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IEEE Communications Magazine • February 2011
102 0163-6804/11/$25.00 © 2011 IEEE
1A full list of results from
all partners is available at
http://www.easy-c.com.
2See ict-artist4g.eu.
INTRODUCTION
High spectral efficiency (i.e., high aggregated
cell data rate per unit of spectrum) is especial-
ly important for data networks. Mobile data
traffic has recently surged due to the availabili-
ty of affordable data dongles, notebooks, tablet
computers with third-generation (3G) radio
modules, and smartphones with web-oriented
user interfaces. Vodafone, for example, has
observed 70 percent growth of data traffic
within one year for their European mobile net-
works. So far, 3G networks could support the
traffic growth. However, eventually, more effi-
cient wireless technology and novel deploy-
ment concepts like small cells and
heterogeneous networks are needed to provide
the required capacity.
Ubiquitous user experience is key for the end
user to have a guaranteed minimum service
quality corresponding to a minimum data rate.
Denser network deployments address this issue
caused by low link budget at the cell edge. How-
ever, this goes along with larger areas where the
transmission is limited by interference.
Long Term Evolution (LTE) and mobile
WiMAX use multiple-input multiple-output
(MIMO)-orthogonal frequency-division multi-
plexing (OFDM) and achieve improved spectral
efficiency within one cell. However, inter-cell
interference is still preventing these technolo-
gies from coming close to the theoretical rates
for multi-cell networks. There are two funda-
mental ways to deal with inter-cell interference:
Coordination of base stations to avoid interfer-
ence and constructive exploitation of interfer-
ence through coherent base station cooperation.
Conceptually, we extend single-cell MIMO tech-
niques, such as multi-user (MU-MIMO), to
multiple cells.
This article shows results from the EASY-C1
project, which focused on coordinated multi-
point (COMP) from 2007 to 2010 and set up two
multisite testbeds for LTE-based COMP in
Dresden and Berlin. ARTIST4G2and other
forthcoming projects will continue to use these
platforms.
COMP is a main element on the LTE
roadmap beyond Release 9. In LTE Release 11,
some simpler COMP concepts may appear, but
it is generally expected that advanced COMP
concepts will take longer to be mature enough
for commercial use.
The main scope of this article is to outline
the basic COMP concepts, and highlight the
potentials and technical challenges when intro-
ducing them in future mobile networks. More-
over, we sketch practical COMP schemes for
uplink and downlink, assess their performance in
large-scale network simulations, and use field tri-
als in urban areas to demonstrate the maturity
of COMP.
ABSTRACT
Coordinated multipoint or cooperative
MIMO is one of the promising concepts to
improve cell edge user data rate and spectral
efficiency beyond what is possible with MIMO-
OFDM in the first versions of LTE or WiMAX.
Interference can be exploited or mitigated by
cooperation between sectors or different sites.
Significant gains can be shown for both the
uplink and downlink. A range of technical
challenges were identified and partially
addressed, such as backhaul traffic, synchro-
nization and feedback design. This article also
shows the principal feasibility of COMP in two
field testbeds with multiple sites and different
backhaul solutions between the sites. These
activities have been carried out by a powerful
consortium consisting of universities, chip man-
ufacturers, equipment vendors, and network
operators.
IMT-ADVANCED AND NEXT-GENERATION
MOBILE NETWORKS
Ralf Irmer, Vodafone
Heinz Droste, Deutsche Telekom
Patrick Marsch, Michael Grieger, and Gerhard Fettweis, Technische Universität Dresden
Stefan Brueck, Qualcomm CDMA Technologies GmbH
Hans-Peter Mayer, Alcatel-Lucent Bell Labs
Lars Thiele and Volker Jungnickel, Fraunhofer Heinrich-Hertz-Institut
Coordinated Multipoint: Concepts,
Performance, and Field Trial Results
IRMER LAYOUT 1/19/11 3:33 PM Page 102
IEEE Communications Magazine • February 2011 103
COORDINATION AND COOPERATION
IN MOBILE NETWORKS
One key element of mobile radio networks is
spatial reuse (i.e., the reuse of resource elements
such as timeslots or frequency bands) in a geo-
graphical distance, where the signal strength is
reduced due to path loss, shadowing, and so on.
Historically, this was achieved using network
planning with certain frequency reuse patterns,
which have, however, the drawback of poor
resource utilization. 3G and 4G technologies are
using full frequency reuse, which in turn leads to
interference between the cells.
In [1, 2] network coordination has been pre-
sented as an approach to mitigate intercell inter-
ference and hence improve spectral efficiency.
Figure 1 shows the cooperation architecture for
COMP. The same spectrum resources are used
in all sectors, leading to interference for termi-
nals (user equipment [UE] in Third Generation
Partnership Project [3GPP] terminology) at the
edge between the cells, where signals from mul-
tiple base stations are received with similar sig-
nal power in the downlink. Multiple sectors of
one base station (eNB in 3GPP LTE terminolo-
gy) can cooperate in intrasite COMP, whereas
intersite COMP involves multiple eNBs.
The sectors at one site can be different self-
sustained units, or different remote radio heads
linked via fiber to a central baseband unit. The
eNBs may be interconnected by the logical X2
interface. Physically, this could be a direct fast
fiber link, or a multi-hop connection involving
different backhaul technologies.
