Research Article  Open Access
Shuangde Li, Yuanjian Liu, Leke Lin, Zhong Sheng, Xiangchen Sun, Zhipeng Chen, Xiaojun Zhang, "Channel Measurements and Modeling at 6 GHz in the Tunnel Environments for 5G Wireless Systems", International Journal of Antennas and Propagation, vol. 2017, Article ID 1513038, 15 pages, 2017. https://doi.org/10.1155/2017/1513038
Channel Measurements and Modeling at 6 GHz in the Tunnel Environments for 5G Wireless Systems
Abstract
Propagation measurements of wireless channels performed in the tunnel environments at 6 GHz are presented in this paper. Propagation characteristics are simulated and analyzed based on the method of shooting and bouncing ray tracing/image (SBR/IM). A good agreement is achieved between the measured results and simulated results, so the correctness of SBR/IM method has been validated. The measured results and simulated results are analyzed in terms of path loss models, received power, root mean square (RMS) delay spread, Ricean Kfactor, and angle of arrival (AOA). The omnidirectional path loss models are characterized based on closein (CI) freespace reference distance model and the alphabetagamma (ABG) model. Path loss exponents (PLEs) are 1.50–1.74 in lineofsight (LOS) scenarios and 2.18–2.20 in nonlineofsight (NLOS) scenarios. Results show that CI model with the reference distance of 1 m provides more accuracy and stability in tunnel scenarios. The RMS delay spread values vary between 2.77 ns and 18.76 ns. Specially, the Poisson distribution best fits the measured data of RMS delay spreads for LOS scenarios and the Gaussian distribution best fits the measured data of RMS delay spreads for NLOS scenarios. Moreover, the normal distribution provides good fits to the Ricean Kfactor. The analysis of the abovementioned results from channel measurements and simulations may be utilized for the design of wireless communications of future 5G radio systems at 6 GHz.
1. Introduction
The next generation (5G) of wireless communications will use systems operating from 500 MHz to 300 GHz [1, 2]. The 3 GHz–30 GHz spectrum is defined as the super high frequency (SHF) band, while 30–300 GHz spectrum is assigned to the extremely high frequency (EHF) or millimeterwave band (mmWave) [3]. Because radio waves in the SHF and EHF bands share similar propagation characteristics, the 3–300 GHz spectrum, with wavelengths ranging from 1 mm–100 mm, can be referred to as the EHF bands [4, 5]. The EHF bands will be a key component, which can deliver multigigabitpersecond transmission throughputs for various multimedia services for 5G radio systems [6, 7]. Thus, fundamental knowledge of the EHF channel propagation characteristics including accurate and reliable channel models is vital for developing 5G wireless communication systems.
Many extensive EHF wireless channel measurement campaigns have been investigated for different scenarios in multiple outdoor and indoor environments, yielding empirically based path loss models and delay dispersion properties. Some research projects, such as METIS [8], NYU WIRELESS [1, 5, 9, 10], MiWEBA [11], and mmMagic [12], have been developing 5G mmWave channel models which are mainly aimed at the outdoor cellular propagation channel and indoor office scenarios including streetcanyon, shopping malls, and stadium scenarios. Millimeterwave propagation characteristics in the laboratory environment are analyzed at the 28 GHz and 82 GHz [13]. A vehicle channel measurement is conducted for different antenna placements and antenna patterns at 55–65 GHz [14]. Moreover, multiple wireless channel sounding campaigns have been presented in the literature for access environments above 3GHz, for example, at 5.3 GHz [15], 10 GHz [16], 11 GHz [17], 15 GHz [18], 28 GHz [5, 13, 18–21], 32 GHz [22], 38 GHz and 73 GHz [1, 5, 23–26], 60 GHz and 70 GHz [16, 27–30], 83.5 GHz [31], and 110 GHz [32].
Although a large number of channelmeasured results can provide reliable channel model, the expense of highprecision measurement equipment is very high and the number of observation points is limited in measurement campaigns, which are both great challenges for channel sounding. As an another approach, the shooting and bouncing raytracing/image (SBR/IM) method [33] is an effective method, which can extend the sparse empirical datasets and can be utilized to investigate the propagation characteristics of EHF bands. Also, it is able to capture multipath components in timevarying wireless channels and can prove the accuracy and effectiveness of the proposed channel model by comparing simulated channel model with the actual measured channel model. In this paper, we verify that the simulated results provide good agreement with measured results over all the measured paths, as we compared simulations with measurements in the same tunnel environment and distance ranges for verification.
