978-1-4799-5863-4/14/$31.00 ©2014 IEEE

Performance Evaluation & SON Aspects of Vertical Sectorisation in a Realistic LTE Network

Environment

Konstantinos Trichias*, Remco Litjens*,†

*TNO, † Delft University of Technology Delft, The Netherlands

{kostas.trichias,remco.litjens}@tno.nl

Abdoulaye Tall, Zwi Altman, Orange Labs

Issy-les-Moulineaux, France {abdoulaye.tall,zwi.altman}

@orange.com

Pradeepa Ramachandra Ericsson Research Linköping, Sweden

pradeepa.ramachandra@ericsson.com

Abstract— The advent of Active Antenna Systems has provided new ways and features for increasing the capacity of mobile networks in order to cope with the ever increasing traffic demand. One such feature is the vertical sectorisation (VS) technique where a cell is divided into an inner and an outer sector by using a second downtilted beam from one antenna. The two sectors use the same spectrum resources but share the electrical power available. The limitation in available power per sector, raises the question whether there is gain in using VS, and if so, how much and under which circumstances. This paper presents some new numerical results on the performance gains of VS in a realistic LTE network environment, especially for the case of independent activation of the feature across cells in the network. We also present a framework for the design of a SON controller enabling the smart (de)activation of the feature based on the estimated/observed traffic distribution. The controller design follows two steps: first the functional form of the controller is derived based on analytical modelling, and then this functional form is calibrated using realistic measurements in a statistical learning fashion.

Keywords—Vertical Sectorisation, Active Antenna Systems, Self-Organizing Networks, realistic LTE environment.

I. INTRODUCTION

The ever-increasing data demands of the subscribers have driven operators to adopt the resource-efficient LTE (Long Term Evolution) air interface. The combination of LTE with Self Organizing Network (SON) techniques and advanced features such as Active Antenna Systems (AAS), promises even higher network performance, reduced costs and long-term network sustainability. AAS can offer significant capacity enhancements with a high degree of flexibility [1], while its combination with SON features will ensure that the network better adapts to the spatially and temporally varying traffic demands [2].

AAS differ from the traditional antenna in terms of integration of the radio-frequency components with the array of antenna elements (active electronic components). This facilitates a large degree of freedom when exploiting these antenna elements thereby allowing more than one beam from the antenna whose beam shape can be controlled individually [3]. One of the main AAS features to enhance the capacity requirements is to dynamically deploy Vertical Sectorisation

(VS), which refers to the use of two vertically separated beams from the same antenna. Figure 1 illustrates the impact of VS on the split service areas of a sectorised cell.

Figure 1: Split of a cell’s service area when using VS.

The capabilities of AAS and the performance of VS have been previously studied. In [4] and [5] a detailed overview of all AAS features is presented while in [1] Yilmaz et. al focus on the evaluation of the performance of VS for various sector tilts and vertical half-power beamwidths. In [6] Caretti et. al perform a comparison between vertical and horizontal sectorisation using a dynamic LTE system level simulator equipped with the 3GPP AAS model and conclude that significant gains can be observed with VS, when the proper network conditions are met. In [7] Tsakalaki et. al use a realistic AAS model and propose an enhanced vertical sector beam pattern design which leads to higher user throughputs due to reduced inter-sector interference. Finally, in [8] Nguyen et. al evaluate the performance of VS in an HSPA system under more realistic conditions by using a semi-deterministic path loss model as well as realistic network layout and density maps, and they conclude that the empirical 3GPP model over-estimates the gains of VS.

Using system-level simulations of a realistic LTE network environment with a 3D ray tracing propagation model, this paper assesses the impact of VS in highly loaded scenarios and also illustrates the importance of choosing appropriate antenna parameters like transmission power and downtilt for each of the sectors. The gain brought about by VS is shown to

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DOI: 10.1109/ISWCS.2014.6933334

depend on different factors, such as the traffic distribution in the cell and the propagation environment. The paper further proposes a two-step methodology for designing a controller that (de)activates the VS feature as a function of the estimated or observed traffic load distribution between the inner and outer cells. In the first step, the functional form of the controller is derived analytically using a simplified network model. In the second step, results from the afore-mentioned realistic LTE network simulations are used to tune (calibrate) the analytically derived functional form of the controller, as derived in the first step.

