Cell signal adjacent channel interference analysis method and device, electronic equipment and storage medium

Abstract

The invention discloses a cell signal adjacent channel interference analysis method and device and electronic equipment, and belongs to the technical field of communication. The method comprises the following steps: predicting a value of a first factor at a next moment in a target cell by adopting a Markov model; clustering scene data associated with preset adjacent channel interference factors in the target cell to obtain interference data of multiple categories; obtaining a value of at least one second factor based on the interference data of each category; and analyzing the signal adjacent channel interference of the target cell based on the value of the first factor and the value of the at least one second factor. According to the method, the interference analysis result is closer to the real result by combining the interference calculation result and predicting the adjacent channel interference trend, and the accuracy of cell interference signal analysis is improved. In addition, the Markov model is adopted to predict the ascending or descending probability value of the adjacent channel interference trend for interference analysis, the operation speed is high, and the resource consumption is low.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method for analyzing cell signal adjacent frequency interference, a device for analyzing cell signal adjacent frequency interference, an electronic device, and a computer-readable storage medium. Background Art

[0002] Adjacent channel interference refers to the mutual interference between signals of adjacent or close channels. Since FM signals contain an infinite number of sideband components, when some of the sideband components fall into the passband of the adjacent channel receiver, adjacent channel interference will occur. In actual use, adjacent channel interference is mainly the signal interference of the adjacent frequency of the signal frequency used. The performance of the receiving filter is not ideal, so that the adjacent signal leaks into the transmission bandwidth and causes interference.

[0003] In the prior art, the current research on interference coexistence between two wireless communication systems generally adopts a deterministic analysis method. This method is based on the link budget principle and uses numerical calculations to obtain the isolation required for the coexistence of the two systems. However, this method generally selects the link with the most serious interference (such as the link with the smallest path loss, the largest transmission power, and the largest transceiver antenna gain) for calculation. The deterministic analysis method used in the prior art not only has a large amount of calculations, resulting in low prediction timeliness, but also inaccurate interference results.

[0004] It can be seen that the cell signal adjacent frequency interference analysis method in the prior art still needs to be improved. Summary of the invention

[0005] The embodiments of the present application provide a method and device for analyzing adjacent frequency interference of cell signals, which not only helps to shorten the analysis time of adjacent frequency interference of cell signals and reduce the computational complexity, thereby improving the timeliness of interference prediction, but also can improve the accuracy of adjacent frequency interference analysis.

[0006] In a first aspect, an embodiment of the present application provides a method for analyzing cell signal adjacent frequency interference, including:

[0007] The Markov model is used to predict the value of the first factor at the next moment in the target cell;

[0008] Clustering the scene data associated with the preset adjacent frequency interference factors in the target cell to obtain interference data of multiple categories;

[0009] Based on the interference data of each category, obtaining a value of at least one second factor;

[0010] Based on the value of the first factor and the value of the at least one second factor, the signal adjacent channel interference of the target cell is analyzed.

[0011] In a second aspect, an embodiment of the present application provides a cell signal adjacent frequency interference analysis device, including:

[0012] A first factor value acquisition module, used to predict the value of the first factor at the next moment in the target cell using a Markov model;

[0013] An interference data clustering module, used to cluster the scene data associated with the preset adjacent frequency interference factors in the target cell to obtain interference data of multiple categories;

[0014] A second factor value acquisition module, used to acquire a value of at least one second factor based on the interference data of each category;

[0015] The interference analysis module is used to analyze the signal adjacent channel interference of the target cell based on the value of the first factor and the value of the at least one second factor.

[0016] In a third aspect, an embodiment of the present application further discloses an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the method for analyzing adjacent frequency interference of a cell signal described in an embodiment of the present application is implemented.

[0017] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the cell signal adjacent channel interference analysis method disclosed in the embodiment of the present application are implemented.

[0018] The cell signal adjacent channel interference analysis method disclosed in the embodiment of the present application adopts a Markov model to predict the value of the first factor at the next moment in the target cell, with fast calculation speed and low resource consumption; then, by clustering the scene data associated with the preset adjacent channel interference factor in the target cell, multiple categories of interference data are obtained; based on the interference data of each category, the value of at least one second factor is obtained; finally, based on the value of the first factor and the value of the at least one second factor, the signal adjacent channel interference of the target cell is analyzed, so that by combining the interference calculation result and the predicted adjacent channel interference trend, the calculation result is closer to the actual result, thereby improving the accuracy of the cell interference signal analysis.

