Method and device for optimizing signal interference

Abstract

The invention relates to a signal interference optimization method and device. The method comprises the following steps: monitoring network parameters in a mobile communication network, and evaluating the interference level of each target frequency band in the mobile communication network according to the network parameters; generating a frequency allocation strategy according to the interference level, the frequency allocation strategy being used for dynamically matching the communication signal to the optimal frequency band; in addition, according to the interference level and the network parameters, a power adjustment strategy is generated, and the power adjustment strategy is used for dynamically adjusting the transmitting power of each base station in the mobile communication network so as to reduce signal interference. According to the application, the collateral dredging parameters are monitored in real time, and the optimal frequency band is dynamically matched for the communication signal according to the network parameters, so that the frequency band with serious interference is avoided, and the communication quality is ensured; meanwhile, according to the power adjustment strategy, the transmitting power of each base station can be dynamically adjusted based on a feedback mechanism, the signal interference is reduced while the communication requirement is met, the communication quality is further improved, and the increasing communication requirement of the user is met.

Description

Technical Field

[0001] The present application relates to the field of mobile communication technology, and in particular to a method and device for optimizing signal interference. Background Art

[0002] With the rapid development of mobile communication technology, mobile communication networks provide users with higher transmission rates, wider coverage, and greater capacity.

[0003] In mobile communication networks, the co-channel interference problem of indoor and outdoor 2G / 3G / 4G signals seriously affects the communication quality and user experience. Existing power control methods have limitations in dealing with complex and changing interference environments. Specifically, traditional power control methods are often based on static preset parameters and cannot adapt to dynamically changing interference environments in real time. For example, in densely populated urban areas, high-rise buildings lead to serious signal reflection and refraction, which increases the complexity and uncertainty of interference. In large public places such as stadiums and shopping malls, indoor and outdoor signals are intertwined, and existing power control methods are difficult to effectively distinguish and suppress interference signals. In addition, signal coverage in remote areas is insufficient, and improper power control will cause signal quality to further deteriorate. These problems not only affect users' communication experience, but also increase operators' operating costs and maintenance difficulties. Summary of the invention

[0004] Based on this, it is necessary to provide a method and device for optimizing signal interference in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for optimizing signal interference. The method comprises:

[0006] Monitoring network parameters in mobile communication networks;

[0007] evaluating the interference level of each target frequency band in the mobile communication network according to the network parameters;

[0008] generating a frequency allocation strategy based on the interference level;

[0009] generating a power adjustment strategy according to the interference level and the network parameters;

[0010] The frequency allocation strategy is used to dynamically match the communication signal to the optimal frequency band; the power adjustment strategy is used to dynamically adjust the transmission power of each base station in the mobile communication network to reduce signal interference.

[0011] In one of the embodiments, the network parameters include transmit power;

[0012] The step of evaluating the interference level of each frequency band in the mobile communication network according to the network parameters includes:

[0013] Configure corresponding weights for each interference source;

[0014] For each of the target frequency bands, a weighted sum of the transmit powers of the interference sources on the target frequency band is calculated to obtain an interference level of each target frequency band.

[0015] In one of the embodiments, generating a frequency allocation strategy according to the interference level includes:

[0016] Sorting the interference levels of the target frequency bands within a preset time in ascending order;

[0017] Selecting the target frequency band corresponding to the interference level ranked first as the optimal frequency band;

[0018] The communication signal is loaded onto the optimal frequency band.

[0019] In one embodiment, after selecting the optimal frequency band, the method further includes:

[0020] Using carrier aggregation technology, the target frequency bands are combined to obtain a combined frequency band;

[0021] The communication signal is loaded onto the optimal frequency band in the combined frequency band.

[0022] In one of the embodiments, the network parameters include signal strength and signal-to-noise ratio;

[0023] The generating a power adjustment strategy according to the interference level and the network parameter includes:

[0024] Extracting characteristic information of the interference level and the network parameters, wherein the characteristic information includes average signal strength and maximum interference frequency band;

[0025] According to the characteristic information, a decision algorithm is used to determine the adjustment direction and adjustment range of the transmission power;

[0026] The adjustment direction and amplitude of the transmit power constitute the power adjustment strategy.

[0027] In one of the embodiments, the method further comprises acquiring historical optimization data, wherein the historical optimization data comprises historical signal quality, historical interference level, and historical power adjustment data;

[0028] The generating a power adjustment strategy according to the interference level and the network parameter includes:

[0029] Build the structure of the machine learning model;

[0030] Training the machine learning model according to the historical optimization data to obtain a trained power adjustment model;

[0031] The interference level and the network parameters are input into a trained power adjustment model to obtain a power adjustment strategy so that each base station adjusts the transmission power.

[0032] In one of the embodiments, the method further includes: eliminating interference signals in the mobile communication network.

[0033] In one embodiment, the eliminating the interference signal in the mobile communication network includes:

[0034] Acquire an original signal, wherein the original signal includes an interference signal;

[0035] Extracting characteristic parameters of the interference signal from the original signal, wherein the characteristic parameters include frequency and amplitude;

[0036] According to the characteristic parameters, the original signal is dynamically filtered to weaken the interference signal.

[0037] In one embodiment, the eliminating the interference signal in the mobile communication network includes:

[0038] Acquire an original signal, wherein the original signal includes an interference signal;

[0039] Based on a statistical analysis algorithm, the original signal is analyzed to identify the interference signal;

[0040] Based on a machine learning algorithm, the interference signal is separated from the original signal.

[0041] In one embodiment,

[0042] In a second aspect, the present application also provides a signal interference optimization device. The device comprises:

[0043] A monitoring module, used for monitoring network parameters in a mobile communication network;

[0044] An evaluation module, configured to evaluate the interference level of each target frequency band in the mobile communication network according to the network parameters;

[0045] A first strategy generating module, configured to generate a frequency allocation strategy according to the interference level; wherein the frequency allocation strategy is used to dynamically match the communication signal to an optimal frequency band;

[0046] The second strategy generating module is used to generate a power adjustment strategy according to the interference level and the network parameters; wherein the power adjustment strategy is used to dynamically adjust the transmission power of each base station in the mobile communication network to reduce signal interference.

[0047] The above signal interference optimization method and device have at least the following advantages:

[0048] The present application monitors the network parameters in the mobile communication network, evaluates the interference level of each target frequency band in the mobile communication network according to the network parameters, and then generates a frequency allocation strategy according to the interference level, wherein the frequency allocation strategy is used to dynamically match the communication signal to the optimal frequency band; it also generates a power adjustment strategy according to the interference level and network parameters, wherein the power adjustment strategy is used to dynamically adjust the transmission power of each base station in the mobile communication network to reduce signal interference. The present application monitors the network parameters in real time, dynamically matches the optimal frequency band for the communication signal according to the network parameters, thereby avoiding the frequency band with serious interference and ensuring the communication quality; at the same time, the power adjustment strategy of the present application can dynamically adjust the transmission power of each base station based on the feedback mechanism, while ensuring that the communication needs are met, reducing signal interference, further improving the communication quality, and meeting the increasing communication needs of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 A schematic diagram of the structure of a signal interference optimization system in one embodiment;

[0052] Figure 2 A schematic flow chart of a method for optimizing signal interference in one embodiment;

[0053] Figure 3 A schematic diagram of a flow chart of a step of evaluating the interference level of each frequency band in an embodiment;

[0054] Figure 4 A schematic diagram of a flow chart of steps for generating a frequency allocation strategy in one embodiment;

[0055] Figure 5 A schematic flow chart of the steps after selecting the optimal frequency band in an embodiment;

[0056] Figure 6 A schematic diagram of a flow chart of steps for generating a power adjustment strategy in an embodiment;

[0057] Figure 7A schematic diagram of a flow chart of steps for generating a power adjustment strategy in another embodiment;

[0058] Figure 8 A schematic diagram of a flow chart of a step of eliminating interference signals in an embodiment;

[0059] Fig. 9 A schematic flow chart of a step of eliminating interference signals in another embodiment;

[0060] Fig.10 is a structural block diagram of a signal interference optimization device in one embodiment;

[0061] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0063] While some exemplary embodiments of the present invention have been described for the purpose of illustration, it should be understood that the present invention may be implemented in other ways not specifically shown in the drawings.

[0064] See also Figure 1 In an exemplary embodiment, the present application provides a signal interference optimization system, including multiple base stations 102 and a server 104.

[0065] Specifically, the base station 102 is an important part of the mobile communication network, which is used to communicate wirelessly with mobile terminals and send and receive signals. The server 104 is a computing device or computer program used to store, process or transmit data, which is usually deployed in a data center and connected to the Internet or an enterprise intranet through a wired network (such as optical fiber, Ethernet, etc.). Each base station 102 is connected to the server 104 by wire or wireless means and exchanges data.

[0066] During the process of data interaction between each base station 102 and the server 104, the server 104 monitors the network parameters in the mobile communication network; based on the network parameters, the interference level of each target frequency band in the mobile communication network is evaluated; based on the interference level, a frequency allocation strategy is generated, wherein the frequency allocation strategy is used to dynamically match the communication signal to the optimal frequency band; the server 104 also generates a power adjustment strategy based on the interference level and network parameters, wherein the power adjustment strategy is used to dynamically adjust the transmission power of each base station 102 in the mobile communication network to reduce signal interference.

[0067] Furthermore, the base stations in the mobile communication network in the embodiment of the present application refer to base stations that share the same network in a set area. Generally speaking, the set area may be a residential area, a gymnasium, or a shopping mall.

[0068] The set area refers to an area where multiple base stations of the same type are located, which are divided according to certain factors. In this way, the signal strength distribution of each base station in the set area is more even, and the high interference area screened out is more reasonable.

[0069] For example, the mobile terminal may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, IoT devices and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc.

[0070] In the above-mentioned signal interference optimization system, the server 104 monitors the network parameters in the mobile communication network, evaluates the interference level of each target frequency band in the mobile communication network according to the network parameters, and then generates a frequency allocation strategy according to the interference level, wherein the frequency allocation strategy is used to dynamically match the communication signal to the optimal frequency band. The server 104 also generates a power adjustment strategy according to the interference level and network parameters, wherein the power adjustment strategy is used to dynamically adjust the transmission power of each base station 102 in the mobile communication network to reduce signal interference. The present application monitors the communication parameters in real time, and dynamically matches the optimal frequency band for the communication signal according to the network parameters, thereby avoiding the frequency band with serious interference and ensuring the communication quality; at the same time, the power adjustment strategy of the present application can dynamically adjust the transmission power of each base station based on the feedback mechanism, while ensuring that the communication needs are met, reducing signal interference, further improving the communication quality, and meeting the increasing communication needs of users.

[0071] In an exemplary embodiment, the present application provides a method for optimizing signal interference. Figure 1 The server 104 in FIG. 1 is used as an example for explanation.

[0072] See also Figure 2 , Figure 2The following is a flow chart of a signal interference optimization method according to the present embodiment, which specifically includes the following steps:

[0073] Step S202: monitor network parameters in the mobile communication network.

[0074] Specifically, network parameters are indicators that measure whether the network is operating normally, and mainly include the frequency bands used by each base station, the number of carriers supported by the base station, the transmission power of the base station, etc. In addition, when there are interference signals in the mobile communication network, network parameters also include the type, number, frequency range, interference signal strength, and transmission power of the interference signal on the target frequency band.

[0075] Step S204: evaluating the interference level of each target frequency band in the mobile communication network according to the network parameters.

[0076] Specifically, the target frequency band refers to the frequency range used by the mobile communication network in a set area, and the specific value of the frequency range depends on different operators or different network services. It should be understood that there can be multiple target frequency bands, and each target frequency band can be further refined into multiple sub-frequency bands, each of which has a specific frequency range and bandwidth.

[0077] The interference level refers to the numerical value of the mutual influence or interference degree between radio signals within a specific frequency band. Specifically, the interference involved in the embodiments of the present application comes from co-channel interference. Co-channel interference means that when two or more wireless communication systems use the same frequency to communicate, the signals between them will interfere with each other, thereby affecting the communication quality.

[0078] Step S206: Generate a frequency allocation strategy according to the interference level, wherein the frequency allocation strategy is used to dynamically match the communication signal to the optimal frequency band.

[0079] Specifically, in a mobile communication network, communication signals are transmitted on one or more specific frequency bands, which can be continuous or discrete, depending on resource planning or availability. When a target frequency band is transmitting communication signals, by evaluating the interference level of each target frequency band, it is possible to switch to other target frequency bands with less interference when a target frequency band is seriously interfered with, so as to avoid the seriously interfered frequency band and ensure that the communication signal is transmitted on a relatively clean frequency band.

[0080] Step S208: Generate a power adjustment strategy based on the interference level and network parameters. The power adjustment strategy is used to dynamically adjust the transmission power of each base station in the mobile communication network to reduce signal interference.

[0081] Specifically, the strength of the base station's transmission power determines the signal coverage range. The greater the transmission power, the wider the coverage range. This improves the signal strength while also increasing the interference between signals. In addition, in the same network, multiple adjacent base stations may cover the same area. If the transmission power is too strong, it will also cause signal overlap, thereby increasing the possibility of interference. Therefore, under the premise of ensuring communication quality, appropriately reducing the base station's transmission power can effectively reduce signal interference.

[0082] The above-mentioned signal interference optimization method monitors the network parameters in the mobile communication network, evaluates the interference level of each target frequency band in the mobile communication network according to the network parameters, and then generates a frequency allocation strategy according to the interference level, wherein the frequency allocation strategy is used to dynamically match the communication signal to the optimal frequency band. A power adjustment strategy is also generated according to the interference level and network parameters, wherein the power adjustment strategy is used to dynamically adjust the transmission power of each base station in the mobile communication network to reduce signal interference. The present application monitors the communication parameters in real time, and dynamically matches the optimal frequency band for the communication signal according to the network parameters, thereby avoiding the frequency band with serious interference and ensuring the communication quality; at the same time, the power adjustment strategy of the present application can dynamically adjust the transmission power of each base station based on the feedback mechanism, while ensuring that the communication needs are met, reducing signal interference, further improving the communication quality, and meeting the increasing communication needs of users.

[0083] See also Figure 3 Optionally, the network parameter includes a transmission power; the target frequency band includes a plurality of sub-frequency bands; then, according to the network parameter, the interference level of each frequency band in the mobile communication network is evaluated, including:

[0084] Step S302: configure a corresponding weight for each interference source.

[0085] Step S304: for each target frequency band, calculate the weighted sum of the transmit power of each interference source in the target frequency band.

[0086] Specifically, based on the network parameters obtained through monitoring, we can obtain information such as the type and quantity of interference sources, as well as the transmission power in a certain frequency band. For different types of interference sources, we can pre-configure a corresponding weight for them based on experience. By superimposing the weighted sum of each interference source in each frequency band, we can obtain the interference level of each target frequency band, which is expressed as:

[0087]

[0088] Where I(f) represents the interference level at frequency f, which can be a frequency band or a single frequency point; w i represents the weight of the i-th interference source; P i(f) represents the transmission power of the i-th interference source at frequency f; N represents the number of interference sources.

[0089] See also Figure 4 Optionally, a frequency allocation strategy is generated according to the interference level, including:

[0090] Step S402: sort the interference levels of the target frequency bands within a preset time in ascending order.

[0091] Step S404: Select the target frequency band corresponding to the interference level ranked first as the optimal frequency band.

[0092] Step S406, loading the communication signal onto the optimal frequency band.

[0093] Specifically, the preset time is determined based on factors such as network environment, base station load, interference, user needs, and operator maintenance strategy. For example, when the base station load is heavy and interference is frequent, the preset time can be appropriately shortened to ensure network quality.

[0094] In the embodiment of the present application, the lower the value of the interference level, the weaker the interference signal. By sorting in ascending order, the strength of the interference signal on each frequency band can be known.

[0095] Furthermore, the frequency allocation strategy can be described as:

[0096]

[0097] Among them, f opt represents the optimal frequency band; F represents the set of available frequency bands in the target frequency band.

[0098] The above signal interference optimization method calculates the interference level of each frequency band and sorts them to obtain the frequency band with the weakest interference signal. The communication signal is loaded on it, which can avoid the frequency band with serious interference and ensure that the communication signal is transmitted on a relatively clean frequency band, thereby significantly improving the communication quality and user experience.

[0099] See also Figure 5 Optionally, after selecting the optimal frequency band, the method further includes:

[0100] Step S502: using carrier aggregation technology to combine target frequency bands to obtain a combined frequency band.

[0101] Step S504: load the communication signal to the optimal frequency band in the combined frequency band.

[0102] Specifically, in the field of communications, the frequency band used to transmit communication signals is called a narrowband carrier. Generally speaking, the frequency range occupied by a narrowband carrier is relatively small, generally between 3kHz and 500kHz. Specifically in this embodiment, a target frequency band refers to a narrowband carrier. Through carrier aggregation technology, each narrowband carrier is combined into a broadband carrier, which can increase the data transmission rate and spectrum utilization. At the same time, the broadband carrier covers a wider frequency range, which means that the communication signal can be transmitted on multiple frequency bands at the same time, which helps to disperse the interference on the narrowband carrier, thereby reducing the impact on the overall signal quality. Furthermore, carrier aggregation technology (Carrier Aggregation, CA) is an advanced wireless communication technology that aims to improve the data transmission rate and spectrum efficiency of the mobile communication system by combining multiple continuous or non-continuous carriers into a wider transmission bandwidth and using multiple frequency bands or carriers for data transmission.

[0103] For example, in a dense urban environment, three target frequency bands f1, f2, and f3 are monitored, and the interference level of each frequency band is calculated respectively, and the values ​​are I(f1) = 0.8, I(f2) = 0.5, and I(f3) = 0.3. The interference levels are sorted, and it is identified that the interference level of the target frequency band f3 is the smallest, then the target frequency band f3 is selected as the main carrier of the communication signal, that is, the above-mentioned optimal frequency band. At the same time, the carrier aggregation technology is used to aggregate the above-mentioned three target frequency bands f1, f2, and f3 to form a broadband carrier.

[0104] The above-mentioned signal interference optimization method adopts carrier aggregation technology to combine the target frequency bands, which can increase the data transmission rate and spectrum utilization; at the same time, the wider frequency range obtained by the combination means that the communication signal can be transmitted on multiple frequency bands at the same time, which helps to disperse the interference on the narrowband carrier, thereby reducing the impact on the overall signal quality.

[0105] See also Figure 6 In one embodiment, the network parameters include signal strength and signal-to-noise ratio. Then, according to the interference level and the network parameters, a power adjustment strategy is generated, including:

[0106] Step S602: extracting characteristic information of interference level and network parameters, wherein the characteristic information includes average signal strength and maximum interference frequency band.

[0107] Step S604: According to the characteristic information, a decision algorithm is used to determine the adjustment direction and adjustment range of the transmission power.

[0108] Step S604: the adjustment direction and adjustment range of the transmit power constitute a power adjustment strategy.

[0109] Specifically, a decision algorithm is a technology that uses methods such as mathematics, computer science, and operations research to analyze and process data to automatically or semi-automatically generate decision recommendations or directly make decisions. Decision algorithms mainly include decision trees, random forests, gradient boosting trees, Bayesian networks, Markov decision processes, reinforcement learning, fuzzy logic control, etc. Specifically, in this embodiment, fuzzy logic control is used to determine the adjustment direction and adjustment range of the transmission power. Among them, fuzzy logic control (Fuzzy Logic Control, FLC) is a control method based on fuzzy logic, which imitates the way humans deal with ambiguity and uncertainty in the decision-making process. Fuzzy logic control expresses and processes the ambiguity of input information through fuzzy sets and fuzzy rules, thereby achieving control of complex systems.

[0110] The expression of power adjustment is:

[0111] P new =P current +ΔP

[0112] Among them, P new Indicates the adjusted transmit power; P current represents the current transmit power, ΔP represents the power adjustment amount, and the calculation formula is:

[0113] ΔP=α×(Q target -Q current )+β×(I current -I threshold )

[0114] Among them, α and β represent adjustment coefficients; Q target Indicates the target signal quality; Q current Indicates the current signal quality; I current Indicates the current interference level; I threshold Indicates the interference threshold.

[0115] Further, determining the adjustment direction and adjustment range of the base station transmission power based on fuzzy logic control includes:

[0116] Step 1: Construct the structure and control rules of the fuzzy controller;

[0117] Step 2: Taking the characteristic information as input variable, fuzzy reasoning is performed according to the constructed fuzzy control rules to generate the fuzzy state of the output variable and obtain the output fuzzy set;

[0118] Step 3: fuzzify the output fuzzy set to obtain accurate adjustment direction and adjustment range;

[0119] Step 4: Apply the power adjustment strategy to the target base station to adjust its transmission power, and adjust the parameters or control rules of the fuzzy controller according to the adjusted network parameters.

[0120] Furthermore, before extracting the characteristic information, it also includes: preprocessing the network parameters, such as filtering, removing noise and deleting outliers.

[0121] For example, in a dense urban environment, the current transmission power P of a certain base station is monitored. current =20dBm, current signal quality Q current =-70dBm, current interference level I current =-90dBm, target signal quality Q target =-60dBm, interference threshold I threshold =-85dBm. Assume α = 0.5, β = 0.3.

[0122] Obtain the real-time interference level and network parameters, pre-process them, extract feature information, and calculate the power adjustment amount based on the decision algorithm:

[0123] ΔP=0.5×(-60-(-70)0+0.3×(-90-(-85))=3.5

[0124] The adjusted transmit power is:

[0125] P new =20+3.5=23.5dBm

[0126] At this time, the base station's transmit power will be adjusted to 23.5dBm.

[0127] See also Figure 7 In another embodiment, the signal interference optimization method of the present application further includes obtaining historical optimization data, wherein the historical optimization data includes historical signal quality, historical interference level and historical power adjustment data.

[0128] Then, based on the interference level and network parameters, a power adjustment strategy is generated, including:

[0129] Step S702, constructing the structure of the machine learning model.

[0130] Step S704: train the machine learning model according to the historical optimization data to obtain a trained power adjustment model.

[0131] Step S706: Input the interference level and network parameters into the trained power adjustment model to obtain a power adjustment strategy so that each base station adjusts the transmission power.

[0132] Specifically, machine learning models can automatically learn from data and be used for a variety of tasks such as classification, prediction, and decision-making. Through algorithms and statistical methods, features are extracted from input data and functions that can be mapped to output results are constructed. They mainly include support vector machines, random forests, and deep neural networks.

[0133] Before training the model, the historical optimization data is annotated to identify the optimal power adjustment strategy. Feature information is also extracted from the historical optimization data, such as average signal strength, maximum interference frequency band, signal-to-noise ratio, etc.

[0134] The annotated historical optimization data is used to gradually optimize the hyperparameters of the model to obtain a trained power adjustment model. For example, a deep neural network is used as the initial model in the embodiment of the present application.

[0135] The above-mentioned signal interference optimization method can intelligently adjust the transmission power of each base station by real-time monitoring of signal quality and interference level, ensuring that interference is minimized while meeting communication needs; at the same time, dynamically adjusting the transmission power of the base station also reduces co-channel interference, improves communication quality and spectrum utilization; further, the application of intelligent algorithms further optimizes the power control strategy, ensuring the accuracy and effectiveness of the adjustment.

[0136] Optionally, the signal interference optimization method of the present application also includes: eliminating interference signals in the mobile communication network.

[0137] See also Figure 8 In one embodiment, eliminating interference signals in a mobile communication network includes:

[0138] Step S802, obtaining an original signal; wherein the original signal includes an interference signal.

[0139] Step S804: extract characteristic parameters of the interference signal in the original signal, where the characteristic parameters include frequency and amplitude.

[0140] Step S808: Dynamically filter the original signal according to the characteristic parameters to weaken the interference signal.

[0141] Exemplarily, the embodiment of the present application uses an adaptive filter to filter the original signal. Among them, the adaptive filter is a filter that can automatically adjust its filtering parameters according to the characteristics of the input signal. Its basic principle is to optimize the filtering effect by minimizing the error between the desired signal and the filter output signal. It should be understood that the original signal input to the adaptive filter includes a communication signal in addition to the interference signal.

[0142] Furthermore, the adaptive filter converts the time domain signal of the original signal into a frequency domain signal through a signal processing algorithm, such as Fourier transform, and then performs frequency domain analysis to extract the characteristic parameters of the interference signal. In addition, before performing frequency domain analysis, the original signal can also be analyzed in the time domain, that is, the time domain waveform of the signal can be directly observed to preliminarily determine the existence and general characteristics of the interference signal.

[0143] Furthermore, the adaptive filter dynamically adjusts its filtering parameters according to the extracted characteristic parameters to minimize the influence of the interference signal. The adjustment expression of the filtering parameters is:

[0144]

[0145] Among them, θ(n+1) represents the filter parameter at the n+1th moment; θ(n) represents the filter parameter at the nth moment; μ is the learning rate, is the error gradient at the nth moment.

[0146] Through the above filtering process, the interference components in the original signal are significantly weakened.

[0147] See also Fig. 9 In another embodiment, eliminating interference signals in a mobile communication network includes:

[0148] Step S902, obtaining an original signal; wherein the original signal includes an interference signal.

[0149] Step S904: Analyze the original signal based on a statistical analysis algorithm to identify the interference signal.

[0150] Step S906: Separate the interference signal from the original signal based on a machine learning algorithm.

[0151] Specifically, the scheme for weakening the interference signal in this embodiment is based on blind detection technology, wherein blind detection technology is an algorithm that can effectively identify and eliminate interference without knowing the specific information of the interference signal in advance. Its core is to use statistical signal processing and machine learning technology to separate the interference component from the received signal.

[0152] The acquired original signal is preprocessed by denoising, normalization and other methods to improve the accuracy of subsequent processing.

[0153] Optionally, the original signal is parsed based on a statistical analysis algorithm to identify the interference signal, including:

[0154] Extract the signal features of the interference signal from the original signal, for example, by performing time domain analysis on the original signal to extract the time dimension attributes such as mean, peak value, zero crossing rate, etc.; then convert the original signal from the time domain to the frequency domain, analyze the spectrum characteristics of the original signal, and extract the frequency, amplitude, bandwidth, main frequency and other features in the spectrum. Furthermore, match the extracted signal features with the known interference features. If the difference between the two exceeds the preset threshold, it is considered that there is an interference signal in the original signal.

[0155] Machine learning algorithms are methods and techniques for automatically acquiring knowledge and experience from existing data, mainly including support vector machines, neural networks, etc.

[0156] Exemplarily, a neural network model is used to separate the interference signal from the original signal, including: inputting the original signal into the neural network model, multiplying the mask in the model with the original signal, and obtaining the separated communication signal and interference signal. The mask in the model is gradually optimized during the model training process, and the mask is a matrix or tensor with the same time or space dimension as the original signal, and its element value is usually between 0 and 1, which is used to indicate the strength and retention degree of the communication signal and the interference signal in the signal.

[0157] The process of interference separation can be expressed as:

[0158] y=f(x;φ)

[0159] Among them, x represents the original signal; y represents the separated interference signal; φ represents the parameters of the neural network model.

[0160] Furthermore, the neural network model is obtained by training based on the signal characteristics of the interference signal extracted above and historical data.

[0161] By filtering the original signal or separating the interference signal from the original signal, the interference signal can be effectively identified and eliminated, and a pure communication signal can be reconstructed, thereby ensuring the stability and quality of the communication signal and significantly improving the user experience.

[0162] The above signal interference optimization method loads the communication signal on the frequency band with the weakest interference signal, which can avoid the frequency band with serious interference and ensure that the communication signal is transmitted on a relatively clean frequency band, thereby significantly improving the communication quality and user experience. Further, the carrier aggregation technology is used to combine the target frequency bands, which increases the data transmission rate and spectrum utilization. At the same time, the wider frequency range obtained by the combination helps to disperse the interference on the narrowband carrier and reduce the impact on the overall signal quality. Further, the transmission power of each base station is intelligently adjusted to ensure that the interference is minimized while meeting the communication needs; at the same time, the transmission power of the base station is dynamically adjusted to reduce the same-frequency interference, improve the communication quality and spectrum utilization; further, the application of intelligent algorithms further optimizes the power control strategy to ensure the accuracy and effectiveness of the adjustment. Further, the interference signal is identified and eliminated, and a pure communication signal is reconstructed to ensure the stability and quality of the communication signal. This application can increase the data transmission rate, and in a complex and multi-application environment, it can achieve the guarantee of communication needs while minimizing interference, ensuring the stability and quality of the communication signal, and improving the user experience.

[0163] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0164] Based on the same inventive concept, the embodiment of the present application also provides a signal interference optimization device for implementing the signal interference optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in the one or more signal interference optimization device embodiments provided below can refer to the limitations of the signal interference optimization method above, and will not be repeated here.

[0165] See also Fig.10 In an exemplary embodiment, the present application provides a signal interference optimization device, including: a monitoring module, an evaluation module, a first strategy generation module and a second strategy generation module, wherein:

[0166] The monitoring module is used to monitor network parameters in the mobile communication network.

[0167] The evaluation module is used to evaluate the interference level of each target frequency band in the mobile communication network according to the network parameters.

[0168] The first strategy generation module is used to generate a frequency allocation strategy according to the interference level, wherein the frequency allocation strategy is used to dynamically match the communication signal to the optimal frequency band.

[0169] The second strategy generation module is used to generate a power adjustment strategy according to the interference level and network parameters. The power adjustment strategy is used to dynamically adjust the transmission power of each base station in the mobile communication network to reduce signal interference.

[0170] Optionally, the evaluation module evaluates the interference level of each target frequency band in the mobile communication network according to the network parameters, including: configuring a corresponding weight for each interference source; and for each target frequency band, calculating the weighted sum of the transmission power of each interference source on the target frequency band.

[0171] Optionally, the first strategy generation module generates a frequency allocation strategy based on the interference level, including: sorting the interference levels of each target frequency band within a preset time in ascending order; selecting the target frequency band corresponding to the first-ranked interference level as the optimal frequency band; and loading the communication signal to the optimal frequency band.

[0172] Optionally, after selecting the optimal frequency band, the first strategy generation module further includes: using carrier aggregation technology to combine the target frequency bands to obtain a combined frequency band; and loading the communication signal onto the optimal frequency band in the combined frequency band.

[0173] Optionally, the second strategy generation module generates a power adjustment strategy according to the interference level and network parameters, including: extracting characteristic information of the interference level and network parameters. The characteristic information includes average signal strength and maximum interference frequency band; according to the characteristic information, a decision algorithm is used to determine the adjustment direction and adjustment range of the transmission power; the adjustment direction and adjustment range of the transmission power constitute the power adjustment strategy.

[0174] Optionally, the second strategy generation module generates a power adjustment strategy based on the interference level and network parameters, and also includes: constructing the structure of the machine learning model; training the machine learning model based on historical optimization data to obtain a trained power adjustment model; inputting the interference level and network parameters into the trained power adjustment model to obtain a power adjustment strategy so that each base station adjusts the transmission power.

[0175] Optionally, the signal interference optimization device of the embodiment of the present application further includes: an interference elimination module.

[0176] The interference elimination module is used to eliminate interference signals in the mobile communication network.

[0177] Optionally, the interference elimination module eliminates interference signals in the mobile communication network, including: obtaining an original signal; wherein the original signal includes an interference signal; extracting characteristic parameters of the interference signal in the original signal, the characteristic parameters including frequency and amplitude; and dynamically filtering the original signal according to the characteristic parameters to weaken the interference signal.

[0178] Optionally, the interference elimination module eliminates interference signals in the mobile communication network, and also includes: obtaining an original signal; wherein the original signal includes an interference signal; based on a statistical analysis algorithm, parsing the original signal to identify the interference signal; based on a machine learning algorithm, separating the interference signal from the original signal.

[0179] The above-mentioned signal interference optimization device loads the communication signal on the frequency band with the weakest interference signal, which can avoid the frequency band with serious interference and ensure that the communication signal is transmitted on a relatively clean frequency band, thereby significantly improving the communication quality and user experience. Further, the carrier aggregation technology is used to combine the target frequency bands, which increases the data transmission rate and spectrum utilization. At the same time, the wider frequency range obtained by the combination helps to disperse the interference on the narrowband carrier and reduce the impact on the overall signal quality. Further, the transmission power of each base station is intelligently adjusted to ensure that the interference is minimized while meeting the communication needs; at the same time, the transmission power of the base station is dynamically adjusted, which also reduces the same-frequency interference and improves the communication quality and spectrum utilization; further, the application of the intelligent algorithm further optimizes the power control strategy to ensure the accuracy and effectiveness of the adjustment. Further, the interference signal is identified and eliminated, and a pure communication signal is reconstructed to ensure the stability and quality of the communication signal. This application can increase the data transmission rate, and in a complex and multi-application environment, it can achieve the guarantee of communication needs while minimizing interference, ensuring the stability and quality of the communication signal, and improving the user experience.

[0180] Each module in the above-mentioned signal interference optimization device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0181] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Fig.11As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for optimizing signal interference is implemented. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0182] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0183] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned signal interference optimization method when executing the computer program.

[0184] In an exemplary embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned signal interference optimization method are implemented.

[0185] In an exemplary embodiment, a computer program product is provided, including a computer program, which implements the steps in the above-mentioned signal interference optimization method when executed by a processor.

[0186] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0187] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0188] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for optimizing signal interference, characterized in that: The method comprises: Monitoring network parameters in mobile communication networks; evaluating the interference level of each target frequency band in the mobile communication network according to the network parameters; generating a frequency allocation strategy based on the interference level; generating a power adjustment strategy according to the interference level and the network parameters; The frequency allocation strategy is used to dynamically match the communication signal to the optimal frequency band; the power adjustment strategy is used to dynamically adjust the transmission power of each base station in the mobile communication network to reduce signal interference.

2. The method according to claim 1, characterized in that The network parameters include transmission power; The step of evaluating the interference level of each frequency band in the mobile communication network according to the network parameters includes: Configure corresponding weights for each interference source; For each of the target frequency bands, a weighted sum of the transmit powers of the interference sources on the target frequency band is calculated to obtain an interference level of each target frequency band.

3. The method according to claim 2, characterized in that Generating a frequency allocation strategy according to the interference level, including: Sorting the interference levels of the target frequency bands within a preset time in ascending order; Selecting the target frequency band corresponding to the interference level ranked first as the optimal frequency band; The communication signal is loaded onto the optimal frequency band.

4. The method according to claim 3, characterized in that After selecting the best frequency band, it also includes: Using carrier aggregation technology, the target frequency bands are combined to obtain a combined frequency band; The communication signal is loaded onto the optimal frequency band in the combined frequency band.

5. The method according to claim 1, characterized in that: The network parameters include signal strength and signal-to-noise ratio; The generating a power adjustment strategy according to the interference level and the network parameter includes: Extracting characteristic information of the interference level and the network parameters, wherein the characteristic information includes average signal strength and maximum interference frequency band; According to the characteristic information, a decision algorithm is used to determine the adjustment direction and adjustment range of the transmission power; The adjustment direction and amplitude of the transmit power constitute the power adjustment strategy.

6. The method according to claim 1, characterized in that: The method further includes obtaining historical optimization data, wherein the historical optimization data includes historical signal quality, historical interference level, and historical power adjustment data; The generating a power adjustment strategy according to the interference level and the network parameter includes: Build the structure of the machine learning model; Training the machine learning model according to the historical optimization data to obtain a trained power adjustment model; The interference level and the network parameters are input into a trained power adjustment model to obtain a power adjustment strategy so that each base station adjusts the transmission power.

7. The method according to claim 1, characterized in that The method further includes: eliminating interference signals in the mobile communication network.

8. The method according to claim 7, characterized in that The eliminating the interference signal in the mobile communication network includes: Acquire an original signal, wherein the original signal includes an interference signal; Extracting characteristic parameters of the interference signal from the original signal, wherein the characteristic parameters include frequency and amplitude; According to the characteristic parameters, the original signal is dynamically filtered to weaken the interference signal.

9. The method according to claim 7, characterized in that: The eliminating the interference signal in the mobile communication network includes: Acquire an original signal, wherein the original signal includes an interference signal; Based on a statistical analysis algorithm, the original signal is analyzed to identify the interference signal; Based on a machine learning algorithm, the interference signal is separated from the original signal.

10. A signal interference optimization device, characterized in that: The device comprises: A monitoring module, used for monitoring network parameters in a mobile communication network; An evaluation module, configured to evaluate the interference level of each target frequency band in the mobile communication network according to the network parameters; A first strategy generating module, configured to generate a frequency allocation strategy according to the interference level; wherein the frequency allocation strategy is used to dynamically match the communication signal to an optimal frequency band; The second strategy generating module is used to generate a power adjustment strategy according to the interference level and the network parameters; wherein the power adjustment strategy is used to dynamically adjust the transmission power of each base station in the mobile communication network to reduce signal interference.

Images