CN117793857B

CN117793857B - Wireless router access method and system

Info

Publication number: CN117793857B
Application number: CN202410215812.1A
Authority: CN
Inventors: 赵守生; 朱芳芳
Original assignee: Yi Lian Science And Technology Shenzhen Co ltd
Current assignee: Yi Lian Science And Technology Shenzhen Co ltd
Priority date: 2024-02-27
Filing date: 2024-02-27
Publication date: 2024-04-30
Anticipated expiration: 2044-02-27
Also published as: CN117793857A

Abstract

The present invention relates to the field of wireless communications technologies, and in particular, to a wireless router access method and system. The method comprises the following steps: acquiring router signal data and router antenna data, and digitizing a signal propagation environment to acquire a router environment digital model; carrying out antenna dynamic deflection analysis on the router signal data to obtain an antenna deflection strategy; acquiring digital signal processing data, carrying out signal processing priority division, acquiring a signal processing strategy, and transmitting to a baseband processor; acquiring historical broadband resource allocation data and performing resource allocation coupling simulation to acquire optimal resource allocation data; and optimizing the antenna deflection strategy according to the optimal resource allocation data and the antenna deflection strategy, obtaining the optimized antenna deflection strategy and transmitting the optimized antenna deflection strategy to the controller. The invention aims to provide an efficient and stable wireless router access method and system so as to meet network communication requirements in different user scenes and improve user experience and network performance.

Description

Wireless router access method and system

Technical Field

The present invention relates to the field of wireless communications technologies, and in particular, to a wireless router access method and system.

Background

In the field of information technology today, the development of wireless network technology is mature, and a wireless router plays a vital role in home and enterprise networks as one of key devices for connecting to the internet. The conventional wireless router access method has some limitations, such as network congestion, signal interference and other problems possibly occurring in a high-density user scene, so that the user experience is reduced. Therefore, a new wireless router access method and system are provided to cope with the challenges, improve the network performance and the user experience, and have important technical significance and application prospect.

Disclosure of Invention

Accordingly, the present invention is directed to a wireless router access method and system, which solve at least one of the above-mentioned problems.

In order to achieve the above purpose, a wireless router access method is applied to a wireless router, wherein the wireless router comprises a controller, an antenna device electrically connected with the controller, a memory and a baseband processor; the memory and the baseband processor are electrically connected with the controller; the wireless router access method comprises the following steps:

Step S1: acquiring router signal data and router antenna data through antenna equipment, and digitizing a signal propagation environment according to the router signal data, so as to obtain a router environment digital model;

Step S2: carrying out signal data classification on the router signal data so as to obtain signal data to be accessed and access signal data; according to the signal data to be accessed and the access signal data, carrying out wireless network topology analysis on the router environment digital model, thereby obtaining a network topology; carrying out antenna dynamic deflection analysis on the router antenna data according to the network topology structure, thereby obtaining an antenna deflection strategy;

Step S3: acquiring digital signal processing data through a baseband processor, and carrying out signal processing priority division on the digital signal processing data according to the signal data to be accessed and the access signal data so as to acquire a signal processing strategy, and transmitting the signal processing strategy to the baseband processor so as to execute a signal access task;

Step S4: acquiring historical broadband resource allocation data through a memory, and performing resource allocation coupling simulation according to a signal processing strategy and the historical broadband resource allocation data so as to obtain optimal resource allocation data;

Step S5: and optimizing the antenna deflection strategy according to the optimal resource allocation data and the antenna deflection strategy, so as to obtain the optimized antenna deflection strategy, and transmitting the optimized antenna deflection strategy to the controller to execute the antenna deflection adjustment task.

The invention obtains the router signal data and the router antenna data through the antenna equipment, and digitizes the signal propagation environment of the router signal data to obtain a router environment digital model. The signal propagation environment around the router is digitized, which helps to better understand and simulate the propagation of signals in the environment, providing data support for subsequent steps. Network planning and optimization can be performed based on the digital model to improve network coverage and performance. And classifying the router signal data, and analyzing the wireless network topology structure based on the signal data to be accessed and the access signal data to obtain the network topology structure. And then, carrying out antenna dynamic deflection analysis on the router antenna data according to the network topology structure to obtain an antenna deflection strategy. By analyzing the network topology and antenna deflection, the coverage and quality of wireless signals can be optimized, and the network performance and coverage effect can be improved. By dynamically adjusting the antenna direction, signal interference is reduced, and network stability and reliability are improved. And acquiring digital signal processing data through a baseband processor, and carrying out priority division on the digital signal processing data according to the signal data to be accessed and the access signal data to obtain a signal processing strategy. And then, transmitting the signal processing strategy to a baseband processor to execute the signal access task. And according to the priority division, signals of different types are subjected to priority processing, so that the response speed and the processing efficiency of the network to the key data are improved. By optimizing the signal processing strategy, the delay of data transmission can be reduced, and the user experience is improved. And acquiring historical broadband resource allocation data through a memory, and performing resource allocation coupling simulation according to the signal processing strategy and the historical broadband resource allocation data to obtain optimal resource allocation data. And dynamically adjusting resource allocation according to the historical data and the current signal processing strategy so as to improve the utilization rate and efficiency of network resources to the greatest extent. And optimizing the antenna deflection strategy according to the optimal resource allocation data and the antenna deflection strategy to obtain an optimized antenna deflection strategy, transmitting the optimized antenna deflection strategy to the controller, and executing an antenna deflection adjustment task. And dynamically adjusting the antenna direction according to the latest resource allocation data and the optimized antenna deflection strategy so as to improve the signal coverage and the transmission quality to the greatest extent. According to the change of network environment and user demand, the antenna deflection strategy is continuously optimized, and the continuous optimization and adaptability of network performance are ensured. In summary, the wireless router access method realizes intelligent management and optimization of the wireless network through the organic combination of a plurality of steps, thereby improving network performance, reducing interference, optimizing resource utilization efficiency and further improving user experience.

