes by calculating a shortest path between nodes; node position layout is optimized, and node positions are dynamically adjusted based on the coverage range and the transmission loss by adopting an iterative algorithm so as to maximize the coverage range and reduce the transmission loss; a self-adaptive antenna system is introduced, and the antenna direction is dynamically adjusted to optimize the signal receiving effect; a dynamic spectrum allocation algorithm based on the game theory is adopted, spectrum resources are dynamically allocated through a non-cooperative game model, and the spectrum utilization efficiency is improved. Through the technical means, the problems of insufficient coverage range, serious signal attenuation, low spectrum resource utilization rate and the like in an existing power supply station data transmission system are solved, the coverage range, the stability and the efficiency of data transmission are effectively improved, and the requirements of an intelligent power grid are met.

Description

Technical Field

[0001] The invention relates to the technical field of wireless communication network optimization, and in particular to a method for improving the data transmission coverage of a power supply station. Background Art

[0002] With the continuous growth of global energy demand and the intelligent upgrading of power systems, the construction of smart grids has become an important trend in the development of modern society. As a key node of the smart grid, power supply stations undertake the important tasks of power data collection, processing and transmission. However, the traditional power supply station data transmission system faces many challenges, especially in areas with complex terrain, dense buildings and significant environmental interference, signal attenuation, insufficient coverage and low spectrum utilization are particularly prominent.

[0003] In the prior art, multi-hop transmission path construction technology is an effective means to gradually expand the coverage of data transmission through relay transmission of multiple nodes. However, the construction of multi-hop transmission paths usually relies on preset node positions and fixed transmission paths, which makes it difficult to cope with dynamically changing network environments, resulting in a lack of flexibility in the selection of transmission paths in practical applications, and the coverage is still limited. In addition, the reasonable layout of node positions has a direct impact on the coverage and transmission efficiency of data transmission. Traditional node layouts mostly adopt a static layout based on experience, without fully considering the transmission loss between nodes and the actual terrain characteristics, which leads to increased transmission loss and reduced coverage in complex environments, further affecting the stability and reliability of data transmission.

[0004] In order to improve the quality of signal reception, adaptive antenna systems have been used in some scenarios to optimize signal reception by dynamically adjusting the direction of the antenna. However, existing adaptive antenna systems are mostly simple adjustment methods based on real-time signal strength feedback, and fail to make full use of historical data and prediction models for direction optimization. Therefore, in the face of rapidly changing environments, antenna adjustment often lags behind, making it difficult to achieve the best signal reception effect.

[0005] In terms of spectrum resource allocation, the wireless communication network of the power supply station needs to achieve efficient data transmission under limited spectrum resources. Traditional spectrum allocation algorithms are mostly based on static allocation strategies, which fail to fully consider the real-time needs of each node and the network load, resulting in low spectrum resource utilization, easy to cause spectrum congestion and transmission interference, and further limit the coverage and efficiency of data transmission.

[0006] In summary, the existing power supply station data transmission technology has many deficiencies in terms of multi-hop transmission path construction, node location layout, adaptive antenna system and spectrum resource allocation, and cannot meet the needs of smart grid for wide-area, reliable and efficient data transmission. Therefore, there is an urgent need for a method that can comprehensively utilize a variety of advanced technical means, dynamically optimize network configuration, and improve the coverage and stability of power supply station data transmission to meet the growing communication needs of smart grids. Summary of the invention

[0007] In order to solve the technical problems in the prior art of insufficient flexibility in constructing multi-hop transmission paths, insufficient rationality in node location layout, adjustment lag of adaptive antenna systems and inefficient spectrum resource allocation, the present invention provides a method for improving the data transmission coverage of power supply stations.

[0008] The technical solution provided by the present invention is as follows:

[0009] The present invention provides a method for improving the data transmission coverage of a power supply station, comprising:

[0010] S1. Construct a multi-hop transmission path by calculating the shortest path between each node to ensure that the multi-hop transmission is relayed through multiple nodes and finally reaches the destination node;

[0011] S2, node location optimization layout, in order to maximize the coverage, the node location layout adopts an iterative optimization algorithm, and the node location is adjusted based on the coverage and transmission loss;

[0012] S3, adaptive antenna system, to further improve data transmission efficiency, introduce the adaptive antenna system, dynamically adjust the antenna direction to optimize signal reception;

[0013] S4. Dynamic spectrum allocation algorithm based on game theory. A dynamic spectrum allocation algorithm based on game theory is introduced to ensure efficient utilization of spectrum resources. The spectrum allocation problem is modeled as a non-cooperative game. Each node is a player in the game, and the goal is to maximize its utility function by selecting the optimal spectrum.