The organisation of the paper is as follows. In Section II, a brief description of VS and the used control parameters are presented. Modelling and scenario related details are given in Section III and Section IV respectively. In Section V the numerical results from our simulator are presented, while in Section VI a two-step methodology for designing the SON controller for the (de)activation of VS is presented. Finally, in Section VII, concluding remarks are given.

II. VERTICAL SECTORISATION

The traditional technique for sectorising a Base Station (BS) site is in the horizontal plane by using different azimuths for each cell’s antenna (horizontal sectorisation). The development of AAS has enabled the use of antennas transmitting two beams in directions with different elevations (vertical sectorisation).

In VS, the cell is split into an inner and an outer sector with vertical downtilts denoted and , respectively (see Figure 1). Both sectors have the same azimuth. The tilts are generally performed electrically, allowing to set them dynamically as VS is activated. The inner and outer sectors share the total transmit power available for the cell, but the available spectral resources (Physical Resource Blocks (PRBs)) are fully reused. Each of the vertical sectors is a distinct cell with its own Physical Cell ID. Performance gains from VS are brought by the fact that the resources are spatially reused, but also by the reduced inter-cell interference due to the decreased power of the outer sectors. Hence, VS should offer higher gains when the loads are significant in both the inner and the outer sectors.

The choice of the antenna tilts and transmit powers for the inner/outer sectors is critical to the performance of VS. They both have a significant impact on the additional interference brought about by VS, on neighboring cells and between the inner and outer sectors. The size of the inner sector is much smaller than that of the outer sector, typically of the order of twenty percent, which makes VS beneficial only in cases of high traffic demand close to the BS. In case of low traffic demand in the inner sector, network performance can degrade due to the additional interference of the inner sector and the reduced transmit power in both sectors.

The conditions under which VS is beneficial should be carefully studied in order to determine its deployment strategy. To obtain maximum gains from VS, intelligent decision rules are needed for the (de)activation of VS. Furthermore an optimization of the antenna electrical tilts (

)

and of the proportion of total power allocated to inner sector ( , is essential.

III. MODELLING

This section describes the key modelling aspects underlying the presented downlink performance assessment of VS in a realistic LTE network.

A. System Aspects

We consider a realistic network layout of outdoor sectorised LTE sites covering a 5 × 7 km2 area in the city of Hannover, Germany, as depicted in Figure 2. The area covers both urban and a suburban environments. Although the entire network area (63 sites, 84 cells) is addressed in the dynamic event-driven system-level simulations, the performance statistics are collected only in the indicated central area of 3 × 5 km2 (36 sites, 51 cells).

Each cell is equipped with a ‘vertically sectorisable’ active antenna operating in the 1800 MHz frequency band, with a maximum transmit power ptotal of 40 W (46 dBm), a main lobe antenna gain of 18 dBi and a fixed mechanical downtilt of 4o. On top of this mechanical downtilt, a single electrical downtilt can be applied to the unsectorised antenna, or distinct electrical downtilts can be applied to each sector

in case the antenna operates in a vertically sectorised mode. The antenna model used in this work is based on [12], [13] and presents a more realistic pattern than the 3GPP model. In case of vertical sectorisation, ptotal is split into Pinner and Pouter = Ptotal – Pinner.

B. Traffic Characteristics

Traffic is generated according to a spatially heterogeneous Poisson arrival process, with the relative traffic intensities depicted in Figure 2. The given relative intensity map (with 10 × 10 m2 pixels) is uniformly scaled in order to obtain absolute traffic intensities. A generated session is modelled as a file download with a deterministic size of 16 Mb.

C. Propagation Environment

Using real three-dimensional building data and applying advanced ray tracing modelling [10], a realistic propagation model is developed to cover both in- and outdoor pixels (10 × 10 m2). Considering that antenna masking is adaptively done within the simulations, depending on the specifically applied electrical downtilts, the propagation model covers isotropic propagation losses, comprising the effects of distance-based path loss, shadowing, diffraction, reflections and building penetration. For a given radio link between a sector antenna s and a pixel p, the propagation loss is characterised by the number of rays Ksp, the isotropic complex gain electrical field strength Esp,k and the vertical (ϑsp,k) and horizontal (sp,k) angles of departure (at the sector antenna) for each ray k = 1, …, Ksp. Applying the appropriate antenna masking AG() for the actual tilt settings, we find the overall path gain by aggregating over all rays:

(∑ | √ (

)

)

(∑ | √ ( )

)

Figure 2: Considered LTE network in Hannover and spatial traffic intensity.

