CN101547449B - Frequency sweep and mobile phone measurement report-based method for automatic frequency optimization - Google Patents

Abstract

The present invention provides an autonomous frequency optimization method based on frequency scanning and mobile phone measurement reports. Through the association and integration of frequency scanning data and mobile phone measurement report (MR) data, the network quality and user perception are associated from the user group level, which is targeted and efficient. Accurately grasp the improvement of wireless coverage quality to user groups, achieve a better balance between user perception, network quality and various assessment indicators, reduce blindness, and avoid network quality fluctuations.

Description

Autonomous Frequency Optimization Method Based on Frequency Sweep and Mobile Phone Measurement Report

technical field

The invention belongs to the technical field of core networks in mobile communication networks, and relates to an autonomous frequency optimization method based on frequency scanning and mobile phone measurement reports.

Background technique

In GSM networks, frequency planning optimization directly affects network performance and user perception. Currently, there are three main technologies applied to GSM network frequency planning optimization:

1) Automatic frequency planning technology based on propagation model coverage prediction.

2) Automatic frequency optimization technology based on frequency sweep measurement statistics.

3) Automatic frequency optimization technology based on mobile phone measurement report (MR). For example, the application number is "2008100721495", the invention name is "Network Frequency Optimization Method Based on Measurement Report", the publication number is "CN101409884", and the invention patent with the publication date "2009.04.15" introduces this technology in detail.

The first type is widely used in planning tools in the initial stage of network construction. Due to the limited update cycle of the electronic map, the automatic frequency optimization based on the signal propagation model has a certain gap from the data accuracy required by the actual network optimization when dealing with multipath (diffraction) interference in urban areas. At the same time, the automatic frequency planning technology based on the coverage prediction of the propagation model cannot reflect the user traffic distribution in time. Therefore, the automatic frequency planning technology based on the coverage prediction of the propagation model cannot meet the increasingly refined network optimization management requirements.

The second is to perform automatic frequency optimization based on the actual measurement sweep statistics of the network. The current network optimization services basically provide automatic frequency optimization platforms and tools based on actual network measurement frequency sweep data. This technology has a better effect on improving coverage blind spots, and is currently a traditional technology that is widely used in network optimization.

The third is an automatic frequency optimization technology based on mobile phone measurement reports. The existing major wireless communication equipment manufacturers have successively provided automatic frequency optimization tools for their equipment. The advantage is:

1) The source of massive test data objectively reflects the actual perception of the end user group;

2) Read network configuration data, mobile statistical data, etc. from OMCR. The data source is reliable, and it is convenient and low-cost to collect the required data;

This technology has gradually become the mainstream value-added service content of major wireless communication equipment manufacturers.

The technical limitations of the first type: With the expansion of network scale, the coverage indicators of VIP base stations and complaints from key users, the fine optimization of urban areas is becoming more and more urgent, and urban buildings and dense urban areas are intertwined, and indoor distribution systems are becoming more and more important. Splitting, the coverage area keeps getting smaller and overlapping, and the coverage of mobile phone signals mainly depends on multipath diffraction, which does not satisfy the line-of-sight propagation model. The automatic frequency planning technology based on propagation model coverage prediction cannot meet the requirements of refined network optimization management.

The limitations of the second technique are:

1) The workload of comprehensive frequency scanning is heavy and the duration is long;

2) Frequency scanning of urban areas and roads, some buildings are inconvenient to enter and exit frequently for measurement, and data collection is incomplete;

3) It is impossible to effectively and consistently collect various field frequency sweep data. At present, the latest value is often used in the adoption of frequency sweep data. The correlation and stability analysis of frequency sweep data in different periods and locations is not fully utilized.

4) The drive test and CQT frequency sweep data cannot be reasonably organized and correlated. A comprehensive network quality monitoring network has not yet been formed.

5) The frequency scanning data only indicates the actual coverage quality of the network, and cannot objectively represent the user's service perception.

The limitations of the third technique are:

1) Massive data lacks feature information extraction and cannot be used effectively;

2) The collection process requires timely monitoring and correction of parameter changes, which may affect the normal parameters and network quality of the live network.

