Selected Papers

Beamforming Method for Broadband
MIMO-OFDM Systems
Yasushi Takatori†, Riichi Kudo, Kentaro Nishimori,and Koichi Tsunekawa
Abstract
We propose two methods for MIMO with beamforming to overcome the inherent problems in MIMOOFDM with beamforming: calculation complexity and low MIMO effect in line-of-site environments.
The multiple-input-multiple-output (MIMO) technique is the most attractive candidate for improving
spectrum efficiency in the next-generation wireless communication systems. The combination of MIMO
with orthogonal frequency division multiplexing (OFDM) is a promising way to achieve a higher data
rate in indoor wireless local area networks. After introducing MIMO-OFDM with multiple access points
to enhance the channel capacity even in line-of-site cases, we describe a novel approximate eigenvector
beam generation method for reducing the calculation complexity by using the power method and theGram–Schmidt orthogonalization procedure. Simulation and experimental results show that the combination of these two methods has a very high potential to improve the system capacity and drasticallyreduce the calculation complexity.

1. Introduction
The recent popularity of wireless local area networks (LANs) has pushed the demand for even higher data rates enabling real-time multimedia applications [1]. However, frequency resources are strictly
limited and most frequencies located in the
microwave band, which are suitable for wirelessLANs, have already been assigned to various radio
systems. Thus, broadband services will be provided
using limited frequency bands, so spectrum efficiency will be even more important for next-generation
wireless LAN systems than it already is in current
systems [2].
Multiple-input-multiple-output (MIMO) is one of
the most attractive candidates for achieving spectrum
efficiency [3], [4]. In identically independently distributed fading channels, it can linearly increase thechannel capacity as the number of antenna branches
increases. And a higher channel capacity is expected† NTT Network Innovation Laboratories
Yokosuka-shi, 239-0847 Japan
E-mail: takatori.yasushi@lab.ntt.co.jp

42

if the channel state information (CSI) is known at thereceiver as well as at the transmitter [5], [6]. However, in the MIMO data transmission scheme, different
signal streams are transmitted among multiple spatialchannels and this creates high-interference environments. Moreover, in broadband wireless access systems, delayed waves make the interference condition
worse so highly sophisticated demodulators are
required at receivers, especially for single-carrier systems.
Thus, orthogonal frequency division multiplexing(OFDM) systems in time division duplexing (TDD)with MIMO techniques have been receiving muchattention because OFDM can alleviate the influenceof the long-delayed waves and MIMO techniques
using CSI can be easily implemented for each subcarrier [7]. This is because a perfect calibration
method has been proposed [8] that enables CSI to be
used in the uplink as well as in the downlink.
Although the MIMO data transmission scheme
increases the hardware complexity of the system, a
MIMO receiver in which the MIMO demodulators
for the sub-carriers work simultaneously has beentested in trials [9] and practical application is
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Selected Papers

approaching. In general, MIMO is effective only inrich multipath environments and its effect decreases
in correlated fading environments such as in a line-ofsight (LOS) condition [10]. To overcome this problem, a beamforming technique for both transmitterand receiver has been proposed [11]. This method canenhance the desired signal power and improve thesignal-to-noise performance. However, it cannot utilize the spatial multiplexing effect, so the improvement in channel capacity is limited.
In this paper, we introduce a new MIMO data transmission scheme for wireless LANs that combines asingle-frequency-network (SFN) with TDD-OFDMMIMO. SFN with OFDM has been proposed forbroadcasting systems to improve the transmission
quality in overlapping cells [11]. In our proposal, we
advocate the use of SFN for multiple access point(MAP) MIMO data transmission. Thus, the proposed
scheme achieves very high channel capacity in both
LOS and non-line-of-sight (NLOS) environments.
Since multiple access points cooperate with eachother in the proposed scheme, higher performancecan be achieved as the number of access points (APs)increases, while the performance is significantly
degraded by the strong interference from adjacentAPs in the conventional scheme.
From the viewpoint of practical implementation,
the calculation cost in the proposed method must be
reduced. This is because it becomes high as the number of APs increases. To overcome this problem, thispaper also introduces a suboptimal approach for spatial channel control to reduce the calculation complexity. The performance with a suboptimal approachis shown and the feasibility of the suboptimal
approach is discussed.

2. Data transmission scheme in MAP-MIMO
systems
The proposed configuration for wireless LANs
with OFDM is shown in Fig. 1. Multiple APs are connected to one access controller (AC). Since OFDM
can mitigate the influence of the delayed waves, thissystem can also compensate for the delay caused by
the different distances between APs and wireless terminals (WTs). Thus, it creates a virtual large array ofantennas and enhances the MIMO effect in both theuplink and downlink. The data transmission scheme
in the uplink and downlink with CSI at both APs and
WTs is as follows.
In the uplink, OFDM data frames consist of a training period and a data period. In the training period,
known signals are transmitted from each antennabranch of the WT. After that, multiple OFDM data
symbols are transmitted using multiple beams. Then,
all signals received at all APs are delivered to the AC.
At the AC, all channel responses are estimated at thesame time in each sub-carrier in the training period
and multiple beams for the virtual large antenna arraycan be optimized by the minimum mean squared
error (MMSE) algorithm. After data has been
received using the multiple beamforming network, it
is demodulated. Note that this system works in TDD
systems and the channel responses in the downlink
can be regarded as being equal to those in the uplink.
And by using the perfect calibration method at eachAP, the generated beam patterns can also be
employed in the downlink and the transmission
power of each beam is optimized by the water pouring theory. In the downlink, multiple OFDM data
frames with training symbols are conveyed to multiple beamforming networks, and transmission signals

To network
Access point
Accesscontroller

Wireless terminal

Fig. 1. System model.

