As an important part of information transmission, communication technology has attracted more and more attention and has been studied extensively [1]. With the rapid development of mobile communication technology, the data transmission rate provided by the communication system is also getting faster and faster [2]. The upgrade of communication service technology provides more convenience for people's life and work [3]. However, with the large-scale application of LTE technology, we have to face more and more challenges while carrying out large-scale communication network business construction [4]. The high-speed integration of the wireless mobile network and Internet makes the broadband development of the mobile communication network more urgent [5], and the planning and coverage optimisation of the wireless network has become the most urgent task to be solved today [6].

The wireless network propagation model is a mathematical model based on the comparison between the theoretical calculation and measured data [7]. It is also an expression related to the measured data obtained from the environment, such as frequency, distance, height and other factors. It is a simulation and prediction of the information loss of the propagation path between the receiver and transmitter [8]. This is an important prerequisite for communication network planning and coverage optimisation [9]. Due to the difference in the signal transmission environment, different network communication structures, uneven spatial distribution of users and uncertain use behaviour, wireless network transmission is always a dynamic network [10]. Due to different environments and parts of the city landscape, the communication model is different [11] and must adapt to the environment of the real response through the spread of the actual environment model testing and calibration [12]. The spread of the wireless propagation model provides better coverage for the wireless network system construction, larger channel capacity and more high-quality and efficient communications [13]. This is particularly important for the current LTE communication system [14].

At present, researchers at home and abroad have conducted a large number of theoretical studies and practical tests on wireless network propagation models [15], and they concluded various classical network propagation models, including deterministic model, empirical model and semi-deterministic semi-empirical model [16]. The deterministic model is based on the theory of electromagnetic field and is directly calculated to obtain the actual environmental conditions, such as signal ray tracking and signal geometric diffraction [17]. The empirical model is also called the statistical model based on test results, and the expression obtained after the statistical analysis of a large number of test results is called the path loss formula [18]. The formula of the empirical model usually involves only the height of the communication antenna, the distance between sending and receiving information, frequency and other factors; and these variable pieces of information can be easily obtained by equipment; its calculation process is simple, so the empirical model is widely used in reality [19]. Famous empirical models include Okumura–Hata [20], Cost 231–Hata [21] CCIR and Cost 231–Walfisch–Ikegami model [22]. For example, based on the principle of reverse ray tracing, Villaluz et al. [23] proposed a diffraction algorithm of arbitrary wedge on the basis of the urban scene model and established an urban multipath prediction model by tracking all possible paths of signal from the source point to the field point. Mardeni and Priya [24] summarised the prediction model based on the ray tracing algorithm applied to high-speed railway tunnel scenarios through comparative analysis of the application of the mirror method, bounce ray method and ray density normalisation. Jimoh et al. [25] compared the prediction effects of nine empirical models in Nigeria, including Hart model, Davidson model, CCIR model and Ericsson model, and verified the accuracy of CCIR. Lee and Lee [26] integrated the models of different prediction radii in Lee's model and proposed an improved model for deploying and optimising the comprehensive prediction of skin cells, microcells and macrocells with different cell types. Jain and Shrivastava [27], on the basis of Cost 231–Hart model, tested data in a multi-scene environment based on a nonlinear least square method and proposed the correction factor of this model in a multi-geomorphic environment.

On the other hand, in the study of the propagation model, the propagation model of the wireless communication network can be divided into two types: one is a large-scale propagation model and the other is a small-scale propagation model. The large-scale propagation model mainly describes the changes in the size of the signal received by the transmitting and receiving devices within a certain distance (several hundred metres or longer). Such changes are due to the influence of various obstacles encountered on the wireless propagation path between the receiver and receiving antennas on the wireless signals. Major large-scale propagation models include SPM universal model, Cost 231–Hata model, Lee model, Walfisch–Ikegami model and Okumura–Hata model [28]. The small-scale propagation model mainly describes the model in which the intensity of the received signal changes rapidly within a short time (second level) or a short distance (several wavelengths). The main small-scale propagation models include Raleigh time-varying channel model, AGWN model [29] and Roentgen fading model. The two models mentioned here can coexist with each other. In the actual communication environment, there exists both large-scale signal attenuation and small-scale signal attenuation in the same wireless communication system. Under the current situation of very limited signal spectrum resources, propagation characteristics of the two wireless networks should be based on. The quality of communication transmission channel and information utilisation rate can be improved in targeted and effective ways [30]. However, in wireless network planning and design, it is usually only necessary to consider the fading of the large-scale model mainly because of the mean value of the signal field intensity received by the receiving device in unit time [31]. However, small-scale fading is usually used in theoretical research for the design of digital receiving equipment and the selection of signal transmission technology, which is generally not considered in the planning stage of the wireless communication network.