3) The data acquisition of this technology is generally limited to the application of the equipment of this manufacturer, which limits other applications in a wider range.

In view of the above technical limitations, the technical solution of this proposal is based on the automatic frequency optimization technology of frequency sweep data and mobile phone measurement report (MR). The data comes from the existing frequency scanning data and the mobile phone measurement report (MR) reported by OMCR. The data source is well guaranteed, and it highlights the relationship between actual network coverage and end user service perception.

Contents of the invention

The purpose of the present invention is to provide an autonomous frequency optimization method based on frequency scanning and mobile phone measurement reports, so as to solve the problems in the prior art that the data source is not well guaranteed, and the actual network coverage and terminal user service perception are poorly correlated.

The technical scheme adopted by the system of the present invention is, based on the autonomous frequency optimization method of frequency scanning and mobile phone measurement reports, is carried out according to the following steps:

(1) In the coverage area that needs to be optimized, the cell division of the area can be based on the dimensions of distinguishing and locating different user groups, different coverage areas, traffic fluctuations, and optimization priorities, and reasonably plan to determine the shape and size of the cells;

(2) Setting and dynamic adjustment of C/I distribution correlation degree;

(3) Establishing an adjacent cell interference relationship matrix based on frequency sweep data;

(4), based on the mobile phone measurement report (MR), the adjacent cell interference relationship matrix is established;

(5) Correlate and match frequency sweep and mobile phone measurement report (MR) data to form an interference matrix;

(6), for different groups of frequency allocation schemes, calculate the adaptability and optimized degree of improvement of each frequency allocation scheme in the frequency allocation scheme group respectively according to the interference matrix obtained;

(7) The above step (6) is repeated continuously until one of the new frequency allocation schemes is optimal; this frequency allocation scheme is the optimized network frequency configuration scheme of the existing network.

The beneficial effects of the present invention are:

1) It is not limited by the types of current communication equipment, and the technical solution is stable and the convergence speed is fast.

2) From the user group level, correlate network quality and user perception, and grasp the improvement of wireless coverage quality to user groups in a targeted and efficient manner.

3) Quantitatively assess the actual impact of the C/I interference matrix on objective user groups, and avoid focusing on the network environment based on the frequency sweep algorithm and the automatic frequency change technology solution based on the mobile phone measurement report. Although the user perception is known, the improvement cannot be implemented. limited. Strong pertinence and practicality.

4) Massive data accumulation and analysis through mobile phone measurement reports can effectively analyze tidal cycle traffic, hot spots that have been neglected or difficult to monitor for a long time, and hot spot traffic.

5) Through parameter adjustment, it is convenient to implement different automatic frequency changing strategies and effect evaluation. Based on the mass data perceived by user terminals, it can be combined with different seasons, different working days, different coverage areas, and different user protection efforts to propose and evaluate the key points of network optimization and the automatic frequency planning and effects that need to be adopted. At the same time, it can also predict and predict trends over a period of time. Various trade-off parameters are adjustable and can be modified flexibly.

6) For the associated data, in addition to the current drive test, CQT test data and mobile phone measurement reports, as well as basic base station geography and configuration information, no additional data import is required. Does not increase the workload of network optimization personnel;

In addition, this technical solution can automatically optimize the frequency of the entire network, and can also optimize the frequency of individual cells. It can also solve the problem of frequency selection for new stations without affecting the operation of the existing network. It can intuitively evaluate the network performance indicators before and after independent frequency change. In comparison, it meets the requirements for smooth maintenance of the operator's network.