[0014] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0015] (1) In the present invention, by constructing a multi-hop transmission path and optimizing the node position layout, the coverage of power supply station data transmission is effectively expanded. By dynamically calculating the shortest path between nodes and adjusting the node position layout in combination with an iterative optimization algorithm, the signal coverage can be maximized, ensuring that wide-area and stable data transmission can still be achieved in complex terrain and environment;

[0016] (2) In the present invention, an adaptive antenna system is introduced, which can dynamically adjust the antenna direction according to the real-time signal strength and historical data to optimize signal reception. The adaptive antenna system significantly improves the accuracy and response speed of antenna adjustment, reduces signal attenuation, and improves the overall efficiency and stability of data transmission through the combination of direction prediction algorithm and machine learning model;

[0017] (3) In the present invention, through the dynamic spectrum allocation algorithm based on game theory, limited spectrum resources can be effectively allocated in a multi-node communication environment. By modeling spectrum allocation as a non-cooperative game and combining it with Nash equilibrium solution, each node can select the optimal spectrum according to its utility function, thereby achieving efficient utilization of spectrum resources, reducing spectrum congestion and interference problems, and further improving the stability and coverage of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A flowchart of a method for improving data transmission coverage of a power supply station provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0021] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0022] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0023] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0024] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0025] Reference Manual Attached Figure 1 , showing a flow chart of a method for improving the data transmission coverage of a power supply station provided by an embodiment of the present invention.

[0026] An embodiment of the present invention provides a method for improving the data transmission coverage of a power supply station, and the processing flow may include the following steps:

[0027] S1. Construct a multi-hop transmission path by calculating the shortest path between each node to ensure that the relay transmission is carried out through multiple nodes and finally reaches the destination node.

[0028] It is understandable that by calculating the shortest path between each node and constructing a multi-hop transmission path, data can be transmitted through multiple relay nodes and finally reach the destination node. This multi-hop transmission method can significantly expand the coverage of the network, especially in the case of scattered power distribution or complex terrain. Through relay transmission of multiple nodes, it can effectively overcome the problem of insufficient coverage caused by signal attenuation in single-hop transmission.

[0029] S2. Node location optimization layout. To maximize the coverage, an iterative optimization algorithm is used in the node location layout to adjust the node location based on the coverage and transmission loss.

[0030] It is understandable that in order to maximize the network coverage of the power supply station, an iterative optimization algorithm is used to optimize the node position. After the initial layout, the node position is gradually optimized by calculating the coverage and transmission loss of each node to minimize the total transmission loss. This step ensures that the nodes can be reasonably laid out in complex terrain or environment, reducing transmission loss and improving signal transmission efficiency and coverage.

[0031] S3, Adaptive antenna system, in order to further improve data transmission efficiency, an adaptive antenna system is introduced to dynamically adjust the antenna direction to optimize signal reception.

[0032] It is understandable that the adaptive antenna system is introduced to dynamically adjust the antenna direction to optimize signal reception. By monitoring the signal strength and historical reception data in real time, the adaptive antenna system can automatically adjust the antenna direction to maximize the signal reception power. This dynamic adjustment mechanism greatly improves the signal reception quality, reduces signal attenuation, and ensures the stability and reliability of data transmission, which is especially important when the power supply station communication network needs to cope with frequently changing environmental conditions.

[0033] S4. Dynamic spectrum allocation algorithm based on game theory. A dynamic spectrum allocation algorithm based on game theory is introduced to ensure efficient utilization of spectrum resources. The spectrum allocation problem is modeled as a non-cooperative game. Each node is a player in the game, and the goal is to maximize its utility function by selecting the optimal spectrum.

[0034] It is understandable that in order to ensure efficient use of spectrum resources, the present invention introduces a dynamic spectrum allocation algorithm based on game theory. The spectrum allocation problem is modeled as a non-cooperative game, and each node, as a player in the game, selects the spectrum that can maximize its utility function. By solving the Nash equilibrium and determining the optimal spectrum allocation scheme, each node can achieve the best transmission performance under limited spectrum resources, reducing spectrum congestion and interference problems, thereby further improving the efficiency and coverage of data transmission.

[0035] It should be noted that S1 specifically includes:

[0036] Assume that the node set in the power supply network is N = {n1,n2,...,n k}, where n i represents the i-th node, node n i and node n j The distance between them is d ij , assuming that node n i and node n j The transmission loss between ij , then the signal receiving power between two nodes is It is expressed as:

[0037]

[0038] Among them, P t is the transmission power, G t and G r are the transmitting and receiving antenna gains respectively, λ is the signal wavelength, L ij For the signal at node n i and node n j The propagation loss between them.

