A network optimization method and device, a network device, and a storage medium
By determining compensation parameters in the router to correct the original network signal, the problem of wireless network distortion in routers is solved, network stability is improved, and cost and power consumption are reduced.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- RUIJIE NETWORKS CO LTD
- Filing Date
- 2025-01-09
- Publication Date
- 2026-07-10
AI Technical Summary
The wireless network provided by the router is prone to distortion, which affects network stability and user experience. Existing technologies improve stability by adding radio frequency circuits, but this increases cost and power consumption.
When the received original network signal and the amplified network signal have a non-linear relationship, compensation parameters are determined, the original network signal is corrected to make it linear with the amplified network signal, and the corrected signal is sent to the terminal.
It improves the stability of network signals, avoids WiFi signal distortion, reduces R&D costs, and reduces power consumption.
Smart Images
Figure CN122372008A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of communication technology, and in particular to a network optimization method, apparatus, network device, and storage medium. Background Technology
[0002] With the development of technology, people's daily lives are becoming increasingly intertwined with the internet. Users use routers to provide wireless networks for their devices. However, the wireless networks provided by routers can sometimes experience distortion, affecting network stability and the user's network experience.
[0003] In related technologies, the stability of the wireless network provided by the router is improved by adding radio frequency circuits. Summary of the Invention
[0004] Exemplary embodiments of this application provide a network optimization method, apparatus, network device, and storage medium.
[0005] In a first aspect, embodiments of this application provide a network optimization method, including:
[0006] When the received original network signal and the amplified network signal corresponding to the original network signal have a nonlinear relationship, the compensation parameter corresponding to the original network signal is determined.
[0007] The original network signal is corrected based on the compensation parameters; the corrected network signal has a linear relationship with the original network signal.
[0008] Send the corrected network signal to the terminal.
[0009] The above method can promptly detect whether the WiFi signal is distorted. If the WiFi signal is distorted, compensation parameters can be used to quickly correct the WiFi signal to avoid the user's terminal device disconnecting from the Internet, thus increasing network stability.
[0010] In one possible implementation, the method further includes:
[0011] The received raw network signal is sampled at set time intervals to obtain multiple sampled signals;
[0012] The multiple sampled signals are combined to obtain the digital signal corresponding to the original network signal;
[0013] The digital signal is amplified to obtain the amplified network signal corresponding to the original network signal.
[0014] By sampling the original network signal at time intervals using the above method, the characteristics of the signal can be accurately captured. This discretization process allows the digital signal to more accurately reflect the changes and details of the original signal, thereby improving the signal processing accuracy. Furthermore, by merging multiple sampled signals and converting them into digital signals, various digital signal processing can be performed more conveniently.
[0015] In one possible implementation, merging the multiple sampled signals to obtain the digital signal corresponding to the original network signal includes:
[0016] Determine the digital signal characteristics of each of the plurality of sampled signals;
[0017] The correction magnitude for the digital signal characteristics of each sampled signal is determined based on the compensation parameters, and each sampled signal is corrected based on the correction magnitude.
[0018] The corrected sampled signals are combined to obtain the digital signal corresponding to the original network signal.
[0019] In one possible implementation, after sending the corrected network signal to the terminal, the method further includes:
[0020] The network signal quality of the terminal is obtained; if the network signal quality is greater than or equal to the signal quality threshold, the set time interval is increased.
[0021] If the network signal quality is less than the signal quality threshold, then the set time interval is reduced.
[0022] Using the above method, the sampling time interval can be adjusted based on the network signal quality of the terminal. If the network signal quality of the terminal is poor, the sampling time interval can be reduced to improve the accuracy of the sampled signal. Similarly, if the network signal quality of the terminal is good, the sampling time interval can be increased to reduce resource consumption.
[0023] In one possible implementation, determining the compensation parameters corresponding to the original network signal includes:
[0024] Based on the amplified network signal and power amplification ratio corresponding to the original network signal, the target network signal corresponding to the amplified network signal is determined.
[0025] The compensation parameters are determined based on the difference between the target network signal and the original network signal.
[0026] Using the above method, the compensation parameter can be accurately determined by the difference between the target network signal and the original network signal. The compensation parameter is used to make up for the difference between the target network signal and the original network signal, which can avoid the distortion of WiFi signal.