Optionally, step S1 specifically includes:

step S11: acquiring router signal data and router antenna data by an antenna device:

Step S12: extracting the characteristics of the router signal data, thereby obtaining signal strength characteristic data and signal multipath characteristic data;

Step S13: performing environmental topography analysis according to the signal intensity characteristic data so as to obtain router environmental obstacle data;

Step S14: carrying out propagation environment analysis according to the signal multipath characteristic data so as to obtain signal propagation environment data;

step S15: digital environment modeling is performed based on the router environment obstacle data and the signal propagation environment data, so that a router environment digital model is obtained.

The invention collects the signals sent by the router and the related data of the antenna. The data captured by the antenna device includes information such as signal strength, signal frequency, etc. These data are the basis for subsequent environmental analysis and modeling. Various features are extracted from the collected signal data. The signal strength characteristics may provide information about the attenuation and fading conditions of the signal in space, while the signal multipath characteristics may describe multipath effects that occur during signal propagation, which information is important to understanding the propagation characteristics of the signal in the environment. The signal strength characteristic data is used to analyze the environmental topography and determine obstructions, such as walls, buildings, etc., that may be present during signal propagation. These obstructions may block and attenuate the propagation of the signal, affecting the coverage and quality of the wireless communication. And analyzing the phenomena of reflection, refraction, diffraction and the like of the signal in the propagation process by utilizing the multipath characteristic data of the signal. This information helps to understand the propagation path and characteristics of the signal in a complex environment, thereby optimizing the design and deployment of the wireless communication system. Combining the router environment obstacle data obtained by the previous analysis with the signal propagation environment data to establish a digital environment model. The model may be a three-dimensional model for modeling the propagation of signals under different environmental conditions. The digital environment model can help engineers to carry out network planning, optimize signal coverage, solve the problems of signal interference and the like, and improve the performance and stability of the wireless communication system.

Optionally, step S13 specifically includes:

Step S131: carrying out continuous intensity fluctuation statistics on the signal intensity characteristic data so as to obtain signal intensity fluctuation data;

Step S132: performing Fourier transform on the signal intensity fluctuation data to obtain a signal intensity spectrum, and performing time sequence feature extraction on the signal intensity spectrum to obtain signal intensity time sequence data;

step S133: carrying out statistical analysis according to the signal intensity spectrum, thereby obtaining a short-range signal intensity spectrum and a long-range signal intensity spectrum;

Step S134: performing inverse Fourier transform on the short-range signal intensity spectrum to obtain short-range signal intensity fluctuation data, and performing short-range signal reflection distance calculation according to the short-range signal intensity fluctuation data to obtain short-range signal reflection distance data;

Step S135: performing inverse Fourier transform on the long-range signal intensity spectrum to obtain long-range signal intensity fluctuation data, and performing long-range signal reflection distance calculation according to the long-range signal intensity fluctuation data to obtain long-range signal reflection distance data;

Step S136: and carrying out router obstacle profile analysis according to the signal intensity time sequence data, the short-range signal reflection distance data and the long-range signal reflection distance data, thereby obtaining router environment obstacle data.

According to the invention, through carrying out continuous intensity fluctuation statistics on the signal intensity characteristic data, the instantaneous change and fluctuation condition of the signal can be identified. This helps to understand the stability and instability of the signal and the possible external disturbances or obstructions. The signal intensity fluctuation data is converted into a frequency domain through Fourier transformation, so that the frequency spectrum information of the signal can be obtained, and further, the time sequence characteristic extraction is carried out on the frequency spectrum of the signal. These features may include the principal frequency components of the spectrum, frequency distribution, etc., to aid in further analysis of the signal's characteristics. The signal intensity spectrum is statistically analyzed, so that the spectrum characteristics of the signal, such as spectrum distribution, spectrum intensity, etc., can be known. This information is important to understand the propagation characteristics of the signal at different frequencies and the interference experienced. The short-range and long-range signal strength spectra are converted back to the time domain by inverse fourier transformation, thereby obtaining short-range and long-range signal strength fluctuation data. These data can be used to calculate the reflection distance of the signal, i.e. the distance the signal propagates after being reflected in the environment. To help determine the location and nature of an obstacle or reflector that may be present. And according to the signal intensity time sequence data and the reflection distance data, performing router obstacle profile analysis, and identifying and positioning obstacles around the router. These obstacles may include walls, buildings, or other objects, which are critical to evaluating signal propagation paths and optimizing network layout.

Optionally, step S136 specifically includes:

Carrying out three-dimensional space combination on the short-range signal reflection distance data and the long-range signal reflection distance data, thereby obtaining signal reflection space data;

performing convolution structure calculation according to the signal reflection space data so as to obtain a space convolution neural network;

Constructing a distance prediction space-time 3D convolutional network according to the signal intensity time sequence data and the spatial convolutional neural network;

performing environment distance prediction on the short-range signal reflection distance data and the long-range signal reflection distance data through a distance prediction space-time 3D convolution network, so as to obtain signal source obstacle distance prediction data;

Performing obstacle profile connection on the signal source obstacle distance prediction data so as to obtain obstacle profile data;

and carrying out space combination on the obstacle profile data so as to obtain router environment obstacle data.

The invention combines the short-range and long-range signal reflection distance data into a three-dimensional space, and can integrate reflection information of different distances into a unified space representation for subsequent processing and analysis. By applying convolution operations to the reflected spatial data, features in space can be extracted, providing a more informative representation of the subsequent distance prediction task. Such processing also helps the model capture the distribution and variation of the signal in space. The time sequence data of the signal intensity is combined with the space convolution neural network to construct a space-time 3D convolution network. The combination can fully utilize time sequence information and space information, and improves the prediction accuracy of the environmental distance. And (3) performing distance prediction on the short-range and long-range signal reflection distance data by using the constructed space-time 3D convolution network, so as to infer the distance of the obstacle around the signal source. These predictive data provide estimates of the location and distribution of obstructions in the environment. Contour connection is performed on the predicted obstacle distance data, so that the scattered point type prediction result can be converted into a continuous obstacle contour. To facilitate a clearer understanding of the distribution of obstructions in the environment. And carrying out space combination on the barrier profile data to obtain barrier data in the whole router environment, and providing reference for subsequent network layout and optimization.