Despite many measurement campaigns were conducted up to now, there are still omitted environments and frequency bands, which require to be intensively investigated. Hence, extensive actual channel measurements and simulations should be performed for various scenarios and significant frequency bands. By analyzing the measured results and simulated results, a standardized channel model should be proposed. The contribution of this paper is fivefold. First of all, channel measurements and simulations in the tunnel environments are performed and analyzed at 6 GHz, which have not been detailed in previous work. Therefore, propagation characteristics are analyzed at 6 GHz in the tunnel environment. Second, the omnidirectional path loss models are characterized based on closein (CI) freespace reference distance model and the alphabetagamma (ABG) model. In addition, the comprehensive parameter table of path loss models including measured results and simulated results for all scenarios is given. Third, the statistical analysis of the RMS delay spread for tunnel scenarios is described using measurement datasets. The fourth part of the contribution is to investigate the different distribution models based on the cumulative distribution functions (CDF) in terms of the parameters of received power, Ricean Kfactor, and angle of arrival (AOA). Finally, some important angle of arrival is extracted based on the subtractive clustering algorithm.
The rest of the paper is organized as follows. The measurement setup and environments are described in Section 2. The results and analysis of path loss models are presented in Section 3. Specially, the parameters of received power, Ricean Kfactor, and angle of arrival (AOA) are evaluated in Section 3. Finally, conclusions are drawn in Section 4.
2. Measurement Setup and Environments
2.1. Measurement Setup and Procedure
Measurements were conducted in the frequency domain in which the channel impulse responses (CIRs) were measured and recorded. Figure 1 illustrates an overview of the channel measurement setup. The height of the transmission antenna is 1.8 m, while the height of the receiver antenna is 1.6 m above the floor in the tunnel environments. Table 1 shows the 6 GHz channel sounding system parameters, and more detailed information of the channel sounder and calibration method can be found in [34].

2.2. Environment Description
Two tunnel propagation measurement campaigns were conducted in the tunnel chamber of Beijing Jiaotong University (BJTU) in Beijing, China, in two different scenarios as shown in Figures 2(a) and 2(b), respectively. The receiver antenna moves along a straight line. The first to be taken (LOS1 measurement) was performed at a sampling distance of 1 m. The transmitter (TX1) was placed almost at one end of a long tunnel, while the receiver (RX) was moving along a LOS route (AB, 19 m), which corresponds to 20 samples as shown in Figure 2(c). The second to be taken (LOS2 measurement) was also taken at a sampling distance of 1 m. The transmitter (TX2) was placed at the corner, while the receiver (RX) was moving along a LOS route (CD, 11 m), which corresponds to 12 samples as shown in Figure 2(c). Moreover, the third to be taken (NLOS measurement), the transmitter (TX1) was also placed almost at one end of a long tunnel, while the receiver (RX) was moving along a route (ABCD, 30 m), which corresponds to 31 samples as shown in Figure 2(c). The last to be taken (LOS3 measurement) was taken at a sampling distance of 0.5 m. The transmitter (TX) was indicated in the tunnel scenario, while the receiver (RX) was moving along a LOS route (AB, 8 m), which corresponds to 17 samples as shown in Figure 2(d). At each receiver location, measurements were acquired using a circular track with 8 equally spaced local area measurement points separated by increments. The radius of the circular track yielded and separation distance between consecutive points along the circular track in tunnel 1 and tunnel 2 scenarios, respectively. Note that in the measurement, no one was moving in the tunnel, so as to eliminate the influence of moving persons.
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2.3. RayTracing Simulations
The shooting and bouncing raytracing/image (SBR/IM) method is developed to deal with the radio wave propagation for complex environment. It can track all the triangular ray tubes bouncing with high accuracy and computational efficiency. If the RX is within a ray tube, the ray tube will have contribution to the received field at RX and the equivalent source (image) can be determined. So SBR/IM method is an effective method which can be used to predict the propagation characteristics at 6 GHz.
The raytracing simulation performs the method of SBR/IM using the software tool, Wireless InSite [35]. In our simulation settings, the raytracer accounts for up to 6 reflections, 2 penetrations, and 1 diffraction for each ray. The material properties are frequencydependent, and the parameters of dielectric constant and conductivity in this paper are estimated for the 6 GHz spectrum band based on the material properties in different bands [36–39]. The buildings are assumed to be concrete with the dielectric constant and respectively. For each simulated RX point, a discrete equivalent baseband CIR was composed from the simulated rays based on their complex amplitude and delay spread. Furthermore, the raytracing simulation performed for the purpose of validation is presented for proper comparison and validation of simulated results and empirical results. This is further discussed in Section 3.
3. Channel Models and Statistical Analysis
3.1. Path Loss Models
The path loss is a significant parameter which can be applicable to describe the largescale effects of the propagation channel [40]. In this work, two kinds of path loss models are considered based on the measurements and simulations, namely, the closein (CI) freespace reference distance path loss model [41, 42] and the alphabetagamma (ABG) path loss model [9].
Two wellknown models are used to develop omnidirectional path loss models in this paper. First of all, the equation for the CI model is given by where is the closein freespace reference distance, is the carrier frequency in GHz, is the speed of light, denotes the path loss exponent (PLE), is the distance between transmitter and receiver, and is a zeromean Gaussian random variable with a standard deviation in dB (shadowing effect). The minimum mean square error (MMSE) is used to calculate PLE and standard deviation. The physical interpretation of is a guided wave in one plane, corresponds to a freespace path loss, and corresponds to a situation, where low antenna heights cause the first Fresnel zone to be obstructed [15].
The ABG model is another famous model which can be used to discuss the frequency dependence of path loss. It can be defined as [24] where denotes the path loss in dB over frequency and distance; and are coefficients showing the dependence of path loss on distance and frequency, respectively; is an optimized offset value for path loss in dB; and is a zeromean Gaussian random variable with a standard deviation in dB derived from MMSE closedform optimization. When used at a single frequency, the ABG model reverts to the existing 3GPP floatingintercept (FI) model with three parameters where set to 0 or 2 [5, 23]. Moreover, to verify the model accuracy, we use the simulated channel parameters to compare them with that of measured results.
Different path loss models have been deduced based on extensive wideband measurements at 6 GHz in terms of LOS and NLOS scenarios. Figures 3(a), 3(b), 3(c), and 3(d) show the scatter plots of all measured and simulated path loss values in dB versus TXRX separation distance optimized for the CI, CIopt, and ABG models in both LOS and NLOS environments. As a reference, freespace path loss models are also included. Table 2 summarizes the parameters of path loss models including measured results and simulated results for all scenarios including ABG models and CI models which consist of 1 m CI models and optimized models (CIopt models).
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From Figure 3, we can see that the values of path loss increase with increasing distance between transmitter and receiver. From Table 2, we can also see that the measured PLEs of CI are 1.72, 1.69, and 1.58 for LOS1, LOS2, and LOS3 paths, respectively. Also, the simulated PLEs of CI are 1.66, 1.50, and 1.74, respectively. The measured PLEs of CIopt are 1.75, 1.45, and 1.45, respectively. Furthermore, the simulated PLEs of CIopt are 1.61, 1.55, and 1.71, respectively. The values of PLE are identical for measured CIopt models in both LOS2 and LOS3 paths. These results indicate that the values of PLE are less than the freespace PLE (), implying that the multipath components (MPCs) from both side walls and floor add up constructively in the tunnel environments, as a guided wave phenomenon. It is seen that the measured PLE and simulated PLE of CI models are 2.18 and 2.20 for NLOS paths, respectively. Also, the measured PLE and simulated PLE of CIopt models are 2.41 and 2.11 for NLOS paths, respectively, indicating faster signal level degradation over the distance between transmitter and receiver. Obvious explanation for this is that there are different dominating propagation mechanisms due to different structures of the tunnel scenarios. As indicated in Table 2, the standard deviations for CI models vary between 0.53 dB and 3 dB for LOS paths and the standard deviations for CI models vary between 2.74 dB and 4.2 dB for NLOS paths. Also, the standard deviations for CIopt models vary between 0.38 dB and 2.10 dB for LOS paths and the standard deviations for CI models vary between 1.38 dB and 2.74 dB for NLOS paths. It is interesting to note that the difference of standard deviations for CIopt models and CI models is always less than 1 dB for majority measurement and simulation sets, which demonstrate that there is no essential difference in standard deviations between CI models and CIopt models. However, the standard deviations for ABG models vary between 1.06 dB and 8.27 dB for LOS paths and the standard deviations for ABG models vary between 7.41 dB and 7.68 dB for NLOS paths. It is worth noting that the difference of standard deviations for ABG models and CI models is always larger than 1 dB for majority measurement and simulation datasets indicating the higher accuracy and reliability of CI models compared to ABG models.
From these analyses, we can conclude that CI model with the reference distance of 1 m provides more accuracy and stability in LOS and NLOS tunnel scenarios at 6 GHz. These simulated results are in agreement with previous works [15, 16, 22, 32, 43, 44] which indicate that the path loss is best modeled by a logdistance relationship.
3.2. Received Power Results and Analysis
In wireless communication channels, the signal is transmitted and then undergoes direct reflection, transmission, scattering, and diffraction. Hence, the signal arriving at the receiver is the superposition of the various multipath components [45]. All cumulative distribution functions (CDF) of received power including measured results and simulated results are demonstrated for LOS and NLOS as shown in Figure 4. In both LOS and NLOS tunnel scenarios, the raytracing predictions agree fairly well with the measured results. There are slight differences between measured and simulated results as shown in Figure 4. They could be attributed to scattering by small objects within the tunnel scenarios and are not expected to influence the performance of wireless communication system. Moreover, the statistical method to determine the distribution of the CDF of received power after regression analysis is needed. Notice that the CDF is close to a normal distribution. The parameters of the distribution are listed in Table 3. and denote the mean and standard deviations of the normal distribution, respectively.
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3.3. Statistical Analysis of RMS Delay Spreads
The mean excess delay and rootmeansquare (RMS) delay are two important parameters used to characterize the temporal dispersive properties of multipath channels. The mean excess delay is defined as the first moment of the power delay profile (PDP) [46]. where and are the gain coefficient delay and PDP of the th MPC, respectively. The RMS delay spread () is the square root of the second central moment of the PDP and is defined to be where
The RMS delay spread for LOS2, LOS3, and NLOS paths is shown in Figure 5. The RMS delay spread values vary between 2.77 and 18.76 ns. From Figure 5, we can see that the correlation between RMS delay spread and the distance between transmitter and receiver is very low. Also, it is seen that the RMS delay spread values in LOS paths are lower than the values in NLOS path due to the difference of structure of tunnel and the small scattering objects. In fact, the presence of obstructions in tunnel environment (compared with the equivalent empty room) has inconsistent effects on the multipath [47]. Moreover, RMS delay spread increases when reflections and scattering induce larger propagation delay times in wireless channel. Figures 6(a), 6(b), 6(c), and 6(d) show the empirical CDFs of RMS delay spreads for all scenarios, which include the Poisson, Exponential, Rayleigh, and Gaussian distribution. In order to identify the distribution, the Loglikelihood test is adopted. After estimating the maximum likelihood of the distribution parameters, it is, therefore, evident that the Poisson distribution best fits the measured data of RMS delay spreads for LOS scenarios and the Gaussian distribution best fits the measured data of RMS delay spreads for NLOS scenarios as shown in Figure 6. Moreover, it is apparent that 90% of the energy arrived at the RX within 13.2 ns for LOS scenarios and 14.6 ns for NLOS scenarios. The mean, minimum, and maximum of RMS delay spreads are listed in Table 4 for all tunnel environments.
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3.4. Ricean KFactor Analysis
Kfactor is a significant parameter in wireless communications because it is able to characterize the type of fading environments [48]. Ricean Kfactor is the power ratio between the LOS component and the sum of other propagation components [42]. Kfactor can be calculated as where is the maximum power for LOS path and is the total power for all paths.
Ricean Kfactor (KF) values at different distances between transmitter and receiver for LOS paths are shown in Figure 7. The maximum Kfactor for LOS1 is 12.67 dB, and the mean is 7.67 dB. Specially, the maximum Kfactor for LOS3 is 11.21 dB and the mean is 6.73 dB which are consistent to the values for LOS1. However, it is seen that the maximum Kfactor for LOS2 is 18.30 dB and the mean is 9.61 dB, which are larger than the values of LOS1. The difference between them is mainly because tunnel 1 and tunnel 3 are straight tunnel and tunnel 2 is curved tunnel which may result in fewer multipath components. It is seen that the correlation is low between Ricean Kfactor and the distance between transmitter and receiver. Moreover, the CDF plots of Kfactor are shown in Figure 8. The results lead to an observation that the normal distribution provides good fits to the Ricean Kfactor for LOS tunnel scenarios. The statistical parameters for Kfactor (expressed in dB) are shown below:
3.5. AOA Analysis
The angles and with reference to the spherical coordinate system give the direction from which the propagation path arrives at receiver point. The mean angle of arrival from which energy arrives at the receiver is defined as where is the power carried by th path and is the unit vector in the direction from which the th path arrives at the receiver.
In terms of the angle of arrival, some conclusions are presented. The distribution of AOA in the elevation plane has been researched [49, 50]. The distribution of AOA in the elevation plane is described as Gaussian [49] and uniform [50]. However, there are few reports on the characteristics of distribution of AOA at 6 GHz. The CDF distributions of mean AOA are shown in Figure 9. From Figure 9, we can see that the normal distribution provides good fits to the mean AOA for LOS1, LOS3, and NLOS paths. Moreover, the best distribution that fits the measured data is the uniform distribution in LOS2 as depicted in Figure 9. These results are in agreement with previous works. The statistical parameters for mean AOA are shown below
In addition, some important arrival angles are extracted based on subtractive clustering algorithm [51] as shown in Figures 10(a), 10(b), 10(c), and 10(d). Furthermore, the extracted parameters of AOA are listed in Table 5. These parameters presented here can be used for wireless communication system design in indoor environments and can be used for adaptive transmission technologies for 5G wireless network.
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4. Conclusion
In this paper, extensive measurements and characterizations of wideband tunnel channel have been proposed. Channel characteristics such as path loss models, received power, RMS delay spread, Ricean Kfactor, and AOA are described and modeled. Based on extensive radio channel sounding campaigns and simulations, it is found that the raytracing predictions agree fairly well with the measured results. Comparison with path loss models illustrates the fact that the CI model with the reference distance of 1 m was shown to be the most suitable because of its accuracy and simplicity in tunnel scenarios. The CI path loss models indicated that the PLEs vary between 1.50 and 1.74 in LOS scenarios and between 2.18 and 2.20 in NLOS scenarios. The CDF of received power follows the normal distribution. The normal distribution provides good fits to the Ricean Kfactor for LOS scenarios. The Poisson distribution model best fits the measured data of RMS delay spreads for LOS scenarios, and the Gaussian distribution model best fits the measured data of RMS delay spreads for NLOS scenarios. Moreover, the normal distribution and the uniform distribution reasonably fit the AOA well for all tunnel scenarios. In addition, propagation characteristics with the effects of human movement and vehicular motion on wireless channels require further measurement and analysis in the complex environments, which is the next research direction.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Acknowledgments
This work has been supported by the National Natural Science Fund under Grant no. 61372045 and by the Postgraduate Research & Practice Innovation Program of Jiangsu Province in 2016 under Grant no. KYLX16_0650. The work of this paper is funded by Radio Wave Environment Characteristics and Modeling Technology Key Laboratory under Grant no. 201600013. Moreover, the authors would like to thank Dr. R. C. Sun, Q. C. Li, Y. Lei, and Y. H. Wu for their help with the measurements and associate Professor L. Liu from Institute of Broadband Wireless Mobile Communications at Beijing Jiaotong University, Beijing, for providing radio propagation measurement equipment used for preliminary measurements.
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Copyright © 2017 Shuangde Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.