Description of drawings

Fig. 1 is a flow chart of the autonomous frequency optimization method of the present invention;

FIG. 2 is a schematic distribution of base stations and cell coverage under a certain coverage area in an embodiment of the present invention;

Fig. 3 is a three-dimensional view of a user group according to an embodiment of the present invention;

Fig. 4 is a two-dimensional view of user groups in an embodiment of the present invention;

Fig. 5 is a schematic diagram of interference signal strength in an embodiment of the present invention;

FIG. 6 is a schematic diagram of the distribution of interference signals in an embodiment of the present invention;

Fig. 7 is a schematic diagram of the C/I distribution generated according to the parameter f(N/1+e ^a+b×CI ) in the embodiment of the present invention;

Fig. 8 is a schematic diagram of C/I distribution generated according to the parameter f(N ³ /1+e ^a+b×CI ) in the embodiment of the present invention.

Detailed ways

The technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings

Implementation premise of the technical solution of the present invention:

The implementation and effect of the automatic frequency changing technical solution are inseparable from the following four situations:

1) Consistency and accuracy of daily basic configuration information.

2), adding, deleting cells, adjacent cells and related parameter adjustments are updated in a timely manner.

3) The data integrity and continuity of the mobile phone measurement report (MR) are consistent with the statistical caliber.

4) The frequency sweep data meets the regional measurement specifications, and the detailed data measurement and re-measurement are strengthened in complex and changeable areas and key areas of concern as needed to ensure the authenticity and reliability of the frequency sweep data samples.

The implementation process of the technical solution of the present invention:

During the implementation of this technical solution, the following points need to be paid attention to:

1) Pay attention to the authenticity and long-term accumulation of data, which is the premise to ensure the high efficiency of the technical solution;

2) Pay attention to the coverage characteristics of different areas and the actual changes of user groups, and make corrections and inspections in time during the analysis. Through a large number of data corrections, implement and refine model parameters to ensure high efficiency in future promotion.

The specific implementation steps are described as follows:

1. In the coverage area that needs to be optimized, the cell division of the area can be based on the dimensions of distinguishing and locating different user groups, different coverage areas, traffic fluctuations, and optimization priorities, and rationally plan to determine the shape and size of the cells.

Initially, the cells in the coverage area can be approximately the same. Due to the limitation of the accuracy of the mobile phone measurement report, it is initially suggested that the area of 100 meters X 100 meters is more secure. Within the same cell, assume that the C/I distribution is approximately uniform.

2. Setting and dynamic adjustment of C/I distribution correlation degree

In different adjacent cells, set different C/I distribution correlation degrees according to the coverage of the field. The rules are based on:

2.1 For similar outdoor coverage environments, such as roads, open land, and the same floor distributed indoors, the C/I distribution correlation degree of different adjacent cells can be appropriately increased, and vice versa, it can be appropriately decreased.

2.2 In the case of C/I distribution correlation degrees in different adjacent directions, the C/I distribution correlation degree can be set according to the actual situation; it is also possible to select some representative coverage areas in the target coverage optimization area for comprehensive Scan the frequency of the network, such as: dense business areas, schools and government agencies, open areas, important highways, etc., to obtain the C/I distribution correlation degree of different adjacent directions in the representative coverage area.

2.3 The setting of the C/I distribution correlation degree of different units is rolling and dynamically corrected according to the actual situation. The source of the corrected data is the continuously updated frequency sweeping drive test and CQT data.

2.4 The filling of the missing data of the frequency scanning data for the first time can be determined according to the frequency scanning data of adjacent cells and by means of a simple average value, etc. And after the actual measured frequency sweep data, determine the C/I distribution correlation degree and C/I distribution value of different cells.

2.5 In multi-story three-dimensional buildings, the division of cells can be layered or not layered according to the needs of actual network optimization. However, the division of cells needs to determine the C/I distribution correlation degree and C/I distribution value of different cells according to the above rules.

3. Establish the adjacent cell interference relationship matrix based on the frequency sweep data

Through frequency scanning drive test and DQT test, the regional C/I distribution of different points in the cell is collected, and the entire BCCH and TCH spectrum of all cells in the area of interest is traversed. These C/I distributions are respectively filled in the cells of the same latitude and longitude determined in step 1. In the same cell, the C/I distributions of different BCCH and TCH spectrums are approximately the same. According to the cell division of the above-mentioned coverage area and the C/I distribution correlation degree rule, the C/I distribution of the interference relationship of adjacent cells based on the frequency scanning data is established and filled in different cells.

