CN115118364B - Method and system for analyzing and early warning interference of 5G signal different system - Google Patents
Method and system for analyzing and early warning interference of 5G signal different system Download PDFInfo
- Publication number
- CN115118364B CN115118364B CN202210918881.XA CN202210918881A CN115118364B CN 115118364 B CN115118364 B CN 115118364B CN 202210918881 A CN202210918881 A CN 202210918881A CN 115118364 B CN115118364 B CN 115118364B
- Authority
- CN
- China
- Prior art keywords
- antenna
- interference
- probability
- strong interference
- isolation
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/345—Interference values
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
- H04B17/3913—Predictive models, e.g. based on neural network models
Abstract
The invention discloses a method and a system for analyzing and early warning 5G signal different-system interference, which aim at the problems of weak cell signals and poor user experience caused by different-system interference of cells of which different telecom operators select common sites. By analyzing the reason of the interference of different systems in the interference analysis process, the main analysis indexes are the distance of antennas between the systems, the main lobe direction and the like, and the theoretical space isolation degree is calculated to prepare for interference qualification, so that the interference degree of the system is theoretically determined. The spatial isolation estimation is an important stage of interference judgment, and the occurrence probability of the inter-system 5G signal interference is obtained by analyzing the index data of the horizontal isolation and the vertical isolation of the spatial isolation estimation and combining training of an artificial intelligence model, so that the signal quality influence caused by the signal interference is avoided.
Description
Technical Field
The invention belongs to the technical field of 5G, and particularly relates to a method for analyzing and early warning interference of a 5G signal different system.
Background
In order to rapidly construct a 5G network, an initial operator usually adopts a deployment mode that the 5G network and an existing network share a common site, so that on one hand, the cost can be reduced, and on the other hand, the efficiency of engineering construction can be improved. However, the co-site will bring the problem of inter-system interference, and how to eliminate the mutual interference becomes a problem that needs to be researched and solved by equipment manufacturers and telecom operators. How to solve the problem of interference of a plurality of telecommunication operators in a cell with one station and another station and different systems and how to eliminate mutual interference becomes a problem which needs to be researched and solved intensively by equipment manufacturers and telecommunication operators.
Disclosure of Invention
The invention belongs to the network communication environment, mainly comprising: in order to reduce the network construction cost, different telecom operators can select a network construction scheme with a common site, so that the cost can be reduced on one hand, and the efficiency of engineering construction can be improved on the other hand. However, the co-site will bring the problem of inter-system interference, and how to eliminate the mutual interference becomes a problem that needs to be researched and solved by equipment manufacturers and telecom operators. How to solve the problem of interference of a plurality of telecommunication operators in a cell with one station and another station and different systems and how to eliminate mutual interference becomes a problem which needs to be researched and solved intensively by equipment manufacturers and telecommunication operators.
The invention aims at the problems that the cell signal of the cell of the common station address selected by different telecom operators is weak and the user experience is poor due to the interference of different systems. By analyzing the reason of the interference of different systems in the interference analysis process, the main analysis indexes are the distance of antennas between the systems, the main lobe direction and the like, and the theoretical space isolation degree is calculated, so that the preparation can be made for interference qualification, and the interference degree of the system is theoretically determined. The spatial isolation estimation is an important stage of interference judgment, and the occurrence probability of the inter-system 5G signal interference is obtained by analyzing the index data of the horizontal isolation and the vertical isolation of the spatial isolation estimation and combining with the training of an artificial intelligence model, so that the signal quality influence caused by the signal interference is avoided.
The invention highlights the early warning position of artificial intelligence on the interference problem of a plurality of telecommunication operators common station and different systems, and adopts the artificial intelligence to analyze and predict the signal interference occurrence probability in advance and carry out the operations of optimization (automatically adjusting the antenna main lobe, horizontal and vertical angles) and the like in advance, thereby avoiding the signal quality influence caused by the signal interference, reducing the adverse effects of signal interruption and the like caused by the signal interference and improving the use experience of users.
A method for analyzing and early warning interference of a 5G signal different system comprises the following steps:
s01, according to antenna directional diagrams after antenna parameters of an interference antenna and an interfered antenna of a common station are adjusted in each antenna mode and relative positions of the interference antenna and the interfered antenna, estimating the antenna isolation between the interference antenna and the interfered antenna in each antenna mode, storing the antenna isolation between the interference antenna and the interfered antenna in a real-time monitoring database, defining data with the antenna isolation found in real time smaller than a gain value range threshold as a strong interference state, storing the antenna mode and the isolation corresponding to the strong interference state into strong interference data, classifying the strong interference data into historical databases of each antenna type according to the antenna mode, and combining the found strong interference data of the antenna type into future strong interference data in a state without changing the antenna type after the strong interference state is found;
s02, constructing a Bayesian strong interference probability prediction model: p (a | B) = (P (B | a) × P (a))/(P (B | a) × P (a) + P (B | a ') × P (a')), calculate the estimated interference probability P (a | B);
wherein, P (a) represents the probability of occurrence of an event a, i.e. the ratio of the number of records of strong interference data of the antenna type to be estimated to the number of records in the antenna type history database, P (B | a) is a conditional probability representing the probability of occurrence of an event B under the condition that the event a occurs, P (B | a) represents the ratio of the number of records of strong interference data in the future when the antenna type to be estimated is in a strong interference state to the number of records in the antenna type history database, P (a ') =1-P (a), P (B | a ') represents the probability of occurrence of an event B under the condition that an event a ' occurs, P (B) = P (B | a) P (a) + P (B | a ') P (a '), and P (a | B) represents the probability of occurrence of a strong interference state at present when the antenna type to be estimated is in a strong interference state in the future;
and S03, when the estimated interference probability is greater than the probability threshold, giving out an early warning.