[0027] In one possible implementation, the method further includes:
[0028] The original network signal and the amplified network signal are linearly fitted to obtain the linear fitting result;
[0029] The original network signal and the amplified network signal are subjected to nonlinear fitting to obtain the nonlinear fitting result;
[0030] If the difference between the linear fitting result and the set fitting threshold is greater than or equal to the difference between the nonlinear fitting result and the fitting threshold, then it is determined that the original network signal and the amplified network signal have a linear relationship.
[0031] If the difference between the linear fitting result and the fitting threshold is less than the difference between the nonlinear fitting result and the fitting threshold, then it is determined that the original network signal and the amplified network signal have a nonlinear relationship.
[0032] Using the above method, the nonlinear fitting results, linear fitting results, and fitting threshold can be used to determine whether there is a nonlinear relationship between the original network signal and the amplified network signal. If it is determined that there is a linear relationship between the original network signal and the amplified network signal, no correction is required. If there is a nonlinear relationship between the original network signal and the amplified network signal, the original network signal needs to be corrected to avoid distortion of the WiFi signal.
[0033] In one possible implementation, the correction of the original network signal based on the compensation parameters includes:
[0034] A target mathematical model is generated based on the compensation parameters. The original network signal is then input into the target mathematical model to obtain the corrected network signal output by the target mathematical model.
[0035] The above method can be used to correct the original network signal using the target mathematical model, optimize the quality of the WiFi signal, and avoid WiFi signal distortion.
[0036] In one possible implementation, before determining the compensation parameters corresponding to the original network signal, the method further includes:
[0037] It is determined that the signal strength of the original network signal is lower than a set signal strength threshold.
[0038] In one possible implementation, determining the compensation parameters corresponding to the original network signal includes:
[0039] Determine a first relationship between the original network signal and the network signal after the original network signal has been corrected, and a second relationship between the original network signal and the amplified network signal;
[0040] The target difference between input and output is determined based on the first relationship and the second relationship;
[0041] The compensation parameter corresponding to each digital signal feature in the original network signal is determined based on the minimum value of the target difference.
[0042] In one possible implementation, the method is applied to network devices that use the Internet Protocol (IP) as their network protocol.
[0043] Secondly, embodiments of this application provide a network optimization apparatus, comprising:
[0044] The compensation parameter determination unit is used to determine the compensation parameter corresponding to the original network signal when the received original network signal and the amplified network signal corresponding to the original network signal have a nonlinear relationship.
[0045] The original signal correction unit is used to correct the original network signal based on the compensation parameters; the corrected network signal has a linear relationship with the original network signal.
[0046] The correction signal transmission unit is used to send the corrected network signal to the terminal.
[0047] Thirdly, embodiments of this application provide a network optimization model, including: at least one adjustment unit, an amplification module, and at least one delay module; each adjustment unit includes a channel and an adjustment module.
[0048] The delay module is used to transmit the original network signal to different adjustment units according to a set time interval;
[0049] The channel in each adjustment unit is used to transmit the original network signal;
[0050] The adjustment module in each adjustment unit is used to correct the network signal according to the compensation parameters when it is determined that the signal quality of the original network signal is lower than the set signal quality threshold.
[0051] The amplification module is used to amplify the network signal after it has been corrected by the adjustment module.
[0052] Fourthly, embodiments of this application provide a network device, including:
[0053] Memory, used to store program instructions;
[0054] A processor is configured to invoke program instructions stored in the memory and execute the steps included in the method described in the first aspect according to the obtained program instructions.
[0055] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method described in the first aspect.
[0056] This application provides a network optimization method, apparatus, network device, and storage medium. When the received original network signal and its corresponding amplified network signal exhibit a non-linear relationship—meaning distortion occurred during the amplification process—a compensation parameter is determined by comparing the original network signal with the target network signal (after the amplified network signal has been reduced by a power amplification ratio). Based on this compensation parameter, the original network signal is corrected to achieve a linear relationship with the original signal. In other words, the distorted network signal is corrected to a normal network signal before being sent to the terminal device. By correcting the distorted network signal to a non-distorted one, the stability of the network signal is improved without requiring additional hardware, reducing development costs and power consumption during use. Attached Figure Description
[0057] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0058] Figure 1 This is a schematic diagram illustrating an application scenario of a network optimization method provided in an embodiment of this application.