Traditionally, the C/I distribution of the interference relationship between adjacent cells is established based on the frequency scanning data: through statistics, it is estimated that in the C/I distribution, the value of the inferior part ≤ 5% is used as the interference matrix value of the cell. Traverse the entire BCCH spectrum of all cells in the area of interest to generate an interference matrix.

Advantages: Since the value of the inferior part ≤ 5% is used as the value of the interference matrix, it is more cautious. The poor coverage area inside the cell is fully considered.

Disadvantages: Whether this value is reasonable needs to be tested in practice. Due to the lack of user group cell distribution, further analysis cannot be taken. Whether the simple and uniform processing method is applicable to the entire network and at all times is debatable.

Through this step, the technical solution objectively and dynamically reflects the C/I distribution of the interference relationship in the entire coverage area, avoiding the influence of simple uniform processing on the interference relationship of the entire adjacent cells.

4. Establish the adjacent cell interference relationship matrix based on the mobile phone measurement report (MR)

According to the GSM specification, in the power control and handover control, the mobile phone in the call state regularly reports the measurement reports of the serving cell and neighboring cells it has measured to the network at a period of 480 milliseconds. It mainly includes BCCH of the serving cell, signal level, call quality and TA value, etc. In addition, it also includes BCCH of the 6 neighboring cells with the strongest signal, signal level and BSIC (network color code), etc. The mobile phone does not measure all frequency points, and the reported neighbor cell information is limited to the cells whose BCCH frequency points are located in the BA table defined by the serving cell, that is, the neighbor cell list.

The mobile phone uplink measurement report includes all users in the network talking at all times, and the signal strength of each cell in their location. By collecting a large number of mobile phone measurement reports (MR), the signal interference between all cells in the network is obtained, and the interference matrix C/I distribution is formed.

Interference matrix generation:

4.1 Generation of measurement report: The mobile phone collects the BCCH received power value of the leading cell of the cell and the received power value of the adjacent BCCH, and reports it to the network. Massive measurement report data is formed after the reports and accumulations of the mobile phones are accumulated;

4.2 Establishment of adjacent cell interference relationship: In the measurement report, traverse the entire BCCH spectrum, focus on the cell, establish the power distribution between different BCCHs, and form the C/I distribution of the dominant cell accordingly.

Advantages and disadvantages of traditional interference matrix generation based on mobile phone measurement reports (MR):

Advantages: Since the value of the inferior part ≤ 5% is used as the value of the interference matrix, it is more cautious. The poor coverage area inside the cell is fully considered.

Disadvantages: Whether this value is reasonable needs to be tested in practice. Due to the lack of user group cell distribution, further analysis cannot be taken. It is debatable whether the simplistic treatment is applicable to the whole network and all time.

Through this step, the technical solution does not follow the statistical distribution, and simply intercepts the value of the inferior part ≤ 5% as the interference matrix value, avoiding the influence on the interference relationship of the entire adjacent cell.

5. Correlate and match frequency sweep and mobile phone measurement report (MR) data to form an interference matrix

5.1 The principle of autonomous frequency optimization technology scheme based on frequency sweep and mobile phone measurement report (MR)

On the one hand, there is the autonomous frequency optimization based on the actual frequency scanning data reflecting the actual network coverage, and on the other hand, the frequency optimization reflecting the perception of the end user group comes from the massive mobile phone measurement report data collected by the system. When the user accepts the GSM network service, the network receives the signal quality in real time and adjusts it continuously. However, due to complex coverage environments such as urban areas, coverage blind spots and call drops may occur. Correlative matching of actual frequency sweep data and massive mobile phone measurement report data is the key point of the autonomous frequency optimization technology solution based on frequency sweep and mobile phone measurement report (MR).

5.2 Scheme implementation of autonomous frequency optimization technology based on frequency sweep and mobile phone measurement report (MR)

So far, we have obtained the C/I distribution of actual network coverage and the actual perceived C/I distribution of user groups in different cells in the target coverage optimization area.