The cooperation techniques aim to avoid or
exploit interference in order to improve the cell-
edge and average data rates. COMP can be
applied both in the uplink and downlink. All
schemes come with the cost of increased
demand on backhaul (high capacity and low
latency), higher complexity, increased synchro-
nization requirements, more channel estimation
effort, more overhead, and so on. The aim of
this article is to highlight the potentials of
COMP and its technical challenges to be
addressed for introducing it in next-generation
mobile networks.
EVALUATION BY SIMULATION AND
FIELD TRIALS
Different approaches to COMP can be analyzed
using system-level simulations with hexagonal
cells and evaluation methodologies customary in
the 3GPP, Next Generation Mobile Networks
(NGMN), and International Telecommunication
Union (ITU). Unless otherwise specified, the
intersite distance in all computer simulations
has been set to 500 m, a terminal speed of 3
km/h is assumed, and the system bandwidth is
10 MHz.
The results of such simulations will be pre-
sented in this article. However, it is not enough
to evaluate the feasibility of an approach solely
based on simulations. Field trials are essential to
find out the critical technical issues, and they
encourage an end-to-end view. The EASY-C
project has set up two outdoor testbeds with
slightly different underlying technology and
focus, as shown in Table 1; see also [3–5].
UPLINK COORDINATED MULTIPOINT
OVERVIEW
Theoretical work has shown that uplink (UL)
COMP offers the potential to increase through-
put significantly [1, 2], in particular at the cell
edge, which leads to enhanced fairness overall.
Modeling some practical aspects such as a rea-
sonably constrained backhaul infrastructure
and imperfect channel knowledge, UL COMP
promises average cell throughput gains on the
order of 80 percent, and roughly a threefold
cell edge throughput improvement [6]. The
channel information is available in the network
without resource-consuming feedback transmis-
sions in the uplink. Also, the terminals need no
modifications in order to support UL COMP.
Therefore, base station cooperation may be
easier to implement than in the downlink (DL).
Only the interface between base station sites
(X2) needs to be defined. In case of joint
detection in the UL, higher X2 capacity is
needed than for joint transmission in the DL.
Although the UL capacity is not the bottleneck
in today’s networks, guaranteeing a minimum
data rate, especially for cell edge users, is
improving user experience, and UL COMP
may be used to carry control traffic necessary
to implement DL COMP.
In general, the UL COMP schemes can be
classified as:
Interference-aware detection: Here, no coop-
eration between base stations is necessary;
instead, base stations also estimate the links to
interfering terminals and take spatially colored
interference into account when calculating
receive filters (interference rejection combining).
Joint multicell scheduling, interference pre-
Figure 1. Base station cooperation: intersite and intrasite COMP.
X2 interface
(eNB-eNB)
Cell /
sector Mobile
terminal (UE)
eNB
eNB eNB
eNB
Inter-Site COMP
Cell edge
Intra-Site COMP
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IEEE Communications Magazine • February 2011
104
diction, or multicell link adaptation, requiring
the exchange of channel information and/or
scheduling decisions over the X2 interface
between base stations [7].
Joint multicell signal processing. Here,
degrees of freedom exist in the way that decod-
ing of terminals may take place in a decentral-
ized or centralized way, and to which extent
received signals are preprocessed before infor-
mation exchange among base stations. In gener-
al, there is a trade-off between using backhaul
efficiently by a maximum extent of preprocessing
(e.g., as in distributed interference subtraction,
DIS, where decoded data is exchanged), but
obtaining less CoMP gain, or using a large back-
haul capacity (as in the case of the distributed
antenna system, DAS, where quantized receive
signals are exchanged) and obtaining a better
performance.
SELECTED SIMULATION RESULTS
In the following section selected UL cooperation
schemes are introduced. During performance
evaluation it is distinguished between gains of
intrasite and intersite cooperation, where inter-
site cooperation needs X2 backhaul capacity.
Uplink Interference Prediction — The basic
idea of UL interference prediction [7] is to per-
form link adaptation based on predicted signal-
to-interference-plus-noise ratio (SINR) values
that are likely to occur during the associated
data transmissions. Prediction is enabled by
exchange of resource allocation information
within a cluster of cooperating cells. In addition,
the UL receivers provide channel state informa-
tion related not only to their associated termi-
nals, but also to the strongest terminals of
neighboring cells. Due to interference predic-
tion, more appropriate link adaptation can be
realized, and hence the performance can be
improved. The exchange of resource allocation
information between two cells causes only mod-
erate backhaul traffic in the range of 8 Mb/s.
Whereas performance gains with intrasite coop-
eration prove to be rather low, we observe up to
25 percent gain in spectral efficiency and 29 per-
cent gain with respect to baseline cell edge
throughput if intersite cooperation including up
to six interfering cells is simulated. The predic-
tion accuracy degrades if the channel state infor-
mation gets outdated. Therefore, the X2 latency
should not exceed 1 ms, even at low terminal
speed.
Uplink Joint Detection — Uplink joint detec-
tion means that signals received at different sec-
tors are jointly processed [8]. Hence, virtual
MIMO antenna arrays may spread out over dif-
ferent users as well as different base station sec-
tors at the network side. Most of the
information exchange between cooperating cells
is caused by sharing the quantized baseband
samples received in each cell. Channel state
information and resource allocation tables are
shared in the cooperation cluster as well. First
estimates reveal that even with consideration of
less than half the cooperation cluster size as
described above for interference prediction, the
cell-to-cell X2 traffic would exceed 300 Mb/s for
10 MHz system bandwidth. This high amount of
backhaul traffic motivates the investigation of
intrasite joint detection. In case of intersite joint
detection including up to three sectors per ter-
minal, gains in spectral efficiency and cell edge
throughput account for 35 and 52 percent,
respectively (2). Sticking with intra-site joint
detection, the improvements drop only moder-
ately to 25 percent on average and 24 percent at
the cell edge (3).