Optionally, step S14 specifically includes:

Step S141: carrying out multipath signal attenuation calculation on the signal multipath characteristic data so as to obtain multipath signal attenuation distribution data;

Step S142: carrying out terrain attenuation value statistics on multipath signal attenuation distribution data according to router environment obstacle data so as to obtain obstacle attenuation value data;

Step S143: calculating signal terrain attenuation values of the obstacle attenuation data and the multipath signal attenuation distribution data, so as to obtain signal terrain attenuation data;

step S144: and carrying out attenuation value marking on the multipath signal attenuation distribution data according to the signal topography attenuation data and the obstacle attenuation value data, thereby obtaining signal propagation environment data.

The invention carries out multipath signal attenuation calculation on the multipath characteristic data of the signal to obtain multipath signal attenuation distribution data; multipath signal attenuation refers to the attenuation of signal power during propagation due to the signal experiencing different path lengths. By calculating the multipath signal attenuation, the propagation characteristics of the signal in space can be known, and basic data is provided for subsequent signal attenuation value calculation. Terrain attenuation refers to the additional attenuation of a signal during propagation that is affected by a terrain obstruction. By carrying out terrain attenuation value statistics on multipath signal attenuation distribution data, the influence degree of terrain on signal propagation can be known, and data support is provided for subsequent signal terrain attenuation value calculation. And combining the obstacle attenuation data with the multipath signal attenuation distribution data to calculate the terrain attenuation suffered by the signal in the propagation process. This step helps to understand in depth the effect of the terrain on the signal during its propagation. The signal topography attenuation data and the obstacle attenuation value data obtained through calculation are applied to multipath signal attenuation distribution data, and various attenuation values received in the signal propagation process are marked. This step can provide detailed signal propagation environment data, which can help to more accurately evaluate the propagation of a signal in an environment.

Optionally, step S2 specifically includes:

Step S21: extracting MAC address data from the router signal data to obtain MAC address data, and carrying out statistical analysis on the MAC address data to obtain high-frequency MAC address data and unique MAC address data;

Step S22: classifying and calculating router signal data according to the high-frequency MAC address data so as to obtain access signal data; classifying and calculating the router signal data according to the unique MAC address data, thereby obtaining signal data to be accessed;

Step S23: according to the signal data to be accessed and the access signal data, carrying out wireless network topology analysis on the router environment digital model, thereby obtaining a network topology;

Step S24: dividing the equipment dense region of the network topology structure, thereby obtaining an access equipment dense region and an equipment dense region to be accessed;

step S25: extracting antenna deflection characteristics and working condition characteristics of the router antenna data, so as to obtain the router antenna deflection data and the router antenna working condition data; carrying out load time sequence statistical analysis on the router antenna working condition data so as to obtain high-load working condition time sequence data and low-load working condition time sequence data;

Step S26: according to the high-load working condition time sequence data and the access equipment dense area, carrying out access high-load working condition antenna deflection division on the router antenna deflection data, thereby obtaining first high-load antenna deflection data; according to the high-load working condition time sequence data and the non-accessed equipment dense area, performing non-accessed high-load working condition antenna deflection division on the router antenna deflection data, so as to obtain second high-load antenna deflection data;

Step S27: according to the low-load working condition time sequence data and the access equipment dense area, the router antenna deflection data are accessed to the low-load working condition antenna deflection division, so that first low-load antenna deflection data are obtained; according to the low-load working condition time sequence data and the non-accessed equipment dense area, non-accessed low-load working condition antenna deflection division is carried out on the router antenna deflection data, so that second low-load antenna deflection data are obtained;

Step S28: and combining the antenna dynamic deflection time sequence of the first high-load antenna deflection data, the second high-load antenna deflection data, the first low-load antenna deflection data and the second low-load antenna deflection data, thereby obtaining an antenna deflection strategy.

The invention extracts MAC address data and performs statistical analysis to help identify the number and type of active devices in the network. This may help the administrator to understand network loading conditions, identify potential security risks, and provide a data basis for subsequent steps. By distinguishing between high frequency MAC addresses and unique MAC addresses, the liveness and type of the device can be better understood. This helps to distinguish between connected devices and potentially new devices, thereby optimizing network management policies. By analyzing the access signal data and the signal data to be accessed, the topology structure of the wireless network can be established, and the connection mode and the relation between the devices can be known. This helps identify network bottlenecks, optimize signal coverage, and improve communication efficiency between devices. The distribution situation of the devices in the network can be better understood by dividing the network topology structure into the device-intensive areas. This helps to optimize signal coverage, adjust device layout, and improve overall network performance. The antenna deflection characteristics and the working condition characteristics are extracted, so that the working state and the performance of the router antenna can be known. By analyzing the antenna load time sequence data, the working states of high load and low load can be identified, and data support is provided for the subsequent steps. The deflection data of the router antenna are divided according to different load working conditions, so that the understanding of the performance of the antenna under different working conditions is facilitated. This can be used to optimize antenna tuning strategies, improving network coverage and signal quality. And carrying out time sequence combination on the antenna deflection data under different working conditions, so as to generate a comprehensive antenna adjustment strategy. The dynamic antenna adjustment is facilitated, and the network performance and coverage are optimized according to the actual network load condition and the equipment distribution condition.

Optionally, step S3 specifically includes:

Step S31: acquiring digital signal processing data through a baseband processor;

Step S32: carrying out signal priority division on signal data to be accessed and access signal data according to the first high-load antenna deflection data and the first low-load antenna deflection data, thereby obtaining priority signal processing data;

step S33: carrying out signal priority division on the signal data to be accessed and the access signal data according to the second high-load antenna deflection data and the second low-load antenna deflection data, thereby obtaining sub-priority signal processing data;

Step S34: and carrying out signal processing prioritization on the digital signal processing data according to the priority signal processing data and the secondary priority signal processing data so as to obtain a signal processing strategy, and transmitting the signal processing strategy to a baseband processor so as to execute a signal access task.

