Non-regular Layout for Cellular Network System Simulations

Jussi Turkka Department of Communications Engineering

Tampere University of Technology Tampere, Finland jussi.turkka@tut.fi

Andreas Lobinger Nokia Siemens Networks

Munich, Germany andreas.lobinger@nsn.com

Abstract—This paper defines a synthetic non-regular Springwald network layout which is easy to take into use in cellular network system simulations. The performance of the non-regular layout was compared with two regular 3GPP simulation scenarios. The benefit of the non-regular layout is that it reflects better the live network deployments and therefore results in more realistic benchmarking metrics. The results shows that the Springwald layout provides a simulation environment where the system level performance is comparable with the reference scenarios, however, with some additional features of the non-regular effects.

Keywords - Radio Network Planning; RAN Dimensioning; Non-regular network topology; Springwald;

I. INTRODUCTION Wireless system performance is often evaluated with detailed link and system level simulations which give quite accurate indicators about the system behavior in different situations. Increasing complexity of the systems and the blooming development of the new modeling methods have made the system simulations more popular than ever. This enables easier and more cost-efficient research and development process for the radio network algorithms.

However, even if the system level simulators allow one to model the wireless systems accurately parameter by parameter, one should also configure the simulators in such a way that they model the real networks as accurately as possible. One of the simplifications which is usually made in case of the cellular network simulations is the assumption about the regular layout of the base station sites. This is rarely true in the real, live networks. Several definitions for the regular layouts are used and one of the most commonly used layout is the hexagonal clover-leaf layout [1], which is also used in the 3rd Generation Partnership Project (3GPP) [2]. The layout defines the regular locations for the base station sites based on the hexagonal geometry which is depicted in Fig. 1. The sites can be configured to have one, three, or six equally spaced antennas in the azimuth plane and this defines

the type of the regular grid. The regular layouts can be scaled by the cell size or the distance of the sites to adjust the simulation scenario. However, they still model a rather homogeneous environment.

The network topology in real networks is rarely regular due to the several reasons such as: the restrictions in the site acquisition, the geographically varying radio propagation conditions and the uneven capacity requirements per area. The restriction in site acquisition arises due to the fact that the sites, masts and antennas cannot be placed on the wanted locations. Local regulations or the land owners may restrict the placement, the location might be impractical or the site-sharing is preferred instead of building a new site. This causes variations to the physical site locations compared with the regular layout. Earlier studies showed that small random variations in the regular layout did not significantly affect the system level performance in case of the WCDMA systems [3].

The geographically varying radio propagation conditions also set limitations to the deployment of the regular layouts. The characteristics of the environment and the local clutter results in a cell specific path loss models. In some cells the radio signal decays faster as a function of distance and therefore some cells must be placed closer to others in order to guarantee the continuous coverage between the cells. However, the main reason for the non-regularities on the cellular networks is the uneven capacity requirement per area. Users are distributed non-uniformly between the cells and the capacity requirements per user can vary. This forces the

Fig. 1. Regular layouts. a) Nominal grid. b) Clover-leaf grid. c) Triangular grid.

Y-C

OO

RD

Fig. 2. The non-regular grid based on the Archimedean spiral

21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

978-1-4244-8016-6/10/$26.00 ©2010 IEEE 1929

operators to place the required sites more dense as the available capacity per cell is limited.

This paper is organized as follows: The properties of the Springwald layout are described in section II. In Section III the dynamic simulation tool and the configuration for the LTE simulations are explained. Finally the comparison between the regular and non-regular layouts is done in section IV.

II. NON-REGULAR SPRINGWALD LAYOUT

A. Non-regular grid There are different ways to create non-regular layouts. One

option to create the non-regularities is to add randomness to the site locations, antenna directions, number of antennas per site or the cell specific path loss models. However, one of the challenges in this approach is the repeatability of the simulations.

The Springwald layout is a synthetic non-regular layout which is easy to implement and describe providing a simple non-regular simulation scenario which reflects some effects from the practical network deployments. The approach to define the site locations is similar to the Archimedes’s spiral (1) as shown in Fig. 2.

r a b (1) where the radius r depends on the constants a, b and the angle . The sampling is done at equal sized steps of the angle and

therefore the radius increases in equal sized steps, too. The location of the first site is an exception as the difference in the angle is larger because the radius r is set to 0. In this paper the network layout is defined for 12 sites and the site locations are scaled with the minimum inter-site distance (ISD) of 500 meters. The equations which can be used to generate the site locations are shown in (2)

*[0,1: 3/10 : 4]

3/10

* j n

r n D

n n

p n r n e

(2)

where r(n) is a vector containing the radius samples for the sites. The variable D defines the minimum inter-site distance and it is used to scale the dimensions of the scenario. The variable (n) defines the angles which are used to define the polar coordinates p(n). Given the variable a is 0.4 and the variable b is 1/ then (1) approximates the Springwald layout and this even can be used to generate an additional tier of the interferers if needed. However, the Springwald layout can also be used with a wrap-around and in that case the 12 sites are enough from inter-cell interference point of view.