Combining high throughput and low latency
as required by joint detection will cause a cost
burden for the backhaul, specifically the X2
interface. Therefore, a combination of intrasite
joint detection (no X2 needed) and intersite
interference predictions (low throughput
demand) has been considered. This even outper-
forms the throughput-demanding intersite joint
detection, as shown in Fig. 2. However, the bur-
den of low-latency X2 remains.
Table 1. COMP testbeds developed within the EASY-C project.
Dresden testbed Berlin testbed
Environment Dense urban
Trial setup 10 sites with up to a total of 28 sectors 4 sites with up to 10 sectors
Frequency 2.68 GHz DL, 2.53 GHz UL
Baseline technology
OFDMA in DL and UL, scalable bandwidth 5–20
MHz, transmissions limited to a maximum of 40
resource blocks (PRBs) in UL and 10 PRBs in DL.
DL: 2 ×2 MIMO-OFDMA, UL: 1 ×2 SC-FDMA,
scalable bandwidth 1.5–20 MHz, full bandwidth
can be used in both up- and downlink
Processing
Real-time DL transmission. For uplink COMP
offline processing. Scheduling is investigated in
quasi-realtime.
Real-time PHY, adaptive MIMO multiple access and
network layer. PHY is extended for DL CoMP.
Backhaul and interconnects 5.4/5.8 GHz microwave with a net data rate of
100 Mb/s and 1 ms delay
1 Gb/s Ethernet over optical fiber and free-space-
optical links.
Testbed scope UL and DL MU-MIMO COMP, relaying, practical
issues
DL MU-MIMO, COMP, relaying, real-time demos
such as high-definition mobile video conference
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IEEE Communications Magazine • February 2011 105
SELECTED FIELD TRIAL RESULTS
Joint decentralized and centralized detection of
terminals was evaluated in the Dresden testbed
[9]. Two terminals with one transmit antenna
each transmitted continuous sequences of modu-
lation and coding schemes, which were received
by two base stations with one receive antenna
each (KATHREIN 80010541). The scenario
resembled a symmetric cell edge scenario, but
the terminals were moved such that interference
conditions changed continuously. The receive
signals were recorded so that different coopera-
tion schemes could be applied and evaluated
offline.
The result plot in Fig. 3 shows the average
rates that could be achieved with different coop-
eration strategies vs. the backhaul required.
Here, square and round markers are used to dis-
tinguish both UE types. We can see that in an
LTE Release 8 system, where each UE unit is
decoded only by the serving base station, an
average rate of about 1.5 b/channel use is possi-
ble for UE 1 (square marker). This can be
improved to about 2.2 b/channel use simply if a
flexible (i.e., transmission time interval [TTI]-
wise) assignment of UE to eNBs is enabled, with
the option of local decoding with successive
interference cancellation (SIC). A further rate
improvement of UE 1 is possible if DIS is
enabled, where one UE unit is decoded first at
one eNB, and the decoded data are then for-
warded to the other eNBs for interference sub-
traction, requiring a smaller extent of backhaul.
This scheme turns out to reduce the outage
probability significantly. The remaining points
show the performance of a DAS, where the
eNBs exchange quantized received signals, with
either 6 or 12 bits per antenna both for I and Q
signal dimensions. As compared to LTE Release
8, in this scenario full DAS-based cooperation
can improve the average throughput by about 70
percent, but the backhaul required is more than
two orders of magnitude larger than for decen-
tralized concepts (DIS). Further measurements
have shown that DIS schemes become even
more valuable in asymmetric scenarios, such that
an adaptive usage of centralized and decentral-
ized cooperation schemes depending on the
interference situation appears promising.
The presented results provide evidence of the
potential benefits of using CoMP in specific sce-
narios. Figure 4 shows the COMP gains in a
large-scale setup in the EASY-C testbed in
downtown Dresden with 12 eNBs on five sites.
The spacing between the sites is 350–600 m, with
an antenna height of 15–35 m. Two UE units are
Figure 2. Performance of selected uplink COMP schemes: 1) inter-site interference prediction, 2) inter-site joint detection, 3) intra-site
joint detection, 4) combining inter-site interference prediction with intra-site joint detection.
Cell edge throughput gain (%)
Spectral effeiciency gain (%)
Cell-to-cell (X2) throughput requirement (Mb/s)
30
25
0
0 40 50 60 70
30
35
40
45
50
55
LTEA scheme
2
340
1
8
43
8
50
0
100
150
200
250
300
350
2
4
1
3
Figure 3. Achieved rates vs. required backhaul for different uplink cooperation
schemes, as measured in field trials.
Average backhaul (b/channel use)
LTE Rel. 8
DIS
BS1
UE1 UE2
BS2
DAS (6-bit)
DAS (12-bit)
Flexible assignment
No backhaul required here
100
10-1
0.5
Average rate (b/channel use)
0
1.0
1.5
2.0
2.5
3.0
3.5
4.0
101 102
IRMER LAYOUT 1/19/11 3:33 PM Page 105
IEEE Communications Magazine • February 2011
106
carried on a measurement bus on a 7.5 km length
route, as depicted in Fig. 4, which passes through
different kinds of surroundings, including an
underpass, apartment buildings, a train station,
and open spaces like parking areas. Convention-
al non-cooperative decoding is compared to
cooperative joint decoding. Through coopera-
tion, average spectral efficiency gains of about
20 percent were achieved. In certain areas, how-
ever, gains above 100 percent were observed.