D. Resource Management

A generated session is assigned to the cell towards which it experiences the strongest RSRP1. Given the dynamics of the number of served calls in each cell, the cell’s PDSCH

2 transmits at either full power or is idle. At any time, the experienced SINR of a given session associated with a user in pixel p and served by sector s is calculated based as follows:

∑ { }

1 Reference Signal Received Power 2 Physical Downlink Shared CHannel

with N the noise level and ps the transmit power of sector s, which depends on whether s is an inner or an outer sector, or an unsectorised cell, and in the sectorised case, on the power split given by Pinner.

Subsequently, the obtained SINR is mapped to an attainable bit rate R(SINR), expressed in Mb/s, using the modified Shannon formula taken from [11]:

{ }

with BW the available bandwidth in MHz. Assuming resource-fair channel sharing, the experienced bit rate of the considered user is then equal to R(SINR) / M, with M the current number of active sessions in the user’s serving cell. Once a file is fully transferred, the session’s experienced throughput is administered for post-processing and the session disappears from the system.

IV. SCENARIOS

In order to evaluate the capabilities and the performance potential of VS and to comprehend under what network conditions gains can be observed, a series of different scenarios were simulated with various VS parameters.

A. Network-level VS

In this scenario, a high load is applied onto the network and the performance of the original unsectorised network (unsectorised only in vertical domain, horizontal sectorization is present, i.e. not omni-cells) is evaluated and used as a baseline. Subsequently, the same scenario is simulated again, but this time all the cells in the network are vertically sectorised, using the exact same VS parameters (electrical downtilts and power split). Multiple simulations are run for various uniform tilt settings and power splits. The values used per parameter are listed in Table I. The outer sector electrical tilt

was kept at 0o (total tilt = mechanical tilt = 40) in order to maintain the same coverage as in the unsectorised case. By comparing the results of the unsectorised and the sectorised cases, the gain of VS and the effect of the different control parameters can be calculated.

B. Cell-level VS

In this scenario, the more realistic case of only one cell being sectorised at a time, is examined. The most loaded cell of the network is selected in order to illustrate the effects of VS in a loaded cell scenario. Scenarios with all the parameter values that are mentioned in Table I were also simulated in this case.

TABLE I CONSIDERED VERTICAL SECTORISATION PARAMETERS

inner sector outer sector

Antenna electrical downtilt

{6o, 8o, 10o}

= 0o

power split

Pinner {0.2,0.5,0.8} × Ptotal

Pouter = Ptotal - Pinner

C. Performance evaluation

In order to evaluate the performance of VS and to measure the offered gains and trade-offs, a series of Key Performance Indicators (KPIs) were used. The average and 10th user throughput percentile per cell were used to evaluate the experienced performance of the users, while the coverage percentage was used to estimate the level of service availability. A user is considered ‘in coverage’ if its RSRP from the serving cell is above -120 dBm. Finally, the load (in terms of a cell’s resource utilization) and the number of users served per cell were used to evaluate the cell benefits.

V. NUMERICAL RESULTS

In the following plots, the various scenarios are denoted with the naming convention ‘θx-y / Pz-r’ where x and y are the electrical tilts ( ) of the inner and outer sector respectively and z and r are the percentage of total power (P) allocated to the inner and outer sector, respectively.

Before going deeper into the analysis of the reported KPIs, it is important to understand the impact that VS has on the network layout by looking into the area being served by each cell before and after VS. Figure 3 below, depicts the best server areas for the Hannover scenario without (left) and with vertical sectorisation, assuming sectorisation scenario θ8-0 / P50-50 (right).

Figure 3: Best server areas in a scenario without (left) and with vertical sectorisation (right).

In most cells of Hannover, the inner sector created by VS is clearly visible, while the area covered by the inner sector of each cell differs significantly depending on the environment (urban vs suburban) and the propagation conditions at the respective area. In some cases, the inner sector is not visible at all which is attributed to the positioning of the antennas on the rooftops and the resulting low vertical angular spread of that cell (the targeted inner sector is blocked by the roof edge).