The baseband processor in the invention is responsible for acquiring the digital signal processing data from the router. The original signal data transmitted in the network is obtained, and a basis is provided for subsequent processing and optimization. And carrying out signal prioritization on the signal to be accessed and the access signal data according to the first high-load and first low-load antenna deflection data and the second high-load and second low-load antenna deflection data. This helps to separate the signals in the network into two priorities, a priority signal and a sub-priority signal. In this way, the network can process signals more efficiently, improving the speed of response to important signals. And according to the division of the priority signal and the secondary priority signal, carrying out priority division on the digital signal processing data to form a signal processing strategy. The division can make the baseband processor process signals with different priorities more pertinently, and optimize the utilization of network resources. This policy is transmitted to the baseband processor to ensure that the network can process the signals by priority.

Optionally, step S4 specifically includes:

Step S41: acquiring historical broadband resource allocation data through a memory;

step S42: calculating the signal resource allocation similarity of the historical broadband resource allocation data according to the signal processing strategy, so as to obtain the signal resource allocation similarity;

Step S43: similar resource allocation data extraction is carried out on the historical broadband resource allocation data according to the signal resource allocation similarity, so that similar access resource allocation data and similar resource allocation data to be accessed are obtained;

Step S44: performing resource allocation coupling simulation on the similar access resource allocation data and the similar resource allocation data to be accessed, so as to obtain resource allocation coupling simulation data;

step S45: and carrying out statistical analysis on the resource allocation coupling simulation data so as to obtain the optimal resource allocation data.

According to the invention, historical broadband resource allocation data is acquired through the memory. Such data may include allocation of network resources over time, such as bandwidth utilization, signal transmission efficiency, etc. These data provide a reference basis for subsequent resource allocation. And according to the current signal processing strategy, similarity calculation is carried out on the historical broadband resource allocation data. The aim is to determine the adaptation degree of the historical resource allocation data and the current strategy so as to evaluate the feasibility and effectiveness of the historical resource allocation data under the current network environment. And extracting data similar to the current strategy from the historical broadband resource allocation data based on the signal resource allocation similarity. The method and the system are beneficial to identifying the resource allocation scheme which is matched with the current strategy best so as to allocate resources in the current environment. And carrying out resource allocation coupling simulation on the similar access resource allocation data and the similar resource allocation data to be accessed. This may involve modeling the effect of different resource allocation schemes and evaluating their impact on network performance and signal processing. By simulating the coupling resource allocation, the advantages and disadvantages of different allocation schemes can be better known, and a more intelligent decision can be made for final resource allocation. And carrying out statistical analysis on the resource allocation coupling simulation data to determine optimal resource allocation data. This may involve comparing performance metrics of different resource allocation schemes, such as bandwidth utilization, delay, throughput, etc. The final goal is to determine the best resource allocation strategy to optimize network performance and improve signal processing efficiency.

Optionally, step S5 specifically includes:

step S51: extracting features of the optimal resource allocation data so as to obtain resource allocation feature data;

Step S52: extracting characteristics of the antenna deflection strategy, so as to obtain antenna deflection adjustment data;

Step S53: calculating the antenna deflection correction angle according to the antenna deflection adjustment data and the resource allocation characteristic data by using an antenna deflection correction angle calculation formula, so as to obtain the antenna deflection correction angle;

the calculation formula of the antenna deflection correction angle specifically comprises the following steps:

；

in the method, in the process of the invention, For correcting antenna deflection angle,/>Is the height of the antenna,/>Is the deflection angle of the antenna,/>For antenna transmission frequency,/>To transmit frequency at antenna/>Signal intensity at time,/>For the type parameter of the antenna,/>In order to average the amount of network data traffic,For maximum network data transmission quantity,/>To transmit frequency at antenna/>Electromagnetic interference value at time/>Is the relief parameter of the surrounding topography of the router,/>For the number of users in the antenna deflection area,/>For the signal processing speed of the device,/>Is ambient temperature/>For the volume of the antenna,Is of atmospheric density,/>Is the router volume;

The invention obtains an antenna deflection correction angle calculation formula by referring to related data and expert experience to carry out formula deduction. The formula fully considers influencing the deflection correction angle of the antenna Height/>Deflection angle/>, of antennaAntenna transmission frequency/>Transmitting frequency at antenna/>Signal intensity at time/>Type parameter of antenna/>Average network data traffic/>Maximum network data traffic/>Transmitting frequency at antenna/>Electromagnetic interference value/>Surrounding topography relief parameters of router/>Number of users of antenna deflection area/>Signal processing speed of device/>Ambient temperature/>Antenna volume/>Atmospheric density/>Router volume/>A functional relationship is formed:

；

Wherein, This section first calculates the parameters/>And/>Natural logarithm of square root of sum of squares of (c) and then divided by the parameter/>And/>And finally taking an inverse cosine function. This part can be understood as adjusting the correction angle of the antenna by the height and deflection angle of the antenna, as well as the signal strength at a specific frequency and the antenna type. /(I)This part represents the average network data traffic/>And maximum network data traffic/>Square root ratio of (c). It is used to adjust the antenna correction angle to take into account the effect of the network loading conditions on the antenna direction. /(I)This part first calculates the parametersAnd/>Square root of product of (2), then divided by the parameter/>And/>Square root of the sum of squares of (c). This section is used to take into account the combined effect of electromagnetic interference values at specific frequencies, the topography parameters around the router, the number of users in the antenna deflection area and the signal processing speed of the device on the antenna correction angle. /(I)Calculate the parameters/>And parameters/>、/>Is added with the product of the parameters/>Is the ratio of the five times root of the formula (i). This section is used to take into account the effect of ambient temperature on the antenna correction angle, and the effect of the antenna volume, the air density and the router volume on this effect. In the art, the antenna deflection correction angle is generally calculated by adopting technical means such as an antenna array, electromagnetic simulation and the like, and the antenna deflection correction angle can be more accurately calculated by adopting an antenna deflection correction angle calculation formula.

Step S54: and carrying out deflection correction on the antenna deflection strategy according to the antenna deflection correction angle, so as to obtain an optimized antenna deflection strategy, and transmitting the optimized antenna deflection strategy to a controller to execute an antenna deflection adjustment task.