A system for analyzing and early warning interference of a 5G signal different system comprises:
the data processing unit is used for acquiring the antenna isolation between the interference antenna and the interfered antenna after adjusting the antenna parameters of the interference antenna and the interfered antenna of the common station under each antenna mode, storing the antenna isolation between the interference antenna and the interfered antenna into a real-time monitoring database, defining the data of which the antenna isolation is smaller than the threshold value of the gain value range, which is found in real time, as a strong interference state, storing the antenna parameters, the antenna modes and the isolation corresponding to the strong interference state into strong interference data, classifying the strong interference data into historical databases of each antenna type according to the antenna parameters and the antenna modes, and combining the found strong interference data of the antenna type into future strong interference data under the state that the antenna type is not changed after the strong interference state is found;
the Bayes strong interference probability prediction model construction unit is used for constructing a Bayes strong interference probability prediction model: p (a | B) = (P (B | a) × P (a))/(P (B | a) × P (a) + P (B | a ') × P (a')), calculate the estimated interference probability P (a | B);
wherein, P (a) represents the probability of occurrence of an event a, i.e. the ratio of the number of records of strong interference data of the antenna type to be estimated to the number of records in the antenna type history database, P (B | a) is a conditional probability representing the probability of occurrence of an event B under the condition that the event a occurs, P (B | a) represents the ratio of the number of records of strong interference data in the future when the antenna type to be estimated is in a strong interference state to the number of records in the antenna type history database, P (a ') =1-P (a), P (B | a ') represents the probability of occurrence of an event B under the condition that an event a ' occurs, P (B) = P (B | a) P (a) + P (B | a ') P (a '), and P (a | B) represents the probability of occurrence of a strong interference state at present when the antenna type to be estimated is in a strong interference state in the future;
and the pre-estimated probability processing unit is used for comparing the relation between the pre-estimated interference probability and the probability threshold value and sending out an early warning when the pre-estimated interference probability is greater than the probability threshold value.
Compared with the prior art, the invention has the following beneficial effects:
compared with the prior art, the main advantages are that:
the prior art (CN 202011340711.5) provides a method, device and system for cross-system interference avoidance, which relates to the field of communication technology and can improve inter-system interference between communication systems on an unlicensed spectrum. The method comprises the following steps: the method comprises the steps that a first device sends a wireless fidelity WiFi message including duration indication information to a second device through a first communication system; the first communication system is a WiFi system; the duration indication information is used for indicating a first duration, and the first duration is used for indicating that the second equipment does not occupy the first channel within the first duration; the frequency spectrum of the first channel is an unauthorized frequency spectrum; the first equipment sends first information comprising service data and/or control information to the third equipment through the second communication system; the second communication system is a sidelink unlicensed SL-U system, a new air interface unlicensed spectrum NR-U system or an licensed spectrum assisted access LAA system. According to the scheme, the first step, the estimation of the antenna isolation degree is an important stage of interference judgment, the horizontal isolation degree and the vertical isolation degree index data of the estimation of the antenna isolation degree are analyzed and combined with the training of an artificial intelligence model, so that the foreknowledge before the occurrence of the inter-system 5G signal interference is realized, and the occurrence probability of the inter-system 5G signal interference is obtained, so that the signal quality influence caused by the signal interference is avoided. And step two, aiming at the directional antenna with horizontal isolation/vertical isolation, the main lobe angle of the antenna and parameters such as horizontal angle and vertical angle need to be adjusted frequently to realize interference minimization, so that a more flexible Bayesian algorithm is adopted to construct an intelligent interference prediction model. And extracting directional antenna type data of the antennas at the two ends of the interference and the interfered ends in a historical log on a base station server through program execution for analysis. And carrying out probability prediction according to the common gain indexes. The prior art is different from the calculation method and the adopted method realized by the scheme.