There are a few design principles which are fulfilled with this kind of configuration. First, the minimum and maximum ISD can be controlled to provide a flexible and less discrete ISD distribution, where the emphasis is on the medium sized sites. Second, the configuration provides several tiers of sites due to the large enough sampling angle. Finally, the ISD between the sites in different tiers matches to the wanted ISD distribution behavior. Moreover, the ISD distribution can be

adjusted with the Archimedean parameters, minimum ISD or the sampling rate.

B. Antenna placements The Springwald sites were configured to have three sectors

per site which is commonly used in the live network deployments. 32 of the 36 antennas were configured in a regular way, so that the azimuth angle between the intra-site sectors would be 120 degrees as depicted in Fig. 3. Therefore the azimuth angles for the three sectors are 30, 150 and -90 degrees. Moreover, 4 of the 36 antennas were adjusted with additional offsets. The offsets were used, because otherwise either the interference or the coverage conditions in some cells would have been too challenging. The antenna azimuth angles are shown later in Table III.

Several other things could have been taken into account as well. The antenna heights, cell specific down tilt angles and cell specific path loss models can be used together with the Springwald layout to emphasize he non-regularities in the cellular network deployments.

III. SIMULATION AND MODELLING ASPECTS

A. Simulation Tool The results in this paper are derived by using a fully

dynamic system simulation tool modeling UTRAN LTE release 8 in downlink and uplink and it has been used in several other publications as in [4]. The simulator maps link level SINR to system level following the methodology in [5]. Both the downlink and the uplink are modeled in a symbol resolution with several radio resource management, scheduling, mobility, handover and traffic modeling functionalities.

The main simulation parameters are based on the 3GPP specifications defining used bandwidth, center frequency, network topology, and radio environment [2]. The following sections describe the used system specific and scenario specific parameters in more detail.

-4 -3 -2 -1 0 1 2 3-5

-4

-3

-2

-1

0

1

2

3

1/6 5/6

3/2

7/5

4/15

1/15

7/5

X-coordinate

Y-c

oord

inat

e

Fig. 3. Antenna configurations for the Springwald cells

1930

B. Simulated scenario In the simulation three layouts are used, two reference cases

with regular hexagonal grid, 500m and 1732m constant inter-site distances and the Springwald layout. The sorted inter-site distances for each site to the adjacent neighbors in relation to the minimum ISD are shown in Fig. 4. This figure shows a profile of the geometric relations, so relation of small cell sizes to the largest cells in the layout and the distribution of the cell sizes. For a comparison to real networks, a suburban and an urban deployment are shown as well.

The profile of the Springwald shows similarities for both of the real deployments: a slope of increasing cell sizes and a maximum variation of the cell size. Springwald can be categorized as a suburban deployment with some additional density.

In the regular and the non-regular case, there was an additional tier of interfering cells causing background interference to the outer tier cells. This results in a similar kind of interference conditions to all simulated cells.

The maximum downlink transmission power is set to 46 dBm and the path loss model shown in Table I is a simple distance dependant model incorporated with 20 dB building penetration loss [2]. One should note that the path loss model was kept same in all cases. However, the denser network deployment might have been done in micro cell level in practice and in that case the propagation characteristics would have been different compared with the scarce macro cell deployment.

C. Simulation parameters Simulation parameters are described here in more details and

later all the relevant values are shown in Table II. In this paper only downlink results are shown. Maximum instantaneous number of users over the simulation area was set in such a way that average number of pedestrian users per eNodeB was 10. The pedestrian mobility model with the speed of 3 km/h was used to minimize the effect of the fast fading in observed SINR curves.

Users’ traffic profile was set to infinite buffer with best effort packet data. This guarantees full network load and the most challenging interference conditions in the cells. The call length was based on a distribution with the mean connection length being approximately 20 seconds in average. The goal of the study was to see how the essential topology related parameters vary between the regular and non-regular simulation cases and how the Springwald performance indicators would behave compared with the regular layouts.

IV. RESULTS

A. Downlink Simulation Results Fig. 5 shows the cumulative distribution functions for the

pathloss in different scenarios measured and merged from the all cells. Fig. 6. shows the probability density function for the interference over thermal noise (IoT) and Fig. 7. shows the cumulative distribution function for the carrier to interference

ratio (CIR) for the scheduled subcarriers. Note that in all cases the Springwald results were collected over the all 36 cells resulting in a nice single curve even there were significant differences in the cell specific curves. This can be seen by observing the cell specific 95% pathloss values in Table III.