[0059] Figure 2 A flowchart illustrating a network optimization method provided in this application embodiment;
[0060] Figure 3 A schematic diagram of a network optimization model provided in an embodiment of this application;
[0061] Figure 4 A schematic diagram of an input-output curve provided for an embodiment of this application;
[0062] Figure 5 A detailed flowchart of a network optimization method provided in an embodiment of this application;
[0063] Figure 6 A structural block diagram of a network optimization device provided in an embodiment of this application;
[0064] Figure 7 This is a structural block diagram of a router provided in an embodiment of this application. Detailed Implementation
[0065] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.
[0066] In the description of the embodiments of this application, unless otherwise stated, "and" means "or", for example, A / B can mean A or B; "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone.
[0067] Specifically, in the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "connection" and "linkage" used in this application, unless otherwise specified, include both direct and indirect connections (linkages).
[0068] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "a plurality of" means two or more, unless otherwise explicitly specified.
[0069] Before introducing the network optimization method provided in the embodiments of this application, for ease of understanding, the technical background of the embodiments of this application will be described in detail below.
[0070] With the development of technology, people's daily lives are becoming increasingly intertwined with the internet. Users provide wireless networks for their devices through network devices such as routers. However, the network signal provided by routers may be weak or distorted, affecting network stability and the user's network experience.
[0071] In related technologies, routers do not perform correction processing on the modulated network signal. Instead, they directly transmit the modulated network signal via radio frequency to each terminal device connected to the router, or they improve the stability of the wireless network provided by the router by adding radio frequency circuitry. However, adding radio frequency circuitry can significantly increase manufacturing costs and power consumption, which does not meet the needs of daily life.
[0072] The present application will now be described in further detail with reference to the accompanying drawings and specific embodiments.
[0073] In one possible embodiment, Figure 1 This diagram illustrates an application scenario of a network optimization method provided in an embodiment of this application. See also... Figure 1 As shown, Figure 1 It includes a network device 100 and terminal devices 210, 220, and 230. Network device 100 can be a router, wireless access point (AP), or other device capable of providing network access to the terminal devices. Terminal devices 210, 220, and 230 can all be mobile phones, tablets, or portable wearable devices. Portable wearable devices can be smartwatches, smart bracelets, head-mounted devices, etc. The number of terminal devices can be more or less, such as two or four terminal devices, or other numbers; this application does not limit this.
[0074] Specifically, terminal devices 210, 220, and 230 can all connect to the Wireless Fidelity (WiFi) network provided by network device 100 using Internet IP protocols such as IPv4 and IPv6 as the network protocol via wired or wireless connections. Router 100 can obtain the network signal quality information of the networks accessed by terminal devices 210, 220, and 230 respectively. Terminal devices 210, 220, and 230 can also use the "iwconfig" and "iwlist" commands in the Wireless Tools suite to obtain or adjust the parameters of the wireless network in the router.
[0075] Figure 1 The application scenario described above is merely an example of one application scenario for implementing the embodiments of this application, and the embodiments of this application are not limited to the above. Figure 1 The application scenarios described above are illustrated below with reference to the accompanying drawings. It should be noted that the application scenarios described above are merely illustrative for the purpose of understanding the spirit and principles of this application, and the implementation methods of this application are not limited in any way.
[0076] Figure 2 A flowchart of a network optimization method provided in an embodiment of this application is shown, such as... Figure 2 As shown, the method may include the following steps:
[0077] Step S201: When the received original network signal and the amplified network signal corresponding to the original network signal have a nonlinear relationship, determine the compensation parameters corresponding to the original network signal.
[0078] In one possible embodiment, after receiving the original network signal, the network device does not directly send the original network signal to the terminal device. Instead, it amplifies the original network signal and sends the amplified network signal to the terminal device. The network source of the original network signal received by the network device can be a modulated network signal provided by an internet service provider via broadband or fiber optic broadband, a network hotspot provided by a user using a mobile data network, or a network signal provided by satellite internet. This application does not limit the network source of the original network signal received by the network device.