The prior art (CN 105075318B) discloses a method and an apparatus for avoiding interference of different systems in a reuse spectrum scheme, and relates to the field of mobile communication. And determining an interference sector causing non-negligible interference of the different system to the terminal by obtaining an interference measurement result of the interference of the different system to the terminal by the base station and performing comparison judgment based on the interference measurement result, and allocating transmission resources capable of avoiding the interference of the different system to the terminal. Compared with the prior art, the embodiment of the scheme avoids the transmission resources on all the reuse frequency bands, can effectively improve the accuracy of avoiding the interference of different systems when the transmission resources are reused on the reuse frequency spectrum, and further improves the frequency spectrum use efficiency. According to the scheme, the first step, the estimation of the antenna isolation degree is an important stage of interference judgment, the horizontal isolation degree and the vertical isolation degree index data of the estimation of the antenna isolation degree are analyzed and combined with the training of an artificial intelligence model, so that the foreknowledge before the occurrence of the inter-system 5G signal interference is realized, and the occurrence probability of the inter-system 5G signal interference is obtained, so that the signal quality influence caused by the signal interference is avoided. And step two, aiming at the directional antenna with horizontal isolation/vertical isolation, the main lobe angle of the antenna and parameters such as horizontal angle and vertical angle need to be adjusted frequently to realize interference minimization, so that a more flexible Bayesian algorithm is adopted to construct an intelligent interference prediction model. And extracting directional antenna type data of the antennas at the two ends of the interference and the interfered from a historical log on a base station server through program execution for analysis. And carrying out probability prediction according to the common gain indexes. The prior art is different from the calculation method and the adopted method realized by the scheme.
The invention creatively aims at the problems that the cell signal of the cell of the co-site selected by different telecom operators is weak and the user experience is poor due to the interference of different systems in the network communication environment. By analyzing the reason of the interference of different systems in the interference analysis process, the main analysis indexes are the distance of antennas between the systems, the main lobe direction and the like, and the theoretical space isolation degree is calculated, so that the preparation can be made for interference qualification, and the interference degree of the system is theoretically determined. The spatial isolation estimation is an important stage of interference judgment, and the occurrence probability of the inter-system 5G signal interference is obtained by analyzing the index data of the horizontal isolation and the vertical isolation of the spatial isolation estimation and combining with the training of an artificial intelligence model, so that the signal quality influence caused by the signal interference is avoided. The invention highlights artificial intelligence among multiple telecom operators
The early warning position in the interference problem adopts the analysis and prediction of artificial intelligence to predict the signal interference occurrence probability in advance and carry out operations such as optimization in advance, thereby avoiding the signal quality influence caused by the signal interference, reducing the adverse effects such as signal interruption caused by the signal interference and the like, and improving the use experience of users.
Detailed Description
The present invention will be described in further detail with reference to examples for the purpose of facilitating understanding and practice of the invention by those of ordinary skill in the art, and it is to be understood that the present invention has been described in the illustrative embodiments and is not to be construed as limited thereto.
System interference minimization in base station failure has always been the ultimate goal of 5G base stations. Interference is divided into intra-system interference and extra-system interference. The system-internal interference is generated by the system, such as inter-cell or intra-cell interference caused by unreasonable coverage, AAU failure and intermodulation interference; the external system interference mainly refers to an interference device, a high-power transmitting station, different system interference and the like. Both types of interference can affect network quality. The invention is mainly applied to the problem of different system interference caused by selecting common station sites by different telecom operators in a cell, and the different system interference is classified in detail.
Secondly, by analyzing the reason of the interference of different systems in the interference flow, the main analysis indexes are the distance of antennas between the systems, the main lobe direction and the like, and the theoretical space isolation degree is calculated, so that the preparation can be made for the interference qualification, and the interference degree of the system is theoretically determined. The space isolation estimation is an important stage of interference judgment, and the space isolation is calculated to obtain theoretical space isolation through the antenna distance between systems and the main lobe direction so as to determine the interference degree of the system. The horizontal isolation and vertical isolation index data of the spatial isolation estimation are analyzed and the training of an artificial intelligence model is combined, so that the foreknowledge of the inter-system 5G signal interference is made before the occurrence of the pre-premise, and the signal quality influence caused by the signal interference is avoided by the operations of automatically adjusting the main lobe of the antenna, the horizontal vertical angle and the like.
In order to solve the above problems, a method for analyzing and warning the interference of a 5G signal different system mainly comprises the following steps:
step 1, classifying interference of different systems and calculating interference degree:
wang Jiang, liu Hailin, huang Jie, lin Yan, zhang Wenjun, gong Chenbao.5G wireless network optimization [ M ], people's post and telecommunications press, 2020-08, it is described that spatial isolation refers to coupling loss between antennas, which refers to the ratio of the power of a transmitter transmitting signal and the power of the signal reaching another transmitter output terminal (or a receiver input stage) which may generate intermodulation products, and the spatial isolation index is mainly composed of two parts of horizontal isolation and vertical isolation after being classified.