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0021] Figure 1 It is a flow chart of the method for analyzing cell signal adjacent frequency interference disclosed in the embodiment of the present application;

[0022] Figure 2 It is a schematic diagram of a cell signal adjacent channel transmission and reception scenario in the prior art;

[0023] Figure 3 It is a schematic diagram of a cell signal adjacent frequency interference scenario disclosed in an embodiment of the present application;

[0024] Figure 4 This is a schematic diagram of the trusted execution environment architecture disclosed in the embodiment of the present application;

[0025] Figure 5 It is a schematic diagram of the structure of the cell signal adjacent frequency interference analysis device disclosed in the embodiment of the present application;

[0026] Figure 6 A block diagram schematically shows an electronic device for executing the method according to the present application; and

[0027] Figure 7 A storage unit for holding or carrying a program code for implementing the method according to the present application is schematically shown. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] Reference Figure 1 A method for analyzing adjacent-channel interference of cell signals disclosed in an embodiment of the present application includes steps 110 to 140.

[0030] Step 110: Use the Markov model to predict the value of the first factor at the next moment in the target cell.

[0031] Optionally, the first factor is an adjacent channel parameter.

[0032] In some optional embodiments, using a Markov model to predict the value of the first factor in the target cell at the next moment includes: using a Markov model to predict the value of the first factor at the next moment based on the value of the first factor in the target cell at the current moment.

[0033] In the embodiment of the present application, the Markov model can be expressed as: X(k+1)=X(k)×P, where X(k) represents the state vector of the trend analysis and prediction object at time t=k, P represents the one-step transition probability matrix, and X(k+1) represents the state vector of the trend analysis and prediction object at time t=k+1. In the transition probability matrix P, each element of the matrix is ​​a non-negative value, and the sum of each row of elements is equal to 1. Each element is represented by probability and is mutually transferred under certain conditions.

[0034] Optionally, the first factor includes one or more of the following: adjacent channel selectivity trend, adjacent channel interference power ratio trend, and adjacent channel leakage suppression ratio trend. The following is an example of a prediction scheme for each first factor.

[0035] 1. Adjacent channel selectivity trend

[0036] Taking the first factor including: adjacent channel selectivity (ACS) trend as an example, the Markov model is used to predict the value of the first factor in the target cell at the next moment, including: using the Markov model to predict the adjacent channel selectivity trend at the next moment based on the adjacent channel selectivity trend at the current moment in the target cell.

[0037] Adjacent channel selectivity ACS is used to measure the ability of a receiver to receive useful signals on its designated channel frequency when adjacent channel signals exist. Adjacent channel selectivity is defined as the ratio of the attenuation of the receiver filter on the designated channel to the attenuation on the adjacent channel. In some optional embodiments, the probability of transition from low to high in the short-term ACS trend and the probability of transition from high to low in the medium- and long-term ACS trend are used as the first factor.

[0038] Optionally, the historical ACS trend is used as X(k), the short-term ACS trend from low to high transition probability is used as the first row of data of the transition probability matrix, and the medium- and long-term ACS trend from high to low transition probability is used as the second row of data of the transition probability matrix to obtain the transition probability matrix P. The historical ACS trend is: the historical ACS trend from low to high transition probability, or the historical ACS trend from high to low transition probability.

[0039] In the embodiment of the present application, by calculating the transition probability matrix P in real time, the Markov model can be used to predict the short-term ACS trend transition probability from low to high and the medium- and long-term ACS trend transition probability from high to low at the next moment based on the short-term ACS trend transition probability from low to high and the medium- and long-term ACS trend transition probability from high to low at the current moment.

[0040] Among them, at the current moment, the probability of the short-term ACS trend transition from low to high can be obtained by the following method: obtain the most recent ten ACS historical data in the log data, and perform weighted average on the data with increased interference trends to obtain an average ACS value, such as 0.4. Secondly, count the number of interference increases when the ACS value exceeds 0.4 in the last ten ACS values, and vice versa. If there are 6 times higher than the average value of 0.4 and 4 times lower interference, the probability of the short-term ACS trend transition from low to high is [0.6, 0.4], where 0.6 represents the probability of the short-term ACS trend increasing, and 0.4 represents the probability of the short-term ACS trend decreasing. Referring to the above method, the probability of the medium- and long-term ACS trend transition from high to low is calculated by weighted average based on the most recent 100 ACS values. For example, the probability of the medium- and long-term ACS trend transition from high to low is [0.3, 0.7].

[0041] The historical ACS trend is the historical ACS trend of the previous moment, which is updated in real time during the prediction process. When predicting for the first time, the historical ACS trend can be set according to the empirical value.

[0042] Furthermore, taking the historical ACS trend of [0.3, 0.7] at time t as an example, based on the above data, the Markov model is used to predict the probability of the short-term ACS trend transitioning from low to high and the medium- and long-term ACS trend transitioning from high to low at the next moment. It can be obtained that the probability of the ACS trend increasing at the next moment is: 0.3x0.6+0.7x0.3=0.39; the probability of the ACS trend decreasing at the next moment is: 0.3x0.4+0.7x0.7=0.61.

[0043] When the ACS trend is predicted next time, the ACS trend at the next moment calculated at time t will be used to update the historical ACS trend. That is, X(k) is updated through X(k+1). Taking the above data as an example, the historical ACS trend at time t+1 is [0.39, 0.61].

