Field Trial Results for CoMP Downlink Transmissions in Cellular Systems

Joerg Holfeld, Erik Fischer and Gerhard Fettweis Vodafone Chair Mobile Communications Systems, TV-Dresden, Germany

Email: {joerg.holfeld;erik.fischer;fettweis}@ifn.et.tu-dresden.de

Abstract-This paper presents measurement results for coordinated mUlti-point downlink transmissions as Long Term Evolution - Advanced concept from a cellular test bed in Dresden, Germany. At first, a description of the real-time prototyping system and its signal processing introduces the major modifications from the existing 3GPP-LTE standard. Then, the cellular propagation and measurement environment of the test bed is characterized. Various spatial setups are compared in conjunction with cooperative and non-cooperative base stations as well as cell-center and cell-edge scenarios. Finally, results concerning the geometry factor, time difference of arrivals and spectral efficiencies complete this field trial.

I. INTRODUCTION

In the last decades, the demand in cellular mobile commu­nication systems changed from voice transmissions more and more to applications such as web browsing and multimedia streaming that require high network throughput. With the rollout of 3GPP Long Term Evolution (LTE) release 8 a multiple-input multiple-output (MIMO)-OFDMA based broad­band system enters the market which features a high system capacity, high coverage, low latency and bandwidth flexibility. But already with the standardization of LTE rel.8 the necessity became obvious to overcome drawbacks and conduct techno­logical extensions. Cell-edge users on the same time-frequency resources suffer from spatial multi-cell interference such that frequency planning becomes compulsory with the drawback of a decreased spectral efficiency. The International Telecom­munication Union (lTU) published a call for IMT-Advanced technology proposals with targets for future cellular systems in March 2008 [ 1]. Some months later, the study item phase for Long Term Evolution - Advanced (LTE-A) has been started. At the same time, the German Federal Ministry of Education and Research (BMBF) funded the EASY-C project with the ambition to investigate some of the LTE-A ideas in large scale test beds. One of the LTE-A enablers is the multi-user (MU) interference mitigation in the downlink (DL) between distributed base stations by coordinated multi-point (CoMP) linear precoding. With this paper, we want to present our realization concepts and publish field trial results for cell-edge and cell-center terminals in comparison with an uncoordinated transmission.

A. State of the art comparison The underlying transmission principle incorporates the DL

of a MU-MIMO transmission with orthogonal frequency divi­sion multiple access (OFDMA). With this kind of spatial mul-

978-1-61284-074-11111$26.00 ©2011 IEEE

tiplexing, different spatially distributed and non-cooperating receivers operate on the same time-frequency resources [2] and oblige the transmitter to mitigate or avoid the MU interference. Hence, knowledge of channel state information (CSI) at the transmitter (CSIT) is compulsory which is further classified in [3]. [4] distinguishes between transmission principles and presents capacity regions of interference limited systems. Linear transmit filters are presented for perfect CSIT in [5], but only estimated observations of CSI are available in real systems. The zero forcing transmit filter (TxZF) is analyzed taking the imperfect CSI into account in [6]. In the recent publications [7] and [8], synchronization requirements for joint transmission (JT) base stations are investigated. The impact of a limited backhaul to provide CSIT is studied in [9].

3GPP LTE rel.8 [ 10] is basically defined as single user (SU) system with codebook based precoding for limited transmit beamforming. The mobile terminal (MT) chooses a unitary matrix and reports the codebook entry as suggestion back to the base station (BS). Furthermore, interference mitiga­tion is impossible for cell-edge users with almost symmetric DL power levels between two BSs and without BS coop­eration [ 1 1]. For this purpose, frequency reuse planning is compulsory at the cost of a reduced spectral efficiency.

The subsequent LTE releases contain LTE-A concepts [ 12]. Coordinated scheduling and coordinated beamforming are in­cluded in LTE rel.9. An additional step is the JT in LTE reLlO as CoMP DL transmission in which several distributed BSs ideally suppress the spatial interference among mUltiple MTs by precoding such that a frequency reuse factor of one can be achieved.