[0079] Figure 3 A schematic diagram of a network optimization model provided in an embodiment of this application is shown. Figure 3 As shown, the network optimization model can include multiple adjustment units. Each adjustment unit can contain a channel and an adjustment module. The channel can be used to transmit network signals. The adjustment module can be used to correct the network signal according to compensation parameters when the signal quality of the original network signal is determined to be lower than a set signal quality threshold. The signal quality of the network signal can be any parameter that measures WiFi signal quality, such as Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), Link Quality Indicator (LQI), packet loss rate, and latency, or it can be a weighted percentage determined by at least one of the above parameters. Each different case corresponds to a signal quality threshold. The amplification module can amplify the network signal. The delay module can set a time interval and transmit the received original network signal to different adjustment units at the set time intervals.
[0080] Specifically, the network device can sample the received original network signal at set time intervals to obtain multiple sampled signals. That is, different adjustment units will sample the original network signal. After sampling the original network signal, the multiple sampled signals can be combined to obtain the digital signal corresponding to the original network signal. Then, the digital signal corresponding to the original network signal is amplified by the amplification module to obtain the amplified network signal corresponding to the original network signal.
[0081] It is important to note that if the digital signal obtained after the original network signal enters the conditioning unit is not linearly related to the original network signal, then the amplified network signal obtained after amplification will also inevitably be non-linearly related to the original network signal. This weakens the amplification module's effectiveness and affects the stability of the wireless network provided by the network device. Therefore, in this embodiment, after determining that the digital signal is not linearly related to the original network signal, the original network signal is corrected to ensure a linear relationship. The corrected digital signal is then input into the amplification module, resulting in a linear relationship between the amplified network signal and the original network signal. This allows the amplification module to fully utilize its amplification function and ensures a stable network signal received by the terminal device.
[0082] In one possible embodiment, whether there is a linear relationship between the original network signal and the amplified network signal can be determined by fitting the signal in the following way:
[0083] A linear regression equation can be established to linearly fit the original network signal and the amplified network signal. For example, the linear regression equation could be y = ax + b, where y is the amplified network signal, x is the original network signal, and a and b are the parameters to be obtained through fitting. After obtaining the specific linear regression equation, the first coefficient of determination can be determined. The first coefficient of determination measures the similarity between the linear regression equation and the actual amplified network signal and the original network signal. The larger the first coefficient of determination, the stronger the linear relationship between the amplified network signal and the original network signal; therefore, the first coefficient of determination can be used as the result of the linear fitting.
[0084] Then, a nonlinear regression equation is established to perform nonlinear fitting between the original network signal and the amplified network signal. For example, the nonlinear regression equation is y = ax. bAlternatively, y = alan(bx), where y is the amplified network signal, x is the original network signal, and a and b are the parameters to be obtained through fitting. After obtaining the specific nonlinear regression equation, the second coefficient of determination can be determined. The second coefficient of determination can be used to measure the similarity between the linear regression equation and the actual amplified network signal and the original network signal. The larger the second coefficient of determination, the greater the nonlinear relationship between the amplified network signal and the original network signal. Therefore, the second coefficient of determination can be used as the result of nonlinear fitting.
[0085] After obtaining the linear and nonlinear fitting results, the differences between the linear fitting result and the set fitting threshold, and the differences between the nonlinear fitting result and the fitting threshold, can be determined respectively. The fitting threshold is typically 1. Since both the nonlinear and linear fitting results are usually numbers greater than 0 and less than 1, the closer the fitting result is to the fitting threshold (i.e., closer to 1), the better it reflects the relationship between the amplified network signal and the original network signal. For example, if the linear fitting result is closer to the fitting threshold than the nonlinear fitting result, it can be determined that the relationship between the amplified network signal and the original network signal is more linear. If the difference between the linear fitting result and the set fitting threshold is greater than or equal to the difference between the nonlinear fitting result and the fitting threshold, it can be determined that the original network signal and the amplified network signal have a nonlinear relationship; if the difference between the linear fitting result and the set fitting threshold is less than the difference between the nonlinear fitting result and the fitting threshold, it can be determined that the original network signal and the amplified network signal have a linear relationship. In the above explanation, the difference between the linear fitting result and the set fitting threshold is the absolute value of the difference between the linear fitting result and the set fitting threshold, and the difference between the nonlinear fitting result and the fitting threshold is the absolute value of the difference between the nonlinear fitting result and the fitting threshold. It should be noted that the relationship between the original network signal and the amplified network signal can also be determined by directly comparing the linear fitting result and the nonlinear fitting result. For example, if the linear fitting result is greater than or equal to the nonlinear fitting result, it can be determined that the original network signal and the amplified network signal have a linear relationship; if the linear fitting result is less than the nonlinear fitting result, it can be determined that the original network signal and the amplified network signal have a nonlinear relationship.