Step 1.1, the horizontal isolation between the transmitting antenna and the receiving antenna is divided into two antenna modes of a directional antenna and an omnidirectional antenna. Therefore, after the interference antenna is adjusted into two antenna modes, the horizontal isolation under the two antenna modes is calculated respectively:
(1) The directional antenna mode is divided into a transmitting antenna mode and a receiving antenna mode, wherein the main lobe of the transmitting antenna mode and the main lobe of the receiving antenna mode are in the same direction and in different directions, and the antenna isolation of each antenna mode is estimated respectively;
antenna main lobe syntropy and heterodromous horizontal isolation formula:
IH =22+20lg (d/lambda) - (Gtx + Grx) formula (1)
Wherein: IH (in dB) is the isolation between the transmitting and receiving antennas at horizontal isolation, which refers to the coupling loss between the antennas, and refers to the ratio of the power of the signal transmitted by the transmitter to the power of the signal at the output of another transmitter (or the input stage of the receiver) that may produce intermodulation products; lg decibel (unit decile) is a measurement unit, and is mainly used for describing the multiple relation between the lg decibel and a reference quantity, wherein 10 times of the multiple between output power and standard power is taken as a base logarithm value 10 times; 22 is a propagation constant, and the propagation constant is a parameter for describing attenuation and phase change of incident waves and reflected waves on transmission lines; λ (unit m) is the radio wavelength in the reception band range; d is the horizontal distance between the transmitting antenna and the receiving antenna. Gtx is the gain of the transmitting antenna on the interference frequency, grx is the gain of the receiving antenna on the interference frequency, and the antenna gain is the ratio of the radiation power density of the antenna in a certain specified direction to the radiation power density of the reference omnidirectional antenna at the same input power, and quantitatively describes the degree of concentrated radiation of the input power by one antenna. Antenna gain is a measure of the ability of an antenna to transmit and receive signals in a particular direction, and is one of the most important parameters for selecting a base station antenna.
As introduced in Wang Jiang et al, 5G wireless network optimization:
(a) And when the main lobes of the transmitting antenna and the receiving antenna are in the same direction:
when the main lobes of the transmitting antenna and the receiving antenna are in the same direction, and the main lobe (angle) widths of the transmitting antenna and the receiving antenna are 0 degree or 180 degrees, both Gtx and Grx are 0dBi.
The horizontal isolation formula is therefore: IH =22+20lg (d/lambda)
(b) And when the main lobes of the transmitting antenna and the receiving antenna are different in direction:
and then, the Gtx and Grx need to check the gain value on the antenna directional diagram according to the included angle between the transmitting antenna and the receiving antenna, and when the antenna directional diagram is checked and found to be negative, the Gtx and Grx are both made to be 0dBi, considering that the calculation is not carried out according to the negative gain in the actual engineering situation.
The horizontal isolation formula is therefore: IH =22+20lg (d/lambda)
In the selected antenna mode, when antenna parameters such as an antenna lobe angle, an azimuth angle, a downtilt angle and the like are adjusted for a directional antenna mode with horizontal isolation/vertical isolation, an antenna pattern changes, so that the antenna pattern changes, and therefore the antenna isolation also changes, so that in the antenna mode, a new horizontal isolation/vertical isolation needs to be re-estimated along with the change of the antenna parameters, for example, real-time radiation power of an interference antenna corresponds to different half-power lobe widths, gtx and Grx with a half-power wave width of 65 ° are about-18 dBi, gtx and Grx with a half-power wave width of 90 ° are about-9 dBi, and Gtx and Grx with a half-power wave width of 120 ° are about-7 dBi. dBi is the gain value unit of the antenna; the larger the dBi the larger the gain.
The half-power beam width is a lobe angle defined by a half-power level point angle, and is also referred to as a 3dB beam width and a half-power angle. Lobe angle: is the angle formed in the antenna pattern at 3dB below the peak of the main lobe.
The antenna angle refers to the azimuth angle and the downward inclination angle of the directional antenna, and the antenna forms included angles with the due north direction and the horizontal direction respectively, namely the azimuth angle and the downward inclination angle.
Azimuth angle: is an angle formed by horizontally and clockwise rotating from the north direction to the plane of the antenna.
Declination angle: is the angle of the antenna from the horizontal.
(2) Omnidirectional antenna pattern: the transmitting antenna and the receiving antenna transmit or receive signals in a full angle of 360 degrees.
Gtx of the omni-directional antenna is equal to the gain value of the transmitting antenna in the interference direction, and Grx of the omni-directional antenna is equal to the gain value of the receiving antenna in the interference direction.
Step 1.2, calculating vertical isolation: the vertical isolation between the transmit and receive antennas is based on the following equation (2):
iv =28+40lg10 (dv/λ) - (Gtx + Grx) formula (2)
Wherein: iv (unit dB) is the vertical isolation between the transmitting antenna and the receiving antenna during vertical isolation, which refers to the coupling loss between the antennas, and refers to the ratio of the power of the signal transmitted by the transmitter to the power of the signal reaching another transmitter output terminal (or receiver input stage) that may generate intermodulation products; 28 is the propagation constant; lg decibel (decibel-er); dv (m) is the transmit-receive antenna vertical spacing; λ (m) a radio wavelength within a reception frequency band; gtx and Grx are gains of the transmitting antenna and the receiving antenna on interference frequencies respectively, the antenna gain is a ratio of a radiation power density of the antenna in a certain specified direction to a radiation power density of a reference omnidirectional antenna when the same input power is obtained, the antenna gain quantitatively describes the degree of concentrated radiation of the input power by an antenna, the antenna gain is used for measuring the capacity of the antenna for transmitting and receiving signals towards a specific direction and is one of the most important parameters for selecting the base station antenna, the gain obviously has a close relation with an antenna directional diagram, and the narrower the main lobe of the directional diagram, the smaller the secondary lobe and the higher the gain.