Experimental conducted results with CoMP DL are rare in literature so far. [ 13] presents an alternative realization concept with TxZF and for a fixed spatial configuration. [ 14] describes the test bed in Dresden and includes system level simulation results. A companion paper has already published implementa­tion details and indoor results for the order-recursive precoder that is used during these field trials [ 15]. Furthermore, JT and relaying concepts for LTE-A are studied in [ 16].

B. Outline of this paper This publication presents field trial results of CoMP DL

in a typical urban area. We compare spectral efficiencies for various MIMO configurations and point out places with gains compared to non-cooperative base stations. We describe the differences and extensions to LTE rel.8 with implementation

details of our test bed hardware in the next Section II. A description of our system model follows in Section III. The measurement methodology is introduced in Section IV and verified on selected test bed positions. The Section V characterizes the cellular environment and emphasizes the cell­center and cell-edge regions to motivate the BS cooperation areas. For this purpose, we have chosen the geometry factor (GF) and the time difference of arrivals (TDOAs) as metric. The final Section VI anticipates our performance results in the large scale.

II. CoMP DL TRANSMISSION SYSTEM

In transmission systems, where multiple BSs, as cooperative transmitters, and MTs, as non-cooperative receivers, operate on the same time-frequency resources, a closed-loop operation must be enabled. The outline encompasses the BS transmitter side, the MT receive algorithms and the channel feedback chain.

Initially, the BSs, which are chosen for a CoMP transmis­sion, are grouped within a cooperative cell cluster. Herein, one BS is selected as the serving enhanced Node B (eNB) that provides solely the physical downlink control channel (PDCCH) with terminal configurations as in LTE rel.8. Every cluster BS must be served with the user data from the core network. The BSs in our CoMP cluster derive from GPS devices a global timing for frame synchronization and a common clock signal for the local oscillators in order to enable a sample-synchronized DL transmission. These strict requirements [7] are realized by Meinberg GPS l70MP devices within our test bed.

On BS transmitter side, the binary user data streams from all MT are grouped in code words and are modulated as complex samples in the baseband like in conventional MIMO­o FD M transmitters [ 17]. The spatial streams and additional MT specific demodulation reference symbol (DM-RS), that we describe afterwards, are mapped by the precoder to antenna streams. Finally, the multiplexing onto the physical resource blocks (PRBs) is performed. The processing of the analog front end concludes the transmitter chain.

In MU systems, linear transmit filters like the zero forc­ing, Wiener transmit filter (TxWF) [5] or block diagonal­ization [ 18] mitigate the spatial MU interference in order to achieve a high frequency reuse. Our filter realization embodies the TxWF which additionally suppresses the inter-stream interference. A spatial noise covariance term is included to provide the optimal trade-off between interference suppression and noise scaling in the sense of the minimum mean square error (MMSE). Two precoding matrices are computed per PRB, i.e, one matrix per six adjacent subcarriers. The TxWF is based on an order-recursive approach as described in [ 15]. We gain the advantage of a low complex algorithm and enable the support of multiple MIMO configurations as shown in the result section. All BSs in the CoMP cluster acquire the global CSIT and compute redundantly the linear filters compared to a centralized precoding solution. Every BS uses its particular precoding coefficients from distinct submatrices and applies

them to the synchronized data stream from the core network to obtain the antenna symbols.

A major modification has been conducted of the DL sub­frame scheme. The physical channel coefficients are reported back to the BSs as quantized feedback message for the allocated PRBs. The CSIT is estimated from cell-specific CSI reference symbols (CSI-RSs) which are referred as common reference symbols (RSs) in LTE rel.8. Furthermore, the sub­frame structure has been amended with MT-specific DM-RS which are also passing the precoding. Hence, it is possible for the MTs to estimate the effective channel as a superposition of the precoder and the physical channel. Also observations of the residual MU interference can be acquired. Both kinds of RSs are multiplexed on time-frequency orthogonal resource elements as it is shown in Figure 1. The O-th, 4-th, 7-th and ll-th OFDM symbols are completely allocated with RSs resulting in an increased pilot overhead. In the figure the indices of CSI-RSxy and DM-RSxy denote the symbols of the x-th BS or MT and the y-th transmit antenna or user data stream.