0 1 2 3 4 5 6 70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Site Distance = x * minimum ISD

p(y)

<=

x

Inter Site Distance Profile

urban deploymentSpringwaldsuburban deploymentregular hex grid

Fig. 4. Inter site distance profile for different scenarios.

TABLE I SCENARIO SPECIFIC SIMULATION PARAMETERS

Parameter Value

Regular layout Hexagonal grid, 19 sites, each with 3 sectors

Non-regular layout Springwald Antenna Type 65 deg, 14 dB gain

ISD 500 m / 1732m / varying Pathloss model PL = 128.1 + 37.6*LOG10(Rkm)

Center frequency 2 GHz Shadowing standard

deviation 8 dB

Channel model 3GPP Typical Urban Mobility model 3 km/h pedestrian

eNodeB max TX power

46 dBm

UE max TX power 24 dBm Bandwidth 10 Mhz

Frequency reuse factor 1

TABLE II SIMULATION SPECIFIC PARAMETERS

Parameter Value

System LTE-FDD Rel.8 Simulation Time 71.5 seconds (1,000,000 symbols)

Hybrid ARQ yes ARQ no

Link Adaptation Both inner and outer loop BLER target 0.2

CQI reporting yes Packet Scheduler TD-PF/FD-PF

Handovers Sliding window size: 200ms Handover margin: 3 dB

Traffic Model Infine Buffer Average number of

users per sector 10

1931

The path loss distributions shown in Fig. 5. were quite predictable. The likelihood for larger path loss values is higher in the large ISD case and in the Springwald layout compared with the regular dense network layout. The large path loss values are not an issue if the system fractional loading is low, since the observed interference in the shared channels is moderate [6]. However, the large cells can perform poorly in case of the high interference and especially in more power limited uplink direction. The 95% pathloss values were -137.5 dB in large ISD case, -133.5 dB in Springwald case and -127.5 dB in small ISD case. It was observed that the standard deviation of the 95% path loss values was around 3.5 dB in Springwald cells.

Fig. 6. shows the probability density function for the downlink interference over thermal as described in [6]. The fully loaded network has a rather high IoT levels even in the largest cell sizes. The IoT is inversely proportional to the cell densities and the cell sizes. Therefore the largest IoT levels are observed in the ISD 500m case where the median IoT was 32.5 dB. The IoT distribution in Springwald case falls in between of ISD 500m and ISD 1732m scenarios as assumed because the cell sizes vary from small cells to large cells. The median value for the Springwald case is 21.5 dB which is still rather large compared to the median value in ISD 1732m case which was 13.5 dB. Rather high IoT values indicate that the cells are interference limited and there is a huge potential in inter-cell interference coordination schemes (ICIC) to improve the cell specific performance [7].

Fig. 7. depicts the CIR curves which were quite similar to the macro cases found in [8]. The median values in all cases were approximately 5 dB and the 5-percentile values were approximately -6.5 dB. This indicates that a small fraction of UEs can have problems on decoding the downlink common channels based on the SINR thresholds and link budget calculations in [8-9]. Even if the median value of the SINR in Springwald was around 5 dB, the standard deviation of the medians in different cells was around 1.8 dB indicating that the cell specific G-distributions varied from cell to cell.

B. Impact of the non-regular layout to the radio network planning process How the non-regularities in the network topology should be

taken into account in the radio network planning? In [3] the conclusion was that the small variations in the site locations and the antenna directions did not degrade the system performance significantly in case of WCDMA radio access technology. The similar kind of conclusions can be made for LTE based on the findings in this paper. The global system G-distributions are roughly the same in all topology cases and this knowledge can be used to approximate the system and cell capacities.

Basically the cell capacity can be approximated by integrating the probability weighted radio link performance curves over the range of the values in the G-distribution as explained in [10]. If the similar link performance is assumed in all scenarios (e.g. the same radio access technology) and the G-distributions are identical, then the system performance is also similar. Moreover, in regular layouts the system performance is assumed to correlate well with the cell specific performance as the interference conditions in all cells should

00.10.20.30.40.50.60.70.80.9

1

-145 -125 -105 -85

Pathloss [dB]

cdf

ISD 500mISD 1732mSpringwald

Fig. 5. Path loss distributions in different simulation cases.

0

0.01

0.02

0.03

0.04

0.05

0.06

-5 15 35 55IoT [dB]

pdf

ISD 500m

ISD 1732m

Springwald

Fig. 6. Behavior of the downlink IoT in different simulation cases.

00.10.20.30.40.50.60.70.80.9

1

-15 -5 5 15CIR [dB]

cdf

ISD 500ISD 1732mSpringwald

Fig. 7. The cumulative G-distributions in different simulation cases.

1932

be the same. This is true if the users are uniformly distributed over the simulation area.