In the selected antenna mode, when antenna parameters such as an antenna lobe angle, an azimuth angle, a downtilt angle and the like are adjusted for a directional antenna mode with vertical isolation, an antenna directional pattern is changed, so that the antenna directional pattern is changed, and the antenna isolation is also changed, so that in the antenna mode, a new vertical isolation is estimated again along with the change of the antenna parameters.
The propagation constant is a parameter for describing attenuation and phase change of incident waves and reflected waves on transmission lines;
for directional antenna mode: gtx and Grx are both 0 (negative numbers may occur).
For the full line antenna mode: gtx equals the antenna gain in the interfering direction and Grx equals the antenna gain in the interfered direction.
Storing the isolation obtained by estimation into a real-time monitoring database, defining the data of which the antenna isolation found in real time is less than a gain value range threshold (6 dBi) as a strong interference state, storing an antenna mode corresponding to the strong interference state, and sorting the isolation into strong interference data, classifying the strong interference data into various antenna type historical databases according to the antenna mode, and merging the found strong interference data of the antenna type into future strong interference data under the state that the antenna type is not changed after the strong interference state is found.
Step 2, constructing a Bayesian strong interference probability prediction model
Aiming at the directional antenna with horizontal isolation/vertical isolation, parameters such as antenna lobe angle, azimuth angle, downtilt angle and the like need to be adjusted frequently to achieve interference minimization, so that a more flexible Bayesian algorithm is adopted to construct a Bayesian strong interference probability prediction model. And extracting the antenna pattern data of a transmitting antenna (interference end) and a receiving antenna (interfered end) in a history log on a base station server through program execution for analysis. And according to the lower limit of the gain value range of all the antenna types of the common station address, probability prediction is carried out on the probability that the isolation between the transmitting antenna and the receiving antenna is lower than the lower limit of the gain value range of all the antenna types of the common station address by 6 dBi. And when the isolation between the transmitting antenna and the receiving antenna is lower than the lower limit of the gain value range of all the antenna classes of the co-site, indicating that the isolation is poor.
Antenna gain index of a common antenna: the gain range of the whip antenna is 6-9dBi, the gain range of the GSM base station antenna is 15-17dBi, and the parabolic directional antenna can easily achieve 24dBi. The higher the dBi the higher the gain. According to the antenna gain indexes of the common antennas, the lower limit of the gain value range of all antenna types of the common station address is calculated to be 6dBi, namely the gain value range threshold.
The Bayesian strong interference probability prediction model is based on the following formula (3):
p (a | B) = (P (B | a) × P (a))/(P (B | a) × P (a) + P (B | a ') × P (a')) equation (3)
Prior probability = P (a) × conditional probability = P (a) × P (B | a) = P (B) × adjustment factor
P (a) is the most basic symbol in the probability, and represents the probability of occurrence of a, i.e. the ratio of the number of records of strong interference data of the antenna type to be estimated to the number of records in the antenna type history database. In this embodiment, when other factors are ignored, the isolation of a certain antenna type is smaller than the total number of the lower limit gain value ranges of all antenna types of the common site by 6 dBi/the isolation of a certain antenna type in the history log is lower than the total number of the lower limit gain value ranges of all antenna types of the common site by 6dBi, for example: 40 percent; other factors include false positives, antenna gain jitter, etc. The antenna types are classified into the same antenna pattern.
P (a') =1-P (a), here 60%;
p (B | a) is a symbol of conditional probability, which represents the probability of occurrence of the event B under the condition of occurrence of the event a, the conditional probability is the key of the bayesian formula, and is also called "likelihood", and P (B | a) represents the ratio of the number of future strong interference data records to the number of records in the antenna type history database when the antenna type to be estimated is in a strong interference state. In the continuous learning process of the interference intelligent prediction model in this embodiment, when the isolation of a certain antenna type is lower than the lower limit of the gain value range of all antenna types of the common-site by 6dBi, when the future antenna parameter changes or does not change, the isolation of the certain antenna type is lower than the total number of the lower limit of the gain value range of all antenna types of the common-site by 6 dBi/the total number of the lower limit of the gain value range of the certain antenna type in the history log which is lower than the total number of the lower limit of the gain value range of all antenna types of the common-site by 6dBi, for example, 50%;
p (B | a ') represents the probability of occurrence of the event B under the condition that the event a ' occurs, and in this embodiment, if the isolation of a certain antenna type in the history log is less than the lower limit of the gain value range of all antenna types of the co-site by 6dBi, P (B | a ') =100% is default;
p (B) = P (B | a) P (a) + P (B | a ') P (a'), in the present embodiment, P (B) =0.5 × 0.4+1 × 0.6=0.8;
then it can be calculated according to the Bayesian formula, i.e., it is calculated
P(A|B) = (0.5*0.4)/(0.8)=0.25
And 0.25, namely when the isolation of a certain antenna type is lower than 6dBi when the parameters of the antenna are changed or not changed in the future, the current probability that the isolation of the certain antenna type is lower than 6dBi, so that the operation of the whole interference intelligent prediction model is completed. Therefore, P (a | B) represents the probability of the antenna type being in a strong interference state at present when the antenna type is predicted to be in a strong interference state in the future.