[QQJ rei O"'F D"'M """Sy= m""bo::'-;lsC"T.in"S""'ub"'fr=am:::e:----, -------7) m.J

I RS I PDCCH I PDSCH I PDSCH I RS I PDSCH I PDSCH I 1

� )@] ! RS tCH I PDSCH I PDSCH I RS I PDSCH I PDSCH I I OFDM Symbol 0 & 7 I I OFDM Symbol 4 & 11 I

� ---ITT ---

I OFDM

Subcarriers in

Physical Resource Block

1 m.J"

j DI1 ____

CSI-RSII

DM-RSII

CSI-RS21

DM-RS21

CSI-RS"

DM-RS31

CSI-RSII

DM-RSII

CSI-RS21

DM-RS21

CSI-RS31

DM-RS31

CSI-RS12

DM-RS12

CSI-RS22

DM-RS22

CSI-RS32

DM-RS32

CSI-RS12

DM-RS12

CSI-RS22

DM-RS22

CSI-RS32

DM-RS32

Fig. 1. The CoMP DL subframe with CSI-RS and DM-RS.

The receiver side signal processing steps are basically com­pliant with those of an LTE rel.8 MT and shown in Figure 2. Initially, the receiver side synchronization in time domain is performed. Two processing chains are distinguished into CSIT feedback and user data processing. In our system, the CSIT is gained by a least-square (LS) estimator [ 19] and quantized into samples with 8 bits per complex dimension. Then, a feedback message is broadcasted via the conventional physical uplink shared channel (PUSCH) on different uplink PRBs to all CoMP BSs. The CSIT signal processing has been realized in real-time such that we are able to generate a message every millisecond. The round trip delay results in approximately 7 milliseconds for the entire closed-loop chain.

Coarse Time Domain Synchronization Timing acquisition and CFO compensation

Computation

UL transmission to CoMP eNBs

Closed-Loop Real-Time Offline Computation

Fig. 2. The MT receiver side signal processing steps.

The BS receiver decodes the PUSCH of each MT individ­ually and merges the CSI into the global channel matrices. There is no additional inter BS CSIT synchronization such that CSIT inconsistencies may occur. On the other hand an exten­sion of the X2 backhaul has been avoided compared to [ 13]. The Table I summarizes further physical layer parameters.

TABLE I TRANSMISSION PARAMETERS FOR PHYSICAL LAYER.

DL carrier frequency

System bandwidth

FFT size

Sampling rate

Subcarrier spacing ill BSs / Tx antennas per BS

MTs / Rx antennas per MT

Feedback message size per MT

Allocated DL resource blocks II Subcarriers per resource block II

BS antennas

BS antenna downtilt / gain

Horizontal half-power beam width

Polarization

Effective isotropically radiated power

MT antennas

MT antenna gain

Horizontal half-power beam width

Polarization

2.68GHz

20 MHz

2048

30.72 MHz

15kHz

2/2

2/2

3072 bits

10

12

KATHREIN 800 10541

8° / 18 dBi

58°

Cross-Dual

61 dBm

KATHREIN 800 10431

2 dBi

360°

Vertical

III. SYSTEM MODEL

The MIMO-OFDMA system model will be simplified to a frequency flat MIMO channel in the latter. The linear precoders W E CNTXNR map subcarrier-wise the NR spatial streams, which are stacked in the vector;r, E cNRXl, onto all NT BS transmit antennas within the cluster. Hence, the precoders form with the physical channels H E CNRXNT

effective channels H E CNR X NR over which the streams are received in vector '!L E CNR X 1 at the distributed MTs with

( 1)

Finally, the received signals are filtered by interference rejec­tion combining (IRC) per MT.