The remainder of the results provide a qualitative and quantitative analysis of the VS performance. Figure 4 depicts the average and 10th user throughput percentile for the entire network, in the case that all the cells are vertically sectorised.

Figure 4: Network-wide average user throughput and 10th user throughput percentile (VS in all cells).

It can be observed that for a loaded network scenario, VS is always beneficial and leads to improvement of up to 95% for the 10th user throughput percentile and up to 33% for the average user throughput. Moreover, the VS parameters that lead to the best performance can be identified. While the results for the tilt setting are very conclusive, since the tilt setting of θ8-0 always gives the higher gain (for the specific traffic distribution), the gain due to the power split between the sectors depends on the chosen KPI. While the average throughput presents the highest gain for the case of an even power split (P50-50), the 10th user throughput percentile improves more in the case that more power is assigned to the outer sector (P20-80). This makes sense since the 10th user throughput percentile is dominated by the users experiencing bad transmission conditions which in their majority are the users residing in the edge of the outer sector (low signal strength and high interference). When assigning more power to the outer sector, these users are greatly benefited and as a result the total 10th user throughput percentile increases.

The same scenarios were simulated for the case that only the most loaded cell in the network (cell 52 – see Figure 2) was vertically sectorised and the network wide KPIs can be seen in Figure 5. Once again, gains in both average and 10th user throughput percentile were observed from a network perspective, but the gain in this case is much lower since the effect of only one vertically sectorised cell, is minimum from an entire network perspective. In the scenarios presented in Figures 4 and 5, the coverage percentage varied by less than 0.003% among scenarios.

Figure 5: Network-wide average user throughput and 10th user throughput percentile (VS in cell 52 only).

In order to understand which factors affect the VS gain, we look at the performance of the most loaded cell and its neighboring cells in detail for both the cases of VS ON and VS OFF. Figure 6 depicts the average and 10th percentile user throughput gain for the sectorised cell (cell 52) and the average gains of its 10 nearest neighboring cells. For clarity reasons only the results of the scenarios with a tilt setting of θ8-0 are displayed, since the rest of the tilt settings offer similar results.

Figure 6: Throughput gains for cell 52 and its neighbors.

As can be seen from Figure 6, when more power is assigned to the inner sector, the performance of the cell actually deteriorates, while the performance of the neighboring cells improves. In order for both the performance of the VS cell and its neighbors to increase, the power needs to be evenly assigned among the sectors or more power has to be assigned to the outer sector. It must be noted that the statistics of both the inner and outer sectors were used to calculate the performance of cell 52 after VS. In order to understand this behavior we need to take into account the load and the number of users assigned to each sector, as depicted in Figure 7.

Figure 7: Number of served users and load per sector.

From Figure 7, it can be observed that even though the number of users assigned to each sector does not change (pilot signal is kept the same), the load of each sector varies greatly depending on the Tx power available to each sector. When the same number of users need to be served with less power, then more resources are needed per user and the load increases. We also observe that the number of users being served by the outer sector is always significantly higher than the number of users being served by the inner sector, since the outer sector covers a significantly larger area (larger footprint).

Figure 6 and Figure 7 give us insight on the appropriate settings to activate VS. When assigning only 20% of the power to the outer sector, then the majority of users in the cell are being served with very little power, thus the performance of the cell deteriorates. Moreover, the outer sector creates very little interference to the neighboring cells and thus the performance of the neighbors improves. On the other hand, when more power is assigned to the outer sector, the improvement of the neighbors is less significant since interference from the sectorised cell increases. In general, it is observed that the better the match between the offered load (served users) to the sectors and the power split is, the better the performance of the VS cell.

The insights gained from the above presented results, emphasize the need for a SON controller which will activate VS in an intelligent way and will optimize the sectorisation parameters, in order to avoid performance degradation due to VS.

VI. SELF-OPTIMISATION

This Section describes the methodology and first results for the VS controller design. The controller implements a policy for activating and de-activating the inner sector, based on the measured or estimated loads of the two sectors. The policy can be viewed as a decision boundary (a curve) in the inner and outer load plane. It is noted that the estimation of the inner and outer sector loads when VS is turned off is not covered in the present paper. The design process is performed in two steps. First, an analytical model is developed to derive the controller functional form. This functional form can be viewed as an expert model. In the second step, the controller form derived in the first step is adjusted using data from the realistic network simulator described in Section III, or conversely, from network measurements.