The invention extracts the characteristics of the optimal resource allocation data, which may include the characteristics of network resource utilization rate, spectrum allocation, data transmission rate and the like. This helps to refine the key information of resource allocation to better understand the characteristics of network performance and resource utilization. The antenna deflection strategy is extracted by features, which may include the direction, inclination angle, coverage area, etc. of the antenna. The current antenna deflection condition can be known, and basic data can be provided for subsequent adjustment. And calculating antenna deflection adjustment data and resource allocation characteristic data by using an antenna deflection correction angle calculation formula. The method is favorable for determining the optimal antenna deflection correction angle so that the direction of the antenna can meet the current network requirements more accurately. And carrying out deflection correction on the antenna deflection strategy according to the calculated antenna deflection correction angle. Through the optimization, the system can better adjust the antenna direction so as to improve the signal coverage and the network performance to the greatest extent. And transmitting the optimized antenna deflection strategy to a controller so as to actually execute the antenna deflection adjustment task.

Optionally, the present specification further provides a wireless router access system for performing a wireless router access method as described above, the wireless router access system comprising:

The signal propagation environment digitizing module is used for acquiring router signal data and router antenna data through the antenna equipment and digitizing the signal propagation environment according to the router signal data so as to acquire a router environment digital model;

the antenna dynamic deflection analysis module is used for classifying the signal data of the router so as to obtain the signal data to be accessed and the access signal data; according to the signal data to be accessed and the access signal data, carrying out wireless network topology analysis on the router environment digital model, thereby obtaining a network topology; carrying out antenna dynamic deflection analysis on the router antenna data according to the network topology structure, thereby obtaining an antenna deflection strategy;

The priority dividing module is used for acquiring digital signal processing data through the baseband processor, dividing the signal processing priority of the digital signal processing data according to the signal data to be accessed and the access signal data, so as to acquire a signal processing strategy, and transmitting the signal processing strategy to the baseband processor to execute a signal access task;

The resource allocation analysis module is used for acquiring historical broadband resource allocation data through the memory, and carrying out resource allocation coupling simulation according to the signal processing strategy and the historical broadband resource allocation data so as to obtain optimal resource allocation data;

and the antenna deflection correction module is used for optimizing the antenna deflection strategy according to the optimal resource allocation data and the antenna deflection strategy, so as to obtain the optimal antenna deflection strategy, and transmitting the optimal antenna deflection strategy to the controller so as to execute the antenna deflection adjustment task.

The wireless router access system can realize any wireless router access method of the invention, is used for combining the operation and signal transmission media among all modules to complete the wireless router access method, and the internal modules of the system cooperate with each other, thereby realizing intelligent management and optimization of a wireless network, improving network performance, reducing interference, optimizing resource utilization efficiency and further improving user experience.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:

fig. 1 is a flow chart illustrating steps of a wireless router access method according to the present invention;

FIG. 2 is a detailed step flow chart of step S1 of the present invention;

FIG. 3 is a detailed flowchart illustrating the step S13 of the present invention;

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

To achieve the above objective, referring to fig. 1 to 3, the present invention provides a wireless router access method, which includes the following steps:

In this embodiment, the router signal and the antenna data are collected by the antenna device, and the digitized signal propagation environment is modeled by using the data. For example, by measuring signal strength, multipath effects, etc., a detailed digital model is created that reflects the signal propagation characteristics of the environment in which the router is located, including obstructions, signal attenuations, etc.

in this embodiment, the router signal data is classified, and the signal to be accessed and the accessed signal are distinguished. Based on the classified signal data, the topology structure of the wireless network is analyzed, including the connection relation between nodes, the signal coverage range and the like. And analyzing the router antenna data according to the network topology structure, and determining a dynamic deflection strategy of the antenna so as to optimize signal coverage and quality.

In this embodiment, digital signal processing data is obtained from the baseband processor, and a signal processing policy is formulated by combining signal data to be accessed and access signal data. And formulating a signal processing strategy according to the characteristics of the signal to be accessed and the accessed signal. High priority signals may acquire more processing resources and bandwidth. And transmitting the formulated signal processing strategy to a baseband processor to ensure that the signal processing task is executed according to the priority so as to meet the network requirement to the greatest extent. For example, high priority signals are processed more quickly to ensure timely access. This strategy is transmitted to the baseband processor to ensure efficient signal access task execution.

Historical broadband resource allocation data is obtained from a memory in the embodiment, and the data may include information such as past network load conditions, spectrum utilization rate and the like. And combining the signal processing strategy and the historical resource allocation data to perform resource allocation coupling simulation so as to determine an optimal resource allocation scheme. And generating optimal resource allocation data, and ensuring effective utilization and performance optimization of network resources.

In this embodiment, the antenna bias strategy is optimized by combining the optimal resource allocation data. This involves adjusting the direction, angle or power output of the antenna to maximize signal coverage and quality. And transmitting the optimized antenna deflection strategy to a controller to ensure that the antenna deflection adjustment task is actually executed. The controller adjusts the direction and parameters of the router antennas according to the optimization strategy of the transmission to optimize signal coverage and network performance.

Optionally, step S1 specifically includes:

In this embodiment, the router signal data and the router antenna data are acquired through the antenna device. In a real scene, professional wireless signal analysis equipment, such as a Wi-Fi analyzer or a spectrum analyzer, can be used to acquire data such as signal strength, frequency, signal to noise ratio and the like by scanning wireless signals in an area where a router is located, and simultaneously record parameters such as position of an antenna, antenna deflection, antenna gain and the like.

In this embodiment, feature extraction is performed on the router signal data. By processing and analyzing the collected signal data, signal strength characteristics and signal multipath characteristics can be extracted. For example, the signal strength data may be used to calculate the decay rate and decay trend of the signal, while the signal multipath data is used to analyze the propagation path and reflection of the signal.

In this embodiment, environmental topography analysis is performed according to the signal intensity characteristic data. The topography and obstacle distribution condition of the environment where the router is located can be analyzed by utilizing the signal intensity characteristic data. For example, the location and type of the obstacle is inferred by the change in signal strength, thereby constructing an environmental terrain model around the router.

In this embodiment, propagation environment analysis is performed according to the signal multipath characteristic data. The multipath effect of the signal in the propagation process, such as multipath fading, multipath interference and the like, can be analyzed by utilizing the multipath characteristic data of the signal. Through the analysis, the propagation characteristics of the signals in the environment can be better understood, and a foundation is laid for establishing an accurate propagation environment model.