The Wiener transmit filter matrix is computed by

W = f3TxWF . HH . ( HHH + tr(;:n) INR ) -1

(2)

with the spatial noise covariance �1l1l E CNRXNR and the en­tire transmit power ETx. In our system, the power scalar f3TxWF is not used according to the MMSE criterion. Instead, it is used as scalar to avoid clipping errors of the fixed-point implemen­tation. The system's transmit power adaptation is performed with power amplifiers as part of the analog front ends. Each of the two CoMP BS selects with WT

= [Wi wr ] T submatrices with the distinct precoding coefficients.

In the remainder, we split the CSIT into local CSI

H = [ H1,1 H1,2 ] (3) H2,1 H2,2

from the two MTs which report [ H1,1 HI,2] and [ H2,1 H2,2] as feedback of the channel coefficients from the two BSs.

IV. MEASUREMENT MET HODOLOGY

The CoMP DL transmission results we present are gained by a twofold measurement approach: The closed-loop real-time operation has been conducted for selected points in the test bed. Then, the received signals at the MTs have been dumped for an additional offline evaluation. Since the effort to cover such a wide area with closed-loop real-time measurements is tremendously high, a simplified and faster physical channel measurement campaign followed that only gathered the CSIT in open-loop operation.

We found a good match between the measured signal­to-interference-and-noise ratio (SINR) per data stream and simulated SINRs from the measured channel coefficients. Based on them, hypothetical transmit filters are computed in a bit-true fixed-point simulation model of the MTs and the post­precoding SINR values could be anticipated. In the latter, the SINR is defined at the receiver as the ratio of the power of the desired user stream to the power of the MU interference plus the spatial antenna noise.

For both evaluation methods, we selected several MIMO setups for a comprehensive performance comparison. We have chosen CoMP setups with four transmit antennas in total and varied the number of data streams for the two involved MTs from four streams in total to a single receive stream per terminal. So the corresponding dimensions of the physical MIMO configurations are 4 x 4, 4 x 3 down to 4 x 2. As perfor­mance reference to conventional systems, a non-cooperative precoding scheme has been also considered where the BSs only acquired local CSIT [ HI,I 0 ] and [ 0 H2,2] from the two MTs without the channel knowledge of the interfering links.

Ij� UJ 0 5 10 15 20

Measured SINR [dB]

Fig. 3. The mapping of measured SINR and estimated SINR values.

From our measurements, LS estimates of the CSIT with the noise powers (j�H(i,j) of each antenna link have been determined. Since a static physical environment can be as­sumed at least over some LTE subframes, the moving average H of the CSIT is computed over 10 milliseconds. In the simulations, the hypothetical precoder is computed based on perturbed mean CSIT H = (H + 8H) with the matrix element

8H( i, j) '" eN ( 0, (j�H( i,j» ) from a Gaussian distribution.

The Figure 3 shows the mapping of mean measured SINR values onto the mean estimated SINRs bounded by its standard deviations. These results are based on various spatial config­urations. Within a range of 5 dB up to 15 dB the SINR is slightly overestimated. The mapping error variance increases on both ends with an underestimation of the MU interference below 5 dB and an overestimation above 15 dB.

In the following, we use this system and mapping model to anticipate the spectral efficiencies of two areas in the test bed.

V. THE CELLULAR ENVIRONMENT

The test bed in Dresden, Germany, in which the field trials have taken place is located at a single site near the city center which was equipped with three sectors steering into 0°, 120° and 240°. On each sector one BS operates on the DL with two cross-polarized antennas of type KATHREIN 8001054 1 (see Table I).