[0039] It can be understood that by setting the node set in the power supply network and the transmission loss formula between each node, the signal receiving power is accurately calculated to ensure the scientificity and effectiveness of the multi-hop path construction. By considering parameters such as transmission power, antenna gain, and signal wavelength, the signal receiving power is calculated, providing a quantitative basis for the optimization of multi-hop transmission paths.

[0040] It should be noted that S2 specifically includes:

[0041] S21, initial layout, according to the terrain characteristics of the power supply station, give the initial node layout N0;

[0042] S22. Calculate the coverage, for each node n i , calculate the coverage R i and the transmission loss L of the adjacent nodes ij ;

[0043] S23, loss optimization, by iteratively adjusting the node position, so that the total transmission loss L total =∑ i,j L ij Minimization,The loss optimization problem is solved by gradient descent method;

[0044] S24, when the total loss change rate ΔL total When it is less than the preset threshold ∈, the iteration is stopped and the final node layout is obtained.

[0045] It can be understood that through the steps of initial layout, calculation of coverage, loss optimization, etc., the node position layout is iteratively adjusted to ensure that the node layout minimizes the total transmission loss while considering the coverage. The gradient descent method is used for loss optimization. By gradually adjusting the node position, the optimal layout solution is found, and finally the optimal node layout in the actual environment of the power supply station is ensured.

[0046] It should be noted that S3 specifically includes:

[0047] Assuming the antenna direction angle is θ, the antenna receiving power model is:

[0048]

[0049] Among them, G(θ) is the antenna gain function, which represents the gain of the antenna at the direction angle θ, and L(θ) is the propagation loss function, which is related to the antenna direction angle;

[0050] By adjusting θ, we can maximize P r (θ), that is:

[0051]

[0052] The adaptive process automatically adjusts by monitoring signal strength in real time to ensure optimal signal reception.

[0053] It can be understood that through the adaptive antenna system, the antenna direction angle is calculated and the signal reception power is optimized according to the antenna gain and propagation loss. By adjusting the antenna direction angle, the signal reception is maximized, and the antenna direction is monitored and dynamically adjusted in real time to ensure the best signal reception effect in different environments.

[0054] It should be noted that S4 specifically includes:

[0055] Let node n i The utility function is U i (f), where f is the value assigned to node n i The spectrum of , the utility function is defined as:

[0056]

[0057] Among them, B i (f) is node n i The bandwidth of the spectrum f, P r (f) is the received power on spectrum f, and C(f) is the amount of interference on spectrum f.

[0058] It is understandable that when initializing spectrum resources, the number and width of spectrum segments are dynamically adjusted to optimize spectrum utilization based on the current network load. When calculating the utility function, the effects of transmission delay and signal-to-noise ratio are considered to ensure that the utility function more fully reflects the transmission needs of the node. Through the direction prediction algorithm and machine learning model, the direction adjustment of the adaptive antenna system is optimized to further improve the accuracy and stability of signal reception.

[0059] In a possible implementation, the most optimal spectrum allocation scheme for each node is determined by solving the Nash equilibrium, and the steps are as follows:

[0060] Initialize spectrum resources: divide the available spectrum resources into several segments, each segment corresponds to a spectrum f j ;

[0061] Calculate the utility function: For each node, calculate the utility function U on different spectrum segments i (f j );

[0062] Game solving: Use an iterative algorithm to solve the game until the utility functions of all nodes are stable, that is, the Nash equilibrium is found;

[0063] Spectrum allocation: According to the solution results, the corresponding frequency band is allocated to each node.

[0064] It can be understood that by dividing the spectrum resources into several segments and solving the game model according to the utility function of each node, it is ensured that each node can obtain the best spectrum allocation plan, thereby optimizing the use of spectrum resources and improving data transmission efficiency and network performance.

[0065] In a possible implementation manner, initializing spectrum resources specifically includes:

[0066] During the initialization process, the number and width of spectrum segments are dynamically adjusted according to the historical spectrum usage of each node and the current network environment. Assuming the initial number of spectrum segments is M0, by calculating the load factor η of the current network, the updated number of spectrum segments M is calculated as follows:

[0067] M=M0×(1+α·η)

[0068] Among them, α is the adjustment coefficient, which is dynamically adjusted according to the current network load to ensure that the number of spectrum segments can be increased under high load conditions, thereby optimizing spectrum utilization.