A. Analytical model

We develop in this section an expression for the boundary decision for activating and deactivating the VS in terms of the inner and outer loads. Let be the load of a cell, - the arrival rate, - the mean user throughput (MUT), - the file size, the expectation of which is denoted by . We use the superscripts ON and OFF when VS is activated (

), or deactivated (

)

respectively. Since the loads for VS ON are different from those for VS OFF, even when assuming the same traffic demand, we will have two decision boundaries: one for activation when inner is OFF and one for deactivation when inner is ON. The boundary is obtained by equating and . For simplicity of notation, inner and outer subscripts are abbreviated by and respectively.

The MUT of a cell can be expressed as

| |∫

(1)

where is the throughput of a user arriving at position

,

| | is the density of arrivals at position and is the area

of the cell. Following queuing theory results [15] , the

throughput can be expressed in terms of the load of the cell and the peak data rate at position , by

. (2)

can be expressed in terms of the traffic distribution and the total arrival rate in the cell as

| | (3)

The load can be written as [15]

∫

(4)

We now distinguish between the inner and outer parts of the cell. The MUT for VS OFF can then be defined as

(

) (5)

where are respectively the total arrival rate and

the mean peak data rate of the users in cell when VS is OFF. The expression is different when the inner is activated since now the inner and outer cells have different loads.

(6)

By expressing the total arrival rates in terms of the loads using (4), we can reformulate the MUTs (5) and (6) in terms of inner, outer loads and quantities independent of the traffic intensity.

The analytical derivation of the equation

then yields a quadratic equation of the following form

(7)

where the coefficients a, b, c, d and e are some constant parameters depending only on the SINR distribution in the inner and outer sectors.

We consider the case of a tri-sector macro site surrounded by 6 interfering macro BSs. We numerically evaluate the different coefficients for activation and deactivation cases and plot the corresponding boundaries as depicted in Figure 8. We recall that in the case VS is OFF, the inner and outer loads correspond to the estimated loads, and their sum is less than one (red curve in the Figure, denoted as the stability limit).

This result suggests that activation of the inner cell is only needed when the inner cell load is high enough. It is noted that the decision boundaries in Figure 8 are obtained for a worst-case SINR scenario, namely all interferers are transmitting all the time with maximum power. The decision boundaries obtained in this step are important since they allow to calibrate the controller (or the decision boundaries) using a small

amount of data which requires lengthy simulations or measurements.

Figure 8: Decision boundaries in the inner/ outer sector loads plane.

B. SON Controller calibration

In the calibration step, we adjust the model using the realistic network simulator described in Section III for scenarios with θ8-0 / P50-50 and variable loading, and statistically evaluate the coefficients. For different traffic demands, we evaluate the loads and the MUT for VS ON/VS OFF cases and plot in the load plane the different activation decisions obtained by choosing the action (VS ON, VS OFF) that gives the highest MUT, as shown in Figures 9 and 10 respectively.

Figure 9: Inner activation decision in the inner/ outer sector loads plane.

In order to find the decision boundaries, we fit a logistic regression with the functional form obtained in (7). The problem is formulated as follows

∑

(8)

where n is the index of a point on the graphs, and for blue stars in Figure 9 and Figure 10, and for the yellow squares in these Figures. The resulting decision boundaries are shown in Figure 11.

0 0.2 0.4 0.6 0.8 10

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o

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r,o

ff

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Stay deactivated

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inner

o

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r

Stability limit of unisector

VS ON VS OFF

Figure 10: Inner deactivation decision in the inner/ outer sector loads plane.

Figure 11: VS (de)activation decision boundaries in the inner/ outer sector

loads plane.

We can see that the form of the experimental boundaries match more or less the analytical ones. It is noted that the form of the controller highly depends on the specific case studied. Indeed, it is impacted by the antenna tilts used, the angular spread available in the sector, the distance to the neighbours and also their interference on the considered sector. So in the case when all sectors in the network are simultaneously activated for example, the decision boundary is expected to get much closer to the axes as additional gain from VS will be brought by reduced interference in the network.