The whole area covers approximately 700m in longitude and 1 100m in latitude and is distinguished between a northern and a southern test bed scenario. The propagation characteristics of the north scenario are determined by a typical residential area with a dense building of up to four stories. Here, a manifold shadowing occurs and for most parts one of the sectors is observed with line-of-sight (LOS) while the other remains in a non-line-of-sight (NLOS) condition. The south scenario represents an urban area with low building density and a LOS dominated propagation environment. Some street canyons with taller buildings separate the two sectors.

The geometry factor (GF) is used to quantify the mean receive power level of the serving BS to the mean interference level from the second BS over N CSI-RS observations with

G = 10 . log 10 ( 2:£=1 ( H1,1[n] )2 ) . (4)

2:n=1 ( H1,2 [n] )2

Based on the GF, places with symmetric and asymmetric receive power levels can be indicated as cell-edge and cell­center. Figure 4(a) highlights for the northern area some

positions with equal power ratios of 0 dB. Here, shadowing through prominent buildings causes a diverse behavior and results in a narrow cell-edge area. The southern area in Figure 4(b) shows a much wider cell-edge with only a view shadowing effects. In both figures the measured cell-edge agrees with the geometric cell-edge of the spanned sectors.

30.0 dB

25.0 dB

20.0 dB

15.0 dB

10.0 dB

5.0 dB

0.00 o.=_.......,. __ ..",..'-"-"""'-.....,. ...... -""" ...... � 0.00 0.15 0.30 0.45 0.60

0.0 dB

Longitude [km]

(a) Northern test bed area.

30.0 dB

25.0 dB

20.0 dB

15.0 dB

10.0 dB

5.0dB

O.OdB

Longitude [km]

(b) Southern test bed area.

Fig. 4. The absolute values of the geometry factors [dB] in the field trial area. The maps are provided by the courtesy of Google maps.

The cell geometry can additionally be described from the timing point of view. For this purpose, an additional offline channel estimation and adjacent subcarrier interpolation is performed with the robust assumption of a uniform power delay profile (PDP) [ 19]. We estimated the PDP of each transmission link based on the CSI-RSs and computed the time difference of arrival (TDOA) as difference of the mean excess delays between each BS per MT. The excess delay is

the first moment of the PDP [20] defined as

_ L.kP(Tk)Tk T = �==---=-.,--:..-.,----L.kP(Tk) (5)

with P(Tk) being the power of the k's channel impulse response (CIR) tap and Tk the corresponding delay. It can be seen in Figure 5(a) and 5(b) that the cell-edges based on the TDOA match quite well with the geometry factors.

E �

0.71

� 0.47 ::J

""

Cii ...J

0.15 0.30 0.45 0.60 Longitude [km]

(a) Northern test bed area.

0.16 0.33 0.49 0.65 Longitude [km]

(b) Southern test bed area.

1.0 us

0.8 us

0.6 us

0.4 us

0.2 us

0.0 us

-0.2 us

-0.4 us

-0.6 us

-0.8 us

-1.0 us

1.0 us

0.8 us

0.6 us

0.4 us

0.2 us

0.0 us

-0.2 us

-0.4 us

-0.6 us

-0.8 us

-1.0 us

Fig. 5. The measured time differences of arrival [J.ts] in the field trial area.

VI. RESULTS

In the remainder of this section want to present the results of our investigation concerning spectral efficiencies at the cell­center and cell-edge regions.

For this purpose, we classified instantaneous channel mea­surements according to their geometry factors G. All channel matrices, whose absolute value of the GF is located within a tolerance of ±1 dB, are assigned to a certain geometry

to. 0.8 Cl

U �0.6 C 0) E �0.4 0-E o UO.2

1.0 r�=======::::;:l

to. 0.8 Cl u

�0.6 C 0)

]0.4 0-E o UO.2

-0- 4x4 CoMP

*" 4x3 CoMP

- 4x2 CoMP

-0- 4x4 Cony

0+--1--+----"1"-.::0,.0""""1 O+---t--j--t-----t"=__J

0 2 4 6 8 10 Spectral Efficiency [bit/s/Hz]

o 2 4 6 8 10 Spectral Efficiency [bit/s/Hz]

(a) Northern test bed. (b) Southern test bed.