Furthermore, the variance of achievable rates at
different UE positions was reduced, correspond-
ing to fairer rate distribution throughout the
measurement area.
CHALLENGES
From the experience of implementing and test-
ing UL COMP, the following key challenges
have become apparent.
Clustering: Suitable clusters of cooperating
base stations have to be found, which can be
done in a static way or dynamically, as discussed
below.
Synchronization: Cooperating base stations
have to be synchronized in frequency such that
intercarrier interference is avoided, and in time
in order to avoid both intersymbol and intercar-
rier interference [10]. The maximum distance of
cooperating base stations is limited since differ-
ent propagation delays of different terminals
may conflict with the guard interval. This aspect
may be compensated through a more complex
equalization.
Channel estimation: A large number of eNBs
in the COMP cluster in the UL will require a
larger number of orthogonal UL pilot sequences.
At some cluster sizes, the COMP gains are out-
weighed by capacity losses due to additional
pilot effort.
Complexity: The above mentioned field trials
have been performed using orthogonal frequen-
cy-division multiple access (OFDMA) in the UL,
as this enables a subcarrier and symbol-wise
MIMO equalization and detection in the fre-
quency domain. If single-carrier (SC)-FDMA
was used as in LTE Release 8, equalization
would be more complex.
Backhaul: It can be a severe issue if central-
ized decoding is applied. Hence, adaptive decen-
tralized/centralized cooperation appears to be an
interesting option. Furthermore, source coding
schemes appear interesting for backhaul com-
pression.
DOWNLINK
COORDINATED MULTIPOINT
OVERVIEW
Base station cooperation in the DL can also
improve average throughput and, more impor-
tant, cell edge throughput [2]. 3GPP distinguish-
es between the following categories of DL
COMP [11].
Coordinated scheduling/beamforming: User
data is only available in one sector, the so-called
serving cell, but user scheduling and beamform-
ing decisions are made with coordination among
the sectors.
Joint processing COMP: User data to be
transmitted to one terminal is available in multi-
ple sectors of the network. A subclass of joint
processing is joint transmission, where the data
channel to one terminal is simultaneously trans-
mitted from multiple sectors.
Both coordinated scheduling/beamforming
and joint transmission have been investigated
within the EASY-C project.
SELECTED SIMULATION RESULTS
Coordinated beam selection [12] and co-
scheduling are part of the investigated COMP
schemes. Co-scheduling draws its gains from
interference avoidance and is less complex than
DL joint transmission. One approach which
includes beamforming per cell is presented
here. Synchronization of the cells is needed;
however, there is no strict requirement on
phase stability as known from coherent tech-
niques. Multicell co-coordinated beamforming
has been assessed in system-level simulations
taking into account the latency for inter-NodeB
communication.
The method is based on an extended precod-
ing matrix index (PMI): the terminals measure
and report the PMIs for their own cells (best
companion) and additionally the PMIs for the
neighboring cells causing the strongest interfer-
ence (worst companion) plus the channel quality
information for the case that these worst inter-
ferers are not used.
The multicell scheduler is based on a dis-
tributed approach, with overlapping clusters of
seven neighboring cells each. The scheduling is
coordinated within the clusters. The following
results are given for four closely spaced antennas
Figure 4. Uplink COMP gains in EASY-C testbed in downtown Dresden.
IRMER LAYOUT 1/19/11 3:33 PM Page 106
at the base station and two UE antennas at 20
MHz system bandwidth.
The simulations show significant gains for
coordinated DL scheduling, in particular for
mobiles at the cell edge. Additionally, the
gains were evaluated for different radio chan-
nels and different latencies for communication
between the sites. As can be observed in Fig.
5, 1 ms latency/hop has only a moderate
impact on the gains. Even with highly time-
variant channels such as urban macrocell
(UMa) at 30 km/h, co-scheduling still provides
a sensible improvement. Assuming 6 ms laten-
cy per hop, the gains are still preserved for
UE velocities up to 3 km/h.
The aggregated additional traffic on the back-
haul sums up to approximately 5 Mb/s for 20
MHz spectrum; as a result this technique is also
economically attractive.
SELECTED FIELD TRIAL RESULTS
Since joint transmission is regarded as the most
challenging CoMP technique from the imple-
mentation perspective, it has been implemented
in both testbeds to investigate the feasibility of
coherent transmission for intra- and intersite
COMP [13, 14]. Significant throughput gains
have been demonstrated for specific interference
scenarios. The same techniques have also been
assessed in wide-area system-level simulations to
study more complex scenarios. The following
enabling features were essential for the trials:
• Sufficient timing and frequency synchro-
nization accuracy: In the trials GPS was
used, although network-based approaches
such as IEEE 1588v2 could also provide
sufficient accuracy.
• Low-phase-noise radio frequency (RF)
oscillators were used.
• Cell-specific reference signals.
• Time-stamped CSI feedback.
• Synchronous exchange of data and channel
state information (CSI) between eNBs over
the X2 interface.
• Distributed precoding and the provision of
precoded pilots.
An example of the DL COMP experiments
conducted in the Berlin testbed is shown in Fig.
7a. A distributed implementation of joint trans-
mission has been demonstrated with synchro-
nized base stations and cell-specific pilots.