[0086] For example, if the linear fitting result between the original network signal and the amplified network signal is 0.5, the nonlinear fitting result is 0.6, and the set fitting threshold is 0.59, then it can be determined that the difference between the linear fitting result and the set fitting threshold is 0.09, and the difference between the nonlinear fitting result and the set fitting threshold is 0.01. The difference of 0.09 between the linear fitting result and the set fitting threshold is greater than the difference of 0.01 between the nonlinear fitting result and the fitting threshold. Since 0.01 is less than 0.09, meaning the difference between the nonlinear fitting result and the fitting threshold is less than the difference between the linear fitting result and the set fitting threshold, it can be determined that the original network signal and the amplified network signal have a nonlinear relationship. It should be noted that determining whether the original network signal and the amplified network signal have a linear relationship can also be done by directly plotting the graphs of the two signals, or by checking whether the regression residuals are randomly distributed, etc., and this application is not limited to these methods.
[0087] In one possible embodiment, after determining that the original network signal and the amplified network signal have a nonlinear relationship, a target difference equation between the input and the output can be established based on a first relationship between the original network signal and the network signal after the original network signal has been corrected, and a second relationship between the original network signal and the amplified network signal. In this way, the corresponding compensation parameters can be determined under the extreme value of the target difference equation.
[0088] First, the relationship between the various signals can be expressed using the following formula, where the first relationship between the original network signal and the network signal after correction of the original network signal can be expressed as: v(n)=G(|u(n)|)u(n), where v(n) corresponds to Figure 3 The signal u(n) that has passed through each adjustment unit but has not yet passed through the amplification module corresponds to the original network signal, and G is the amplitude equation for the adjustment module to correct the original network signal. The second relationship between the original network signal and the amplified network signal can be derived from the first relationship and the network signal after the amplified network signal and the original network signal are corrected. The specific derivation process is as follows: y(n) = f(v(n)) = f(G(|u(n)|)u(n)), where y(n) represents the amplified network signal, and f is the power amplification equation corresponding to the amplification module. Specifically, after the original network signal u(n) passes through the adjustment unit, the amplitude of the corresponding adjustment module to correct the original network signal can be found according to the digital signal characteristics of the original network signal. Multiplying the amplitude of the adjustment module to correct the original network signal by the original network signal can determine the signal v(n) that has passed through each adjustment unit but has not yet passed through the amplification module.
[0089] After establishing the equations for the first and second relations, equations can be established based on system performance:
[0090]
[0091] Where J is the objective function for measuring system performance, which evaluates the difference between the current input and output. This objective function measures the distortion of the network signal during transmission; a larger difference between input and output indicates more severe distortion, while a smaller difference indicates less distortion. E is the expectation, calculated as the average of all possible values of n according to a probability distribution, where n is the discrete-time index of the signal, usually an integer value, and its range depends on the length of the original network signal. If the length of the original network signal is N, then n can be any integer between 0 and N-1, or any integer between 1 and N. G lin The linear gain of the power amplifier is used to reflect the ideal gain relationship. The objective function defines the deviation between the original network signal and the amplified network signal. Therefore, by minimizing the above objective function, that is, minimizing the value of J, the magnitude of the correction of the original network signal by the adjustment module, i.e., the magnitude of G(|u(n)|), can be determined. For the convenience of formula derivation, G can be... lin Setting the value to 1 allows the filter output by the lookup table to be as close as possible to the original input signal u(n).
