Received December 7, 2016, accepted December 17, 2016, date of publication January 9, 2017, date of current version March 8, 2017. Digital Object Identifier 10.1109/ACCESS.2017.2650261
Channel Measurements, Modeling, Simulation and Validation at 32 GHz in Outdoor Microcells for 5G Radio Systems XIONGWEN ZHAO1 , (Senior Member, IEEE), SHU LI1 , QI WANG1 , MENGJUN WANG2 , SHAOHUI SUN2 , AND WEI HONG3 , (Fellow, IEEE) 1 School
of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China Academy of Telecommunication Technology, Beijing 100191, China Key Laboratory of Millimeter Waves, Nanjing 210096, China
2 China 3 State
Corresponding author: S. Li (
[email protected]) This work was supported in part by the China Academy of Telecommunication Technology, in part by the State Key Laboratory of Millimeter Waves, Southeast University, China, under Grant K201517, and in part by the Fundamental Research Funds for the Central Universities under Grant 2015 XS09.
ABSTRACT In this paper, based on outdoor microcellular channel measurements at 32 GHz for 5G radio systems, a comprehensive channel modeling, simulation, and validation are performed. The directionalscan-sounding measurements using a horn antenna rotated with an angular step at the receiver are carried out, which constitutes a virtual array to form a single-input multiple-output radio channel. The directionaland omni-directional path-loss models are developed by using close-in and floating-intercept methods. Nonparametric and parametric methods are applied to extract large-scale channel parameters (LSPs). The nonparametric method is based on the definition of a channel parameter, whereas the parametric method is derived by the space-alternating generalized expectation–maximization (SAGE) algorithm, which can deembed an antenna pattern. It is found that the LSPs in the angular domain are significantly different by using the two methods; however, the LSPs in the delay domain almost stay the same. By comparing the LSPs with the parameter table at 32 GHz with 3GPP standard, it is found that 3GPP LSPs should be corrected at the International Telecommunications Union-assigned millimeter wave (mmWave) frequencies for 5G. In this paper, the channel simulation is implemented by using the quasi-deterministic radio channel generator (QuaDRiGa) platform recommended by 3GPP. By comparing the LSPs with the simulated and measured results, it is found that QuaDRiGa is a good platform at the mmWave band, even if it is originally developed for channel simulation below 6 GHz. The results of this paper are important and useful in the simulations and design of future 5G radio systems at 32 GHz. INDEX TERMS mmWave, 32 GHz, channel measurement, direction-scan-sounding, path-loss, SAGE, QuaDRiGa, simulation, validation. I. INTRODUCTION
The demand in high-data-rate transmission is expected to grow explosively with the advent of the fifth generation (5G) radio systems in the next few years as stated in European METIS (Mobile and wireless communications Enablers for the Twenty-twenty Information Society) project [1]. Millimeter Waves (mmWave) are regarded as the key frequency candidates for 5G, which can offer very high data rate in broadband mobile and backhaul services [2], [3]. Therefore, accurate channel models and parameters at mmWave are urgently needed in the link and system level simulations for 5G radio systems. 1062
In recent years, the characteristics of indoor radio channels have been studied in higher frequency bands (HFB), e.g. 10-11 GHz [4]–[6], 28 GHz [1]–[12], 60 GHz [13], [14], and 70-73 GHz [11], [14], [15]. The measurement campaigns for outdoor urban cellular network have been carried out in HFB of 10, 18, 28, 38, 60, 72 and 81-86 GHz [16]–[20]. In worldwide, there are some research projects related to 5G mmWave channel measurements and modeling work such as METIS [1], NYU WIRELESS [11], [16], mmMAGIC [22], MiWEBA [23], and 3GPP [24] and so on. Based on the available work, 3GPP finalized and published its 5G channel models in this year [24], and its main target was to get
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universal path-loss models and the large scale channel parameters (LSPs) within 6 – 100GHz to meet 5G link and system level simulations at different carrier frequencies. However, 3GPP outputs were based on only a few of measured carrier frequencies with different measurement environments in the world, the frequency dependent path-loss models and LSPs are for sure required to be validated by more coming measurements. This kind of work could be left to International Telecommunications Union (ITU) which has started its 5G channel standardization right now. ITU assigned different spectrum segments between 24 and 86 GHz for 5G radio systems in World Radio Communications Conference (WRC) last year [25], in which 24.25-27.5 GHz and 31.8-33.4 GHz might be the primary choice for 5G wireless access [26]. So far there are some channel measurements and modeling work available at 24.25-27.5 GHz, however, there are no results at 31.8-33.4 GHz by us knowledge, which is why we focus on 32 GHz channel in this work. Channel measurements in mmWave are now carried out with either a network analyzer or a channel sounder, where at least one horn antenna is required to be used at the transceiver to increase the measurement distance. In a practical measurement, because it’s very difficult to align two horn antennas, therefore an omni-directional antenna is commonly used at the transmitter (Tx) and a horn is equipped at the receiver (Rx) to perform directional-scan-sounding (DSS) measurements. To extract channel LSPs, e.g. rms delay- and angular-spread, number of clusters etc. from measurement data, the nonparametric [16]–[19] and parametric methods [4], [12] are applied, the former one is based on the definitions of channel parameters, while the later one is based on high resolution method, e.g. using SAGE (Space-Alternating Generalized Expectation-maximization). While applying SAGE algorithm to estimate the channel parameters, the concept of multipath component (MPC) is defined as a propagation path over the environment and the signal model which reflects the MPCs and antenna array configuration is required. The advantage of parametric method over non-parametric one is that it can de-embed antenna pattern when extracting LSPs, then more accurate LSPs can be extracted from measured channel impulse responses (CIRs), especially in the angular domain [12], [27]. A geometric based stochastic channel model (GSCM) was proposed in Wireless World Initiative New Radio Project (WINNER) [28] and ITU-R [29], which is very popular platform for 4G channel simulation. To extend WINNER and ITU-R model to three-dimensional (3D) MIMO model, quasi deterministic radio channel generator (QuaDRiGa) has been developed first [30]. Most recently, QuaDRiGa was extended to mmWave and recommended for 5G channel simulations by 3GPP [24]. However, the accuracy of the platform is yet to be verified by measurements, especially at ITU assigned mmWave frequency bands. In this paper, the DSS channel measurements for outdoor microcells at 32 GHz with 1 GHz bandwidth are carried out. The horn antenna is rotated in both azimuth and VOLUME 5, 2017
elevation planes, respectively with specific angular steps. Virtual single-input and multiple-output (SIMO) system can be formed by horn rotating positions. Moreover, the horn antenna pattern is measured in an anechoic chamber, then the LSPs and the parameters inside clusters can be extract by SAGE. Two kinds of path-loss models are developed, namely directional and omni-directional path loss models. The former one is to find the maximum received power while the horn is rotating for beamforming and tracking, and the later one is to integrate the received powers in all different horn orientations at a specific measurement location. Finally, a parameter table is summarized as in WINNER [28] to implement channel simulation and validation by using QuaDRiGa platform, the simulation results are compared with the modeling results in [17], mmMAGIC [22], and 3GPP TR 38.900 [24] for outdoor microcells, and also validated by the measurements at 32 GHz in this work. The novelties of this paper include: (1) the first outdoor microcell measurements are performed at 32 GHz ITU assigned frequency spectrum by us knowledge; (2) Channel simulation and validation are done using 3GPP recommended QuaDRiGa platform, which has not been done so far at mmWave. (3) A comprehensive parameter table and path-loss models are given first time at 32 GHz in the link and system level simulations for 5G radio systems, where the LSPs are extracted by SAGE to de-embed antenna pattern. The rest of the paper is organized as follows. The measurement campaign and system are introduced in Section II. In Section III, the SAGE algorithm based on the DSS signal model is described and an example of estimation is presented. In Section IV, the model parameters are extracted and the comparison among this work with mmMAGIC, NYU WIRELESS, and 3GPP are summarized. Section V presents the channel simulation and validation by using QuaDRiGa simulation platform. The conclusion is drawn in Section VI. II. MEASUREMENT CAMPAIGN AND SYSTEM
The outdoor microcellular measurements were conducted in the campus of North China Electric Power University (NCEPU) in Beijing, China in two different scenarios as shown in Fig. 1(a) and (b), respectively. Less building windows at the two sides of the road with sparser but taller trees in Scenario #1 than Scenario #2. In each scenario, a line-of-sight (LoS) and a non-line-of-sight (NLoS) routes are measured as shown in Fig. 2(a) and (b), respectively. The BS and MS are located in a crane and a trolley with heights of 6.1 meter and 1.8 meter, respectively, and equipped an omni-directional antenna and a horn antenna. The horn has 10◦ half-power-beamwidth (HPBW) and is able to rotate in both of the azimuth and elevation planes by using a stepper motor. To get the complete 3D angle-of-arrival (AoA) information, the horn is rotated from 0◦ to 360◦ with 5◦ angular step in the azimuth plane, and several co-elevation angles are selected to repeat such rotating measurement with respect to the distance between the BS and MS. For example, at the first MS measurement location (closest to the BS) in route 1063
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FIGURE 3. Block diagram of measurement system.
TABLE 1. Measurement system parameters.
FIGURE 1. Measurement environments. (a) Scenario #1 and (b) Scenario #2.
very time consuming measurement. The co-elevation angles at each MS location are shown in the legend of Fig. 2(a). In both of the scenarios, there are 20 and 14 MS locations are measured for the LoS and NLoS routes, respectively. At each MS location, about 21,600 CIRs are collected when only 2D measurements are considered, thus more than 430,000 and 300,000 CIRs are recorded in scenarios #1 and #2, respectively. The measurements were mainly conducted at midnight to avoid people’s movement. The measurement system is developed by Keysight with block diagram shown in Fig. 3 with system level parameters listed in Table 1 in this work. The detailed information of the sliding correlated sounder and the calibration method are described in [31]. III. PARAMETER ESTIMATION FOR PROPAGATION PATHS A. SIGNAL MODEL FOR THE DSS SYSTEM
SAGE algorithm was widely used in WINNER to extract multipath parameters for MIMO channel measurements [28], SAGE implementation can be found in [32]. In this measurements, an biconical horn is used at the Tx while the horn antennas is rotated in different directions, therefore the measurement system can be regarded as a virtual SIMO with number of M elements or directions in the Rx, the receive signal of a single impinging wave can be written as FIGURE 2. Layout of measurement campaigns. (a) Scenario #1. (b) Scenario #2. Legend of (a) is suitable for (b) as well.
R1-1 shown in Fig. 2(a), the co-elevation angle is set from −10◦ to 50◦ with step of 10◦ , thus 72∗7 directions are measured in 3D space. As the distance between the BS and MS is larger, the co-elevation angles can be reduced because of 1064
s (t; ρl ) = [s1 (t; ρl ) , . . . , sM (t; ρl )]T = c (θl , ϕl ) αl exp (j2π υl t) u (t − τl )
(1)
where ρl = [τl , θl , ϕl , υl , αl ] is the parameter set of path l to be estimated, where τ is delay, θ is the elevation-angle-ofarrivals (EAoA), ϕ is the azimuth-angle-of-arrivals (AAoA), VOLUME 5, 2017
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υ is Doppler frequency and α is the amplitude. u (t) is the reference signal. The steering vector c (θ, ϕ) can be expressed as c1 (θ, ϕ) .. c (θ, ϕ) = . cM (θ, ϕ)
j2π i he ϕ) , r f ϕ) exp (θ, (θ, 1 1 λ . .. = j2π he (θ, ϕ) , rM i fM (θ, ϕ) exp λ
(2)
where r1 , r2 , . . . rM are the positions of the M elements in Cartesian coordinate system, f is the complex antenna pattern, e (θ, ϕ) is the unit direction vector and hi is inner product. Then the receiving signal Y (t) = [Y1 (t) , . . . , YM (t)]T is given by r L X N0 Y (t) = s (t; ρl ) + N (t) (3) 2 l=1
where N0 is a positive constant, N (t) = [N1 (t) , . . . , NM (t)]T is M - dimensional complex white Gaussian noise.
FIGURE 5. MPCs at location 1 in route R3-4 with θ = 0◦ . (a) MPCs estimated by SAGE algorithm. (b) Concatenated PDPs derived from 72 directional CIRs.
B. EXTRACTION OF CHANNEL PARAMETERS USING SAGE
FIGURE 4. Directional-scan-sounding (DSS) system with the biconical horn and horn antennas at the Tx and Rx, respectively.
DSS is the most widely used method in millimeter wave channel measurement as shown in Fig. 4. Assuming that the channel is time-invariant and the feed point remains in the center when the horn is rotating, then the steering vector c (θ, ϕ) in (2) can be written as c1 (θ, ϕ) f θ − θ 1 , ϕ − ϕ1 .. .. c (θ, ϕ) = = (4) . . cM (θ, ϕ) f θ − θ M , ϕ − ϕM where e θi , ϕi is the unit direction vector of i-th orientation. As the position of the transceiver keeps unchanged with no moving scatterers during the horn rotating, s (t; ρl ) in (1) can be written as s (t; ρl ) = c (θl , ϕl ) αl u (τ − τl ) . VOLUME 5, 2017
(5)
By applying the signal model (4)-(5) in the DSS system, channel parameters for the propagation paths related to delay and angular domains can be extracted by SAGE. Since the full spatial scanning in the azimuth and elevation planes is too time-consuming, for most the measured locations especially those locations far away from the Tx, only 3∼5 elevation angles are scanned. In this case, the EAoAs extracted by SAGE can have estimation error according to [33]. As an alternative, we estimate the delay and AAoAs of the MPCs for each co-elevation angle. The SAGE estimation parameters are set as follows: the number of the MPCs is 200, the maximum iteration number is 10, the MPCs with power at least 3 dB larger than the noise level are picked up from 200 estimated MPCs. As an example, a scatter plot of the delays, AoAs, and gains of the estimated MPCs in the NLoS case measured in route R3-4 at location 1 with co-elevation θ = 0◦ is illustrated in Fig. 5(a) by using SAGE. For sake of comparison, the corresponding plot of the delays, AoAs and gains from original CIRs are displayed in Fig. 5(b). From Fig. 5(a) and (b), it is seen that all distinguishable MPCs are extracted. The performance of the MPCs extraction is evaluated by calculating the percentage of reconstruction energy with respect to the raw 1065
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measured CIRs. The mean reconstruction energy in this work is about 88%. IV. CHANNEL MODELS AND PARAMETERS A. PATH-LOSS MODEL
In this work, two kinds of path-loss models are developed based on the DSS measurements, namely directional and omni-directional path-loss models. The former one is to find the maximum receiver power or minimum path-loss in each measurement location with different horn rotation angles, which could be useful for beamforming or tracking. The path-loss in the later one can be calculated as [11] to integrate power in all the horn rotating directions at a specific location PLi [dB] = Pt [dBm] − 10 log10 " # XX Pri (θel , ϕaz ) [mW] × az
(6)
el
where Pri (θel , ϕaz ) is the received power after removing the antenna gain for azimuth angle ϕaz and elevation angel θel in measured location i, Pt is the transmit power. Two well-known models are used to develop path-loss models in this work. The first one is called close-in (CI) model, in which the path-loss intercept is calculated by assuming 1 meter reference distance in free space [16] and is given by d 4π f + 10n log10 (7) PLCI = 20 log10 c d0 where n is the path-loss exponent, f is the carrier frequency, c is the speed of light, d0 = 1 m is the reference distance and d is the separation of the Tx and Rx in 3D space. The second one is called floating intercept (FI) model [27] , which is calculated as d PLFI = β + 10α log10 (8) d0 where α is the distance dependence coefficient similar with n in CI model and β is the floating path-loss intercept. Figure 6(a) and 6(b) shows the path-loss models as well as shadow fading for Scenarios #1 and #2, respectively. As a reference, free space path-loss models are also included. From Fig. 6(a) it is seen that omni-directional path-loss models are slightly below the directional path-loss models both in the LoS and NLoS routes since the directional path-loss model only contains the path-loss for strongest path, while omnidirectional path-loss model also includes the other propagation paths. The directional and omni-directional path losses for the LoS routes are roughly equal to the free space pathloss. The shadow fading (SF) of the CI model is slightly larger than that in FI model, and the difference is within 1 dB. The path-loss models and shadow fading by the CI method are summarized in Table 2. 1066
FIGURE 6. Directional and omni-directional path-loss models and shadow fading using CI and FI models. (a) Scenario #1. (b) Scenario #2.
B. RMS DELAY SPREAD AND ANGULAR SPREAD
Root mean square (RMS) delay spread (RDS) is calculated by v u PL u Pl τl2 DS = t Pl=1 − τ02 (9) L l=1 Pl VOLUME 5, 2017
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TABLE 2. Comparison of the GSCM parameters in this work and open literature.
where L is the total number of paths estimated by SAGE and τ0 is the mean excess delay given by v u PL u Pl τl τ0 = t Pl=1 . (10) L l=1 Pl The rms angle spread (RAS) is calculated as in [34]. By defining the circular angle spread, differences caused by the zero degree selection could be avoided. v u PL 2 u l=1 ψl,µ (1) Pl t RAS = min σAS (1) = (11) PL 1 l=1 Pl where ϕ + 2$, if ϕ < −$ ψl,µ (1) = ϕ, if | ϕ| ≤ $ ϕ − 2$, if ϕ > $
(12)
where $ = π for the AAoDs (Azimuth Angle-of-Departure) and AAoAs and $ = π/2 for the EAoDs (Elevation Angleof-Departure ) and EAoAs, ϕ = ψl (1) − µψ (1) and PL ψl (1) Pl (13) µψ (1) = l=1 PL l=1 Pl where 1 designates angle shift from −$ to $ and (ψl + 1) + 2$, if (ψl + 1) < −$ ψl (1) = (ψl + 1) , if | (ψl + 1)| ≤ $ (ψl + 1) − 2$, if (ψl + 1) > $ VOLUME 5, 2017
(14)
For the two typical microcell scenarios measured in this work, parametric method based on SAGE as well as nonparametric method are used to get the RDS and RAS. By using non-parametric method , they are derived by the PDPs (Power-Delay-Profiles) and PAPs (Power-AngularProfiles) obtained from original CIRs. The noise floor is estimated by the last two hundred delay samples where no signal received and the noise cut threshold is set as 5dB above noise level. The cumulative probability functions (CDFs) of the RDS for Scenarios #1 and #2 are shown in Fig. 7(a) and (b), respectively. The CDFs of the azimuth RAS (ARAS) for Scenario #1 and #2 are shown in Fig. 8(a) and (b), respectively. It is seen from Fig. 7 that the RDS distributions by nonparametric method and SAGE are very close. However, the ARAS distributions have relative big difference as seen from Fig. 8. Non-parametric method is over-estimate the ARAS. The same phenomenon is also observed in [27], which is because the non-parametric method is based on the rms angular spread definition in (11) to extract it from measured CIRs, the effect of virtual array pattern is included, while SAGE algorithm can de-embed the array pattern from the measured CIRs. The RDS and ARAS for the NLoS scenarios are larger than those for the LoS scenarios, which is consistent with the results below 6 GHz [28]. It is also found from Fig. 8 that RDS in Scenario #2 is much larger than that in Scenario #1 which might be due to the dense building windows in Scenario #2 with specular reflection from windows every now and then. The statistical results of the RDS and ARAS are summarized in Table 2. 1067
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MPCs (i 6 = j) is given by q MCDi,j = MCD2AoA,ij + MCD2τ,ij
(15)
where MCDAoA,ij and MCDτ,ij are the MCD in angular domain and delay domain, respectively, calculated as
MCDAoA,ij = j − i , τj − τi στ (16) MCDτ,ij = ζ 2 1τmax
FIGURE 7. Rms delay spread derived by non-parametric method and SAGE. (a) Scenario #1. (b) Scenario #2.
FIGURE 8. Azimuth rms angular spread derived by non-parametric method and SAGE. (a) Scenario #1. (b) Scenario #2.
C. CLUSTER PARAMETERS AND RICEAN K-FACTOR
Cluster is a widely used concept in the GSCMs such as 3GPP SCM and WINNER models as well as in Saleh-Valenzula (SV) model for ultra-wideband [35]. To extract the MPCs by SAGE, the cluster should be distinguished in delay-angular domain. In order to implement channel simulation, the cluster level parameters, e.g. number of clusters, cluster RDS and RAS etc. are essential. A multipath component distance (MCD)-based method is adopted to group the MPCs [36]. The MCD between the i-th and j-th 1068
where i and j are the directions of arrival given by = [cos (ϕ) cos (θ ) , sin (ϕ) cos (θ) , sin (θ )]T for the i-th and j-th MPCs, respectively, τi and τj are the delays of the i-th and j-th MPCs, respectively, 1τmax = max τj − τi ; ∀i, j ∈ [i, . . . , L] , and ζ is a delay scaling factor to balance the weights of the MCD in angular and delay domains. The clustering algorithm consists of the following three steps [12]: 1) Choose a reference MPC which has the largest power among all the MPCs in a set eligible for extracting clusters. 2) Calculate the MCD between the reference MPC and all other MPCs in the set, select those MPCs with the MCDs less than a predefined threshold denoted with MCDth , and group them together with the reference MPC as one cluster. 3) Remove the MPCs already allocated to a cluster from the MPC set, and re-execute step (1) to find the next cluster. This procedure stops until all the MPCs are assigned to certain clusters. The parameter ζ and MCDth are determined by a visual inspection evaluated by whether the clustering results can map to the physical environment. In this work, we found that ζ = 5 and MCDth = 0.25 are appropriate values to take. The 3D scatter plots (delay-AAoA-relative power) are shown in Fig. 9 in which Fig. 9(a) is by clustering result, and Fig. 9(b) is got by the measured CIRs in delay and angular domains. Clusters grouped by the MPCs are distinguished with different colors in Fig. 9(a) where the Circles represent the scatter locations. It is seen from Fig. 9 that clustering result agrees well with the physical environment. Based on the clustering result, the cluster RDS and RAS can be calculated by (9)-(14) from the propagation paths within each cluster. The cluster level parameters are also summarized in Table 2. Ricean K-factor (KF) is the power ratio between the LoS component and the sum of other propagation components [37]. The CDF plots of K-factor are shown in Fig. 10. It is seen that the K-factor fits well into norm distribution. The K-factor in Scenario #2 is less than that in Scenario #1 due to more windows in the buildings at the two sides of the road are scattered the power in Scenario #2. Figure 11 shows the CDF plots of number of clusters for the four measured routes, they fit well into normal distribution as well. The mean values of number of clusters for the LoS routes are about 8, which is the same as in WINNER microcell scenario [28]. For the NLoS routes, the mean values of number of clusters here are less than 10, which is less than the WINNER value of 16. This is because in mmWave propagation, blockage, e.g. by a VOLUME 5, 2017
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FIGURE 11. Number of cluster for the four measurement routes in Scenarios #1 and #2.
FIGURE 9. Scatter plots for the cluster locations in the NLOS route R3-4. (a) Clustering result. (b) Results by measured CIRs in delay and angular domains.
FIGURE 10. K-factor for the LoS routes in Scenarios #1 and #2.
building corner, can cause big loss, which may resulting in fewer number of clusters in the NLoS cases. The statistical values of K-factor and number of clusters are summarized in Table 2. D. COMPARISON AND SUMMARY OF THE CHANNEL MODELS AND PARAMETERS
In order to implement channel simulations and validations in next Section, Table 2 summarizes the statistical values of VOLUME 5, 2017
the LSPs, the parameters in clusters and the path-loss models. For comparison purpose, the corresponding parameters in [17] at 28 GHz, in mmMAGIC [22] and 3GPP [24] at 32 GHz are also listed in the same table. For the path-loss models and shadow fading, it is found in Table 2 that the pathloss exponents (PLEs) expressed by A for the LoS cases are in range of 1.8 to 2.2, which are close to that in free space of 2.0 in this and other work. For the NLoS cases, the shadow fading in this work is smaller than other work, and the PLE for Scenario #1 is slightly larger than other work. It is also observed from Table 2 that cluster parameters such as number of clusters, cluster ASA and ESA in this work agree well with [17] and [22] except the 3GPP [24]. It is also found that although the scenarios in Table 2 are all for outdoor microcells, there are relatively big differences for the LSPs, e.g. mean values of the RDS and RAS measured in NYC Campus are larger than Daojeon in both of the LoS and NLoS cases, and those values given by 3GPP [24] are larger than mmMAGIC [22]. In this work, these differences are also observed in Scenarios #1 and #2, which is caused by different outdoor environments such as window density on building surfaces, the trees and cars in the road etc. Therefore, despite the differences among these parameters derived by the measurements with different environments, from Table 2 it’s seen that the channel models and parameters in this work have relatively good agreements with [17] and [22]. The 3GPP parameter table should be corrected at the ITU assigned carrier frequencies. It’s also recommend to use the parameter table derived at a specific carrier to implement channel simulation other than using 3GPP frequency dependent parameters because, as mentioned in the introduction, 3GPP has developed frequency dependent channel models and parameters from 6 to 100 GHz, but its parameter table is based on the measurements performed only in a few discrete carrier frequencies with different measurement environments in the world. V. CHANNEL SIMULATION AND VALIDATION
We use 3GPP recommended channel simulation platform called QuaDRiGa [17] to implement channel simulation and validation, where our proposed parameters in Table 2 at 32 GHz are used as the inputs to generate channel coefficient.The implementation of QuaDRiGa is available as an open source in [38]. Because of the usage of 1069
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FIGURE 12. CDF plots of the rms delay spread derived by the simulated channel coefficient and measurements for the LoS and NLoS cases. (a) Scenario #1. (b) Scenario #2.
FIGURE 14. CDF plots of the elevation angular spread of arrival (ERAS) by the simulated channel coefficient and measurements for the LoS and NLoS cases. (a) Scenario #1. (b) Scenario #2.
FIGURE 15. CDF plots of K-factor by simulated channel coefficient and measurements for the LoS cases in Scenarios #1 and #2.
FIGURE 13. CDF plots of the azimuth angular spread of arrival (ARAS) by the simulated channel coefficient and measurements for the LoS and NLoS cases. (a) Scenario #1. (b) Scenario #2.
omni-directional antenna at the transmitter in our 32 GHz measurements, the AAoDs (Azimuth-Angle-of-Departures) and EAoDs (Elevation-Angle-of-Departures) are not available, therefore the AAoDs and EAoDs derived from NYC Campus in [17] are adopted in our simulations. The reasons for selecting [17] are that the measurements were also conducted in university campus, and the other LSPs in Table 2 have relative good agreement with our measurement results. The settings of simulation scenario are as follows: the Tx is equipped with an omni-directional antenna, its height 1070
is 6 meters and is located at the middle of map and surrounded by 250 receivers with height of 1.8 meters, they are equipped with dipole antenna distributed uniformly in a circle with radius of 200 meters. Both Scenarios #1 and #2 are simulated with the LoS and NLoS cases. Figures 12-15 show the comparison between measurement results and simulation outputs of the RDS, ARAS and ERAS as well as the K-factor, respectively. Figures 12(a)–15(a) are for Scenario #1, while Figs. 12(b)–15(b) are for Scenario #2, respectively. In the simulations, the RDSs are calculated by the simulated channel coefficients where the delays are generated by the simulator. The ARASs and ERASs are calculated using the power of each path based on simulated channel coefficient, the AoAs and EoAs are generated by the simulator, respectively. K-factor is calculated by (17). It is seen from Figs. 12–15 that the simulation results agree very well with measured results for both of the LoS and NLoS cases in Scenario #1 and #2. At the same time it can be found that QuaDRiGa is good platform for channel simulation in mmWave band at 32 GHz. VOLUME 5, 2017
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VI. CONCLUSION
In this paper, the channel measurements are carried out for outdoor microcells at 32 GHz ITU assigned mmWave band for 5G radio systems with bandwidth of 1 GHz. SAGE algorithm is used to extract the MPCs based on the measurements by rotating a horn antenna in azimuth and elevation planes to form a virtual array. The directional- and omnidirectional path-loss models are developed by using both of the CI and FI models. Based on the MPC parameters extracted by SAGE, the LSPs are calculated and compared with the results by the non-parametric method. The channel parameters in delay domain, e.g. the RDSs derived by non-parametric method and SAGE algorithm are very close, however, the angular domain parameters, such as the RASs derived by non-parametric method are much larger than those by SAGE which can de-embed array pattern from the MPCs. By using SAGE estimated MPCs, clusters are identified using MCD based clustering approach, and then the number of cluster and cluster level parameters are calculated. Parameter table at 32 GHz is summarized and compared with open literature, project report and 3GPP standard, which shows that our results are more close to the NYU WIRELESS and mmMAGIC outputs, however, a relative big difference from the 3GPP results existed. 3GPP frequency dependent parameter table is based only on the measurements performed in a few discrete carrier frequencies with different environments in the world, its parameter table should be validated and corrected at the ITU already assigned carrier frequencies. In this work, 3GPP recommended QuaDRiGa platform is applied to implement channel simulation, it’s found that QuaDRiGa is a good platform at 32 GHz mmWave band even if it’s originally a platform for channel simulation below 6 GHz. The results in this work are important in 5G link and system level simulations at 32 GHz. The comprehensive studies here in channel measurements, modeling, simulation and validation at 32 GHz could be very useful in 5G channel research, the methodologies can be extended to the other ITU assigned spectra as well. REFERENCES [1] METIS. (2015). Mobile and Wireless Communications Enablers for the Twenty-Twenty Information Society, D1.4, METIS Channel Models. [Online]. Available: http://www.metis2020.com [2] T. S. Rappaport et al., ‘‘Millimeter wave mobile communications for 5G cellular: It will work!’’ IEEE Access, vol. 1, pp. 335–349, May 2013. [3] W. Roh et al., ‘‘Millimeter-wave beamforming as an enabling technology for 5G cellular communications: Theoretical feasibility and prototype results,’’ IEEE Commun. Mag., vol. 52, no. 2, pp. 106–113, Feb. 2014. [4] A. Roivainen, C. F. Dias, N. Tervo, V. Hovinen, M. Sonkki, and M. Latva-Aho, ‘‘Geometry-based stochastic channel model for two-story lobby environment at 10 GHz,’’ IEEE Trans. Antennas Propag., vol. 64, no. 9, pp. 3990–4003, Sep. 2016. [5] M. Kim, J.-I. Takada, Y. Chang, J. Shen, and Y. Oda, ‘‘Large scale characteristics of urban cellular wideband channels at 11 GHz,’’ in Proc. Eur. Conf. Antennas Propag. (EuCAP), Lisbon, Portugal, Apr. 2015, pp. 1–4. [6] K. Belbase, M. Kim, and J.-I. Takada, ‘‘Study of propagation mechanisms and identification of scattering objects in indoor multipath channels at 11 GHz,’’ in Proc. Eur. Conf. Antennas Propag. (EuCAP), Lisbon, Portugal, Apr. 2015, pp. 1–4. VOLUME 5, 2017
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XIONGWEN ZHAO (SM’06) received the Ph.D. degree (Hons.) from Helsinki University of Technology (TKK), Finland, in 2002. From 1992 to 1998, he was a Director and a Senior Engineer with the Laboratory of Communications System Engineering, China Research Institute of Radiowave Propagation, Beijing, China. From 1999 to 2004, he was a Senior Researcher and a Project Manager with the Radio Laboratory, TKK, in the areas of MIMO channel modeling and measurements at 2, 5, and 60GHz and UWB. From 2004 to 2011, he was with Elektrobit Corporation (EB), Espoo, Finland, as a Senior Specialist at EB Wireless Solutions. From 2004 to 2007, he was with European WINNER Project as a Senior Researcher in MIMO channel modeling for 4G radio systems. From 2006 to 2008, he also with the field of wireless network technologies, such as WiMAX and wireless mesh networks (WMNs). From 2008 to 2009, he was in satellite mobile communications for GMR-1 3G, DVB-SH RF link budget, and antenna performance evaluations. From 2010 to 2011, he was involved in the spectrum-sharing and interference management between satellite and terrestrial LTE networks. He is currently a Full Professor of Wireless Communications with North China Electric Power University, Beijing, China, and chairs several projects by the National Science Foundation of China (NSFC), the State Key Laboratories and Industries on channel measurements, modeling, and simulations. Prof. Zhao is a Reviewer of the IEEE transactions, journals, letters, and conferences papers. He received the IEEE Vehicular Technology Society Neal Shepherd Best Propagation Paper Award in 2014. He has served as the TPC Member, Session Chair, and a Keynote Speaker for numerous international and national conferences. SHU LI received the B.Sc. degree in electronic information technology from North China Electric Power University, Beijing, China, in 2012, where he is currently pursuing the Ph.D. degree in electrical engineering and information technology. His current research interests include 3D-MIMO channel modeling, mmWave channel modeling, high-resolution parameter extraction algorithm, time-evolution channels, and D2D communications.
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QI WANG received the B.S. degree from North China Electric Power University, Beijing, China, in 2012, where she is currently pursuing the Ph.D. degree. Her recent research interests include mmWave communication, massive MIMO channel modeling, and human blocking modeling.
MENGJUN WANG received the M.S. degree in communication and information systems from the China Academy of Telecommunication Technology (CATT). He is currently a Senior Engineer with CATT. His research fields include mmWave mobile communications, MIMO technology, and heterogeneous wireless networks.
SHAOHUI SUN was born in Shaoguan, China, in 1972. He received the M.S. degree in computer engineering and the Ph.D. degree in communication and information systems from Xidian University, Xi’an, China, in 1999 and 2003, respectively. From 2003 to 2006, he was a Post-Doctoral Fellow with the China Academy of Telecommunication Technology (CATT), Beijing, China. From 2006 to 2010, he was with the Datang Mobile Communications Equipment Ltd., Beijing, where he has been deeply involved in the development and standardization of the Third-Generation Partnership Project Long-Term Evolution (3GPP LTE). Since 2011, he has been the Chief Technical Officer with the Datang Wireless Mobile Innovation Center, CATT. His current research area of interest includes multiple antenna technology, heterogeneous wireless networks, and relay. WEI HONG (M’92–SM’07–F’12) received the B.S. degree from the University of Information Engineering, Zhengzhou, China, in 1982, and the M.S. and Ph.D. degrees from Southeast University, Nanjing, China, in 1985 and 1988, respectively, all in radio engineering. Since 1988, he has been with the State Key Laboratory of Millimeter Waves, serving as the Director of the Laboratory since 2003, and is currently a Professor and the Dean of the School of Information Science and Engineering, Southeast University. In 1993, and from 1995 to 1998, he was a short-term Visiting Scholar with the University of California at Berkeley and at Santa Cruz, CA, USA, respectively. He has been engaged in numerical methods for electromagnetic problems, mmWave and THz theory and technology, antennas, and RF technology for wireless communications. He has authored and co-authored more than 300 technical publications and two books. Dr. Hong is a Fellow of CIE, MTT-S AdCom Member (2014-2016), Vice-President of the Microwave Society and Antenna Society of CIE, Chairperson of the IEEE MTT-S/AP-S/EMC-S Joint Nanjing Chapter. He has served as the Associate Editor of the IEEE TRANSACTIONS ON MICROWAVE THEORY and TECHNIQUES from 2007 to 2010, and an Editor board member for IJAP, China Communications, and the Chinese Science Bulletin. He was twice awarded the National Natural Prizes (second and fourth class), thrice awarded the First-Class Science and Technology Progress Prizes from the Ministry of Education of China and Jiangsu Province Government. He also received the Foundations for China Distinguished Young Investigators and for Innovation Group issued by NSF of China.
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