[0044] Furthermore, the adjacent frequency interference at the next moment can be analyzed according to the probability of the short-term ACS trend transitioning from low to high and the probability of the medium- and long-term ACS trend transitioning from high to low at the next moment.

[0045] 2. Adjacent channel interference power ratio trend

[0046] In other optional embodiments, taking the first factor including: adjacent channel interference power ratio (ACIR) trend as an example, a Markov model is used to predict the value of the first factor in the target cell at the next moment, including: using a Markov model to predict the value of the adjacent channel interference power ratio trend at the next moment based on the adjacent channel interference power ratio trend at the current moment in the target cell.

[0047] Adjacent channel interference power ratio is an indicator parameter to measure the performance of a communication system, which represents the ratio of the total power from the transmitting source to the total interference power to the interfered receiver. In a specific application scenario, 1 / ACIR=1 / ACLR+1 / ACS.

[0048] The adjacent channel leakage suppression ratio (ACLR) is used to measure the degree to which the adjacent channel transmission signal falls within the receiver passband. It is defined as the ratio of the transmission power spectrum density to the measured power spectrum density on the adjacent channel. In a specific application scenario, the signal diagram of the adjacent channel leakage suppression ratio ACLR is as follows: Figure 2 The signal waveform of the receiving and transmitting signal on the left side is shown in the figure, and the signal diagram of the adjacent channel selective ACS is shown in the figure Figure 2 The waveform of the receiving and transmitting signals on the right side of the figure is shown in FIG. The calculation method of the adjacent channel leakage suppression ratio and the adjacent channel selectivity ACS can be found in the prior art and will not be described in detail here.

[0049] Optionally, the historical interference trend probability between ACIR and ACS is used as X(k), the current trend transition probability between ACIR and ACS from low to high is used as the first row of data of the transition probability matrix, and the current trend transition probability between ACIR and ACS from high to low is used as the second row of data of the transition probability matrix to obtain the transition probability matrix P. The historical interference trend probability between ACIR and ACS is: the trend transition probability between ACIR and ACS from low to high, or the trend transition probability between ACIR and ACS from high to low.

[0050] In an embodiment of the present application, by calculating the transition probability matrix P in real time, a Markov model can be used to predict the transition probability of the trend from low to high between ACIR and ACS at the next moment, as well as the transition probability of the trend from high to low between ACIR and ACS, based on the transition probability of the trend from low to high between ACIR and ACS at the current moment, as well as the transition probability of the trend from high to low between ACIR and ACS.

[0051] Taking the historical probability of interference trend between ACIR and ACS as [0.3, 0.7], the probability of transition from low to high between ACIR and ACS at the current moment as [0.6, 0.4], and the probability of transition from high to low between ACIR and ACS at the current moment as [0.3, 0.7] as examples, after Markov model prediction, it can be obtained that the probability of an increase in the trend between ACIR and ACS at the next moment is 39%, calculated as 0.3x0.6+0.3x0.7=0.39, and the probability of a decrease in the trend between ACIR and ACS at the next moment is 61%, calculated as 0.3x0.4+7x0.7=0.61.

[0052] Among them, the probability of transition from low to high between ACIR and ACS at the current moment, and the probability of transition from high to low between ACIR and ACS can be obtained by the following method: obtain the most recent ten ACS historical data and ACLR historical data in the log data, and calculate the most recent ten historical ACIR values ​​based on the ACS historical data and ACLR historical data, and then perform weighted average of the most recent ten historical ACIR values ​​to obtain the average ACIR value; then, count the number of interference increases when the most recent ten ACIR values ​​exceed the average ACIR value, and vice versa, if the number of interference decreases is 6 times higher than the average ACIR value and the number of interference decreases is 4 times, then the probability of transition from high to low between ACIR and ACS is [0.6, 0.4]. Referring to the above method, the probability of transition from low to high between ACIR and ACS is calculated by weighted average based on the most recent 100 ACS values ​​and ACLR values, for example, the probability of transition from low to high between ACIR and ACS is [0.3, 0.7].

[0053] The historical ACIR and ACS interference trend probability is the interference trend probability between the historical ACIR and ACS at the previous moment, and is updated in real time during the prediction process. When predicting for the first time, the historical ACIR and ACS interference trend probability can be set according to an empirical value.

[0054] When predicting the interference trend probability between ACIR and ACS next time, the interference trend probability between ACIR and ACS at the next moment calculated at time t will be used to update the historical interference trend probability between ACIR and ACS. That is, the historical interference trend probability between ACIR and ACS at time t+1 is [0.39, 0.61].

[0055] 3. Adjacent channel leakage suppression ratio trend.

[0056] In other optional embodiments, the first factor may also include: an adjacent channel leakage suppression ratio trend. Accordingly, a Markov model is used to predict the value of the first factor at the next moment in the target cell, including: using a Markov model to predict the value of the adjacent channel leakage suppression ratio trend at the next moment based on the adjacent channel leakage suppression ratio trend at the current moment in the target cell.