However, in Springwald there are differences in the cell specific path loss and G-distributions. This means that even if the system wide G-distributions are similar, there are some differences in the local performances in Springwald. This is an attractive feature if one wants to have a scenario with comparable system wide performance, but with some local variations which require further optimization (e.g. algorithm benchmarking for self-organizing networks).

Another radio network dimensioning related planning principle can be concluded from the results too. In network dimensioning the operators define the network coverage and capacity based on the coverage and capacity requirements. One target is to minimize the number of base stations as each unnecessary base station is a rather expensive investment for the operators [11]. However, what happens to the available

cell capacity if the site density is increased? Does it remain same even if the interference increases dramatically as seen in Fig. 6? This occurs if the coverage can be provided with scarce site density but the denser topology is required due to the capacity demands. The increased interference levels could indicate that the performance degrades too. However, since the G-distributions do not change in regular-layouts, the assumption is that the cell capacity remains. Therefore the dimensioning process can be simplified. However, in non-regular deployment, further optimization might be needed later.

V. CONCLUSION This paper showed a way to define a non-regular network

topology which is simple to define and utilize. The key performance indicators showed that the system performance in Spingwald is on a suitable level compared with the two 3GPP reference cases. Moreover, the local cell specific differences in cell sizes, cell shapes, neighbor relations, path losses and interference conditions results in a simulation scenario which can be used to benchmark different radio resource algorithms, trying to solve practical problems in non-regular cellular networks.

VI. ACKNOWLEDGEMENTS The author would like to thank colleagues from Nokia,

Nokia Siemens Networks, Magister Solutions Ltd, Jyväskylä University and the Radio Network Group at Tampere University of Technology for their constructive criticism, comments and support with the work.

REFERENCES [1] J. Itkonen, B. Tuzson and J. Lempiäinen, “Assessment of Network

Layouts for CDMA Radio Access”, EURASIP Journal on Wireless Communications and Networking, 2008.

[2] “Physical Layer Aspects for Evolved UTRA, “ 3GPP Technical Report 25.814”, version 7.1.0, September 2006.

[3] J. Niemelä & J. Lempiäinen, “Impact of Base Station Locations and Antenna Orientations on UMTS Radio Network Capacity and Coverage Evolution”,IEEE 6th Int. Symp. On Wireless Personal Multimedia Communications Conf., Yokosuka, 2003.

[4] P. Kela et al., “Dynamic Packet Scheduling Performance in UTRA Long Term Evolution in Downlink”, in conference proceeding of ISWPC2008, 2008.

[5] K. Brueninghaus et al., “Link performance models for system level simulations of broadband radio access systems”, in proceedings of the Personal, Indoor and Mobile Radio Communications (PIMRC’05), vol. 4, September 2005.

[6] J. Turkka, “A Study of G-distribution statistical properties under fractional network loading”, IEEE VTC2010, Taipei, Taiwan, 2010.

[7] G. Fodor et al., “Intercell Interference Coordination in OFDMA Networks and in the 3GPP Long Term Evolution System”, Journal of Communications, Vol.4, No. 7, August 2009.

[8] H. Holma & A. Toskala, “LTE for UMTS: OFDMA and SC-FDMA Based Radio Access”, Wiley & Sons Ltd, 2009.

[9] S. Sesia, I. Toufik and M. Baker, “LTE - The UMTS Long Term Evolution”, Wiley & Sons Ltd, 2009.

[10] P. Mogensen et al., “LTE Capacity Compared to the Shannon Bound” VTC Spring 2007.

[11] J. Lempiäinen, & M. Manninen M., “UMTS Radio Network Planning, Optimization and QOS Management for Practical Engineering Tasks”, Springer, 2004.

TABLE III SITE SPECIFIC CONFIGURATIONS

Site Id

n Cell Id

m Azimuth

Angle (deg.) Area (km2)

95% PL (dB)

1 1 30 0.34 138 2 150 0.11 135 3 -90 0.25 133

2 4 30 0.33 139 5 150 0.30 140 6 -90 0.08 127

3 7 30 0.10 137 8 150 0.33 141 9 -90 0.15 134

4 10 30 0.18 133 11 150 0.36 139 12 -108 0.35 139

5 13 30 0.28 139 14 150 0.24 134 15 -90 0.49 142

6 16 12 0.65 143 17 150 0.33 138 18 -90 0.49 139

7 19 30 0.55 141 20 150 0.41 141 21 -108 0.43 140

8 22 30 0.50 143 23 150 0.51 144 24 -90 0.32 139

9 25 30 0.57 141 26 150 0.65 142 27 -90 0.36 138

10 28 48 0.52 142 29 150 0.47 141 30 -90 0.67 141

11 31 30 0.42 140 32 150 0.49 140 33 -90 0.57 139

12 34 30 0.41 139 35 150 0.44 140 36 -90 0.22 133

1933