Terminals estimate the multicell channel and
feed the CSI back to their serving cells. Base sta-
tions exchange CSI as well as data and indepen-
dently perform pre-coding with the goal to
maximize the desired signals whilst minimizing
mutual interference.
Quantization and compression of the CSI are
important topics, but outside the scope of this
trial setup. CSI is fed back from the terminals
safely using UL resources at a data rate of 4.6
Mb/s. Feedback interval and precoding delay are
10 and 20 ms, respectively. The X2 interface
between base stations is realized using a 1 Gb/s
Ethernet connection over cooper, fiber, or free-
space optics, depending on the setup. The bidi-
rectional load is 300 Mb/s realized with 0.5 ms
latency.
Measurements were taken in the laboratory
[5] and over the air in both indoor and outdoor
environments (Fig. 6, bottom left). It was
observed that the interference situation experi-
enced at a terminal is indeed critical at the cell
edge if both base station signals are received
equally strong on average at full frequency
reuse. Signal and interference links fade inde-
pendently, and sometimes the signal is stronger
than the interference, while after a very short
distance the opposite can be true. This is the
origin of the high outage probabilities observed
at the cell edge in the interference-limited case
(Fig. 6, bottom right). Once DL COMP is
switched on, significantly higher data rates can
be realized in both cells simultaneously, due to
the mutual interference cancellation. Moreover,
the outage probability is remarkably reduced.
Our experiments have shown that COMP gains
are significant for simple interference scenarios,
and that the implementation challenges can be
overcome.
In reality, non-cooperating cells would sur-
round the cluster of cooperative cells, leading to
a remaining interference floor not yet present in
our trials. The presence of such external inter-
ference has been studied in wide-area system-
level simulations using basically the same COMP
concept also tested in the field. Note that the
active set is found so that in each cell two users
are randomly placed, and each user gets only
one stream. In all cells, only those user sets
requesting the same cooperation cluster are
investigated. In Fig. 6 (top right) we observe that
there is no gain from using explicit CSI feedback
in the serving cell, exploited for multi-user DL
beamforming. Performance is equivalent to a
fixed grid-of-beams as in LTE Release 8 if the
terminal estimates in addition the surrounding
interferers coherently, applies interference rejec-
tion combining, and provides implicit frequency-
selective feedback on interference-aware PMI
and CQI, and the base station applies score-
based scheduling [4]. Explicit CSI feedback is
useful for CoMP. With increasing cluster size,
the interference floor is reduced and the perfor-
mance enhanced accordingly, at the cost of addi-
tional effort for overhead and backhaul. For
more details, see [5].
IEEE Communications Magazine • February 2011 107
Figure 5. Downlink co-scheduling: spectral efficiency vs. cell edge throughput
for ITU UMa and SCME radio channels and different backhaul latencies.
Spectral efficiency (b/Hz/sector)
DL spectral efficiency vs. cell edge throughput
1.90
3.00E+05
5%-ile CDF DL UE throughput (b/s)
4.00E+05
5.00E+05
6.00E+05
7.00E+05
8.00E+05
9.00E+05
1.00E+06
2.00E+05
2.00 2.10 2.20
alpha 3.0
alpha 2.0
alpha 1.0
alpha 0.5
2.30 2.40 2.50 2.60 2.70
3GPP_SCME_1ms
3GPP_SCME_6ms
3GPP_SCME_baseline
ITU_UMa_1ms
ITU_UMa_6ms
ITU_UMa_baseline
IRMER LAYOUT 1/19/11 3:33 PM Page 107
IEEE Communications Magazine • February 2011
108
CHALLENGES
Our results indicate that the complexity of DL
COMP can be managed in real-world scenarios
and that significant gain can be realized by form-
ing small cooperation clusters in large-scale net-
works. However, solutions for the following are
needed before it can be integrated in next-gener-
ation mobile networks:
• Reduced cost of base station synchroniza-
tion and low-phase-noise transmitters
• Efficient feedback compression
• Reduced feedback delay
• Efficient channel prediction at the precoder
• Flexible formation of cooperation clusters
• Handling of outer interference within the
cluster
• Efficient multi-user selection
• Flexible networking behind COMP
• Integration of COMP into higher layers
CLUSTERING OF CELLS
As demonstrated in previous sections, COMP
has the capability to enhance spectral efficien-
cy and cell edge throughput significantly. How-
ever, COMP requires additional signaling
overhead on the air interface and over the
backhaul in case of intersite cooperation.
Therefore, in practice only a limited number of
base stations can cooperate in order to keep
the overhead manageable. The cooperating cell
clusters should be set up adaptively based on
RF channel measurements and UE positions in
order to exploit the advantages of COMP effi-
ciently at limited complexity. A key require-
ment for any adaptive cluster algorithm is that
it fits into the architecture of the radio access
and/or the core network of LTE. The 3GPP
standard already offers a framework for self-
organizing networks (SONs) to support auto-
matic configuration and optimization of the
network. Within EASY-C an adaptive mobile-
station-aware clustering concept has been
designed that can be integrated with small
standard changes to the existing network archi-
tecture and the SON concept of LTE.
In order to evaluate the performance of the
adaptive clustering concept, system-level simu-
lations were run employing a hexagonal net-
work layout shown in Fig. 7a. The scenario was
configured with 19 3-sector sites of 500 m inter-
site distance. The 3GPP UMa spatial channel
model (SCM) at 2 GHz was used. The shadow
fading standard deviation was set to 2 dB. One
hundred UE unitss were placed at random loca-
tions within each of four hotspot areas. Figure
Figure 6. Top left: Distributed implementation of Joint transmission COMP. Top right: Performance and backhaul traffic vs. cluster size
obtained from system-level simulations. Bottom left: Intra- and inter-site test scenarios in Berlin [5]. Bottom right: Measured throughput
with full frequency reuse in a two-cell scenario w/o external interference relative to the case of isolated cells.