In the same cell, the C/I distributions of different BCCH and TCH spectrums are approximately the same. In the target coverage area, in the same C/I distribution of different cells of BCCH and TCH spectrum generated based on the frequency sweep data, it is assumed that the same BCCH is evenly distributed in the C/I distribution cells of the same BCCH and TCH frequency spectrum. , Mobile phone measurement report (MR) user data of C/I distribution of TCH spectrum. Mobile phone measurement report of C/I distribution of different BCCH and TCH spectrum is apportioned in different cell C/I distribution of different BCCH and TCH spectrum (MR) user data, user groups are gradually clear and refined in the target coverage area, and the accuracy is continuously improved. That is, the mobile phone measurement report (MR) is combined with the frequency sweep data, and the user group distribution is preliminarily determined through the maximum Bayesian likelihood estimation.

The distribution of user groups is expressed as follows:

$\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; CI\; fre</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; min</span>}}{\overset{\mathrm{<span\; class="notranslate">\; max</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; min</span>}}{\overset{\mathrm{<span\; class="notranslate">\; max</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; CI</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; mb</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; CI</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; |</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; mb</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; CI</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; CI</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>$

In the above formula, N represents the number of users in the cell. P(mb_CI) represents the C/I value collected through the mobile phone measurement report. P(CI) is the C/I value obtained through frequency sweep data collection. P(mb_CI)=P(CI) means when the two match with the maximum likelihood. x, y, h respectively represent the geographical location information such as the longitude, latitude and height of the cell, and t represents the time.

f(CI, x, y, h, mb_CI, t|P(mb_CI)=P(CI)) indicates that when P(mb_CI)=P(CI), the mobile phone measurement assigned to the cell according to the maximum likelihood probability The number of reports is converted into the number of users through the regular reporting mechanism every 480ms.

After obtaining the C/I distribution and user group distribution of different BCCH and TCH spectrums in different cells of the target area, different weights are used to evaluate the degree of C/I interference of different frequencies for users in the covered cell. The stable and convergent cyclic iterative optimization technology scheme is adopted to achieve the joint automatic frequency change frequency template and evaluation based on frequency sweep and mobile phone measurement report (MR). In the formula, the analysis of adjacent frequency and co-channel interference of non-same-colored cells can also be added. The principle is similar to that described above.

According to the actual situation, this technical solution can analyze the key points of the network optimization strategy, adjust the weight of different parameters, and obtain the best solution.

After using different weights, the C/I distribution of a coverage area is obtained as follows:

$\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; =</span>\left[\begin{array}{cccc}{\mathrm{<span\; class="notranslate">\; CI</span>}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}& {\mathrm{<span\; class="notranslate">\; CI</span>}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}}& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& {\mathrm{<span\; class="notranslate">\; CI</span>}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}}\\ {\mathrm{<span\; class="notranslate">\; CI</span>}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>\\ <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>\\ {\mathrm{<span\; class="notranslate">\; CI</span>}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& {\mathrm{<span\; class="notranslate">\; CI</span>}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}\end{array}\right]<span\; class="notranslate">\; =</span>\left[\begin{array}{ccc}{\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; cell</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}{\overset{\mathrm{<span\; class="notranslate">\; max</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; cell</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}{\overset{\mathrm{<span\; class="notranslate">\; max</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; CI</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>\\ <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>\\ <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& <span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span>& {\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; cell</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}{\overset{\mathrm{<span\; class="notranslate">\; max</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; cell</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}{\overset{\mathrm{<span\; class="notranslate">\; max</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; CI</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; the\; y</span>}}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}\end{array}\right]$

The above formula expresses the distribution of C/I interference among all frequency bands in a certain coverage area. Indicates the C/I interference value between frequency _f1 and frequency _f1 . N _{x, y} represents the number of users in the coverage area, which is used as a weight value for the distribution of C/I interference among all frequency bands.

6. For different groups of frequency allocation schemes, the fitness and optimized improvement of each frequency allocation scheme in the frequency allocation scheme group are respectively calculated according to the obtained interference matrix.