In this embodiment, the digitized environment modeling is performed based on the router environment obstacle data and the signal propagation environment data. And integrating and processing the obtained router environment obstacle data and the signal propagation environment data, and establishing a digital router environment model. The model can comprise information such as the surrounding terrain of the router, the distribution condition of obstacles, the propagation path of signals, attenuation characteristics and the like, and provides accurate reference basis for subsequent router optimization and network planning.

Optionally, step S13 specifically includes:

In this embodiment, continuous intensity fluctuation statistics is performed on the collected signal intensity feature data. The change in signal intensity over time will be analyzed to detect the stability and fluctuation of the signal. For example, the mean, variance, or standard deviation of the signal strength over different time periods may be calculated to obtain signal strength fluctuation data.

In this embodiment, fourier transformation is performed on the signal strength fluctuation data, and the signal strength fluctuation data is converted into a frequency domain representation, so as to obtain a signal strength spectrum. Then, time series feature extraction may be performed on the signal strength spectrum, for example, to extract a main frequency component, energy distribution, etc. of the spectrum, so as to obtain time series data of the signal strength.

In this embodiment, the short-range signal intensity spectrum and the long-range signal intensity spectrum can be obtained by performing statistical analysis according to the signal intensity spectrum. The spectral data can reflect the signal intensity distribution characteristics in different distance ranges, and provide a basis for subsequent signal analysis and processing.

in this embodiment, for the short-range signal intensity spectrum, inverse fourier transform is performed, frequency domain data is converted back to the time domain, and short-range signal reflection distance calculation is performed according to short-range signal intensity fluctuation data. This can help determine the reflection of the signal over a short distance and the position of the obstacle, thereby obtaining short range signal reflection distance data.

In this embodiment, for the long-range signal intensity spectrum, inverse fourier transform is also performed to obtain long-range signal intensity fluctuation data, and reflection distance data of the long-range signal is calculated according to the long-range signal intensity fluctuation data. Helping to understand the attenuation and environmental characteristics of signals during long distance propagation.

In this embodiment, the signal strength time sequence data, the short-range signal reflection distance data and the long-range signal reflection distance data are integrated together, and necessary preprocessing, such as noise removal, missing value filling, etc., is performed. The signal reflection distance data is converted into obstacle position information around the router based on the known router position information by a triangulation method or the like. And determining the position of the obstacle according to the distance relation between the router and the reflection point. And marking the position of the router in a map or an environment model by utilizing the position information of the router and the reflection distance data, and drawing the outline of the obstacle according to the reflection distance data. This may employ various image processing and spatial analysis techniques such as edge detection, contour extraction, etc. The extracted obstacle profile data is analyzed to learn about environmental features around the router, such as walls, furniture, human body, etc. This can be achieved by analyzing the profile shape, size, density, etc. characteristics. And according to the environmental characteristic analysis result, evaluating the influence degree of the obstacle on signal transmission. For example, a blocked area of signal transmission, a multipath propagation area, etc., are identified and their impact on signal quality and coverage is evaluated. And obtaining router environment obstacle data according to the obstacle influence evaluation result.

Optionally, step S136 specifically includes:

In this embodiment, the reflection distance data from the short range and long range signals are integrated to form a three-dimensional data set. For example, two data are aligned with time stamps, creating a three-dimensional tensor, where two dimensions represent spatial coordinates and the third dimension represents time.

In this embodiment, the convolutional neural network structure is designed by using the signal reflection space data to capture the complex relationship of signal reflection in the space. And adopting structures such as a convolution layer, a pooling layer and the like, for example, a 3D convolution kernel is used for constructing a depth network model by considering space-time information.

In the embodiment, a space-time 3D convolutional network is constructed by combining signal intensity time sequence data and a spatial convolutional neural network. The network can comprehensively consider the characteristics of the change of the signal intensity along with time and the spatial distribution, and can realize the prediction of the distance.

In the embodiment, the constructed distance prediction space-time 3D convolution network is utilized to predict the short-range and long-range signal reflection distance data, so as to obtain the distance prediction data from the signal source to the obstacle.

in this embodiment, the distance prediction data is converted into an obstacle profile, and adjacent points are connected to form a complete profile. This may employ clustering algorithms or edge connection techniques in image processing to make the shape of the obstacle more accurate.

In this embodiment, the connected obstacle profile data are spatially combined to obtain the environmental obstacle data around the router. And the specific environment barrier distribution situation of the area where the router is located is obtained through aggregation and analysis of the profile data, so that network optimization and planning are facilitated.

Optionally, step S14 specifically includes:

in this embodiment, the multipath signal attenuation calculation is performed by using a suitable mathematical model and algorithm with respect to the collected multipath signal characteristic data by considering factors such as propagation path length, obstacle influence, signal frequency, and the like. To obtain attenuation values on each propagation path. For example, multipath signal attenuation may be calculated using a path loss model, such as a free space propagation model or an environmental attenuation model, in combination with measured data.

In this embodiment, terrain attenuation value statistics is performed on the obtained multipath signal attenuation distribution data based on the environmental obstacle data around the router. This may include analyzing the type, density and location of obstructions around the router, and then evaluating the signal for terrain attenuation in different directions based on this information. For example, attenuation values in different directions may be estimated by numerical simulation or in-field measurements.

In this embodiment, the obtained obstacle attenuation data and multipath signal attenuation distribution data are combined to calculate the signal terrain attenuation value. This involves combining multipath signal attenuation data with the effects of terrain obstructions to obtain a more accurate signal attenuation profile. For example, a weighted average or a point-by-point addition method may be used to calculate the terrain attenuation value based on the degree of influence of the obstacle on the signal propagation.

In this embodiment, attenuation value marking is performed on the multipath signal attenuation distribution data according to the obtained signal terrain attenuation data and the obtained obstacle attenuation value data. The topographic features and the obstacle effects in the signal propagation environment are fully considered, and an accurate data basis is provided for subsequent signal propagation analysis. For example, attenuation values on different propagation paths may be marked to indicate their influence by terrain and obstructions

Optionally, step S2 specifically includes:

In this embodiment, MAC address data is extracted from router signal data, and then statistical analysis is performed. For example, the most frequently occurring MAC address is identified, as well as the unique MAC address that occurs only once, and this data is used for subsequent signal classification and network topology analysis.