[0057] Optionally, the historical adjacent channel leakage suppression ratio trend is used as X(k), the short-term adjacent channel leakage suppression ratio trend from low to high transition probability is used as the first row of data of the transition probability matrix, and the medium- and long-term adjacent channel leakage suppression ratio trend from high to low transition probability is used as the second row of data of the transition probability matrix to obtain the transition probability matrix P. The historical adjacent channel leakage suppression ratio trend is: the historical adjacent channel leakage suppression ratio trend from low to high transition probability, or the historical adjacent channel leakage suppression ratio trend from high to low transition probability.

[0058] Among them, the method for obtaining the adjacent channel leakage suppression ratio trend refers to the prior art, and the specific calculation methods of the short-term adjacent channel leakage suppression ratio trend from low to high transition probability and the medium- and long-term adjacent channel leakage suppression ratio trend from high to low transition probability refer to the method for calculating the short-term ACS trend from low to high transition probability and the medium- and long-term ACS trend from high to low transition probability mentioned above, which will not be repeated here.

[0059] In the embodiment of the present application, by adopting the Markov model, the next scene data is predicted based on the current scene data. Compared with the prior art of obtaining the interference signal trend by analyzing all historical data, the operation speed is fast and the resource consumption is small.

[0060] Step 120: clustering the scenario data associated with the preset adjacent frequency interference factors in the target cell to obtain multiple categories of interference data, where each category of interference data belongs to a unique transceiver.

[0061] In actual application scenarios, such as Figure 3 As shown in the figure, in addition to the power generated by the useful signal allocated by the base station to the terminal, the signal in the cell also includes the mutual interference between the terminals in the cell and the signal interference generated by the external system. Therefore, when calculating adjacent channel interference, in addition to considering the useful signal, that is, the power allocated by the base station to the terminal, it is also necessary to calculate the useless power.

[0062] Below, according to Figure 3 In the adjacent channel interference scenario shown in the figure, the adjacent channel interference of the 5G uplink channel and the adjacent channel interference of the downlink channel are qualitatively analyzed. Among them, the adjacent channel interference of the 5G downlink channel can be calculated by the following formula:

[0063]

[0064] Among them, SINR d_irepresents the adjacent channel interference of the 5G downlink channel, that is, the received signal-to-noise ratio of user i in base station K; N0 represents the receiver noise power; ACIR represents the adjacent channel interference power ratio; N represents the number of sectors in a single system; P k,i represents the transmission power of the user occupying channel i; G k,i represents the sum of the transmit and receive antenna gains between base station K and user i; PL k,i represents the path loss between base station k and user i; P m,i represents the transmission power of the user occupying channel i in sector m of this system; G m,i represents the sum of the transmit and receive antenna gains between base station m and user i; represents the path loss between base station m and user i; P n,i represents the transmission power of the user occupying channel i in the external system sector n; G n,i represents the sum of the transmit and receive antenna gains between system sector n and user i; represents the path loss between external system sector n and user i.

[0065] The adjacent channel interference of the 5G uplink channel can be calculated by the following formula:

[0066]

[0067] Among them, SINR u_i represents the adjacent channel interference of the 5G uplink channel, that is, the received signal-to-noise ratio of user i in base station K; N0 represents the receiver noise power; ACIR represents the adjacent channel interference power ratio; N represents the number of sectors in a single system; N u Indicates the number of uplink users per sector in the external system; P k,i represents the transmission power of the user occupying channel i; G k,u represents the sum of the transmit and receive antenna gains between base station K and user i; PL k,i represents the path loss between base station k and user i; P m,i represents the transmission power of the user occupying channel i in sector m of this system; G m,i,k represents the sum of the transmit and receive antenna gains between the user occupying channel i in sector m of the system and base station K; represents the path loss between the user occupying channel i in sector m of the system and base station K; P n,j represents the transmission power of the user occupying channel i in the external system sector n; G n,j,k represents the sum of the transmit and receive antenna gains between the user occupying channel j in the external system sector n and the base station K; Represents the path loss between the user occupying channel i in the external system sector n and the base station K.

[0068] Through the calculation methods of the adjacent channel interference of the 5G downlink channel and the adjacent channel interference of the 5G uplink channel, it can be concluded that the useless power that needs to be considered when calculating the adjacent channel interference includes but is not limited to the following three parts: receiver noise power, internal interference (i.e., interference generated by other users in the system) and external interference (i.e., interference generated by external systems). These interferences are distributed in network or system factors, transceiver parameters, propagation models, etc.