[0069] It is understandable that the number and width of spectrum segments are dynamically adjusted to optimize the configuration of spectrum resources according to network load conditions to adapt to different network load conditions and improve spectrum utilization and overall network performance.

[0070] In a possible implementation, calculating the utility function specifically includes:

[0071] When calculating the utility function, the transmission delay T and signal-to-noise ratio SNR of each node are considered, and the utility function U i (f) is updated as follows:

[0072]

[0073] Where T(f) is the node n i The transmission delay on spectrum f, SNR(f) is the signal-to-noise ratio of the node on spectrum f. By introducing delay and signal-to-noise ratio parameters, the guidance of the utility function on spectrum allocation is improved.

[0074] It is understandable that by introducing the transmission delay and signal-to-noise ratio parameters, the utility function more accurately reflects the actual transmission requirements of the node, thereby taking into account the actual network conditions in spectrum allocation and improving the scientificity and efficiency of resource allocation. In a possible implementation, the adaptive antenna system specifically includes:

[0075] The direction prediction algorithm based on historical signal reception data is calculated by dividing the antenna direction θ at the previous moment into t-1 and the current signal strength change ΔP r (θ t-1 ), predict the optimal direction of the antenna at the next moment θt , the prediction formula is as follows:

[0076] θ t =θ t-1 +β×ΔP r (θ t-1 )

[0077] Among them, β is the direction adjustment coefficient, which is dynamically adjusted according to historical data to achieve faster antenna direction optimization.

[0078] It can be understood that the antenna direction is dynamically adjusted through the direction prediction algorithm of historical signal data to optimize the signal reception quality, improve the stability of signal reception and the communication efficiency of the network.

[0079] In a possible implementation, the direction prediction algorithm specifically includes:

[0080] The machine learning model is used to train the data of multiple iterations and optimize the value range of β, so that the antenna system can predict and adjust the direction more accurately and improve the stability of signal reception. The optimized β is expressed as:

[0081]

[0082] Among them, the function f is obtained through machine learning model training, and the direction adjustment coefficient is dynamically adjusted according to the rate of change of signal reception power.

[0083] It is understandable that by optimizing the directional adjustment coefficient through machine learning model training, the antenna system can more accurately predict and adjust the antenna direction, thereby improving the stability and accuracy of signal reception and ensuring more efficient data transmission.

[0084] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0085] (1) In the present invention, by constructing a multi-hop transmission path and optimizing the node position layout, the coverage of power supply station data transmission is effectively expanded. By dynamically calculating the shortest path between nodes and adjusting the node position layout in combination with an iterative optimization algorithm, the signal coverage can be maximized, ensuring that wide-area and stable data transmission can still be achieved in complex terrain and environment;

[0086] (2) In the present invention, an adaptive antenna system is introduced, which can dynamically adjust the antenna direction according to the real-time signal strength and historical data to optimize signal reception. The adaptive antenna system significantly improves the accuracy and response speed of antenna adjustment, reduces signal attenuation, and improves the overall efficiency and stability of data transmission through the combination of direction prediction algorithm and machine learning model;

[0087] (3) In the present invention, through the dynamic spectrum allocation algorithm based on game theory, limited spectrum resources can be effectively allocated in a multi-node communication environment. By modeling spectrum allocation as a non-cooperative game and combining it with Nash equilibrium solution, each node can select the optimal spectrum according to its utility function, thereby achieving efficient utilization of spectrum resources, reducing spectrum congestion and interference problems, and further improving the stability and coverage of data transmission.

[0088] The above contents are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0089] There are a few points to note:

[0090] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0091] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0092] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0093] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for improving the data transmission coverage of a power supply station, characterized in that: include: S1. Construct a multi-hop transmission path by calculating the shortest path between each node to ensure that the multi-hop transmission is relayed through multiple nodes and finally reaches the destination node; S2, node location optimization layout, in order to maximize the coverage range, an iterative optimization algorithm is used in the node location layout to adjust the node location based on the coverage range and transmission loss; S3, adaptive antenna system, to further improve data transmission efficiency, introduce the adaptive antenna system, dynamically adjust the antenna direction to optimize signal reception; S4. Dynamic spectrum allocation algorithm based on game theory. A dynamic spectrum allocation algorithm based on game theory is introduced to ensure efficient utilization of spectrum resources. The spectrum allocation problem is modeled as a non-cooperative game. Each node is a player in the game, and the goal is to maximize its utility function by selecting the optimal spectrum.