[0092] J={u * (n)u(n)-u * (n)f(G(|u(n)|)-u(n)f * (G(|u(n)|)
[0093] +f(G(|u(n)|)f * (G(|u(n)|)}
[0094] To minimize the objective function, the first-order difference can be determined in adaptive filtering theory. Through derivation, a recursive expression for the magnitude G of the correction applied to the original network signal by a single adjustment module can be determined: G i+1 =G i +γu * (n)[u(n)-y(n)], where γ is the learning step size, which controls the update rate. * This represents the complex conjugate of the input signal. u(n)-y(n) is the error term, which can represent the difference between the input and output. The amplitude equation for the correction of the Mth adjustment module can be derived from the recursive expression of the amplitude G of the correction of the original network signal by a single adjustment module. The expression for the (i+1)th iteration of the update equation is as follows:
[0095]
[0096] The dot (·) in parentheses after G can represent the digital signal characteristics of the network signal. Therefore, the compensation parameter corresponding to each digital signal characteristic in the original network signal can be determined using the above equation for the target difference. This is the correction magnitude for each digital signal characteristic in the original network signal. Furthermore, the normalization algorithm expression can be obtained as follows:
[0097]
[0098] This can be further simplified into an expression that facilitates implementation by adjusting the module:
[0099]
[0100] The above expression can also be simplified to an expression that does not require a multiplier:
[0101]
[0102] In this formula, if b in sign[b] is greater than 0, then sign[b] is 1; if b in sign[b] is equal to 0, then sign[b] is 0; and if b in sign[b] is less than 0, then sign[b] is -1. It can be considered that G determined in the above formula... m The compensation parameters that need to be determined in step S201 are the correction amplitudes corresponding to each digital signal feature in the original network signal.
[0103] Step S202: Correct the original network signal based on the compensation parameters.
[0104] In one possible embodiment, the target mathematical model y = f(x, G) can be generated based on the compensation parameters determined in step S201. m ), where y is the digital signal corresponding to the original network signal obtained through the channel, x is the original network signal, and G m The target mathematical model can be used to correct each digital signal feature in the original network signal by a predetermined amplitude. Since x and y have a non-linear relationship, the target mathematical model is typically a non-linear model to adjust them to a linear relationship. This model can be applied to the adjustment module, ensuring that the original network signal entering the adjustment unit outputs the corresponding digital signal after correction.
[0105] The original network signal is input into the target mathematical model, thereby obtaining the corrected network signal output by the target mathematical model. Figure 4 This illustration shows a schematic diagram of an input-output curve provided in an embodiment of this application, such as... Figure 4 As shown, Figure 4 The graph is divided into three sections: left, middle, and right. In the left graph, the horizontal axis X represents the original network signal, and the vertical axis U represents the digital signal corresponding to the original network signal obtained through the channel. It can be seen that in the left graph, the input X and U are not linearly related. If U is directly amplified, WiFi signal distortion will occur, causing the user's terminal device to be unable to access the internet. Therefore, the digital signal U corresponding to the original network signal obtained through the channel needs to be input into the target mathematical model determined by the compensation parameters. After inputting U into the target mathematical model and amplifying it, the functional relationship between Y and U, as shown in the middle graph, is obtained. Here, the horizontal axis U in the middle graph has the same meaning as U in the left graph, namely, the digital signal corresponding to the original network signal obtained through the channel. The vertical axis Y represents the amplified signal. In the right graph, the input X and output Y are linearly related. This confirms that after amplifying X, the resulting Y is linearly related to X, thus obtaining an undistorted WiFi signal, and the network access of the terminal device remains stable.
[0106] Step S203: Send the corrected network signal to the terminal.
[0107] In one possible embodiment, after obtaining a distortion-free WiFi signal, the corrected network signal can be sent to each terminal device connected to the network device. The network device can obtain the network signal quality of each terminal device through the network device's management interface or through network protocols (such as SNMP or NETCONF). The network signal quality can be any parameter that can measure WiFi signal quality, such as Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), Link Quality Indicator (LQI), packet loss rate, and latency. It can also be a percentage determined by weighting at least one of the above parameters. Each different situation corresponds to a signal quality threshold. For example, when using signal strength to determine network signal quality, the signal strength can reach -30dBm when the network signal quality is good, while it is -70dBm when the network fluctuates. Therefore, the signal quality threshold can be set to -55dBm. If the network signal quality is greater than or equal to the signal quality threshold, the sampling time interval in step S201 can be increased. If the network signal quality is below the signal quality threshold, the set time interval can be reduced. It should be noted that the acquired network signal quality can also be processed in other ways, such as determining the number of terminal devices with network signal quality below the threshold and the number of terminal devices with network signal quality greater than or equal to the threshold, and comparing the two. If the number of terminal devices with network signal quality below the threshold is significantly less than the number of terminal devices with network signal quality greater than or equal to the threshold, it can be considered a terminal device malfunction, rather than a problem with the set time interval in this application.