In this embodiment, based on the obtained high-frequency MAC address data, the router signal data is classified and calculated to obtain access signal data. And meanwhile, carrying out another round of classification calculation according to the unique MAC address data to obtain signal data to be accessed. To help understand which devices are actively connected to the network and which devices are not yet connected.

In this embodiment, the router environment digital model is analyzed by using the obtained signal data to be accessed and the access signal data to perform wireless network topology analysis. Modeling and analysis of network topology is accomplished using graph theory, complex network analysis, or deep learning models. By analyzing the connection condition and the signal transmission path between different devices, the topology structure of the network can be established, which is beneficial to the subsequent device management and optimization.

In this embodiment, the device-intensive area in the network is divided according to the established network topology. This process may involve complex spatial analysis methods and algorithms to identify and partition areas that are densely populated with devices. This may include using Geographic Information Systems (GIS), spatial statistical analysis, or deep learning models to enable identification and partitioning of device-intensive areas.

In the embodiment, antenna deflection characteristic extraction and working condition characteristic extraction are performed on router antenna data. This process may involve complex signal processing and feature extraction methods to identify the antenna's biased features and operating conditions. And then, carrying out load time sequence statistical analysis on the antenna working condition data so as to identify high-load and low-load working condition time sequence data. This may include using a time series analysis, spectral analysis, or deep learning model to enable analysis and identification of antenna operating condition data.

In the embodiment, based on high-load working condition time sequence data and equipment dense areas, the router antenna deflection data are accessed to high-load working condition antenna deflection division. This process may involve complex spatial and temporal analysis methods to identify antenna deflection characteristics under high load conditions. Similarly, the antenna deflection data of the non-accessed device dense area is subjected to non-accessed high-load working condition antenna deflection division. This may include using a Geographic Information System (GIS), time series analysis, or deep learning model to enable the partitioning and identification of antenna bias data.

in the embodiment, according to the low-load working condition time sequence data and the equipment dense area, the router antenna deflection data are accessed to the low-load working condition antenna deflection division. This process may involve complex spatial and timing analysis methods to identify antenna deflection characteristics under low load conditions. Meanwhile, antenna deflection division under the unaccessed low-load working condition is carried out on antenna deflection data of the dense area of the unaccessed equipment. This may include using a Geographic Information System (GIS), time series analysis, or deep learning model to enable the partitioning and identification of antenna bias data.

In this embodiment, the antenna deflection data under different load conditions are combined to form the dynamic deflection time sequence of the antenna. This process may involve complex data fusion and model integration methods to comprehensively consider antenna bias characteristics under different conditions. This helps to formulate antenna tuning strategies to accommodate different load conditions, thereby optimizing network performance and user experience.

Optionally, step S3 specifically includes:

The baseband processor in this embodiment may perform a series of digital signal processing operations, such as demodulation, filtering, modulation, etc., by receiving the raw signal data from the router to convert the raw signal into a digital form for further processing.

According to the first high-load antenna deflection data and the first low-load antenna deflection data, if the antenna load of a certain area is found to be high, the situation may indicate that the user density of the area is high, at this time, the access signal data of the area can be divided into priority signal processing data, so that the user of the area can be ensured to access the network quickly, and the user experience is improved.

According to the second high-load antenna deflection data and the second low-load antenna deflection data, if the antenna load of a certain area is found to be low, it may indicate that the user density of the area is low, and at this time, the signal data to be accessed in the area can be divided into sub-priority signal processing data, so that network resources can be reasonably utilized while the requirement of the priority area is met, and the overall network efficiency is improved.

In this embodiment, the data are further prioritized according to the network policy and the resource allocation. For example, higher signal processing frequencies or greater bandwidth allocations may be employed for priority signal processing data to ensure timely response of high priority regions; and a more flexible resource allocation strategy can be adopted for the secondary priority signal processing data so as to adapt to the changing requirements of different areas. And finally, transmitting the formulated signal processing strategy to a baseband processor to execute a corresponding signal access task, so as to realize effective management and allocation of network resources.

Optionally, step S4 specifically includes:

in this embodiment, the past broadband resource allocation data is retrieved from the memory, and these data record the use condition, allocation policy and performance information of the past network resources, so as to provide a basis for subsequent analysis.

In this embodiment, a signal processing strategy is adopted to analyze historical broadband resource allocation data, and calculate the similarity between resource allocation schemes. This allows for quantification of the similarity between different modulation schemes, providing an important basis for subsequent steps.

in this embodiment, similar resource allocation data extraction is performed on historical wideband resource allocation data according to the signal resource allocation similarity, so as to obtain similar access resource allocation data and similar resource allocation data to be accessed. Based on the calculated similarity, screening out a data set similar to the current situation in the historical allocation data, and respectively extracting similar access resource allocation data and resource allocation data to be accessed for further simulation and analysis.

In the embodiment, similar resource allocation data is utilized to perform coupling simulation, the mutual influence and the coupling effect of different resource allocation schemes in an actual network environment are simulated, resource allocation coupling simulation data are generated, and a simulation result is provided for the selection of optimal resource allocation. For example, environmental parameters of the resource allocation simulation, including network topology, device configuration, user distribution, etc., are determined to ensure that the simulated environment matches the actual network conditions. And loading the similar access resource allocation data and the similar resource allocation data to be accessed into a simulation environment to serve as a simulated allocation scheme. Executing a resource allocation scheme in a simulation environment, and simulating the allocation and allocation process of resources. This may involve adjusting bandwidth allocation, optimizing signal transmission routes, adjusting device parameters, and the like. Performance indexes in the simulation process, including network throughput, delay, packet loss rate and the like, are monitored in real time so as to evaluate the actual effect of each allocation scheme. And analyzing the coupling effect between different resource allocation schemes, namely allocating the influence of one resource on allocation of other resources and the interaction between different resources. And recording key data and performance indexes in the simulation process for subsequent statistical analysis and evaluation.