Step 3, processing the estimated result
And when the estimated interference probability is greater than the probability threshold, sending out early warning. Table 1 shows the probability calculation result of the current strong interference state when each antenna type is in the strong interference state in the future.
TABLE 1
A system for analyzing and early warning interference of a 5G signal different system comprises:
the data processing unit is used for acquiring the antenna isolation between the interference antenna and the interfered antenna after adjusting the antenna parameters of the interference antenna and the interfered antenna of the common station under each antenna mode, storing the antenna isolation between the interference antenna and the interfered antenna into a real-time monitoring database, defining the data of which the antenna isolation is smaller than the threshold value of the gain value range, which is found in real time, as a strong interference state, storing the antenna parameters, the antenna modes and the isolation corresponding to the strong interference state into strong interference data, classifying the strong interference data into historical databases of each antenna type according to the antenna parameters and the antenna modes, and combining the found strong interference data of the antenna type into future strong interference data under the state that the antenna type is not changed after the strong interference state is found;
the Bayes strong interference probability prediction model construction unit is used for constructing a Bayes strong interference probability prediction model: p (a | B) = (P (B | a) × P (a))/(P (B | a) × P (a) + P (B | a ') × P (a')), calculate the estimated interference probability P (a | B);
wherein, P (a) represents the probability of occurrence of an event a, i.e. the ratio of the number of records of strong interference data of the antenna type to be estimated to the number of records in the antenna type history database, P (B | a) is a conditional probability representing the probability of occurrence of an event B under the condition that the event a occurs, P (B | a) represents the ratio of the number of records of strong interference data in the future when the antenna type to be estimated is in a strong interference state to the number of records in the antenna type history database, P (a ') =1-P (a), P (B | a ') represents the probability of occurrence of an event B under the condition that an event a ' occurs, P (B) = P (B | a) P (a) + P (B | a ') P (a '), and P (a | B) represents the probability of being in a strong interference state at present when the antenna type is in a strong interference state in the future;
and the pre-estimated probability processing unit is used for comparing the relation between the pre-estimated interference probability and the probability threshold value and sending out an early warning when the pre-estimated interference probability is greater than the probability threshold value.
The invention creatively aims at the problems that the cell signal of the cell of the co-site selected by different telecom operators is weak and the user experience is poor due to the interference of different systems in the network communication environment. By analyzing the reason of the interference of different systems in the interference analysis process, the main analysis indexes are the distance of antennas between the systems, the main lobe direction and the like, and the theoretical space isolation degree is calculated, so that the preparation can be made for interference qualification, and the interference degree of the system is theoretically determined. The spatial isolation estimation is an important stage of interference judgment, and the occurrence probability of the inter-system 5G signal interference is obtained by analyzing the index data of the horizontal isolation and the vertical isolation of the spatial isolation estimation and combining training of an artificial intelligence model, so that the signal quality influence caused by the signal interference is avoided. The invention highlights the early warning position of artificial intelligence on the interference problem of a plurality of telecommunication operators common station and different systems, and adopts the artificial intelligence to analyze and predict the occurrence probability of signal interference in advance and carry out optimization and other operations in advance, thereby avoiding the signal quality influence caused by the signal interference, reducing the adverse effects such as signal interruption and the like caused by the signal interference and improving the use experience of users.
It should be noted that the specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (2)
1. A method for analyzing and early warning interference of a 5G signal different system is characterized by comprising the following steps:
s01, according to antenna directional diagrams after antenna parameters of an interference antenna and an interfered antenna of a common station are adjusted in each antenna mode and relative positions of the interference antenna and the interfered antenna, estimating the antenna isolation between the interference antenna and the interfered antenna in each antenna mode, storing the antenna isolation between the interference antenna and the interfered antenna in a real-time monitoring database, defining data with the antenna isolation found in real time smaller than a gain value range threshold as a strong interference state, storing the antenna mode and the isolation corresponding to the strong interference state into strong interference data, classifying the strong interference data into historical databases of each antenna type according to the antenna mode, and combining the found strong interference data of the antenna type into future strong interference data in a state without changing the antenna type after the strong interference state is found;
s02, constructing a Bayesian strong interference probability prediction model: p (a | B) = (P (B | a) × P (a))/(P (B | a) × P (a) + P (B | a ') × P (a')), calculating an estimated interference probability P (a | B);
wherein, P (a) represents the probability of occurrence of an event a, i.e. the ratio of the number of records of strong interference data of the antenna type to be estimated to the number of records in the antenna type history database, P (B | a) is a conditional probability representing the probability of occurrence of an event B under the condition that the event a occurs, P (B | a) represents the ratio of the number of records of strong interference data in the future when the antenna type to be estimated is in a strong interference state to the number of records in the antenna type history database, P (a ') =1-P (a), P (B | a ') represents the probability of occurrence of an event B under the condition that an event a ' occurs, P (B) = P (B | a) P (a) + P (B | a ') P (a '), and P (a | B) represents the probability of occurrence of a strong interference state at present when the antenna type to be estimated is in a strong interference state in the future;
and S03, when the estimated interference probability is greater than the probability threshold, giving out an early warning.