[0108] In one possible embodiment, Figure 5 A detailed flowchart of a network optimization method provided in an embodiment of this application is shown, as follows: Figure 5 As shown, the method includes the following steps:
[0109] Step S501: Sample the received raw network signal according to a set time interval to obtain multiple sampled signals.
[0110] Step S502: Combine multiple sampled signals to obtain the digital signal corresponding to the original network signal.
[0111] Step S503: Amplify the power of the digital signal to obtain the amplified network signal corresponding to the original network signal.
[0112] Step S504: Perform linear fitting on the original network signal and the amplified network signal to obtain the linear fitting result, and then perform nonlinear fitting to obtain the nonlinear fitting result.
[0113] Step S505: When it is determined that the received original network signal and the amplified network signal have a nonlinear relationship based on the linear fitting results and the nonlinear fitting results, the compensation parameters are determined based on the difference between the digital signal corresponding to the original network signal and the original network signal.
[0114] Step S506: Generate a target mathematical model based on the compensation parameters, input the original network signal into the target mathematical model, and obtain the corrected network signal output by the target mathematical model.
[0115] Step S507: Send the corrected network signal to the terminal.
[0116] Step S508: Obtain the network signal quality of the terminal and determine whether the network signal quality is less than the signal quality threshold. If yes, proceed to step S509; otherwise, proceed to step S510.
[0117] Step S509: Reduce the set sampling time interval.
[0118] Step S510: Increase the set time interval for sampling.
[0119] Based on the same inventive concept Figure 6 This application provides a structural block diagram of a network optimization device, such as... Figure 6 As shown, the network optimization device 600 may include:
[0120] The compensation parameter determination unit 601 is used to determine the compensation parameter corresponding to the original network signal when the received original network signal and the amplified network signal corresponding to the original network signal have a nonlinear relationship.
[0121] The original signal correction unit 602 is used to correct the original network signal based on the compensation parameters; the corrected network signal has a linear relationship with the original network signal.
[0122] The correction signal sending unit 603 is used to send the corrected network signal to the terminal.
[0123] In one possible implementation, the compensation parameter determination unit 601 is further configured to sample the received original network signal at a set time interval to obtain multiple sampled signals.
[0124] The multiple sampled signals are combined to obtain the digital signal corresponding to the original network signal;
[0125] The digital signal is amplified to obtain the amplified network signal corresponding to the original network signal.
[0126] In one possible implementation, the compensation parameter determination unit 601 is further configured to obtain the network signal quality of the terminal, and if the network signal quality is greater than or equal to the signal quality threshold, then increase the set time interval.
[0127] If the network signal quality is less than the signal quality threshold, then the set time interval is reduced.
[0128] In one possible implementation, the compensation parameter determination unit 601 is specifically used to determine the target network signal corresponding to the amplified network signal based on the amplified network signal and the power amplification ratio corresponding to the original network signal.
[0129] The compensation parameters are determined based on the difference between the target network signal and the original network signal.
[0130] In one possible implementation, the compensation parameter determination unit 601 is further configured to perform linear fitting on the original network signal and the amplified network signal to obtain a linear fitting result.
[0131] The original network signal and the amplified network signal are subjected to nonlinear fitting to obtain the nonlinear fitting result;
[0132] If the difference between the linear fitting result and the set fitting threshold is greater than or equal to the difference between the nonlinear fitting result and the fitting threshold, then it is determined that the original network signal and the amplified network signal have a linear relationship.
[0133] If the difference between the linear fitting result and the fitting threshold is less than the difference between the nonlinear fitting result and the fitting threshold, then it is determined that the original network signal and the amplified network signal have a nonlinear relationship.
[0134] In one possible implementation, the original signal correction unit 602 is specifically used to generate a target mathematical model based on the compensation parameters, input the original network signal into the target mathematical model, and obtain the corrected network signal output by the target mathematical model.
[0135] Based on the same inventive concept, embodiments of this application provide a network device that can implement the functions of the network optimization methods described above. Please refer to... Figure 7 The network device 700 includes a memory 701, a processor 702, and a bus 703.