VII. CONCLUDING REMARKS

This paper has presented new results on the gains brought about by the vertical sectorisation (VS) technique in mobile networks, and proposed a framework for designing a SON controller for smart activation of this feature. The numerical results have been obtained using a dynamic LTE system-level simulator with realistic propagation conditions (using ray-tracing) and traffic modelling. The numerical results especially for the case of independent activation of the feature across cells in the network have shown that the gain of VS highly depends on the traffic distribution. Indeed, for low loads in a sector there is generally no gain in activating VS. These results show that in order to fully benefit from the feature, an intelligent activation strategy must be devised.

A novel two step design methodology has been proposed for the controller. In the first step, an analytical model is used to derive the functional form of the decision boundary for activating / deactivating the VS feature, and served as an expert knowledge. In the second step, results from a realistic network simulator (or alternatively, from measurements) are utilized to calibrate the controller using statistical learning. First results have demonstrated the applicability of the proposed design methodology. Future work will focus on the impact of vertical angular spread on the site selection for the VS feature, and on the estimation of the inner and outer loads when the VS is deactivated. Moreover, further research will be carried out, targeting the optimization of VS parameters (tilt and power per sector) based on current traffic distribution.

Acknowledgment

The research leading to these results has received funding from the European Union, Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 316384. The authors would like to thank Dr. Delphine Lelaidier of Orange Labs, France, for the help in modelling the active antennas.

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Coverage and Capacity in LTE using Adaptive Antenna Systems’, Master’s thesis report, Feb 2010.

[2] J. Ramiro and K. Hamied, Self-Organizing Networks (SON): Self-Planning, Self-Optimization and Self-Healing for GSM, UMTS and LTE. John Wiley & Sons Ltd, 2011.

[3] 3GPP TR 37.840, ‘Technical Specification Group Radio Access Network Study of AAS Base Station (Release 11)’

[4] White paper “Active Antenna Systems: A step-change in base station site performance”, Nokia-Siemens Networks, 2012.

[5] White paper “Active Antennas: The next step in Radio and Antenna Evolution”, COMMSCOPE.

[6] M.Caretti, M.Crozzoli G.M Dell’Aera, A.Orlando “Cell splitting based on Active Antennas: Performance Assesment for LTE System”, Telecom Italia, 2012.

[7] Elpiniki P. Tsakalaki, Luis Angel Maestro Ruiz de Temino, Tomi Haapala, Javier Lopez Roman and Miguel Arranz Arauzo, “Deterministic Beamforming for Enhanced Vertical Sectorisation and Array Pattern Copensation”, Aalborg University, Nokia-Siemens Netwroks, Vodafone Technology Networks, 2011.

[8] Huan Cong Nguyen, Jarmo Makinen and Wolfgang Stoermer, “Performance of HSPA Vertical Sectorisation System under Semi-Deterministic Propagation Model”, Aalborg University, Nokia Siemens Networks, Deutsche Telekom, IEEE, 2013.

[9] D.M. Rose, T. Jansen, T. Werthmann, U. Türke and T. Kürner, ‘The IC 1004 Urban Hannover Scenario - 3D Pathloss Predictions and Realistic Traffic and Mobility Patterns’, COST IC1004 TD(13)08054, Ghent, Belgium, 2013.

[10] Y. Lostanlen and T. Kürner, ‘Ray-Tracing Modeling’ in ‘LTE Advanced and Next Generation Wireless Networks: Channel Modeling and Propagation’, Wiley, 2012.

[11] 3GPP, ‘Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Frequency (RF) system scenarios’, TR 36.942, v11.0.0, 2012.

[12] 3GPP TSG-RAN WG4 meeting #62, R4-120908, “Proposal to modeling active antennas”, Katherein, Feb. 2012.

[13] 3GPP TSG-RAN WG4 meeting #62bis, R4-121835, “Modeling Active Antennas”, Katherein, Mar, 2012.

[14] 3GPP TSG-RAN WG4 R4-120305, by Kathrein, February 2012

[15] T. Bonald and A. Proutière, “Wireless downlink data channels: User performance and cell dimensioning,” in ACM Mobicom, 2003.

0 0.2 0.4 0.6 0.8 10

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