2. The utility model provides a system of different system interference analysis of 5G signal and early warning which characterized in that includes:
the data processing unit is used for acquiring the antenna isolation between the interference antenna and the interfered antenna after adjusting the antenna parameters of the interference antenna and the interfered antenna of the common station under each antenna mode, storing the antenna isolation between the interference antenna and the interfered antenna into a real-time monitoring database, defining the data of which the antenna isolation is smaller than the threshold value of the gain value range, which is found in real time, as a strong interference state, storing the antenna parameters, the antenna modes and the isolation corresponding to the strong interference state into strong interference data, classifying the strong interference data into historical databases of each antenna type according to the antenna parameters and the antenna modes, and combining the found strong interference data of the antenna type into future strong interference data under the state that the antenna type is not changed after the strong interference state is found;
the Bayes strong interference probability prediction model construction unit is used for constructing a Bayes strong interference probability prediction model: p (a | B) = (P (B | a) × P (a))/(P (B | a) × P (a) + P (B | a ') × P (a')), calculating an estimated interference probability P (a | B);
wherein, P (a) represents the probability of occurrence of an event a, i.e. the ratio of the number of records of strong interference data of the antenna type to be estimated to the number of records in the antenna type history database, P (B | a) is a conditional probability representing the probability of occurrence of an event B under the condition that the event a occurs, P (B | a) represents the ratio of the number of records of strong interference data in the future when the antenna type to be estimated is in a strong interference state to the number of records in the antenna type history database, P (a ') =1-P (a), P (B | a ') represents the probability of occurrence of an event B under the condition that an event a ' occurs, P (B) = P (B | a) P (a) + P (B | a ') P (a '), and P (a | B) represents the probability of occurrence of a strong interference state at present when the antenna type to be estimated is in a strong interference state in the future;
and the pre-estimated probability processing unit is used for comparing the relation between the pre-estimated interference probability and the probability threshold value and sending out an early warning when the pre-estimated interference probability is greater than the probability threshold value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210918881.XA CN115118364B (en) | 2022-08-02 | 2022-08-02 | Method and system for analyzing and early warning interference of 5G signal different system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210918881.XA CN115118364B (en) | 2022-08-02 | 2022-08-02 | Method and system for analyzing and early warning interference of 5G signal different system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115118364A CN115118364A (en) | 2022-09-27 |
| CN115118364B true CN115118364B (en) | 2022-12-13 |
Family
ID=83335075
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210918881.XA Active CN115118364B (en) | 2022-08-02 | 2022-08-02 | Method and system for analyzing and early warning interference of 5G signal different system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115118364B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106900002A (en) * | 2016-12-07 | 2017-06-27 | 中国移动通信集团设计院有限公司 | A kind of interference estimation method and device of multiple wireless communication systems |
| CN109714777A (en) * | 2019-01-15 | 2019-05-03 | 西安航空学院 | The acquisition methods that TD-LTE system is isolated with DTMB coexistence of systems time space interval discrete frequency |
| CN112213697A (en) * | 2020-10-09 | 2021-01-12 | 合肥工业大学 | Feature fusion method for radar deception jamming recognition based on Bayesian decision theory |
| CN112449352A (en) * | 2019-09-03 | 2021-03-05 | 丽水青达科技合伙企业(有限合伙) | Method for measuring and calculating space isolation |
| CN112950243A (en) * | 2019-12-10 | 2021-06-11 | 大唐移动通信设备有限公司 | 5G station address planning method and device, electronic equipment and storage medium |
| CN113163436A (en) * | 2021-03-24 | 2021-07-23 | 苏州攀登者信息科技有限公司 | Method for measuring and calculating space isolation |
| WO2021249637A1 (en) * | 2020-06-10 | 2021-12-16 | Lenovo (Singapore) Pte. Ltd. | Model based predictive interference management |
| CN114793140A (en) * | 2022-06-21 | 2022-07-26 | 深圳粤讯通信科技有限公司 | 5G antenna interface board port isolation measurement system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130106236A (en) * | 2012-03-19 | 2013-09-27 | 한국전자통신연구원 | Method for analyzing interference between heterogeneous wireless communication system |