[0069] Among them, the network or system factors include: network structure (such as cell structure, density and distribution of activated users, geometric relationship between interference source and interfered party, protection distance, outdoor and indoor mixing, etc.), single station coverage area size (such as cell radius), power control; transceiver parameters (including antenna parameters, etc.); propagation model (such as slow fading, etc.). Mobile communication transceivers mainly have the following parameters: adjacent channel interference, receiving parameters, antenna parameters, etc. Among them, adjacent channel interference includes: transmission parameters, transmission frequency, transmission power, transmission intermodulation characteristics, channel bandwidth adjacent channel leakage suppression ratio, spurious emission, transmission template, etc. The receiving parameters include: receiver sensitivity, adjacent channel suppression ratio (selectivity), receiver intermodulation index bit error rate, required carrier-to-interference ratio [C / I, C / (N+I)], allowed interference signal strength, etc. The antenna parameters include: the height, gain, directivity diagram and polarization mode or antenna model of the transceiver antenna.

[0070] In specific implementation, one or more of the above interference factors may be selected for interference analysis. Optionally, network or system influence factor classification, transceiver parameter factor classification, propagation model influence factor classification, etc. are selected from the above interference factors as the preset adjacent frequency interference factors.

[0071] In order to analyze the interference signal, it is necessary to first classify the interference data that may exist in the signal received by the base station, for example, to divide the scene data into: network or system influencing factor classification, transceiver parameter factor classification, propagation model influencing factor classification, etc. After classification, the interference data that may belong to multiple categories is changed to belong to a single category, so that the fuzzy scene data classification is clear, and the effect of data filtering is achieved. Through such clustering data preprocessing, the accuracy of interference prediction is improved.

[0072] In some optional embodiments, the scenario data associated with the preset adjacent frequency interference factors in the target cell are clustered to obtain interference data of multiple categories, including: taking the scenario data of the same user terminal at the same time as belonging to a unique category as a clustering constraint, clustering the scenario data where the preset adjacent frequency interference factors in the target cell are located, and obtaining interference data of multiple categories.

[0073] After the interference data are clustered, one piece of interference data belongs to a unique classification, which avoids inaccurate prediction caused by one piece of interference data participating in the calculation of multiple interference classifications.

[0074] In some optional embodiments, the scene data of the same user terminal at the same time belonging to a unique category is used as a clustering constraint, and the scene data where the preset adjacent frequency interference factors are located in the target cell are clustered to obtain interference data of multiple categories, including: clustering the scene data using the K-means++ algorithm to obtain K initial clustering centers, wherein the distance between the data point and the clustering center is configured to be calculated based on the physical distance between the data point and the clustering center and / or the signal transmission path; based on the generation time of the interference data and the user terminal to which it belongs, the classified data corresponding to the K clustering centers are denoised respectively, so that the scene data of the same user terminal at the same time belongs to a unique category.

[0075] Optionally, a K-means++ algorithm may be used to cluster the scene data, and the steps are shown in the following steps S1 to S5.

[0076] Step S1, randomly select a cluster center u among the data points i .

[0077] Step S2, for each data point x that has not been selected, calculate the shortest distance between the data point x and the selected cluster center.

[0078] Step S3, using weighted probability distribution to randomly select a new data point as the next cluster center, so that data points that are farther away from the existing cluster center have a greater probability of being selected as the new cluster center.

[0079] In some optional embodiments, the distance between the data point and the cluster center may be calculated based on the geographical location of the user terminal corresponding to the data point, the received signal strength, etc.

[0080] Step S4, repeating steps S2 and S3 until K cluster centers (ie, j=K) are selected as initial cluster centers.

[0081] Step S5, based on the initial cluster centers, the clustering results are corrected using the standard K-means clustering method.

[0082] For example, the initial cluster centers and the data in each category are modified according to the rationality of the classification results.

[0083] On the other hand, after predicting the value of the first factor, the data in the test report uploaded by the cell user terminal to the base station can be aggregated and classified according to the possible interference factors in an orderly manner according to the interference trend rising probability corresponding to the first factor, for example, according to the base station coverage area classification, signal gain classification, user density and distribution classification. Among them, the base station coverage area is based on the data classification with the cell edge coverage coordinates as the K center point to the edge coverage area coordinates as the boundary; the signal gain classification is based on the industry standard 3db as the K center point to the lowest -3db data classification.

[0084] Through clustering, the user terminal data that may appear in the data clustering process at the same time may belong to multiple categories to retain the data, for example, cluster it into a category among the base station coverage area classification, signal gain classification, user density and distribution classification, and remove the data from other data classifications. Through such clustering data preprocessing, the accuracy of data analysis is improved.

[0085] After clustering processing, K categories of interference data can be obtained, where K is an integer greater than 1.

[0086] Step 130: Obtain a value of at least one second factor based on the interference data of each category.

[0087] In some optional embodiments, obtaining the value of at least one second factor based on the interference data of each category includes: using a trusted execution environment based on a trusted innovation environment to classify the interference data of each category and perform privacy calculations to obtain the value of at least one second factor, wherein the privacy data in the interference data is calculated in a separate layer of the trusted execution environment to obtain a layered calculation result, and then the layered calculation result is mixed with the public data in the interference data in a trusted environment.