7. The above step (6) is repeated continuously until one of the new frequency allocation schemes is optimal.

8. The frequency allocation scheme is the optimized network frequency configuration scheme of the existing network.

The automatic frequency changing technology solution based on frequency scanning relies more on daily monitoring, DT and CQI testing, and user complaint handling to improve network service quality, which takes a long time to process and cannot actively and efficiently improve network optimization quality. Although the mobile phone measurement report based on massive data already contains many parameters such as user location and reception level, it cannot associate the frequency sweep data with the mobile phone measurement report. The automatic frequency change technical solutions based only on frequency scanning or mobile phone measurement reports cannot understand the distribution of user groups in the cell, so as to extract more dominant features.

The notable feature of the present invention is that it makes up for the deficiency that the existing method can only roughly estimate the frequency allocation performance, but cannot correlate with each other based on the current network frequency sweep and mobile phone measurement report (MR) data, forming an interference evaluation allocation scheme. It solves the problem of call drop rate caused by co-channel interference in the network. The frequency optimization of the existing network improves the quality of frequency allocation, improves the utilization rate of spectrum resources, and optimizes the interference of the entire network.

Example:

Assumptions: 1), the research area is a 100×100 square coverage area;

2), the coverage area is composed of 9 irregular cells with continuous coverage;

3) The source of frequency sweep data is obtained according to the propagation attenuation model, which can be substituted by the actual frequency sweep data in further simulation;

4), MR measurement report, estimated according to normal distribution with distribution interval [-15, 10].

As shown in Figure 2, the coverage areas of different base stations and cells in a certain coverage area:

According to the assumed conditions, the C/I distribution user data in the same cell in the mobile phone measurement report (MR) is continuously allocated and matched according to the C/I distribution of the frequency sweep data, and then the user group distribution of the target coverage area is obtained.

The three-dimensional view of user group distribution obtained after simulation is shown in Figure 3.

The two-dimensional top view of the user group distribution obtained after simulation is shown in Figure 4.

The schematic diagram of interference signal strength in the coverage area is shown in Figure 5, and the schematic diagram of interference signal distribution is shown in Figure 6: in the entire coverage cell, the dominant coverage signal is generally greater than the interference signal. Interfering signals are randomly distributed in the coverage area. If a strong interference signal coincides with the distribution of user groups, it will cause a large number of user perception degradation. Therefore, in automatic frequency planning, it is necessary to consider not only the distribution of user groups, but also the signal interference situation on the ground.

In the entire coverage cell, temporarily consider introducing a parameter f(N/1+e ^a+b×CI ) as the distribution of a certain coverage area. This parameter not only considers the distribution of user groups in a certain coverage area, but also considers the signal coverage quality in this coverage area. It is a comprehensive balance between increasing network coverage investment and satisfying user perception indicators as much as possible. If the parameter is too large, it means that the number of users in the coverage area (cell) is large, or the signal quality of the coverage area (cell) is poor (the C/I distribution is low), so the coverage area ( cells) is the coverage area that needs to be optimized as quickly as possible. And if the parameter is small, it means that the number of users in the coverage area (cell) is small, such as in areas such as open farmland, or the signal quality of the coverage area is better (higher C/I distribution), for example: Key protection road sections, etc.

The specific expression is as follows:

$\mathrm{<span\; class="notranslate">\; A</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; CI</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\underset{\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}}{\overset{\mathrm{<span\; class="notranslate">\; max</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; cell</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}{\overset{\mathrm{<span\; class="notranslate">\; max</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; cell</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}{\overset{\mathrm{<span\; class="notranslate">\; max</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; a</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; b</span>}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; CI</span>}}<span\; class="notranslate">\; )</span>}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}}{{\underset{\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; cell</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}{\overset{\mathrm{<span\; class="notranslate">\; max</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\underset{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; cell</span>}<span\; class="notranslate">\; \_</span>\mathrm{<span\; class="notranslate">\; min</span>}}{\overset{\mathrm{<span\; class="notranslate">\; max</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; N</span>}}_{{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}}$

The above formula is a parameter optimization for the C/I distribution of a coverage area. N represents the number of users in the coverage area. In a+b×CI, a is the lowest threshold value influenced by C/I distribution. b represents the weight value of the influence of C/I distribution. a and b can be adjusted in real time according to actual network conditions. A(N/1+e ^a+b×CI ) represents an optimized key area obtained after comprehensive consideration of user distribution and C/I distribution.