2. A method for improving the data transmission coverage of a power supply station according to claim 1, characterized in that: The S1 specifically includes: Assume that the node set in the power supply network is N = {n1,n2,...,n k }, where n i represents the i-th node, node n i and node n j The distance between them is d ij , assuming that node n i and node n j The transmission loss between ij , then the signal receiving power between two nodes is It is expressed as: Among them, P t is the transmission power, G t and G r are the transmitting and receiving antenna gains respectively, λ is the signal wavelength, L ij For the signal at node n i and node n j The propagation loss between them.

3. A method for improving the data transmission coverage of a power supply station according to claim 2, characterized in that: The S2 specifically includes: S21, initial layout, according to the terrain characteristics of the power supply station, give the initial node layout N0; S22. Calculate the coverage, for each node n i , calculate the coverage R i and the transmission loss L of the adjacent nodes ij ; S23, loss optimization, by iteratively adjusting the node position, so that the total transmission loss L total =∑ i,j L ij Minimization,The loss optimization problem is solved by gradient descent method; S24, when the total loss change rate ΔL total When it is less than the preset threshold ∈, the iteration is stopped and the final node layout is obtained.

4. A method for improving the data transmission coverage of a power supply station according to claim 1, characterized in that: The S3 specifically includes: Assuming the antenna direction angle is θ, the antenna receiving power model is: Where G(θ) is the antenna gain function, which represents the gain of the antenna at the direction angle θ, and L(θ) is the propagation loss function, which is related to the antenna direction angle; By adjusting θ, we can maximize P r (θ), that is: The adaptive process automatically adjusts by monitoring signal strength in real time to ensure optimal signal reception.

5. A method for improving the data transmission coverage of a power supply station according to claim 1, characterized in that: The S4 specifically includes: Let node n i The utility function is U i (f), where f is the value assigned to node n i The spectrum of , the utility function is defined as: Among them, B i (f) is node n i The bandwidth of the spectrum f, P r (f) is the received power on spectrum f, and C(f) is the amount of interference on spectrum f.

6. A method for improving the data transmission coverage of a power supply station according to claim 5, characterized in that: Specifically include: By solving the Nash equilibrium, the most optimal spectrum allocation scheme for each node is determined. The steps are as follows: Initialize spectrum resources: divide the available spectrum resources into several segments, each segment corresponds to a spectrum f j ; Calculate the utility function: For each node, calculate the utility function U on different spectrum segments i (f j ); Game solving: Use an iterative algorithm to solve the game until the utility functions of all nodes are stable, that is, the Nash equilibrium is found; Spectrum allocation: According to the solution results, the corresponding frequency band is allocated to each node.

7. A method for improving the data transmission coverage of a power supply station according to claim 6, characterized in that: The initialization of spectrum resources specifically includes: During the initialization process, the number and width of spectrum segments are dynamically adjusted according to the historical spectrum usage of each node and the current network environment. Assuming the initial number of spectrum segments is M0, by calculating the load factor η of the current network, the updated number of spectrum segments M is calculated as follows: M=M0×(1+α·η) Among them, α is the adjustment coefficient, which is dynamically adjusted according to the current network load to ensure that the number of spectrum segments can be increased under high load conditions, thereby optimizing spectrum utilization.

8. A method for improving data transmission coverage of a power supply station according to claim 6, characterized in that: The calculation utility function specifically includes: When calculating the utility function, the transmission delay T and signal-to-noise ratio SNR of each node are considered, and the utility function U i (f) is updated as follows: Where T(f) is the node n i The transmission delay on spectrum f, SNR(f) is the signal-to-noise ratio of the node on spectrum f. By introducing delay and signal-to-noise ratio parameters, the guidance of the utility function on spectrum allocation is improved.

9. A method for improving data transmission coverage of a power supply station according to claim 4, characterized in that: The adaptive antenna system specifically includes: The direction prediction algorithm based on historical signal reception data is calculated by dividing the antenna direction θ at the previous moment into t-1 and the current signal strength change ΔP r (θ t-1 ), predict the optimal direction of the antenna at the next moment θ t , the prediction formula is as follows: i t =θ t-1 +β×ΔP r (i t-1 ) Among them, β is the direction adjustment coefficient, which is dynamically adjusted according to historical data to achieve faster antenna direction optimization.

10. A method for improving data transmission coverage of a power supply station according to claim 9, characterized in that: The direction prediction algorithm specifically includes: The machine learning model is used to train the data of multiple iterations and optimize the value range of β, so that the antenna system can predict and adjust the direction more accurately and improve the stability of signal reception. The optimized β is expressed as: Among them, the function f is obtained through machine learning model training, and the direction adjustment coefficient is dynamically adjusted according to the change rate of the signal receiving power.

Images