Fig. 6. Complementary COF of cell-center sum spectral efficiencies.

factor class. A pair of physical channel matrices is composed into global CSIT by selecting two arbitrary elements from the same GF class that exhibit a different sign. This represents the fact that both MTs are assigned to different BSs and thus the serving BS of the one MT is the interfering BS for the other MT and vice versa. We define the cell-center with IGI 2 10 dB and the cell-edge as IGI ::; 5 dB.

Since the SINR determines the data throughput, we use the theoretical spectral efficiency [bit/slHz] per stream as upper bound for coded transmissions over Nsc = 120 sub-carriers of 10 PRBs that result in a block size of NRE = 1080 resource elements with

C(SINR) B

NRE . log2 (1 + SINR) / (1 ms) Nsc·l:1f

and the SINR values in the linear domain.

(6)

At first, we compare the complementary cumulative dis­tribution functions (CCDFs) of the sum spectral efficiencies of the northern and southern cell-centers in Figure 6. In the northern area, the transmit diversity with the rich scattering environment is reflected in increasing sum spectral efficiencies of the four spatial configurations. Compared to this, the LOS dominated southern area does not show the gains in terms of the transmit diversity. Common to both is an almost equal spectral efficiency between the 4 x 4 CoMP and the conventional transmission.

In the Figure 7 and Figure 8, the CCDFs of the stream and sum spectral efficiency respectively for the cell-edge MTs are depicted. In the first figure, the performance of a single stream is considered while in the second the sum over instantaneous streams is provided. Common to both figures, the performance loss of the missing interference mitigation of non-coordinated BSs becomes obvious. Figure 7 points out that a spatial stream can suffer from an outage while other spatial streams can be still transmitted at very low rates (see 8(a) and 8(b )).

The sum spectral efficiencies of the CoMP scenarios in the north raise with the number of spatial streams (Figure lO(a)) but even, if we observe the sum spectral efficiency, we see no appreciable improvements for the southern scenario (Figure 10(b)). Additional spatial streams cannot be mitigated

1.0 1.0

-0- 4x4 CoMP

0.8 0.8 *" 4x3 CoMP t..t.. t..t.. Cl Cl - 4x2 CoMP u u

� 0.6 \ � 0.6 \ <: :: \ " " \

E E \ " 0.4 � 0.4 is. Q. \ E E \ 0 0 \

u 0.2

u 0.2 , , � , ; ,

\: 0,

0 0 "

0 1 2 3 4 5 0 2 3 4 5 Spectral Efficiency [bit/slHz] Spectral Efficiency [bit/s/Hz]

(a) Northern test bed. (b) Southern test bed.

Fig. 7. Complementary CDF of cell-edge spectral efficiencies per stream.

1.0 'R�========:::;l \

t..t.. 0.8 Cl U

�0.6 :: " ] 0.4 Q. E o

UO.2

\ -0- 4x4 CoMP

" *" 4x3 CoMP

\. - 4x2 CoMP \: -0- 4x4 Cony .. 'I: . \: '=-""""=-o= ...... -=o!J LJ :\

.... " .. : \ . \

, . . . . 0 . . . . ,,0.

o " , , o +--f---1----¥-�i'--'1

0 2 4 6 8 10

Spectral Efficiency [bit/s/Hz]

(a) Northern test bed.

1.0 �G;::::=======:::;l

t..t.. 0.8

Cl U

�0.6

:: " E

�0.4 Q. E o

uO.2

-0- 4x4 CoMP

*" 4x3 CoMP

- 4x2 CoMP

-0- 4x4 Cony

0 2 4 6 8 10

Spectral Efficiency [bit/slHz]

(b) Southern test bed.

Fig. 8. Complementary CDF of cell-edge sum spectral efficiencies.

by the precoder which causes a higher MU interference and thus decreases the spectral efficiency per stream. In the smaller north, more homogeneous results can be achieved over the entire area while in the larger south spectral efficiency degra­dations are obvious that result from a higher noise limitation.

In a second group of results, we present the mean spectral efficiencies as a function of the mean absolute geometry factors. Here, basically the same observations of the spatial diversities in the north and the degradation of the MU in­terference mitigation in the south with additional streams can be made. In Figure 9(b) a two times higher spectral efficiency per stream can be realized if the number of streams is reduced from four down to two. In the Figures 9(a) and 9(b) the CoMP performances per stream are normalized to the conventional system. At the cell-edge of the northern scenario a gain of approximately 140% can be achieved in the 4 x 4 CoMP case which vanishes into the direction of the cell­center GFs. Furthermore, a base station cooperation does not achieve performance gains in every spatial configuration, e.g. in our dense urban environment. Figure Il(a) shows that for G > 6 dB the 4 x 2 CoMP and for G > 10 dB the 4 x 3 CoMP has no advantage compared to a conventional system.

E 5 E

5

'" -0- 4x4 CoMP '" -0- 4x4 CoMP � � en 4 *" 4x3 CoMP en

4 *" 4x3 CoMP � � " - 4x2 CoMP

" - 4x2 CoMP Q. Q. <: <: '0; 3 '0; 3 0 0 >. C u <: <: " 2 " 2 'u 'u !E !E L.Ll L.Ll OJ OJ � � u u " " Q. Q. en

0 en 0

0 3 6 9 12 15 0 3 6 9 12 15

IGI [dB] IGI [dB] (a) Northern test bed. (b) Southern test bed.

Fig. 9. The spectral efficiency gain per stream compared to non-cooperating BSs with a frequency reuse of one as function of the geometry factors.

10

¥ 8 +:> e >. 6 u <: " 'u !E 4 L.Ll OJ � u

2 " Q. en 0

Fig. 10. factors.

3.0 <: 82.5 >. u ij2.0

'u !E L.Ll1.5 OJ � �1.0

en

§0.5 en

0

10

-0- 4x4 CoMP -0- 4x4 CoMP N

*" 4x3 CoMP � 8 *" 4x3 CoMP

- 4x2 CoMP +:> - 4x2 CoMP e -0- 4x4 Cony >. 6 u

1) <: "

'u !E 4 L.Ll OJ � u

2 " Q. en

0 0 3 6 9 12 15 0 3 6 9 12 15

IGI [dB] IGI [dB]

(a) Northern test bed. (b) Southern test bed.

The mean sum spectral efficiency as function of the geometry

0

-0- 4x4 CoMP

*" 4x3 CoMP

- 4x2 CoMP

3 6 9 12

IGI [dB]

(a) Northern test bed.

15

3 .0

<:

8 2.5

>. u ij2.0

'u !E L.Ll1.5 OJ � �1.0 en §0.5

en

0

0

-0- 4x4 CoMP

...... . . . . . ..... . . . .

3 6 9 12 15 IGI [dB]

(b) Southern test bed.

Fig. II. The sum spectral efficiency gain compared to non-cooperating BSs with a frequency reuse of one as function of the geometry factors.

VII. SUMMARY

We presented a detailed description of a cooperative base station concept with linear precoding to mitigate multi-user interference. We stated the major differences and extensions in comparison to the 3GPP LTE release 8 standard. A char­acterization of our real-time hardware and the cellular system followed. We have been able to predict post-precoding SINRs from physical channel measurements in the small scale and applied this mapping to the large-scale of two urban test bed areas. Finally, spectral efficiency results are presented for various CoMP DL configurations in comparison to a conventional cellular system without base station cooperation.

ACKNOWLEDGMENTS

The authors acknowledge the excellent cooperation of all project partners within the EASY-C project and the support by the German Federal Ministry of Education and Research (BMBF). The authors further acknowledge the substantial support of A. Navarro-Caldevilla, J. Heft, M. Potschke and S.-E. Breuer.

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