A schematic diagram of the C/I distribution generated according to the parameter f(N/1+e ^a+b×CI ), as shown in FIG. 7 .

In the actual network optimization process, this parameter can be changed according to different areas of optimization focus on the target coverage optimization area. For the target coverage optimization area, more attention should be paid to the impact of the dynamic fluctuation of the number of users on the coverage area. If the adjustment parameter is f(N ³ /1+e ^a+b×CI ), the change of key areas is schematically shown in Figure 8 .

If it is necessary to focus on optimizing the signal quality of the coverage area, the automatic frequency changing technical solution can ensure the effective distribution of network wireless resources by defining the sensitivity of C/I interference in different areas. Achieve a better balance between user perception, network quality and assessment indicators. Reduce blindness and avoid network quality fluctuations.

Claims (3)

1., based on the autonomous frequency optimization method of frequency sweep and mobile phone measurement report, it is characterized in that performing as follows:

(1), in the overlay area needing to optimize, the cell in region divide can according to differentiation location different user group, separate distinct coverage region, traffic variation and optimize emphasis dimension, the shape of determining unit of making rational planning for lattice and size;

(2), C/I distributes the setting of the degree of association and dynamic conditioning;

(3), neighbor cell interference relational matrix is set up based on frequency sweep data;

(4), neighbor cell interference relational matrix is set up based on mobile phone measurement report (MR);

(5), association mates frequency sweep and mobile phone measurement report (MR) data, formation interference matrix;

(6), for different class frequency allocative decision, the fitness of each frequency allocation plan in frequency allocation plan group and the improvement after optimizing is calculated respectively according to obtained interference matrix;

(7) above-mentioned (6) step, is constantly repeated, until there is a kind of frequency allocation plan optimum in new frequency allocation plan; This frequency allocation plan is the network allocation plan after existing network optimization;

Formed in described step 5 in interference matrix process and mobile phone measurement report (MR) and frequency sweep data combined, estimated by maximum Bayesian likelihood, tentatively determine that customer group distributes,

As shown in the formula expression:

$N=\underset{\mathrm{CI}\_\mathrm{fre}=\min }{\overset{\max }{\mathrm{\&Sigma;}}}\underset{x,y=\min }{\overset{\max }{\mathrm{\&Sigma;}}}f(\mathrm{CI},x,y,h,\mathrm{mb}\_\mathrm{CI},t|P(\mathrm{mb}\_\mathrm{CI})=P\left(\mathrm{CI}\right))$

In above formula, N represents the number of users in this cell; P (mb_CI) represents the C/I value collected by mobile phone measurement report; P (CI) is the C/I value obtained by frequency sweep data acquisition; Maximum likelihood coupling both P (mb_CI)=P (CI) represents; X, y, h represent the longitude and latitude of this cell and the geographical location information of height respectively, and t represents the time; F (CI, x, y, h, mb_CI, t|P (mb_CI)=P (CI)) when representing P (mb_CI)=P (CI), according to maximum likelihood probability apportion the mobile phone measurement report number of this cell, report mechanism by every 480ms timing, be converted into number of users.

2. the autonomous frequency optimization method based on frequency sweep and mobile phone measurement report according to claim 1, is characterized in that the size of cell in step 1 is 100 meters of X100 rice.

3. the autonomous frequency optimization method based on frequency sweep and mobile phone measurement report according to claim 1, it is characterized in that in step 3 neighbor cell interference relational matrix process of establishing by statistics, estimation C/I distribution, accounts for the interference matrix value of bad part≤5% value as this community.