[0088] When the clustered data is used to analyze the source of cell signal interference, the interference signal is relatively complex and the data of the user terminal is personal privacy data. In order to ensure the security of the data during the analysis process, in the embodiment of the present application, privacy computing based on a trusted execution environment under a trusted innovation environment is adopted for data classification. During the calculation process, public and private computing are processed in layers. The public data is calculated in a trusted environment, and the private data is calculated separately and then mixed with the public data in the trusted environment for calculation.

[0089] The trusted innovation environment refers to an innovative ecological environment with information technology as the core, including policy support, industrial chain collaboration, talent training, technology research and development and other aspects. The core goal of the trusted innovation environment is to achieve innovation in information technology applications and improve national information security and independent innovation capabilities. The trusted execution environment is a computing environment that can protect information security through the combination of hardware and software. The architecture of the trusted innovation environment can be found in the prior art and will not be repeated in the embodiments of this application.

[0090] Optionally, the privacy data in the interference data is calculated in a separate layer of the trusted execution environment to obtain a layered calculation result, including: a hardware-based trusted execution environment and a cryptography-based privacy computing layer, performing layered calculations on the privacy data in the interference data to obtain a layered calculation result.

[0091] Among them, the trusted execution environment based on the trust creation environment is as follows Figure 4 shown.

[0092] Optionally, the trusted execution environment is implemented as follows: utilizing the trusted computing technology of the security chip to construct a CPU-based trust chain at system startup and runtime, and passing the trust relationship from the CPU embedded instructions all the way up to the operating system layer; at the same time, by expanding the operating system, enabling the kernel integrity measurement function for it, and further extending the trust chain to the application layer, that is, the privacy computing framework layer; and all integrity measurement values ​​will be stored in the hardware device of the domestically produced trusted platform module (such as TPM2.0) to ensure that it is trustworthy and cannot be tampered with; finally, through remote authentication technology, a trusted execution environment is constructed in a more controllable manner to realize trusted computing of upper-level applications.

[0093] The privacy computing layer includes: all execution modules of the platform, user-defined operators, user data, etc. are maintained in the trust chain to ensure their integrity and non-tamperability. The platform completes privacy computing and realizes the mining of data value through cryptographic technologies such as homomorphic encryption, differential privacy, and oblivious transmission, as well as blockchain technologies such as smart contracts and consortium chains.

[0094] The practice of privacy computing applications based on domestically produced chips proves that domestically produced chips can also perfectly meet the hardware requirements of privacy computing, and verifies the feasibility of the privacy computing industry to get rid of its dependence on foreign chips. It also proves that privacy computing can help multiple parties output models suitable for their own businesses while avoiding the risk of data leakage, thereby reducing the risk of data leakage.

[0095] Step 140: Analyze adjacent channel interference of the signal of the target cell based on the value of the first factor and the value of the at least one second factor.

[0096] Taking the first factor including: adjacent channel selectivity trend, adjacent channel interference power ratio trend, and adjacent channel leakage suppression ratio trend as an example, after the adjacent channel selectivity trend, adjacent channel interference power ratio trend, and adjacent channel leakage suppression ratio trend at the next moment are predicted by the Markov model, the interference state of the target cell can be analyzed by comparing the adjacent channel selectivity trend, adjacent channel interference power ratio trend, and adjacent channel leakage suppression ratio trend at the current moment and the next moment. For example, when the probability of the medium- and long-term ACS trend transitioning from high to low at the current moment and the probability of the medium- and long-term ACS trend transitioning from high to low at the next moment are both increased, or both decreased, it can be considered that the adjacent channel interference in the cell is normal, otherwise, it can be considered that the adjacent channel interference in the cell is abnormal.

[0097] On the other hand, taking the second factor including the transmission frequency as an example, if the transmission frequency calculated at the current moment is attenuated by more than -3db compared to the transmission frequency at the previous moment, it can be considered that there is signal interference in the target cell at the current moment.

[0098] For a specific implementation method of analyzing the signal adjacent channel interference of the target cell based on the value of the first factor and the value of the at least one second factor, refer to the prior art and will not be described in detail in the embodiments of the present application.

[0099] In summary, the cell signal adjacent channel interference analysis method disclosed in the embodiment of the present application adopts the Markov model to predict the value of the first factor at the next moment in the target cell; clusters the scene data associated with the preset adjacent channel interference factor in the target cell to obtain multiple categories of interference data; based on the interference data of each category, obtains the value of at least one second factor; finally, based on the value of the first factor and the value of the at least one second factor, analyzes the signal adjacent channel interference of the target cell, thereby combining the interference calculation results and the predicted adjacent channel interference trend to make the calculation results closer to the actual results, thereby improving the accuracy of the cell interference signal analysis. Furthermore, by adopting the Markov model to predict the probability value of the adjacent channel interference trend rising or falling for interference analysis, the operation speed is fast and the resource consumption is low.

[0100] Based on the above embodiments, the present application also discloses a cell signal adjacent frequency interference analysis device, such as Figure 5 As shown, the device comprises:

[0101] A first factor value acquisition module 510, configured to use a Markov model to predict the value of the first factor at the next moment in the target cell;

[0102] The interference data clustering module 520 is used to cluster the scene data associated with the preset adjacent frequency interference factors in the target cell to obtain multiple categories of interference data;

[0103] A second factor value acquisition module 530, configured to acquire a value of at least one second factor based on the interference data of each category;

[0104] The interference analysis module 540 is used to analyze the adjacent channel interference of the signal of the target cell based on the value of the first factor and the value of the at least one second factor.

[0105] Optionally, the first factor includes one or more of the following: an adjacent channel selectivity trend, an adjacent channel interference power ratio trend, and an adjacent channel leakage suppression ratio trend. The first factor value acquisition module 510 is further configured to perform one or more of the following operations:

[0106] The Markov model is used to predict the adjacent road selectivity trend at the next moment based on the adjacent road selectivity trend at the current moment in the target cell.

[0107] The Markov model is used to predict the value of the adjacent channel interference power ratio trend at the next moment based on the adjacent channel interference power ratio trend at the current moment in the target cell.

[0108] The Markov model is used to predict the value of the adjacent channel leakage suppression ratio trend at the next moment based on the adjacent channel leakage suppression ratio trend at the current moment in the target cell.

[0109] Optionally, the interference data clustering module 520 is further used to:

[0110] The scenario data of the same user terminal at the same time belongs to a unique category as a clustering constraint, the scenario data where the preset adjacent frequency interference factors in the target cell are located are clustered to obtain interference data of multiple categories.

[0111] Optionally, acquiring a value of at least one second factor based on the interference data of each category includes:

[0112] A trusted execution environment based on a trusted innovation environment is adopted to perform privacy calculation on the interference data of each category to obtain the value of at least one second factor, wherein the privacy data in the interference data is calculated in a separate layer of the trusted execution environment to obtain a layered calculation result, and then the layered calculation result is mixed with the public data in the interference data in a trusted environment.

[0113] Optionally, the privacy data in the interference data is calculated in a separate layer of the trusted execution environment to obtain a layered calculation result, including:

[0114] Based on a hardware-based trusted execution environment and a cryptography-based privacy computing layer, hierarchical calculations are performed on the privacy data in the interference data to obtain hierarchical calculation results.

[0115] The cell signal adjacent frequency interference analysis device disclosed in the embodiment of the present application is used to implement the cell signal adjacent frequency interference analysis method described in the embodiment of the present application. The specific implementation methods of each module of the device will not be repeated here, and reference may be made to the specific implementation methods of the corresponding steps in the method embodiment.

[0116] The cell signal adjacent channel interference analysis device disclosed in the embodiment of the present application adopts a Markov model to predict the value of the first factor at the next moment in the target cell; clusters the scene data associated with the preset adjacent channel interference factor in the target cell to obtain multiple categories of interference data; based on the interference data of each category, obtains the value of at least one second factor; finally, based on the value of the first factor and the value of the at least one second factor, analyzes the signal adjacent channel interference of the target cell, thereby combining the interference calculation result and the predicted adjacent channel interference trend to make the calculation result closer to the actual result, thereby improving the accuracy of the cell interference signal analysis. Furthermore, by adopting the Markov model to predict the probability value of the adjacent channel interference trend rising or falling for interference analysis, the operation speed is fast and the resource consumption is low.

[0117] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0118] The above is a detailed introduction to a cell signal adjacent frequency interference analysis method and device provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and scope of application. In summary, the content of this specification should not be understood as a limitation on the present application.

[0119] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0120] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components in the electronic device according to the embodiment of the present application. The present application can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0121] For example, Figure 6 An electronic device that can implement the method according to the present application is shown. The electronic device can be a PC, a mobile terminal, a personal digital assistant, a tablet computer, etc. The electronic device traditionally includes a processor 610 and a memory 620 and a program code 630 stored on the memory 620 and can be run on the processor 610, and the processor 610 implements the method described in the above embodiment when executing the program code 630. The memory 620 can be a computer program product or a computer-readable medium. The memory 620 can be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk or a ROM. The memory 620 has a storage space 6201 for the program code 630 of a computer program for executing any method step in the above method. For example, the storage space 6201 for the program code 630 can include various computer programs for implementing the various steps in the above method respectively. The program code 630 is a computer-readable code. These computer programs can be read from one or more computer program products or written into the one or more computer program products. These computer program products include program code carriers such as hard disks, compact disks (CDs), memory cards or floppy disks. The computer program includes a computer readable code, and when the computer readable code is executed on an electronic device, the electronic device is caused to execute the method according to the above embodiment.

[0122] The embodiment of the present application also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the cell signal adjacent frequency interference analysis method as described in the embodiment of the present application are implemented.