In this embodiment, detailed statistical analysis is performed on the coupling simulation data of resource allocation, including consideration of performance index, efficiency, stability, etc., so as to determine an optimal resource allocation scheme, and provide an optimal broadband resource allocation strategy for the network.

Optionally, step S5 specifically includes:

In this embodiment, feature extraction is performed on the optimal resource allocation data, and statistical analysis may be performed first on each parameter in the resource allocation data, such as bandwidth utilization, signal strength distribution, and device load conditions. Then, a machine learning or statistical analysis method can be adopted to extract key features, such as average bandwidth requirements, equipment utilization fluctuation and the like, so as to form resource allocation feature data.

In this embodiment, the characteristic extraction is performed on the antenna deflection strategy, and key characteristics, such as antenna adjustment frequency, adjustment amplitude, adjustment direction, etc., can be extracted by analyzing the historical antenna adjustment data. These features may reflect the stability, flexibility, and effect of different antenna bias strategies.

in this embodiment, the antenna deflection correction angle calculation is performed on the antenna deflection adjustment data and the resource allocation feature data by using an antenna deflection correction angle calculation formula, and the correction angle of each antenna can be calculated by combining the resource allocation feature and the antenna adjustment data according to a specific correction formula. This calculation process may involve complex mathematical models and algorithms.

；

in the method, in the process of the invention, For correcting antenna deflection angle,/>Is the height of the antenna,/>Is the deflection angle of the antenna,/>For antenna transmission frequency,/>To transmit frequency at antenna/>Signal intensity at time,/>For the type parameter of the antenna,/>In order to average the amount of network data traffic,For maximum network data transmission quantity,/>To transmit frequency at antenna/>Electromagnetic interference value at time/>Is the relief parameter of the surrounding topography of the router,/>For the number of users in the antenna deflection area,/>For the signal processing speed of the device,/>Is ambient temperature/>For the volume of the antenna,Is of atmospheric density,/>Is the router volume; /(I)

The invention obtains an antenna deflection correction angle calculation formula by referring to related data and expert experience to carry out formula deduction. The formula fully considers influencing the deflection correction angle of the antennaHeight/>Deflection angle/>, of antennaAntenna transmission frequency/>Transmitting frequency at antenna/>Signal intensity at time/>Type parameter of antenna/>Average network data traffic/>Maximum network data traffic/>Transmitting frequency at antenna/>Electromagnetic interference value/>Surrounding topography relief parameters of router/>Number of users of antenna deflection area/>Signal processing speed of device/>Ambient temperature/>Antenna volume/>Atmospheric density/>Router volume/>A functional relationship is formed:

；

In this embodiment, the antenna deflection strategy is deflected and corrected according to the antenna deflection correction angle, and the original antenna deflection strategy can be adjusted according to the calculated correction angle. For example, if the correction angle of an antenna is positive, the original antenna may be biased toward the angle to optimize signal coverage. And then, transmitting the optimized antenna deflection strategy to a controller, and executing an antenna deflection adjustment task by the controller to ensure that the network coverage effect is optimized.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The wireless router access method is characterized by being applied to a wireless router, wherein the wireless router comprises a controller, antenna equipment electrically connected with the controller, a memory and a baseband processor; the memory and the baseband processor are electrically connected with the controller; the wireless router access method comprises the following steps:

Step S2: carrying out signal data classification on the router signal data so as to obtain signal data to be accessed and access signal data; according to the signal data to be accessed and the access signal data, carrying out wireless network topology analysis on the router environment digital model, thereby obtaining a network topology; carrying out antenna dynamic deflection analysis on the router antenna data according to the network topology structure so as to obtain an antenna deflection strategy, wherein the step S2 specifically comprises the following steps:

Step S28: combining the antenna dynamic deflection time sequence of the first high-load antenna deflection data, the second high-load antenna deflection data, the first low-load antenna deflection data and the second low-load antenna deflection data, thereby obtaining an antenna deflection strategy;

step S3: the method comprises the steps of obtaining digital signal processing data through a baseband processor, carrying out signal processing priority division on the digital signal processing data according to signal data to be accessed and access signal data, obtaining a signal processing strategy, and transmitting the signal processing strategy to the baseband processor to execute a signal access task, wherein the step S3 is specifically as follows:

Step S34: performing signal processing prioritization on the digital signal processing data according to the priority signal processing data and the secondary priority signal processing data, so as to obtain a signal processing strategy, and transmitting the signal processing strategy to a baseband processor to execute a signal access task;

step S4: acquiring historical broadband resource allocation data through a memory, and performing resource allocation coupling simulation according to a signal processing strategy and the historical broadband resource allocation data to obtain optimal resource allocation data, wherein the step S4 specifically comprises:

step S45: carrying out statistical analysis on the resource allocation coupling simulation data so as to obtain optimal resource allocation data;

Step S5: performing antenna deflection strategy optimization according to the optimal resource allocation data and the antenna deflection strategy, so as to obtain an optimal antenna deflection strategy, and transmitting the optimal antenna deflection strategy to a controller to execute an antenna deflection adjustment task, wherein the step S5 specifically comprises:

；

in the method, in the process of the invention, For correcting antenna deflection angle,/>Is the height of the antenna,/>Is the deflection angle of the antenna,/>The frequency is transmitted for the antenna and,To transmit frequency at antenna/>Signal intensity at time,/>For the type parameter of the antenna,/>For average network data traffic,/>For maximum network data transmission quantity,/>To transmit frequency at antenna/>Electromagnetic interference value at time/>For the relief parameters of the topography surrounding the router,For the number of users in the antenna deflection area,/>For the signal processing speed of the device,/>Is ambient temperature/>For antenna volume,/>Is of atmospheric density,/>Is the router volume;

2. The wireless router access method according to claim 1, wherein step S1 is specifically:

3. The wireless router access method according to claim 2, wherein step S13 is specifically:

4. The wireless router access method according to claim 3, wherein step S136 is specifically:

5. The wireless router access method according to claim 4, wherein step S14 is specifically:

6. A wireless router access system for performing a wireless router access method as claimed in claim 1, the wireless router access system comprising: