Advanced Topics in Accurate Propagation Modelling ...

7th European Conference on Antennas and Propagation (EUCAP 2013) - Convened Sessions

Propagation Models For Heterogeneous Networks Florian Letourneux, Sylvain Guivarch, Yves Lostanlen Wireless Expertise and Research Centre, SIRADEL, Toronto, Ontario, Canada, [email protected] Abstract—In this paper, the different categories of propagation modeling are introduced, namely empirical, semiempirical and deterministic. Semi-empirical models can provide a first insight into the coverage of each layer (macro-cell, outdoor and indoor small-cells) but present many limitations for HetNet coverage prediction. On the other hand, deterministic ray-based models provide high accuracy for all configurations and radio links met in HetNet. Small and large propagation ranges are all addressed in a coherent way, thus cross-correlation between various links is inherently finely estimated resulting in an accurate HetNet coverage prediction. Besides, variations of signal levels at different reception floors can be reproduced by the construction of precise 3D ray paths. Thereby, coverage and system simulation tools can be fed with realistic scenarios and accurate 3D path loss predictions to investigate the impact of small-cells, to define a deployment strategy and finally to design and optimize HetNets. Index Terms—Canyoning; Heterogeneous Propagation model; Path loss; Ray-based, Small-cell

I.

Figure 1. Categorizes of Propagation Models.

Network;

INTRODUCTION

The evaluation of network performance with simulation tools for radio-planning and optimization tasks requires to accurately assess the path loss between a transmitter and a receiver using radio propagation models. A propagation model refers here to a path loss model; wideband channel models are not in the scope of this document. The LTE-advanced technology, recently introduced in 3GPP LTE Releases 10 and 11 [1], [2], offers different interference management techniques that enable dense deployments of outdoor and indoor small-cells leading to Heterogeneous Networks (HetNet). Propagation models shall address the various topologies and radio links in a coherent way to provide homogenous coverages of the different layers. Then, they can be combined to get reliable service coverage and capacity of HetNet. Besides, traffic demand is non-uniform with hot spots and a large indoor part and the coverage radius of small-cells are short (from a few ten to a few hundred meters). Thus, it requires accurate models and realistic 3D environments. The remaining of this paper is organized as follows: section II introduces the different categories of propagation models. Then, section III discusses the HetNet specificities and the different propagation modeling solutions. Section IV covers the challenges of HetNet performance analysis. Finally, section V draws conclusion and perspective.

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Figure 2. Path loss coverage maps from an empirical model (on the left) and from a deterministic model (on the right).

II.

OVERVIEW OF PROPAGATION MODELS

A. Categorization Investigations of the propagation phenomena can be done either experimentally based on measurements or derived from theory, as illustrated in Fig. 1. In the pre-GSM era mainly empirical propagation models have been derived from measurements [3], [4], [5]. With the availability of digital terrain data, in the 1990s, deterministic models based on theoretical approaches became more popular, see e. g. [6], [7], [8], [9], [10]. These developments enabled the integration of realistic propagation characteristics into simulation tools. Generally speaking, propagation models can be subdivided into the categories site-general and site-specific models. An example of path loss coverage maps from an empirical model and from a deterministic model is given in Fig. 2. B. Empirical Models Empirical path loss models are useful to study the principle behavior of system-level concepts or to enable a rough

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estimation of the number of required sites in a large area for example in greenfield planning during a license auction. These models do not require site-specific terrain information. Instead input parameters are e. g. path loss decay exponents, effective antenna heights or average clutter loss factors characterizing the average propagation environment. Prominent models of this type are the Okumura-Hata-Model [4], [5] and Lee’s model [3]. C. Semi-empirical Models The widespread availability of digital terrain data, which include terrain height, land use information and building data, triggered the development of site-specific propagation models and their integration into radio planning tools. Site-specific propagation models are based on the detailed terrain characteristics extracted along the individual propagation paths between transmitter and receiver. The first site-specific models applicable to cellular planning tasks are semi-empirical models, where the path loss calculation is based on a combination of deterministic approaches and empirical models. These models use Low Resolution (LR) geographic data. They give reasonably good results for the coverage prediction of large macro cell sites even deployed in urban areas [11], but due to their limited resolution these models have their limits in predicting for example indoor coverage problems in dense urban areas. Combinations of Knife-Edge diffraction models to consider shadowing by terrain deterministically with the empirical Okumura-Hata model [4], [5] and its extensions [12] are still the most frequently used semi-empirical macro-cell path loss models applied in commercial radio planning tools.

that are integrated in the radio-planning tools make use of that kind of multiple knife-edge diffraction loss. Ray-based models generate a high interest, as they succeed in predicting with a high accuracy the field strength around low transmitters in urban environments. When compared to VP model, a RB approach provides a much more accurate insight into the scattered radio energy around the transmitters. Fig. 3 illustrates a coverage footprint comparison between a VP and RB model for a small-cell configuration. Many propagation prediction techniques have been proposed over the last decade [14], [15], [16], [17]. For very low transmitter heights, the propagation is confined between the buildings and thus the first approaches have mainly been in two-dimensions (horizontal plane) [18], [19]. RB prediction methods rely on geographical data that require high-resolution accuracy and an adaptation to the propagation problem (e.g. limiting the number of useless diffracting edges, internal courts, and vertical details). This kind of accurate geographical data may be expensive. Yet the main restrictive aspect of the 3D ray-tracing (image method) for a large usage in radio-planning remains generally the computation time. Some techniques have been recently elaborated and are subject of continuous work to speed up the computation, especially for coverage computation. When the 3D ray-tracing is based on computer image theory, the acceleration techniques generally involve a pre-processing of the vector database. Often the simplifications are efficient in terms of computation time, but they result in the prediction of only dominant paths that may be dependent on transmitter site characteristics.

D. Deterministic Models Deterministic path loss models that rely on a terrain description (building layout, altitude, clutter types, building contours, etc) and precise terminal locations take advantage of the terrain description to simulate part of the shadow fading, approximating the impact of propagation physical mechanisms, then providing realistic and correlated spatial variations of the path loss. Two families of deterministic models are generally integrated in the radio-planning tools: vertical-plane (VP) models are based on multiple knife-edge diffraction and are bidimensional models that analyze the direct path only; ray-based (RB) models calculate the lateral multipath contributions and may be bi- or three-dimensional. The VP models analyze the vertical terrain profile along the direct path transmitter-receiver to estimate the propagation loss. Models based on the multiple knife-edge diffraction are commonly used for planning macro-cells in rural areas with LR geographic data, but as well in urban areas with LR or HR data. The model ITU-526 from [13] is widely used in the planning of cellular radio networks. It calculates the diffraction loss from the spherical surface of the Earth as well as the diffraction loss from one or several obstacles. The calculation of this latter diffraction loss is based on the Deygout method that approximates the main obstacles by an opaque half-plane vertical surface (called a knife-edge). Most of the VP models

Figure 3. Ray-based model vs. verticale-plane model for a small-cell

Mixing ray-tracing and ray-launching separated in two planes (vertical and horizontal) is another way to optimize the computation times [9], [10]. The 3D urban model presented in [11] is an efficient alternative to most recent models as the model gathers the main advantages of each previous solution. The model provides high accuracy for all configurations met in heterogeneous network topologies, i.e. outdoor, outdoor-to-indoor and indoor-tooutdoor radio links from macro, metro or indoor base-stations. Small and large propagation ranges are all addressed in a coherent way, thus cross-correlation between various links is inherently finely estimated. Variations of the signal level and

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channel properties at different reception floors can be reproduced by the construction of precise 3D outdoor-indoor ray paths. Thereby, HetNet studies can consider various cells configurations (Macro-, Metro-, Femto-cells etc.) and environment in the same system simulation. Fig. 4 gives an example of 3D propagation prediction between an outdoor small-cell and a user at different floors. It illustrates the ability of RB models to simulate the difference of the propagation channels according to the reception height.

2. 3. 4. 5. 6. 7.

BS: Base Station; SC: Small-Cell; UE: User Equipment.

Figure 4. 3D propagation predictions between an outdoor small-cell and a user at different floors.

III.

Figure 5. Tx topologies and radio links in a HetNet.

PROPAGATION MODELS FOR HETNET

A. Transmitter Topologies and Radio Links HetNets are made up of macro layer and small-cells that can be located outdoors, indoors or both. The macro-cell configuration refers to transmitter antennas located at a height that provides a fairly clear view over the surrounding building or vegetation. Such configurations can be retrieved in rural and urban environments with transmitters located on pylons or on rooftops. The radio energy with a macro-cell antenna is therefore mainly propagated through diffraction over top of the obstacles. In a HetNet, the macro layer aims at ensuring a seamless coverage since small-cells do not provide coverage everywhere. The outdoor small-cell configuration refers to transmitter antennas located at very low level (below the surrounding obstacles). Outdoor small-cells are present mainly in urban and suburban environments with transmitters located on urban furniture or against walls. The radio energy is mainly propagated by canyoning effect along the streets. Outdoor small-cells aim at reducing the cell size to offer higher data rates and to offload the macro layer by capturing traffic in hotspots. A small-cell will mainly cover its own street as well as a few adjacent streets and the nearby buildings. The indoor small-cell configuration refers to in-building transmitter antennas. A distinction between pico-cells and femto-cells is usually done: the former ones are deployed by the operator while the later ones are installed by the user. Indoor small-cells aim at improving indoor coverage and offering additional capacity. The various resulting radio links in a HetNet are listed hereafter and illustrated in Fig. 5: 1.

Macro outdoor BS to outdoor UE

Macro outdoor BS to indoor UE Indoor SC to indoor UE Indoor SC to outdoor UE Indoor SC to indoor UE in another building Outdoor SC to outdoor UE Outdoor SC to indoor UE

B. Propagation Models The characteristics of the main deterministic and semiempirical models are detailed in Table 1. The general benefits and drawbacks are listed and then specificities to the different topologies are described. Although a semi-empirical model would provide a first insight into the coverage area of a macrocell, VP model or RB models are more suitable to accurately assess the Macro-cell contributions in HetNet configurations. Besides, both VP and RB model offer the capability to simulate multi-floor indoor coverage. The prediction of outdoor small-cells cannot be properly addressed with a semi-empirical model since the radio propagation is strongly dependent upon the transmitter configuration. For street-level transmitters, the best accuracy is achieved with a RB model but comes at the expense of HR map data. The simulation of indoor small-cells can be tackled with a RB model but it requires a detailed description of the indoor environment (internal walls, furniture…). A semi-empirical approach considering a simplified indoor description is a good trade-off for the assessment of indoor-to-indoor radio links. However, the latter does not predict the impact of the indoor small-cell in the street or in the surrounding buildings. IV.

HETNET PROPAGATION MODELING CHALLENGES

A. 3D Simulation Approach The traffic demand is expected to be much non-uniform with hot spots and a large part of users located indoors (typically 60-80%). Small-cells dedicated to capture this traffic have small coverage radius ranging from a few ten to a few hundred meters. A traditional 2D simulation approach used for macro deployment will not enable to assess the coverage footprint and interference impact of these new topologies.

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To design an efficient HetNet in urban areas, a tailored simulation framework and methodology are essential. In particular, it must rely on suitable propagation models as discussed in section III and on a 3D environment description including outdoor and indoor areas. For example, indoor traffic demand might be addressed by outdoor small-cells at the lowest floors, by macro-cells at highest floors or by indoor deployments either in part or in the whole building.

TABLE I.

Overview

Macro-cell

Outdoor small-cell

Indoor small-cell

coverage levels of the different layers (high dynamic range of signal levels, similar prediction accuracy, etc.). Then, they can be combined to get reliable HetNet SINR levels. C. Exploitation of Propagation The usage of HR geographical data and RB propagation models bring realistic spatial correlation and variability in pathloss predictions yielding a coherent 3D HetNet coverage. This approach enables realization of small-scale or illustrative case

COMPARISON OF SOME PROPAGATION MODELS FOR HETNET

Deterministic Models Vertical-plane models Pros: - In-street and multi-floor indoor deterministic predictions. - Any environments. - Large frequency range. - Flexible signal level range (distance). - Fast computation. Cons: - Lack of accuracy if site is not dominant. Ray-based models Pros: - Same as VP models plus: - Accurate for any topologies (canyoning effect). - Can predict path loss, angular and time variables. Cons: - Fast computation requires speed-up techniques. - 3D environment data required. Vertical-plane models Pros: - Accurate prediction of signal and interference levels. Cons: - Lack of accuracy if macro site is not dominant. Ray-based models Pros: - Accurate for any macro configurations. - Can predict path loss, angular and time variables. Vertical-plane models Pros: - Fair prediction of coverage area. Cons: - No prediction of canyoning effect. - Limited accuracy of signal and interference levels prediction. Ray-based models Pros: - Accurate prediction of signal and interference levels taking into account canyoning effect. Ray-based models Pros: - Accurate prediction of signal and interference levels taking into account canyoning effect. Cons: - Detailed building description is required. a

B. Coherent HetNet Coverage Prediction HetNet are made up of a mix of macro BS and low-power nodes geographically distributed to meet the traffic demand. Although both out-band and in-band HetNet deployment scenarios are investigated, it is expected that many LTE smallcells will be deployed on the same frequency bands as the macro layer in order to leverage the scarce spectrum. However, these deployments may create high interference levels leading to poor coverage zones or even dead zones (i.e. zones with no service) [23] [24]. Furthermore, LTE and LTE-A systems use AMC (Adaptive Modulation and Coding), thus the modulation and throughput directly depend on the HetNet SINR levels. Thereby, to accurately predict the (interference-limited) service coverage and the capacity of HetNets a consistent and comprehensive propagation modeling solution is mandatory. The selected propagation models must address the various topologies and radio links (outdoor-to-outdoor, outdoor-toindoor, etc.) in a coherent way to provide homogenous

Semi-empirical Models Pros: - First insight into the coverage area. - Fast computation. Cons: - Limited frequency range. - Limited distance range. - Limited environments (e.g. urban). - No indoor-to-outdoor and outdoor-to-indoor predictions. - More complex to calibrate accurately on few parameters Less applicable to different areas / cities.

E.g. COST-Hata model Cons: - No outdoor-to-indoor prediction (multi-floor indoor coverage).

E.g. Lee microcell model [20]. Cons: - Inaccurate coverage footprint. - No outdoor-to-indoor prediction (multi-floor indoor coverage).

E.g. COST231 model [21]. Cons: - Accuracy limited to short distance range. - No indoor-to-outdoor and indoor-to-outdoor-to-indoor prediction (in-street and other buildings coverage) a. However, extensions of well-known models address these radio inks, as model presented in [22].

studies and also realistic and large-scale heterogeneous network performance evaluation [23], [24]. It may be exploited for engineering rules and methodology definition at early stage, and then for design and optimization. For example, it was shown in [24] that femto-cells have a strong impact locally (gain or degradation depending on the access-mode and user profile) and not only at their own floor. Furthermore, this approach enables to perform simulations and field trials on the same setups. Measurements can be used to calibrate and validate simulations. Then, simulations are even more relevant to extend trial scenarios and carry out sensitivity analysis. Besides, various advanced techniques (MIMO, resource allocation algorithm, beamforming…) have been developed to improve the data rates and spectral efficiency by taking into account the local characteristics of the radio channel. 3D raybased models (or similar techniques) are well suited to estimate the site-specific space-time characteristics of the narrowband or wideband urban propagation channel, which is a key asset for performance simulation of new radio systems. Many works

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form the recent years present comparisons of the multipath predictions to wideband channel characteristics, such as the power delay profile, the delay spread or the angular spread [11], [12] [25]. These techniques are consequently useful and reliable to assess deterministically the wideband radio channel and go beyond SINR-based simulations to evaluate HetNet performances [23-24]. V.

Among the different categories of propagation models, the ray-based models seem to be the best suited to cope with the new challenges brought by HetNets (various topologies and radio links to be combined, 3D environments, etc.). If well implemented, they provide high accuracy and operational efficiency for all configurations and radio links met in HetNet. Small and large propagation ranges are all addressed in a coherent way, thus cross-correlation between various links is inherently finely estimated resulting in an accurate HetNet coverage prediction. Variations of signal levels at different reception floors can be reproduced by the construction of precise 3D ray paths. Thereby, coverage and system simulation tools can be fed with a coherent 3D HetNet coverage to investigate the impact of small-cells, to define a deployment strategy and finally to design and optimize HetNets. However, this 3D approach may be costly (especially the HR geographical data) and less relevant for rural or even suburban deployments. Innovative low-cost solutions tailored to small-cells could complete the ray-based models to provide a consistent and comprehensive propagation modeling solution.

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CONCLUSION

The evaluation of network performance with simulation tools for radio-planning and optimization tasks requires to accurately assess the radio link between a transmitter and a receiver with radio propagation models.

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