[0123] Such a computer program product may be a computer-readable storage medium, which may have a computer-readable storage medium having a Figure 6The memory 620 in the electronic device shown in the figure is similar to the storage segment, storage space, etc. The program code can be compressed and stored in the computer-readable storage medium in an appropriate form. The computer-readable storage medium is generally as shown in the reference Figure 7 The portable or fixed storage unit. Generally, the storage unit includes computer readable code 630', which is a code read by a processor. When the code is executed by the processor, each step in the method described above is implemented.

[0124] References to "one embodiment," "embodiment," or "one or more embodiments" herein mean that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least one embodiment of the present application. In addition, please note that examples of the term "in one embodiment" herein do not necessarily all refer to the same embodiment.

[0125] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0126] In the claims, any reference signs placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present application may be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for analyzing cell signal adjacent frequency interference, characterized in that: The method comprises: The Markov model is used to predict the value of the first factor at the next moment in the target cell; Clustering the scene data associated with the preset adjacent frequency interference factors in the target cell to obtain interference data of multiple categories; Based on the interference data of each category, obtaining a value of at least one second factor; Based on the value of the first factor and the value of the at least one second factor, the signal adjacent channel interference of the target cell is analyzed.

2. The method according to claim 1, characterized in that The first factor includes one or more of the following: adjacent channel selectivity trend, adjacent channel interference power ratio trend, and adjacent channel leakage suppression ratio trend. The using of the Markov model to predict the value of the first factor at the next moment in the target cell includes one or more of the following operations: The Markov model is used to predict the adjacent road selectivity trend at the next moment based on the adjacent road selectivity trend at the current moment in the target cell. The Markov model is used to predict the value of the adjacent channel interference power ratio trend at the next moment based on the adjacent channel interference power ratio trend at the current moment in the target cell. The Markov model is used to predict the value of the adjacent channel leakage suppression ratio trend at the next moment based on the adjacent channel leakage suppression ratio trend at the current moment in the target cell.

3. The method according to claim 1, characterized in that The clustering of the scenario data associated with the preset adjacent frequency interference factors in the target cell to obtain interference data of multiple categories includes: The scenario data of the same user terminal at the same time belongs to a unique category as a clustering constraint, the scenario data where the preset adjacent frequency interference factors in the target cell are located are clustered to obtain interference data of multiple categories.

4. The method according to claim 1, characterized in that The obtaining, based on the interference data of each category, a value of at least one second factor comprises: A trusted execution environment based on a trusted innovation environment is adopted to perform privacy calculation on the interference data of each category to obtain the value of at least one second factor, wherein the privacy data in the interference data is calculated in a separate layer of the trusted execution environment to obtain a layered calculation result, and then the layered calculation result is mixed with the public data in the interference data in a trusted environment.

5. The method according to claim 4, characterized in that The privacy data in the interference data is calculated in a separate layer of the trusted execution environment to obtain a layered calculation result, including: Based on a hardware-based trusted execution environment and a cryptography-based privacy computing layer, hierarchical calculations are performed on the privacy data in the interference data to obtain hierarchical calculation results.

6. A cell signal adjacent frequency interference analysis device, characterized in that: The device comprises: A first factor value acquisition module, used to predict the value of the first factor at the next moment in the target cell using a Markov model; An interference data clustering module, used to cluster the scene data associated with the preset adjacent frequency interference factors in the target cell to obtain interference data of multiple categories; A second factor value acquisition module, used to acquire a value of at least one second factor based on the interference data of each category; The interference analysis module is used to analyze the signal adjacent channel interference of the target cell based on the value of the first factor and the value of the at least one second factor.

7. The device according to claim 6, characterized in that The first factor includes one or more of the following: adjacent channel selectivity trend, adjacent channel interference power ratio trend, and adjacent channel leakage suppression ratio trend. The first factor value acquisition module is further used to perform one or more of the following operations: The Markov model is used to predict the adjacent road selectivity trend at the next moment based on the adjacent road selectivity trend at the current moment in the target cell. The Markov model is used to predict the value of the adjacent channel interference power ratio trend at the next moment based on the adjacent channel interference power ratio trend at the current moment in the target cell. The Markov model is used to predict the value of the adjacent channel leakage suppression ratio trend at the next moment based on the adjacent channel leakage suppression ratio trend at the current moment in the target cell.

8. The device according to claim 6, characterized in that The interference data clustering module is further used for: The scenario data of the same user terminal at the same time belongs to a unique category as a clustering constraint, the scenario data where the preset adjacent frequency interference factors in the target cell are located are clustered to obtain interference data of multiple categories.

9. An electronic device comprising a memory, a processor, and a program code stored in the memory and executable on the processor, characterized in that: When the processor executes the program code, the method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium having program code stored thereon, characterized in that: When the program code is executed by a processor, the steps of the method described in any one of claims 1 to 5 are implemented.

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