Marchstraβe
Cluster size. i.e., number of sectors involved in JP CoMP
non-CoMP
0
0
Median spectral efficiency (b/s/Hz/sector)
Backhaul traffic (payload only) (b/kHz)
5
10
150
100
50
15
2 3 4 5 6 7 8 910
Rate relative to isolated cell rate
0
0
CDF
0.9
1
0.7
0.5
0.3
0.1
0.9 10.80.70.60.50.40.30.20.1
Interference limited, Intersite
Interference limited, Intrasite
CoMP Intersite
CoMP Intrasite
Channel
feedback
MT1
300m
N
Hardenbergstr.
Technical University
TUB
(43 m)
TLabs
(84 m)
540 m
Ernst Reuter
Plaza
Strasse des
17. Juni
Campus
485 m
480 m
HHI
Einsteinuƒer
BS1
H11 H22
H12 H21
Local
precoder
Data
MT1
Data
MT2
MT2
BS2
Channel
feedback
Local
precoder
Inter-BS link (X2)
Channel feedback
exchange
Intersite sector
Intrasite sector
Intersite point
Intrasite point
K – 1
traffic = rate ⋅ (1+ code_rate)
w.r.t. CoMP LTE mapping
LTE 1x1, round robin
LTE 2x2, round robin
LTE 2x2, score-based
CoMP, LTE L2S mapping
IRMER LAYOUT 1/19/11 3:33 PM Page 108
IEEE Communications Magazine • February 2011 109
7b shows the result of the designed clustering
algorithm, which was configured to obtain the
optimal solution for a disjoint set of clusters
with up to three sectors. The colors represent
the different clusters. The clustering algorithm
took only long-term average received power
measurements from UE into account in case
they were higher than –120 dBm. It is apparent
from the figure that this concept managed to
form clusters around the UE hotspots and
avoided clusters in regions where not needed.
The mean geometry gain due to adaptive clus-
tering was about 6 dB for this scenario com-
pared to LTE Release 8.
BACKHAUL FOR COMP
ARCHITECTURE AND TECHNOLOGIES
COMP approaches need to exchange direct
information between cells, with different require-
ments of necessary backhaul throughput and
latency. Intra-site COMP can be realized with-
out any impact on backhaul. In the case of
deployment of remote radio units connected to a
centralized baseband processing unit via Ether-
net or fiber links, COMP backhaul requirements
should also be no obstacle.
For connectivity between sites, the logical X2
interface could be used. This could either be a
direct physical link or a multihop link, depend-
ing on the network’s backhaul architecture. The
delay depends on the network topology, network
node processing delay and line delay (usually
speed of light). Gigabit Ethernet speeds of up 10
Mb/s and delays of 0.1–20 μs with additional
delays due to switching equipment. Other suit-
able candidates are conventional and millimeter-
wave microwave, with speeds up to of 800 Mb/s
or 10 Gb/s, respectively, and delays as low as 150
μs/hop.
LATENCY REQUIREMENTS
COMP has to be integrated with the hybrid
automatic repeat request (HARQ) process; thus,
the backhaul latency will put some limits on this,
suggesting a maximum latency of 1 ms without
LTE standard modification.
Another impact of backhaul latency is that
the exchanged channel information is outdated.
For example, a minor performance degradation
was estimated for coordinated scheduling con-
sidering a X2 latency of 6 ms. In [15] a DL
COMP capacity gain reduction of 20 percent is
estimated for joint transmission with 5 ms back-
haul latency at 3 km/h.
CAPACITY REQUIREMENTS
COMP schemes require the exchange of channel
state information, control data, user data, and
received signals, in a preprocessed or quantized
format.
As shown earlier and in [16, 17], the backhaul
requirements vary strongly from a few megabits
per second up to 4 Gb/s for different COMP
approaches, considering a 10 MHz LTE X2 link.
This also depends on the cluster size. Earlier we
showed an example of how backhaul can be
reduced significantly even without major perfor-
mance losses.
To conclude, state-of-the-art backhaul tech-
nology can support COMP in principle. Howev-
er, the cost of additional backhaul and access
capacity gains has to be balanced in a network
deployment.
CONCLUSIONS AND OUTLOOK
This article has shown that coordination of cells
in wide-area systems is not only beneficial for
average spectral efficiency and cell edge data
Figure 7. Cell layout with UE positions and selected clusters: a) no clustering; b) adaptive clustering.
X-Distance (m)
Original CoMP cluster layout and UE positions
-1000
-1000
Y-Distance (m)
-500
0
500
1000
-500 0 500 1000
X-Distance (m)
Adapted CoMP Cluster Layout and UE Positions
-1000
-1000
Y-Distance (m)
-500
0
500
1000
-500 0 500 1000
01
2
34
5
2122
23
2425
26
2728
29
1011
12
2223
24
67
8
910
11
3637
38
3940
41 4243
44 4546
47
4849
50
1112
13
1415
16
1819
20
1516
17
1213
14
01
2
34
5
2122
23
2425
26
2728
29
1011
12
2223
24
67
8
910
11
3637
38
3940
41 4243
44 4546
47
4849
50
1112
13
1415
16
1819
20
1516
17
1213
14
IRMER LAYOUT 1/19/11 3:33 PM Page 109
IEEE Communications Magazine • February 2011
110
rates, but can also be implemented. COMP was
demonstrated for uplink and downlink in two
testbeds in urban areas. COMP schemes for the
UL range from joint multicell scheduling to
more complex joint detection, and can be cen-
tralized or decentralized. In the DL the schemes
range from less complex coordinated scheduling
to more challenging joint processing approaches.