Additionally, in the current mobile communication network planning, the wireless network propagation model is an important factor affecting the efficiency and accuracy of planning schemes [32]. However, the classical wireless propagation model is usually calculated through statistics and fitting of a large amount of test data in the actual network environment. The empirical model uses statistical methods to conduct statistical analysis of all environmental factors, but the empirical model has a large error in specific physical area scenarios [33]. Additionally, the traditional transmission model mainly considers the influence of signal coverage distance and antenna relative height on signal path loss, and the rational application scenario is relatively simple and only applicable to flat areas such as rural areas and plains [34]. At present, the communication model widely used in the communication network is an improved experiential communication model with more detailed classification. Each classification has a fixed fitting formula, which makes the communication model suitable for the actual complex environment. Among them, typical propagation models include standard propagation model [35]. At present, the classical propagation model is widely adopted in the 4G (4th generation) propagation model in China, and the SPM model is optimised on the basis of the Cost 231–Hata model.

Based on a variety of different types of typical problems of signal propagation (e.g., area coverage, low coverage, interference serious and overlapping coverage). To ask questions that corresponds to the wireless network communication model solutions (e.g., By adjusting the antenna azimuth, hanging high, dip Angle, base station transmission power and other related parameters), and optimization is the typical problem to solve, It is necessary to compare the measured data and verify the error range between the simulated prediction data and the actual test data again after the solution implementation. The propagation model obtained from these works is helpful for the subsequent simulation prediction data so as to accurately judge the areas with various practical problems in the current network, such as over-coverage, weak coverage, overlapping coverage, high interference and low rate. Thus, it can effectively and accurately guide and reference the existing network coverage planning and communication quality optimisation, and improve the efficiency of communication network construction.

Propagation model correction is a curve fitting process in essence, that is, by adjusting the coefficients of the propagation model, the error between the path loss calculated by the propagation model and the measured path loss is minimised. In free space, the propagation model between the receiver and receiver antennas is the simplest. Suppose that the power density of radio wave on the sphere of non-directional point source radiation is

The power received by the receiving antenna at the sphere is as follows:

The effective receiving area of the receiving antenna is as follows:

In Eqs (1)–(3), _{t}_{r}

In engineering, the ratio of _{t}_{r}

In free space, only the loss caused by the energy diffusion of electromagnetic wave exists, which is called path loss. When _{t}_{r}

It can be seen from Eq. (6) that the factors affecting path loss are transmission frequency and transmitting distance. In other words, if _{t}_{r}

If the working frequency of the LTE system is 2.6 GHz, the path loss formula of free-space propagation model is as follows:

Okumura, a Japanese scientist, proposed a signal strength prediction model based on a large amount of propagation loss test data, but the prediction model has to query all kinds of curve charts to predict, which is not conducive to computer processing. Hata was improved on the basis of the Okumura model, and the Okumura–Hata model was obtained by setting restrictions on curve fitting. Almost all the empirical propagation models proposed in the later period are modified and improved on this basis. The model is mainly used for a quasi-flat terrain macrocellular system with a frequency of 150–1500 MHz and a cell radius of >1 km. The effective antenna heights of the base station and mobile station are set as 30–200 m and 1–10 m, respectively. The empirical formula of the Okumura–Hata model is as follows:

Here, _{re}_{te}_{re}_{cell}_{terrain}_{re}

The expressions of cell correction factors in different transmission environments will be different, which can be expressed as follows:

The topographic correction factor _{terrain}

The Okumura–Hata model is only suitable for a low-frequency band, so the European Association for Scientific and Technological Research proposed the Cost 231–Hata model. This model is an extension of the Hata model, which is suitable for urban landforms and suburban wireless network planning. It is generally used in macrocell base stations with a radius of >1 km and an application frequency of up to 2000 MHz. The path loss expression of this model is as follows:

Here, _{c}_{re}_{te}

From the form of Eqs (1) and (4), the two Hata models are almost the same, and the factors taken into consideration are completely the same. The main differences lie in the coefficients of various factors, including the constant term, frequency, correction factor _{re}_{M}_{M}_{re}_{re}

The Cost 231–Walfisch–Ikegami model is an extension of the Cost–Hata model on the basis of the Walfisch–Bertoni model and Ikegami model. The model takes into account the loss of free path, the loss from roof to street and the loss affected by the angle between the antenna and street and can be used to calculate the loss of all kinds of conditions >2000 MHz. When there is a line of sight (LOS) propagation between the transmitter and receiver, the path loss formula is as follows:

When there is non-line-of-sight (NLOS) transmission between the transmitter and receiver, as shown in Figure 1, the path loss formula is as follows:
_{0} is attenuation of free space and _{1} represents diffraction from the roof to the street, denoted as follows:

In the formula, _{R}_{m}_{11}(

Here, _{2} represents the loss caused by diffraction due to multiple obstacles, which can be expressed as follows:

The success of model correction is usually achieved by comparing the predicted value of the model with the measured value of the environment and statistical analysis of the error.

Assume that the prediction error of point

Each parameter of judgement is represented by Eqs (20)–(22).

The mean square deviation (RMS) is as follows:

Mean error is as follows:

The standard deviation (Std Dev) error is as follows:

According to the current site parameters of the LTE system in a city, sparsely populated areas, densely populated areas and densely populated areas were selected for CW test, and the neural network algorithm combined with particle swarm optimisation algorithm was used to correct the LTE system wireless propagation model of each site and predict its coverage.

Site 1 is located in a sparsely populated area, and its location information, frequency and related parameters of transmitting antenna hang high CW test are shown in Table 1.

Parameters of site 1

Environmental conditions | Sparsely populated area | ||
---|---|---|---|

Location | 116.585763E, 25.324614N | ||

Sampling points | 9345 | Frequency (MHz) | 2743 |

Transmission antenna hanging height (m) | 80 | Receiving antenna hanging height (m) | 4 |

Transmitting antenna gain (dBi) | 15 | Receiving antenna gain (dBi) | 15 |

Transmitted power of signal source (dBm) | 47 | Feeder joint loss (dB) | 1 |

During the CW test, 9345 valid sample data were collected, some of which are listed in Table 2.

Partial CW test data

25.324614N | 116.585763E | −66.75 |

25.324614N | 116.585763E | −73.34 |

25.324614N | 116.585763E | −66.97 |

25.324614N | 116.585763E | −66.34 |

25.324614N | 116.585763E | −64.66 |

25.324614N | 116.585763E | −65.78 |

25.324614N | 116.585763E | −61.98 |

25.324614N | 116.585763E | −68.13 |

25.324614N | 116.585763E | −62.13 |

25.324614N | 116.585763E | −63.23 |

25.324614N | 116.585763E | −64.66 |

25.324614N | 116.585763E | −67.34 |

25.324614N | 116.585763E | −65.13 |

25.324614N | 116.585763E | −64.09 |

25.324614N | 116.585763E | −64.25 |

25.324614N | 116.585763E | −60.17 |

25.324614N | 116.585763E | −74.03 |

25.324614N | 116.585763E | −72.19 |

25.324614N | 116.585763E | −82.91 |

25.324614N | 116.585763E | −79.16 |

The SPM model was used to predict the network coverage, and the neural network algorithm combined with the particle swarm optimisation algorithm was used to correct the SPM model based on the effective data collected by the CW road survey. _{3} and _{5} are the correction factors related to the antenna height, and the antenna height in this test is colonisation, so there is no need to correct it. The value of _{4} is related to diffraction, and the terrain change is very small during this test, so the default value of 0.2 is directly taken. _{6} represents the height gain of receiver, which remains unchanged during the test. Therefore, the influence of this factor is not considered, and it is usually set to 0. The parameters obtained after correction are shown in Table 3.

Correction parameters

_{1} | _{2} | _{3} | _{4} | _{5} | _{6} | _{c} |
---|---|---|---|---|---|---|

39.23 | 29.95 | 5.43 | 0.25 | −6.61 | 0 | 1 |

After the corrected propagation model is obtained, the predicted data using the model are compared with the measured data, and the errors are statistically analysed. After the correction of model simulation data compared with the measured data and Figures 1 and 2, respectively, 1 site calibration before and after the correction error distribution of the predicted and the measured values, contrast Figures 1 and 2, the former path loss model calibration is >25 dB occupied >80%, almost all of the error <10%, and more is within error are mainly distributed in 5 dB. Therefore, the SPM model after correction is in better agreement with the data measured in the actual CW environment. Before model calibration, the absolute value of error is mainly distributed in the range of 25–30 dB. According to the engineering standard, the proportion of error within 10 dB is only 0.52%, while the absolute value of error after model calibration is mostly concentrated within 10 dB, and only a small proportion of data deviation is large. It is proved that the corrected propagation model can more accurately represent the propagation characteristics of such terrains.

Site 2 is located in a densely populated area, and its location information, frequency and related parameters of transmitting antenna hang high CW test are shown in Table 4.

Parameters of site 2

Environmental conditions | Sparsely populated area | ||
---|---|---|---|

Location | 116.585763E, 25.324614N | ||

Sampling points | 11981 | Frequency (MHz) | 2743 |

Transmission antenna hanging height (m) | 85 | Receiving antenna hanging height (m) | 3 |

Transmitting antenna gain (dBi) | 15 | Receiving antenna gain (dBi) | 15 |

Transmitted power of signal source (dBm) | 47 | Feeder joint loss (dB) | 1 |

In the CW test of site 2, 11981 valid samples are taken, and some of the test data are shown in Table 5.

Site 2 test data

116.012849E | 25.913642N | −68.94 |

116.012849E | 25.913642N | −77.78 |

116.012849E | 25.913642N | −81.84 |

116.012849E | 25.913642N | −78.97 |

116.012849E | 25.913642N | −78.56 |

116.012849E | 25.913642N | −85.56 |

116.012849E | 25.913642N | −83.47 |

116.012849E | 25.913642N | −77.47 |

116.012849E | 25.913642N | −77.78 |

116.012849E | 25.913642N | −70.22 |

116.012849E | 25.913642N | −79.56 |

116.012849E | 25.913642N | −77.22 |

116.012849E | 25.913642N | −70.81 |

116.012849E | 25.913642N | −81.19 |

116.012849E | 25.913642N | −70.69 |

116.012849E | 25.913642N | −72.5 |

116.012849E | 25.913642N | −74.03 |

116.012849E | 25.913642N | −72.19 |

116.012849E | 25.913642N | −82.91 |

116.012849E | 25.913642N | −79.16 |

The parameter correction of the positive model is consistent with the aforementioned correction method. The parameter correction of the local model after correction is shown in Table 6.

Correction parameters

_{1} | _{2} | _{3} | _{4} | _{5} | _{6} | _{c} |
---|---|---|---|---|---|---|

32.31 | 30.41 | 5.86 | 0.24 | −6.93 | 0 | 1 |

The corrected model simulation data are compared with the measured data. Figures 3 and 4, respectively, show the error distribution of the predicted value and measured value of station 2 before and after correction. It can be clearly seen from Figures 3 and 4 that the path loss before and after model correction is basically at least 40 dB different. The corrected SPM model can better predict the road damage in the CW environment.

According to the comparison between Figures 3 and 4, before the correction of the model, the error >25 dB accounted for >60%, and the error <10 dB only accounted for 0.58%. After the correction of the model, the error <10 dB of the model exceeded 55%. It shows that the accuracy of path loss prediction by the corrected model is greatly improved.

Site 3 is located in a densely populated area, and its location information, frequency and related parameters of the transmitting antenna hang high CW test are shown in Table 7.

Site 3 parameter information

Environmental conditions | Sparsely populated area | ||
---|---|---|---|

Location | 116.743167E, 25.286431N | ||

Sampling points | 14238 | Frequency (MHz) | 2743 |

Transmission antenna hanging height (m) | 80 | Receiving antenna hanging height (m) | 3 |

Transmitting antenna gain (dBi) | 15 | Receiving antenna gain (dBi) | 15 |

Transmitted power of signal source (dBm) | 47 | Feeder joint loss (dB) | 1 |

In the CW test at site 3, there were 14,238 valid samples, and Table 8 is part of the data.

Partial test data of site 3

25.286431N | 116.743167E | −71.47 |

25.286431N | 116.743167E | −70.59 |

25.286431N | 116.743167E | −85.19 |

25.286431N | 116.743167E | −69.41 |

25.286431N | 116.743167E | −62.38 |

25.286431N | 116.743167E | −66.16 |

25.286431N | 116.743167E | −63.64 |

25.286431N | 116.743167E | −61.59 |

25.286431N | 116.743167E | −63.11 |

25.286431N | 116.743167E | −63.34 |

25.286431N | 116.743167E | −61.2 |

25.286431N | 116.743167E | −65.31 |

25.286431N | 116.743167E | −70.72 |

25.286431N | 116.743167E | −79.97 |

25.286431N | 116.743167E | −68.78 |

25.286431N | 116.743167E | −66.56 |

25.286431N | 116.743167E | −74.03 |

25.286431N | 116.743167E | −72.19 |

25.286431N | 116.743167E | −82.91 |

25.286431N | 116.743167E | −79.16 |

The parameter correction of the positive model is consistent with the aforementioned correction method, and the parameter correction of the local model after correction is shown in Table 9.

Correction parameters

_{1} | _{2} | _{3} | _{4} | _{5} | _{6} | _{c} |
---|---|---|---|---|---|---|

26.43 | 31.24 | 3.45 | 0.61 | −5.34 | 0 | 1 |

The corrected model simulation data are compared with the measured data. Figures 5 and 6, respectively, show the error distribution of predicted value and measured value of site 2 before and after correction. It can be clearly seen from Figures 5 and 6 that the error before correction is >25 dB and close to 80%, and only 7% of the data with error is <10 dB. The corrected model error is mainly distributed in the range of 5 dB. The experimental results show that the modified propagation model has good applicability to the propagation prediction of such terrains.

In order to further verify the accuracy of path loss prediction of the propagation model corrected by the BP-PSO algorithm, statistical tests are carried out on the predicted values of the model and the measured values. The statistical test results are shown in Table 10.

Statistical table of errors at each station

Site 1 | Before correction | 19.2319 | 6.3281 | 7.3982 |

After correction | 1.82931 | 5.9832 | 7.2984 | |

Site 2 | Before correction | 18.9326 | 6.1189 | 6.5307 |

After correction | 0.6988 | 5.7982 | 6.4897 | |

Site 3 | Before correction | 18.5942 | 5.6409 | 6.3498 |

After correction | 0.5983 | 5.5982 | 6.1892 |

This study selects three stations in the LTE system frequency band and uses the BP-POS algorithm to conduct propagation model correction. According to the current engineering practice, if the corrected average error of the propagation model is <10 dB, the standard deviation is <10 dB, and the mean square error is <8 dB, then the corrected model basically conforms to the actual test environment. As the propagation model calibration of statistical average error shows logarithmic normal distribution trends, the propagation model calibration standard deviation of the response is the concentration distribution of degree of error; the smaller the standard deviation, average error is the more concentrated; and the ultimate goal of propagation model calibration is the minimum standard deviation between the calibration model, and the error distribution is more concentrated. According to the comparison between predicted values and measured data before and after model calibration shown in Figures 1, 4 and 7, it can be concluded that the propagation model corrected by the BP-POS algorithm is available. As can be seen from the error distribution comparison diagram of the corrected model, the data with errors within 10 dB are more concentrated, indicating that the accuracy of path loss prediction of the corrected model has been greatly improved. Regardless of the LTE dense urban area, medium dense urban area or general dense urban area, the SPM model after correction can better match the measured data in the CW environment, which meets the requirements of mobile communication network planning, fully demonstrating that the application of the BP-POS algorithm in LTE system wireless propagation model correction is feasible.

In this study, the wireless propagation model of LTE system is corrected, and its coverage is predicted through the combination of the neural network algorithm and the particle swarm optimisation algorithm, and the correction method and correction results are verified. Based on the LTE network planning project of a big city in China, the research and application of LTE system wireless propagation model correction are carried out on the basis of classical outdoor propagation model and existing model correction algorithm. The correction results are applied to the actual project, and the actual problems such as over-coverage, weak coverage, overlapping coverage, high interference and low rate can be accurately judged through the prediction data of simulation software. Thus, it can effectively and accurately guide and reference the planning and optimisation of the live network. The main results are as follows:

Use simulation software to simulate the classical correction propagation models such as Okumura–Hata, Cost 231–Hata, Cost 231–Walfisch–Ikegami and SPM. Simulation results show that the SPM model can adapt to a wider range of frequency bands, take more comprehensive loss factors into account and achieve more accurate results. Therefore, the SPM model is selected as the basic model of LTE wireless propagation model correction. Combined with the LTE project of this study, site selection and related equipment of CW test, path planning of the data collection process and data processing steps are given.

According to the advantages and disadvantages of the artificial neural network and particle swarm optimisation algorithm, two algorithms are combined for LTE system propagation model correction, and the iterative optimisation process of the algorithm is compared with the propagation model correction process, and the realisation process of the BP-POS algorithm in propagation model correction is obtained.

Based on the measured CW data of the three stations, the BP-POS algorithm was used to correct the wireless propagation model of the LTE system, respectively, and the corrected propagation model coefficients were obtained. The results show that the corrected propagation model is in better agreement with the data measured in the actual CW environment in LTE sparsely populated areas, densely populated areas or densely populated areas. The error statistics show that the data with an error of <10 dB are more concentrated, indicating that the accuracy of path loss prediction of the corrected propagation model is greatly improved, which meets the test criteria of wireless propagation model correction, thus verifying the feasibility and superiority of the BP-POS algorithm applied to wireless propagation model correction of the LTE system.

#### Site 2 test data

116.012849E | 25.913642N | −68.94 |

116.012849E | 25.913642N | −77.78 |

116.012849E | 25.913642N | −81.84 |

116.012849E | 25.913642N | −78.97 |

116.012849E | 25.913642N | −78.56 |

116.012849E | 25.913642N | −85.56 |

116.012849E | 25.913642N | −83.47 |

116.012849E | 25.913642N | −77.47 |

116.012849E | 25.913642N | −77.78 |

116.012849E | 25.913642N | −70.22 |

116.012849E | 25.913642N | −79.56 |

116.012849E | 25.913642N | −77.22 |

116.012849E | 25.913642N | −70.81 |

116.012849E | 25.913642N | −81.19 |

116.012849E | 25.913642N | −70.69 |

116.012849E | 25.913642N | −72.5 |

116.012849E | 25.913642N | −74.03 |

116.012849E | 25.913642N | −72.19 |

116.012849E | 25.913642N | −82.91 |

116.012849E | 25.913642N | −79.16 |

#### Partial CW test data

25.324614N | 116.585763E | −66.75 |

25.324614N | 116.585763E | −73.34 |

25.324614N | 116.585763E | −66.97 |

25.324614N | 116.585763E | −66.34 |

25.324614N | 116.585763E | −64.66 |

25.324614N | 116.585763E | −65.78 |

25.324614N | 116.585763E | −61.98 |

25.324614N | 116.585763E | −68.13 |

25.324614N | 116.585763E | −62.13 |

25.324614N | 116.585763E | −63.23 |

25.324614N | 116.585763E | −64.66 |

25.324614N | 116.585763E | −67.34 |

25.324614N | 116.585763E | −65.13 |

25.324614N | 116.585763E | −64.09 |

25.324614N | 116.585763E | −64.25 |

25.324614N | 116.585763E | −60.17 |

25.324614N | 116.585763E | −74.03 |

25.324614N | 116.585763E | −72.19 |

25.324614N | 116.585763E | −82.91 |

25.324614N | 116.585763E | −79.16 |

#### Correction parameters

_{1} |
_{2} |
_{3} |
_{4} |
_{5} |
_{6} |
_{c} |
---|---|---|---|---|---|---|

26.43 | 31.24 | 3.45 | 0.61 | −5.34 | 0 | 1 |

#### Parameters of site 2

Environmental conditions | Sparsely populated area | ||
---|---|---|---|

Location | 116.585763E, 25.324614N | ||

Sampling points | 11981 | Frequency (MHz) | 2743 |

Transmission antenna hanging height (m) | 85 | Receiving antenna hanging height (m) | 3 |

Transmitting antenna gain (dBi) | 15 | Receiving antenna gain (dBi) | 15 |

Transmitted power of signal source (dBm) | 47 | Feeder joint loss (dB) | 1 |

#### Site 3 parameter information

Environmental conditions | Sparsely populated area | ||
---|---|---|---|

Location | 116.743167E, 25.286431N | ||

Sampling points | 14238 | Frequency (MHz) | 2743 |

Transmission antenna hanging height (m) | 80 | Receiving antenna hanging height (m) | 3 |

Transmitting antenna gain (dBi) | 15 | Receiving antenna gain (dBi) | 15 |

Transmitted power of signal source (dBm) | 47 | Feeder joint loss (dB) | 1 |

#### Parameters of site 1

Environmental conditions | Sparsely populated area | ||
---|---|---|---|

Location | 116.585763E, 25.324614N | ||

Sampling points | 9345 | Frequency (MHz) | 2743 |

Transmission antenna hanging height (m) | 80 | Receiving antenna hanging height (m) | 4 |

Transmitting antenna gain (dBi) | 15 | Receiving antenna gain (dBi) | 15 |

Transmitted power of signal source (dBm) | 47 | Feeder joint loss (dB) | 1 |

#### Partial test data of site 3

25.286431N | 116.743167E | −71.47 |

25.286431N | 116.743167E | −70.59 |

25.286431N | 116.743167E | −85.19 |

25.286431N | 116.743167E | −69.41 |

25.286431N | 116.743167E | −62.38 |

25.286431N | 116.743167E | −66.16 |

25.286431N | 116.743167E | −63.64 |

25.286431N | 116.743167E | −61.59 |

25.286431N | 116.743167E | −63.11 |

25.286431N | 116.743167E | −63.34 |

25.286431N | 116.743167E | −61.2 |

25.286431N | 116.743167E | −65.31 |

25.286431N | 116.743167E | −70.72 |

25.286431N | 116.743167E | −79.97 |

25.286431N | 116.743167E | −68.78 |

25.286431N | 116.743167E | −66.56 |

25.286431N | 116.743167E | −74.03 |

25.286431N | 116.743167E | −72.19 |

25.286431N | 116.743167E | −82.91 |

25.286431N | 116.743167E | −79.16 |

#### Statistical table of errors at each station

Site 1 | Before correction | 19.2319 | 6.3281 | 7.3982 |

After correction | 1.82931 | 5.9832 | 7.2984 | |

Site 2 | Before correction | 18.9326 | 6.1189 | 6.5307 |

After correction | 0.6988 | 5.7982 | 6.4897 | |

Site 3 | Before correction | 18.5942 | 5.6409 | 6.3498 |

After correction | 0.5983 | 5.5982 | 6.1892 |

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