[0136] The memory 701 is used to store computer programs executed by the processor 702. The memory 701 may mainly include a program storage area and a data storage area. The program storage area may store the operating system and programs required to run instant messaging functions, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.
[0137] Memory 701 may be volatile memory, such as random-access memory (RAM); memory 701 may also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or memory 701 may be any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but is not limited thereto. Memory 701 may be a combination of the above-described memories.
[0138] The processor 702 may include one or more central processing units (CPUs) or digital processing units, etc. The processor 702 is used to implement the network optimization method described above when it invokes a computer program stored in the memory 701.
[0139] This application embodiment does not limit the specific connection medium between the memory 701 and the processor 702 described above. This application embodiment... Figure 7 The memory 701 and the processor 702 are connected via a bus 703, and the bus 703 is in Figure 7 The connections between other components are shown in bold lines only and are not intended to be limiting. The 703 bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, Figure 7 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.
[0140] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium. The computer program product includes computer program code, which, when executed on a computer, causes the computer to perform any of the network optimization methods discussed above. Since the principle by which the computer-readable storage medium solves the problem is similar to that of the network optimization method, the implementation of the computer-readable storage medium can be found in the implementation of the method; repeated details will not be elaborated further.
[0141] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0142] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0143] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0144] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0145] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.
Claims
1. A network optimization method, characterized in that, include: When the received original network signal and the amplified network signal corresponding to the original network signal have a nonlinear relationship, the compensation parameter corresponding to the original network signal is determined. The original network signal is corrected based on the compensation parameters; the corrected network signal has a linear relationship with the original network signal. Send the corrected network signal to the terminal.
2. The method according to claim 1, characterized in that, The method further includes: The received raw network signal is sampled at set time intervals to obtain multiple sampled signals; The multiple sampled signals are combined to obtain the digital signal corresponding to the original network signal; The digital signal is amplified to obtain the amplified network signal corresponding to the original network signal.
3. The method according to claim 2, characterized in that, After sending the corrected network signal to the terminal, the method further includes: The network signal quality of the terminal is obtained; if the network signal quality is greater than or equal to the signal quality threshold, the set time interval is increased. If the network signal quality is less than the signal quality threshold, then the set time interval is reduced.
4. The method according to claim 2, characterized in that, Determining the compensation parameters corresponding to the original network signal includes: Based on the amplified network signal and power amplification ratio corresponding to the original network signal, the target network signal corresponding to the amplified network signal is determined. The compensation parameters are determined based on the difference between the target network signal and the original network signal.
5. The method according to claim 1, characterized in that, The method further includes: The original network signal and the amplified network signal are linearly fitted to obtain the linear fitting result; The original network signal and the amplified network signal are subjected to nonlinear fitting to obtain the nonlinear fitting result; If the difference between the linear fitting result and the set fitting threshold is greater than or equal to the difference between the nonlinear fitting result and the fitting threshold, then it is determined that the original network signal and the amplified network signal have a linear relationship. If the difference between the linear fitting result and the fitting threshold is less than the difference between the nonlinear fitting result and the fitting threshold, then it is determined that the original network signal and the amplified network signal have a nonlinear relationship.
6. The method according to claim 1, characterized in that, The correction of the original network signal based on the compensation parameters includes: A target mathematical model is generated based on the compensation parameters; The original network signal is input into the target mathematical model to obtain the corrected network signal output by the target mathematical model.
7. The method according to claim 1, characterized in that, Before determining the compensation parameters corresponding to the original network signal, the method further includes: It is determined that the signal strength of the original network signal is lower than a set signal strength threshold.
8. The method according to any one of claims 1 to 7, characterized in that, The method is applied to network devices that use the Internet Protocol (IP) as their network protocol.
9. A network optimization device, characterized in that, include: The compensation parameter determination unit is used to determine the compensation parameter corresponding to the original network signal when the received original network signal and the amplified network signal corresponding to the original network signal have a nonlinear relationship. The original signal correction unit is used to correct the original network signal based on the compensation parameters; the corrected network signal has a linear relationship with the original network signal. The correction signal transmission unit is used to send the corrected network signal to the terminal.
10. A network device, characterized in that, include: Memory, used to store program instructions; A processor is configured to invoke program instructions stored in the memory and execute the steps of the method according to any one of claims 1-8.
11. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it implements the method of any one of claims 1-8.