From the technical as well as economic points
of view, intrasite cooperation will be much easier
to realize. However, intersite cooperation will be
needed in order to exhaust the full interference
reduction potential of base station cooperation.
The combination of joint processing at one site
with joint scheduling between the sites is of
great interest as it provides promising gains with
limited backhaul.
The following challenges needs to be
addressed in order to benefit from the promising
COMP gains:
• Backhaul with low latency and high band-
width. Today’s backhaul technologies can
support COMP, but more effort is needed
to reduce the amount of data exchanged
between the sites.
• Clustering and multisite scheduling.
• Channel estimation and efficient feedback
(for DL COMP).
• Synchronization between sites is feasible
today, but the cell area where COMP can
be applied may be limited by the length of
the cyclic prefix.
• Combination of UL and DL COMP and
their integration into the LTE standard.
This article, and the EASY-C project, have
already given some answers on COMP. Ongoing
efforts to address the challenges in the research
community — such as the ARTIST4G project
and 3GPP standardization — are important to
gain more insight into achievable spectral effi-
ciency gains and the complexity of different
approaches.
ACKNOWLEDGMENT
The authors acknowledge the excellent coopera-
tion of all project partners within the EASY-C
project and the support of the German Federal
Ministry of Education and Research (BMBF).
REFERENCES
[1] P. Marsch, S. Khattak, and G. Fettweis, “A Framework
for Determining Realistic Capacity Bounds for Distribut-
ed Antenna Systems,” Proc. IEEE Info. Theory Wksp.
‘06, Chengdu, China, Oct. 22–26, 2006.
[2] K. M. Karakayli, G. J. Foschini, and R. A. Valenzuela.
“Network Coordination for Spectrally Efficient Commu-
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[3] P. Marsch and G. Fettweis, “On Multi-Cell Cooperative
Transmission in Backhaul-Constrained Cellular Systems,”
Annales des Télécommun., vol. 63, no. 5–6, May 2008.
[4] V. Jungnickel et al., “Interference Aware Scheduling in
the Multiuser MIMO-OFDM Downlink,” IEEE Commun.
Mag., vol. 47, no. 6, June 2009.
[5] V. Jungnickel et al., “Field Trials using Coordinated
Multi-Point Transmission in the Downlink,” 3rd IEEE
Int’l. Wksp. Wireless Distrib. Net., IEEE PIMRC, Sept.
2010.
[6] P. Marsch, Coordinated Multi-Point under a Constrained
Backhaul and Imperfect Channel Knowledge, Ph.D. the-
sis.
[7] A. Müller and P. Frank, “Cooperative Interference Pre-
diction for Enhanced Link Adaptation in the 3GPP LTE
Uplink,” IEEE VTC–Spring, 2010.
[8] A. Müller and P. Frank, “Performance of the LTE Uplink
with Intra-Site Joint Detection and Joint Link Adapta-
tion,” IEEE VTC–Spring, 2010.
[9] M. Grieger et al., “Field Trial Results for a Coordinated
Multi-Point (CoMP) Uplink in Cellular Systems,” Proc.
ITG/IEEE Wksp. Smart Antennas ‘10, Bremen, Germany,
Feb. 23–24, 2010.
[10] V. Kotzsch and G. Fettweis. “Interference Analysis in
Time and Frequency Asynchronous Network MIMO
OFDM Systems,” IEEE WCNC ‘10, Sydney, Australia,
Apr. 18–21, 2010.
[11] 3GPP TR 36.814, “Further Advancements for E-UTRA
Physical Layer Aspects,” Release 9, v. 9.0.0, Mar. 2010.
[12] J. Giese and M. A. Awais, “Performance Upper Bounds
for Coordinated Beam Selection in LTE-Advanced,”
Proc. ITG/IEEE Wksp. Smart Antennas ‘10, Bremen, Ger-
many, Feb. 23–24, 2010.
[13] G. Fettweis et al., “Field Trial Results for LTE-Advanced
Concepts,” Proc. IEEE ICASSP ‘10, Dallas, TX, Mar.
14–19, 2010.
[14] L. Thiele, V. Jungnickel, and T. Haustein, “Interference
Management for Future Cellular OFDMA Systems Using
Coordinated Multi-Point Transmission,” IEICE Trans.
Commun., Special Issue on Wireless Distributed Net-
works, Dec. 2010.
[15] S. Brueck et al., “Centralized Scheduling for Joint-
Transmission Coordinated Multi-Point in LTE-Advanced,”
Proc. ITG/IEEE Wksp. Smart Antennas ‘10, Bremen, Ger-
many, Feb. 23–24, 2010.
[16] C. Hoymann, L. Falconetti, and R. Gupta, “Distributed
Uplink Signal Processing of Cooperating Base Stations
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2009.
ADDITIONAL READING
[1] R. Irmer et al., “Multisite Field Trial for LTE and
Advanced Concepts,” IEEE Commun. Mag., vol. 47, no.
2, Feb. 2009, pp. 92–98.