| US10772052B2 (en) * | 2017-06-16 | 2020-09-08 | Qualcomm Incorporated | Controlling coexistent radio systems in a wireless device |
-
2022
- 2022-08-02 CN CN202210918881.XA patent/CN115118364B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106900002A (en) * | 2016-12-07 | 2017-06-27 | 中国移动通信集团设计院有限公司 | A kind of interference estimation method and device of multiple wireless communication systems |
| CN109714777A (en) * | 2019-01-15 | 2019-05-03 | 西安航空学院 | The acquisition methods that TD-LTE system is isolated with DTMB coexistence of systems time space interval discrete frequency |
| CN112449352A (en) * | 2019-09-03 | 2021-03-05 | 丽水青达科技合伙企业(有限合伙) | Method for measuring and calculating space isolation |
| CN112950243A (en) * | 2019-12-10 | 2021-06-11 | 大唐移动通信设备有限公司 | 5G station address planning method and device, electronic equipment and storage medium |
| WO2021249637A1 (en) * | 2020-06-10 | 2021-12-16 | Lenovo (Singapore) Pte. Ltd. | Model based predictive interference management |
| CN112213697A (en) * | 2020-10-09 | 2021-01-12 | 合肥工业大学 | Feature fusion method for radar deception jamming recognition based on Bayesian decision theory |
| CN113163436A (en) * | 2021-03-24 | 2021-07-23 | 苏州攀登者信息科技有限公司 | Method for measuring and calculating space isolation |
| CN114793140A (en) * | 2022-06-21 | 2022-07-26 | 深圳粤讯通信科技有限公司 | 5G antenna interface board port isolation measurement system |
Non-Patent Citations (6)
| Title |
|---|
| 3.5 GHz频段IMT-2020系统对同频异系统干扰分析;吴宏达等;《移动通信》;20190115(第1期);70-73页 * |
| 5G试验网的频率干扰分析;龙杰华等;《现代信息科技》;20190410(第07期);53-55,58页 * |
| Interference Coexistence of 5G NR and LTE System Based on 2.1GHz;Na An等;《2020 12th International Conference on Communication Software and Networks》;20200713;90-94页 * |
| NR 与异系统共址天线隔离度分析;黄嘉铭等;《广东通信技术》;20200415;20-22页 * |
| 基于贝叶斯网络的远程预警雷达作战运用效果评估;周跃等;《四川兵工学报》;20120825(第08期);40-43页 * |
| 多网络共存下基于贝叶斯的垂直切换算法;孔令斌等;《电讯技术》;20160228(第02期);122-127页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115118364A (en) | 2022-09-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11108477B2 (en) | Method and apparatus for determining broadcast beam weighted value in wireless communications system | |
| CN105491601B (en) | Method and system for positioning base station interference | |
| CN108809371B (en) | Beam width optimization method and switching method in large-scale antenna system | |
| CN107534478A (en) | RF Wave beam formings control in communication system | |
| CN110972312B (en) | Inter-cell interference coordination method of millimeter wave cellular network | |
| US8971816B2 (en) | Method and arrangement for assisting in direction adjustment of a directional antenna | |
| Bai et al. | Coverage in dense millimeter wave cellular networks | |
| MX2012013881A (en) | Method and arrangement for reducing interference and enhancing coverage. | |
| EP2247155B1 (en) | Mobile communication system | |
| Rodriguez et al. | A geometrical-based vertical gain correction for signal strength prediction of downtilted base station antennas in urban areas | |
| Tultul et al. | Device-to-device communication in terahertz frequency band: Enhancement of energy efficiency | |
| Elmezughi et al. | Evaluation of Line-of-Sight Probability Models for Enclosed Indoor Environments at 14 to 22 GHz | |
| CN115118364B (en) | Method and system for analyzing and early warning interference of 5G signal different system | |
| Mariam et al. | Coverage probability of uplink millimeter wave cellular network with non-homogeneous interferers’ point process | |
| US10601530B2 (en) | Method for generating measurement result and device | |
| CN111988789B (en) | Wireless network node deployment optimization method, system and device | |
| Ehiagwina et al. | Sensitivity degradation and antenna isolation analyses for a multi-operator global systems for mobile communication base transceiver stations | |
| Armeniakos et al. | Angular Distance-Based Performance Analysis of mmWave Cellular Networks | |
| CN108419266B (en) | Channel interception method and transmitting terminal equipment | |
| Ikechi et al. | A Wavelet Genetic Algorithm Based Signal Path Loss Model for Evolved Nodeb Cell Sites Planning And Effective Coverage of Wireless Communication System | |
| Armeniakos et al. | Comprehensive Analysis of Maximum Power Association Policy for Cellular Networks Using Distance and Angular Coordinates | |
| US11611884B2 (en) | Dynamic configuration of overlapping basic service set preamble detect (OBSS PD) parameters for access points | |
| KR20000060909A (en) | Method for determining distance between diversity antennas | |
| Sasaki et al. | Path loss frequency dependence at 2–26 GHz in an urban macro cell environment | |
| Abdel-Raouf et al. | WiGig Coverage Area Management Based on Wi-Fi Received Signal Strength |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |