survey of stochastic models used for a mimo system ...

TELECOM’2017 & 10èmes JFMMA. 10, 11 & 12 MAI 2017. RABAT, MAROC

SURVEY OF STOCHASTIC MODELS USED FOR A MIMO SYSTEM DEPLOYED ON 5G COMMUNICATION L. SQUALI (1), F. RIOUCH (1) (1) Research laboratory in Telecommunication Systems, Networks and Services (STRS), National Institute of Posts and Telecommunications (INPT), Rabat, Morocco, Email: [email protected] & [email protected] ABSTRACT: The multimedia data traffic conveyed by global mobile networks is booming, and this trend is expected to continue, as indicated by the Cisco network visual index [1]. These requirements of transmission rate and quality of service (QoS) cannot be met with conventional single antenna (SISO systems). Consequently, several antennas must be used for transmission and reception, therefore the name of Multi Input Multi Output (MIMO system). This diversity allows us to take advantage on the spatial dimension of the channel by exploiting the phenomenon of multi-paths, thus the capacity of the channel and the data rate increase considerably by introducing a spatial multiplexing [2] [3]. MIMO technology has been recently adopted by several standards, namely by 5G, which exploit these MIMO advantages so as to attend all the requirements set: network speeds as high as 10 Gbps, cell edge rate greater than 100 Mbps and latency of less than 1 ms. 5G Technology has also adopted a new spectrum bands (from 6GHz up to 100 GHz) with a large carrier bandwidth (up to 2 GHz), deploying the Millimeter waves (Mmwaves), nevertheless, these waves suffer from great limitations in terms of propagation conditions [4] [5]. The transmission channel becomes then a central element and the analysis of its properties represents an important 5G issue. Particularly for MIMO channels modelling of these bands, which are not addressed by existing channel models developed for bands below 6 GHz. Keywords: MIMO system, spatial multiplexing, propagation models, Millimeter waves, 5G coverage, stochastic models.

1.

INTRODUCTION

By comparing the mm-wave and microwave propagation channels, we can distinguish a few differences, such as the significantly higher shadowing and diffraction losses at mm-wave frequencies [6] [7]. As a result, the mm-wave channel is more dominated by specular paths and is presumed to use highly directive antennas array and beamforming. These differences, and the fact that many new environments are considered for mm-wave deployment, necessitate the need for new channel models at mm-wave frequencies [8]. The urge for new channel models has been aided by research projects involving both industry and academia, among these teams we can mention: 3GPP RAN WG1 (RAN1) [9], ITU Study Groups 3 and 5 (SG3, SG5) [10], IEEE 802.11 Task Group n (TGn) and TGac and TGad and TGay [11] [12], METIS 2020 [13], COST2100 [14], IC1004 [15], MiWEBA [16] and NYU WIRELESS [17]. There are two types of propagation models proposed for a 5G Multiple Antenna System (MIMO): deterministic and stochastic. We emphasize in this article the second type; since they are the most deployed and offer more flexibility (less information to provide and treat) and also it produce more general results. The study will examine the pertinence of those stochastic models and compare their performances.

2.

MM-WAVE

AND PROPAGATION CHANNELS :

MICROWAVE

The radio wave will interact with some obstacles in the environment causing multipath propagation, that create delayed copies of the original signal which will be transmitted to the receiver. The mechanisms leading to multipaths are generally divided into reflection, scattering and diffraction. Furthermore, all these paths can be attenuated due to shadowing objects. A. Reflection and transmission : A specular reflection occurs when an electromagnetic field interacts with an electrically large, smooth and distant surface. Due to the importance of permittivity in determining the strength of a reflected wave, many measurement campaigns have been dedicated to dielectric properties of typical urban and indoor materials. At mm-wave the penetration losses are usually very large and therefore accounting for penetration can be simplified to only inducing extra attenuation similarly to the Motley– Keenan model [18] typical building material losses at 60 GHz are shown in the Table below:

Table 1: Relative permittivities and penetration losses of Materials around 60 GHz Where Att is the penetration loss in dB/cm. It can be seen

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TELECOM’2017 & 10ème JFMMA that Att varies wildly even for a single material because the material samples used in measurements are not identical and their composition depends on, e.g., the moisture and possible metallic reinforcements. Also the permittivity is seen to vary depending on the specific material sample. B. Diffusion: When an electromagnetic wave impinges on a surface which is rough or small compared to the wavelength, the propagation phenomenon is referred to as scattering. By introducing an effective reflection coefficient which models the power scattered to the specular direction we have : Rrough = Rsmooth exp[2(kσh cos θi)2], where Rsmooth is the Fresnel reflection coefficient, k = 2π/λ is the wave number for the wavelength λ and σh is the standard deviation of the height distribution. To illustrate the effect of the surface roughness at 60 GHz, the ratio Rrough/Rsmooth, which ideally should be close to 1, is a function of σh for different incident angles.

utilizing deterministic field prediction such as ray tracing [23] [24]. The majority of stochastic model discussed on this article are based on the geometry-based stochastic channel model (GSCM) approach, which theoretically create the radio channel in delay and directional domains, by placing clusters in the simulation environment. The scatterers are placed randomly based on stochastic parameters that are typical for the environment. While non-geometric channel models, describe the propagation parameters themselves in a completely stochastic manner without calculating them based on the geometry of the environment. Some of the most important 5G propagation models are briefly described below by introducing there advantages, limits and the parameters to be optimized. 3.1. PATH LOSS, SHADOW BLOCKAGE MODELING:

FADING,

AND

5G Technology is is an attractive research field for densely populated urban areas (Urban Micro(UMi), Urban Macro (UMa) or Suburban macro-cell (SMa)) and indoor environments (indoor hotspot (InH) ), which are known to be the most demanding areas of mobile data. For these scenarios we can’t really estimate a good LOS between transmitters and receivers. Therefore, to adequately assess the performance of 5G systems, multi-frequency path loss (PL) models, Shadow Fading and blockage models will need to be developed across the wide range of 5G frequency bands.

Table 2: Surface roughness for indoor and urban materials

3.1.1. CI, CIF & ABG PL Models:

Most indoor materials have σh < 0.2 and can thus be considered smooth, implying that surface roughness does not require particular consideration. On the other hand, it can be observed that outdoor materials such as brick and asphalt are clearly rougher.

Three PL models are considered in this paper; namely the close-in (CI) free space reference distance PL model [25] the close-in free space reference distance model with frequency-dependent path loss exponent (CIF) [26] and the Alpha-Beta-Gamma (ABG) PL model [25]. The close-in (CI) PL model, given by:

C. Diffraction: At microwave frequencies, diffraction has been known as a major contributor especially in urban NLOS scenarios. At mm-waves, diffraction is negligible in most cases [19] with the exception of weak NLOS links and when the receiver is in the transition region close to the shadow boundary. When considering diffraction in deterministic field prediction, Huygens’ Principle is commonly used [20]. In short, it states that an object being radiated by a field can be seen as a secondary source which re-radiates a field to the receiver. In wireless communications especially the wedge diffraction is of interest because building corners, both indoors and outdoors, are the main objects causing diffraction.

3. STOCHASTIC MODELS

DEPLOYED ON

5G

SYSTEM : The Stochastic channel models aim at reproducing the statistical properties of a propagation channel in terms of, for instance, received power, delay or angular dispersion. This type of channel models is usually formulated as a set of mathematical equations, including parameters describing the characteristics of the environment and the deployment, such as the antenna height, the street width and the path loss. The parameterization of channel models is done either with channel measurements [21] [22] or by

where f is the frequency in Hz, n is the PLE (path loss exponent), d is the distance in meters, X CIσ is the shadow fading (SF) term in dB, and the free space path loss (FSPL) at 1 m, and frequency f is given as:

where c is the speed of light. In the CI PL model, only a single parameter σ needs to be determined through optimization to minimize the SF standard deviation over the measured PL data set. Alpha-beta-gamma (ABG) PL model includes a frequency and distance-dependent term to describe path loss at various frequencies, The ABG model equation is given by:

where α captures how the PL increase as the transmitreceive in distance (in meters) increases, β is a the floating offset value in dB, gamma captures the PL variation over the frequency f in GHz, and XABGσ is a Gaussian random variable representing the shadowing or large scale signal fluctuations about the mean path loss over distance. In the

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TELECOM’2017 & 10ème JFMMA ABG PL model there are three parameters which need to be optimized ( α, β, γ). the close-in free space reference distance model with frequency-dependent path loss exponent (CIF) is an extension of the CI model, and uses a frequencydependent path loss exponent given by:

where n denotes the path loss exponent (PLE), and b is an optimization parameter that captures the slope, or linear frequency dependency of the path loss exponent that balances at the centroid of the frequencies being modeled. The term f0 is a fixed reference frequency, the centroid of all frequencies represented by the path loss model, found as the weighed sum of measurements from different frequencies, using the following equation:

Where K is the number of unique frequencies, and Nk is the number of path loss data points corresponding to the kth frequency fk. The CIF model reverts to the CI model when b = 0 for multiple frequencies, or when a single frequency f = f0 is modelled. In the CIF model there are 2 optimization parameters (n and b). Dynamic blockage and geometry-induced blockage can be modelled by different modelling approaches. The dynamic blockage could be modelled as a component of the small scale fading by including excess shadowing on a number of paths or clusters, as has been proposed in [METIS 2015] or [IEEE 802.11ad]. The geometry-induced blockage could be modelled as a component of the largescale fading as additional shadow fading with certain occurrence probability. It is worth noting that the environment also causes transient path gains by, for example, motion of surfaces or objects with strong reflections. The effects of transient path gains cause dynamic shadow fading. . 3.1.2. IEEE 802.11: The IEEE 802.11ad channel model was developed by the IEEE 802.11 task group AD for 60 GHz wireless local area networks (WLANs) [11] [12]. The model aims at taking into account all the relevant characteristics of 60 GHz propagation channels, and supports beamforming and non-stationary channels due to moving people. The model is based on clustered rays and provides accurate spacetime and polarization characteristics of each ray, which consist of the LOS path and first and second order reflections. Channel sounding and ray tracing has been used to parameterize the model for three indoor scenarios, namely a conference room, a cubicle and a living room. Since the model parameters are created deterministically, the parameterization for each scenario is site-specific and may not be valid for other similar environments.

3.1.3. METIS channel model: The European 7th framework project METIS (Mobile and wireless communications Enablers for the Twentytwenty Information Society) was founded to lay a foundation for 5G as the first 5G channel model [13]. There are three METIS channel models; map-based, stochastic and hybrid models. They are 3D models with a

frequency range from 0.45 GHz to 70 GHz and beyond. The map-based model is meant for use cases where realistic spatial channel characteristics are needed, such as for large antenna arrays. The model is based on simple 3D geometries and ray tracing, including the propagation mechanisms. On the other hand, the METIS stochastic model follows the WINNER framework, and is specified separately for a number of scenarios and frequency bands. The METIS hybrid channel model takes advantage of the map-based and stochastic models for varying levels of demands in accuracy and complexity. It obtains the path loss and shadowing from the map-based model, and other parameters from the stochastic model. The usability of the METIS model at mm-wave frequencies is still unknown as very little validation work has been conducted. 3.1.4. WINNER II/+ models The 3rd generation partnership project (3GPP) released the spatial channel model (SCM) in 2002, which was developed for cellular systems with multiple antennas (MIMO) in the frequency range 2–5 GHz. It was parametrized in two dimensions, considering only azimuth angles and neglecting the elevation domain. Later the development of the SCM lead to the WINNER(wireless world initiative new radio) [27], WINNER II [28], WINNER+ [29]. According to the WINNER-II model the path loss can be calculated as:

Here d is the separation between the transmitter and receiver in meters, fc is the frequency in GHz, A is the path loss exponent, B is the intercept and C is the frequency dependent parameter. X is the environment specific parameter such as path loss due to a wall. PLfree is the path loss in a free space in LOS environment. Parameters used in the WINNER II Channel Models have been listed and shortly explained below: The first set of parameters is called large scale (LS) parameters, because they are considered as an average over a typical channel segment: Large Scale Parameters  Delay spread and distribution  Angle of Departure spread and distribution  Angle of Arrival Spread and distribution  Shadow Fading standard deviation  Ricean K-factor Support Parameters  Scaling parameter for Delay distribution  Cross-polarisation power ratios  Number of clusters  Cluster Angle Spread of Departure  Cluster Angle Spread of Arrival  Per Cluster Shadowing  Auto-correlations of the LS parameters  Cross-correlations of the LS parameters  Number of rays per cluster WINNER II channel model implementation was purely a 2D model, thereafter, the model was updated with a 3D representation of antenna arrays to facilitate the move to 3D channel modelling, known as WINNER+ Model, then a new LS parameter distributions is created: - ESD (RMS elevation spread in departure (BS)) - ESA (RMS elevation spread in arrival (UE))

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TELECOM’2017 & 10ème JFMMA - MED (Mean/median elevation in departure (BS)) - MEA (Mean/median elevation in arrival (UE)) Spatial consistency, meaning that two closely located mobile stations (MSs) experience similar power, delay and angular dispersion, is thus not supported. Therefore the WINNER model is incapable of modeling, for instance, device-to-device (D2D) links, where both link ends are moving. Furthermore, the WINNER model assume that antenna arrays are electrically small, which may not be a valid assumption for very large antenna arrays. 3.1.5. COST 2100: This channel model has been developed within the COST (European cooperation in science and technology) framework [14]. In contrast to WINNER, the COST 2100 model has fixed cluster positions. In most cases, the propagation paths, known as the multipath components (MPCs) are mapped to the corresponding scatterers and are characterized by their delay, azimuth-of-departure (AoD), elevation-of-departure (EoD), azimuth-of-arrival (AoA), and elevation-of-arrival (EoA). Clusters are formed by grouping scatterers that generate MPCs with similar delays and directions (azimuth and elevation). A cluster is depicted as an ellipsoid in space as viewed from the BS and from the MS, as illustrated in Figure 1, Three type of clusters are defined; Local clusters are located around the MS or the BS, and those are characterized by single-bounce scatterers only. Far clusters are divided into single-bounce and multiplebounce clusters. Single-bounce clusters can be explicitly mapped to a certain position by matching their delay and angles through a geometric approach. On the contrary, the multiple- bounce clusters are described by two representations, as viewed on figure 1 from the BS and the MS sides respectively, and called twin clusters. Visually, a twin cluster contains therefore two identical images of one cluster, appearing at both sides.

Figure 1. General structure of the COST 2100 channel model Like the WINNER family, the COST 2100 model is also designed for cases where one end of the link is fixed, and thus is not adequate for all 5G propagation scenarios (D2D). Furthermore the parameterizing of COST 2100 model on different environments is challenging, because the cluster characteristics cannot be easily extracted from propagation measurements.

CONCLUSION Despite the many 5G channel modelling informations presented in this survey, there is still a great effort required to ensure the successful deployment of mm-wave networks. Among these, the next task for as will be to validate the existing channel model frameworks by means of channel sounding and accurate field prediction tools in various mm-wave bands and environments. The results

from these actions can be used to derive models which are valid in an extremely wide range of frequencies, or to point out possible model deficiencies and propose more optimizations and solutions.

REFERENCES: [1]

D. Mcqueen, “The momentum behind LTE adoption,” IEEE Communications Magazine, vol. 47, no. 2, pp. 44–45, Feb. 2009. [2] I. E. Telatar. Capacity of Multi-Antenna Gaussian Channels. European Trans. on Telecomm., 10(6): 585{595, November 1999. [3] G. J. Foschini and M. J. Gans. On Limits of Wireless Communications in a Fading Environment When Using Multiple Antennas. Wireless Personal Communications, 6(3) :311, March 1998. [4] Y. Niu, Y. Li, D. Jin, L. Su, and A. V. Vasilakos, “A survey of millimeter wave (mmwave) communications for 5g: Opportunities and challenges,” arXiv preprint arXiv:1502.07228, Feb 2015. [5] T. S. Rappaport, S. Sun, R. Mayzus, H. Zhao, Y. Azar, K. Wang, G. N. Wong, J. K. Schulz, M. Samimi, and F. Gutierrez, “Millimeter wave mobile communications for 5g cellular: It will work!” Access, IEEE, vol. 1, pp. 335–349, May 2013. [6] S. Geng, “Millimeter wave and UWB propagation for high throughput indoor communications,” Ph.D. dissertation, Aalto Univ., Helsinki, Finland, 2011. [7] M. Peter and W. Keusgen, “Analysis and comparison of indoor wideband radio channels at 5 and 60 GHz,” in Proc. 3rd European Conf. Antennas Propag. (EuCAP), Berlin, Germany, Mar. 23-27, 2009, pp. 3830–3834. [8] Q. Li, H. Shirani-Mehr, T. Balercia, H. Niu, A. Papathanassiou, and G.Wu,“Millimeter wave channel model and system design considerations,” in Proc. 2015 IEEE Int. Conf. Commun. Workshop (ICCW), London, UK, June 8-12, 2015, pp. 1214–1219. [9] K. Haneda, G. R. MacCartney, Jr., et al. “Indoor 5G 3GPP-like Channel Models for Office and Shopping Mall Environments,” IEEE International Conference on Communications Workshops (ICCW), May 2016. [10] “Propagation data and prediction methods for the planning of indoor radiocommunication systems and radio local area networks in the frequency range 300 MHz to 100 GHz” International Telecommunication Union, ITU-R P. 1238-8, Geneva, 2015. [11] V. Erceg, L. Schumacher, P. Kyritsi, A. Molisch, et al., IEEE P802.11 Wireless LANs, TGn Channel Models, IEEE 802.11-03/940r3, May 2004. [12] IEEE P802.11 Wireless LANs, TGac Channel Model Addendum, IEEE 802.11-09/0308r8, September 2009. [13] J. Medbo, et al. "Radio propagation modeling for 5G mobile and wireless communications." IEEE Communications Magazine 54.6, June 2016. [14] http://www.cost2100.org/. [15] http://www.ic1004.org/. [16] Quasi-Deterministic Millimeter-Wave Channel Models in MiWEBA Richard J. Weiler, Michael Peter, Wilhelm Keusgen, Alexander Maltsev, Ingolf Karls, Andrey Pudeyev, Ilya Bolotin, Isabelle Siaud, Anne-Marie Ulmer-Moll EURASIP Journal on Wireless Communications and Networking, 2016:84, Mar. 2016. [17] G. R. MacCartney, Jr. et al., “Indoor office wideband millimeter-wave propagation measurements and channel models at 28 and 73 GHz for ultra-dense 5G wireless networks,” IEEE Access, vol. 3, pp. 2388–2424, Oct. 2015. [18] A. J. Motley and J. M. P. Keenan, “Personal communication radio coverage in buildings at 900 MHz and 1700 MHz,” Electronics Lett., vol. 24, no. 12, pp. 763–764, June 1988. [19] P. Smulders and L. Correia, “Characterisation of propagation in 60 GHz radio channels,” Electron. Commun. Eng. J.,vol. 9, no. 2, pp. 73–80, Apr. 1997. [20] H. F. Bertoni, Radio Propagation for Modern Wireless Systems. Upper Saddle River, NJ: Prentice Hall, 2000. [21] S. Y. Seidel, T. S. Rappaport, S. Jain, M. L. Lord, and R. Singh, “Path loss, scattering and multipath delay statistics in four European cities for digital cellular and microcellular radiotelephone,” IEEE Trans. Veh. Technol., vol. 40, no. 4, pp. 721–730, Nov. 1991. [22] M. Zhu, G. Eriksson, and F. Tufvesson, “The COST 2100 channel model: Parameterization and validation based on outdoor MIMO measurements at 300 MHz,” IEEE Trans. Wireless Commun., vol. 12, no. 2, pp. 888–897, Feb. 2013. [23] S. Hur, Y.-J. Cho, T. Kim, J. Park, A. Molisch, K. Haneda, and M. Peter, “28 GHz channel modeling using 3D ray-tracing inurban environments,” in Proc. 9th European Conf. Antennas Propag. (EuCAP), Lisbon, Portugal, Apr. 13-17, 2015, pp. 1–5. [24] Y. Chang, S. Baek, S. Hur, Y. Mok, and Y. Lee, “A novel dual-slope mmwave channel model based on 3D ray-tracing in urban environments,” in Proc. IEEE 25th Ann. Int. Symp. Pers. Indoor Mobile Radio Commun. (PIMRC),Washington DC, Sept. 2-5, 2014, pp. 222–226. [[25] G. R. MacCartney, Jr. et al., “Path loss models for 5G millimeter wave propagation channels in urban microcells,” in 2013 IEEE Global Communications Conference (GLOBECOM), Dec. 2013, pp. 3948–3953. [26] G. R. MacCartney, Jr. et al., “Indoor office wideband millimeter-wave propagation measurements and channel models at 28 and 73 GHz for ultra-dense 5G wireless networks,” IEEE Access, vol. 3, pp. 2388–2424, Oct. 2015. [27] D. Baum et al., “IST-2003-507581 WINNER D5.4 v.1.4, Final Report on Link Level and System Level Channel Models,” IST-WINNER, Tech. Rep., Nov. 2005. [28] P. Kyösti et al., “IST-4-027756 WINNER II deliverable 1.1.2. v.1.2, WINNER II channel models,” IST-WINNER2, Tech. Rep., July 2007. [29] P. Heino et al., “D5.3: WINNER+ Final Channel Models,” CELTIC, Tech Rep., June 2010.

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