BIOGRAPHIES
RALF IRMER [SM] (ralf.irmer@vodafone.com) received his
Dipl-Ing. and Dr.-Ing. degrees from Technische Universität
Dresden in 2000 and 2005, respectively. He joined Voda-
fone Group R&D in 2005, where he leads the Wireless
Access Group, which is responsible for evolution of LTE,
WiFi, and other technologies, and defining Vodafone’s
future network architecture. Before, he worked for five
years as a research associate at TU Dresden. He holds sev-
eral patents, and has published more than 30 conference
and journal publications. He had a leading role in several
research projects, including WIGWAM, WINNER, and EASY-
C. He is a member of VDE and IET.
HEINZ DROSTE (Heinz.Droste@telekom.de) received his Dipl.-
Ing degree 1991 from the Open University, Hagen. Since
then he has been working for Deutsche Telekom at a vari-
ety of mobile communication related R&D projects. Anten-
nas and radio wave propagation belong to his knowledge
field as well as system-level simulation and radio network
planning. More recently he extended his expertise to the
field of techno-economical evaluations. In the framework
of EASY-C he coordinates the partner activities in Working
Group 1, “Algorithm and Concepts.”
PATRICK MARSCH (marsch@ifn.et.tu-dresden.de) received his
Dipl.-Ing. and Dr.-Ing. degrees from Technische Universität
Dresden in 2004 and 2010, respectively, after completing
an apprenticeship at Siemens AG and studying at the TU
Dresden and McGill University, Montréal, Canada. After an
internship with Philips Research East Asia in Shanghai, P.R.
China, he joined the Vodafone Chair in 2005. He is the
technical project coordinator of EASY-C, and is currently
heading a research group on the analysis and optimization
of cellular systems.
MICHAEL GRIEGER received his Dipl.-Ing. from DHBW Stuttgart
in 2005 and his M.Sc. from the Technische Universität
Dresden in March 2009. In 2008, funded by the Herbert
Quandt/ALTANA Foundation, he studied at CTU, Prague.
During his Master’s thesis, he conducted research in Prof.
John Cioffi’s group at Stanford University on multicell sig-
COMP schemes for
the uplink range
from joint multi-cell
scheduling to more
complex joint
detection, and can
be centralized or
decentralized. In the
downlink, the
schemes range from
less complex
coordinated
scheduling to more
challenging joint
processing
approaches.
IRMER LAYOUT 1/19/11 3:33 PM Page 110
IEEE Communications Magazine • February 2011 111
nal processing, which continues to be his major research
focus today. An aspect of his research is the comparison of
information theoretic results to those of the “real world”
using field trials.
GERHARD FETTWEIS [F] (fettweis@ifn.et.tu-dresden.de) earned
his Dipl.-Ing. (1986) and Ph.D. (1990) degrees from Aachen
University of Technology (RWTH), Germany. From 1990 to
1991 he was a visiting scientist at the IBM Almaden
Research Center, San Jose, California, working on signal
processing for disk drives. From 1991 to 1994 he was with
TCSI Inc., Berkeley, California, responsible for signal proces-
sor development. Since 1994 he holds the Vodafone Chair
at TU Dresden. He is coordinating the research project
EASY-C.
HANS-PETER MAYER (Hans-Peter.Mayer@alcatel-lucent.de)
received his Ph.D. degree in physics from the University of
Tübingen in 1987. He joined Alcatel-Lucent and worked on
high-speed optoelectronic and WDM components until
1995. From 1996 to 1999, he has been responsible for
early UMTS system studies, followed by the realization of
first UMTS and HSPA trial systems. Within Bell Labs, he is
currently responsible for the Advanced MAC department
with a focus on projects related to LTE-Advanced.
STEFAN BRUECK (sbrueck@qualcomm.com) studied mathe-
matics and electrical engineering at the University of Tech-
nology Darmstadt, Germany, and Trinity College Dublin,
Ireland. He received his Dipl.-Math. and Dr.-Ing. degrees in
1994 and 1999, respectively. From 1999 to 2008 he was
working for Lucent Technologies and Alcatel-Lucent in Bell
Labs and UMTS Systems Engineering, where he was respon-
sible for the MAC layer design of the HSPA base station. In
May 2008 he joined Qualcomm Germany and currently
leads the Radio Systems R&D activities in the Corporate
R&D Centre Nuremberg. He is involved in several research
projects on LTE-Advanced.
LARS THIELE [S‘05] received his Dipl.-Ing. (M.S.) degree in
electrical engineering from TU Berlin in 2005. Currently he
is working towards his Dr.-Ing. (Ph.D.) degree at the
Fraunhofer Heinrich Hertz Institute (HHI), Berlin. He has
contributed to receiver and transmitter optimization under
limited feedback, performance analysis for MIMO trans-
mission in cellular ODFM systems, and fair resource alloca-
tion. He has authored and co-authored about 40
conference and journal papers in the area of mobile com-
munications.
VOLKER JUNGNICKEL [M‘99] (jungnickel@hhi.de) received a Dr.
rer. nat. (Ph.D.) degree in physics from Humboldt Univer-
sität zu Berlin in 1995. He worked on semiconductor quan-
tum dots and laser medicine and joined HHI in 1997. He is
a lecturer at TU Berlin and head of the cellular radio team
at HHI. He has contributed to high-speed indoor wireless
infrared links, 1 Gb/s MIMO-OFDM radio transmission, and
initial field trials for LTE and LTE-Advanced. He has
authored and co-authored more than 100 conference and
journal papers on communications engineering.
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