Vol. 3 No. 9 Sep. 2005

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for each antenna branch are generated. Then, thosesignals are delivered to APs and transmitted fromarray antennas simultaneously. At a WT, channel
responses are estimated in the training period andmultiple beams are optimized by MMSE criteria.
After that spatially multiplexed data are separated bymultiple beams and demodulated.

assumed; their locations are (0, 0, 3 m), (10, 0, 3 m),and (10, 10, 3 m). The height of the WT is 0.7 m. The
center frequency is 5.2 GHz, the number of sub-carriers is 52, and the sub-carrier spacing is 312.5 kHz.
We conducted a large number of trials and evaluatedthe cumulative probability of the channel capacity
and the ergodic channel capacity.
For the multi-user case, we evaluated the averageachievable throughput performance with the multiaccess scheme. In this evaluation, all APs used thesame frequency channel to enable us to evaluate thetotal achievable throughput in the spatial channel. In
the conventional method, each AP works independently, and simultaneous transmission occurs, whereas no collision occurs in the proposed scheme as aresult of employing time division multiple access(TDMA). Note that if all APs are synchronized andeach AP selects a different transmission timing, it
becomes the one-user case. In the evaluation of theconventional methods, an orthogonal spatial filter
was assumed at the WT to decompose the interference from the APs and the channel capacity was calculated after the decomposition. The data packets
were randomly generated for users and the achievabledata rate was calculated from the channel capacity ofeach link.
An example of the data transmission scheme isshown in Fig. 3. In the conventional scheme, users A
and B access AP-1 while users C and D access AP-2
and AP-3, respectively. In the proposed scheme, all

3. Performance evaluation of the proposed
scheme3.1 Simulation model for indoor multipath
environments
The simulation model is shown in Fig. 2. In thismodel, one short-delayed cluster and two exponentially attenuated long-delayed clusters are assumed.
We also assume that the average power of longdelayed clusters is set to be equal regardless of theWT location. Therefore, only the short-delayed cluster power changes as the WT moves. The K-factor,
which represents the ratio between direct-path powerand diffuse power, is set to 0 dB at the zone edge ofeach access point and the number of waves in eachcluster is set to 20. The angular spread of shortdelayed cluster equals 5° and that of the long-delayedclusters is 20°. The delay spread is set to 30 ns [12].
The number of antenna branches at each AP is four.
The same number of antenna branches is assumed at
the WT. The element spaces at both APs and WT are
0.7 λ and a circular array is used. Three APs are

Cluster-1

Cluster-2

Cluster-3

Delay time
Evaluation area

AP-3

10 m

WT
AP-1

AP-2
10 m

Fig. 2. Simulation model.

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Selected Papers

User A
(4)

A
A
A
A

User B
(6)

B
B
B
B
B
B

User C
(5)

C
C
C
C
C

User D
(8)

D
D
D
D
D
D
D
D

1

2

3

4

AP-1
CA' = 2, CB' = 2

A
A

A
A

B
B

B
B

AP-2
CC' = 1

C

C

C

C

AP-3
CD' = 1

D

D

D

D

Time

Conventional scheme

CA = 4, CB = 6
CC = 5, CD = 2

A
A
A
A

B
B
B
B
B
B

C
C
C
C
C

D
D

Proposed scheme

Fig. 3. An example of the schedules for conventional and proposed schemes.

Figure 4 shows the cumulative probability of theeigenvalues of the channel transfer matrix H k in the
proposed scheme compared with those of the conventional MIMO systems. In MIMO systems, eigenvalues indicate the power of each spatial channel. In
this figure, M is the number of antenna branches atConventional (M = 12)

Proposed (M = 4, L = 3)

100

80
Cumulative probability (%)

users access all APs. The maximum throughputs thatcan be achieved for users in the conventional scheme
are assumed to be, CA' = 2, CB' = 2, CC' = 1, and CD'= 1. Those for the proposed scheme are assumed to be
CA = 4, CB = 6, CC = 5, and CD = 2. At t = 0, four, six,
five, and eight packets are generated for users A to D,
respectively. In the conventional method, the data forusers A, C, and D is transmitted during the first
frame, whereas in the proposed scheme, only the datafor user A is transmitted then. Thus, the total numberof data packets in this frame is four in both methods.
However, in the next frame, the proposed scheme
increases the number of packets from four to six, so
the proposed scheme achieves higher throughput.
After four frames, the conventional scheme has only
completed data transmission to user A, while the proposed scheme has completed transmission to all users
except user D. In computer simulations, we evaluated the average of the total achievable throughput forvarious WT locations.

60

40

2nd
3rd

1st

20

3.2 Simulation results for one-user caseWe evaluated the spatial channel separation performances when the WT was located at (5, 0, 0.7 m) andclarified the operation in multipath fading environments. After that, we obtained the ergodic channel
capacity in the various WT position.
Vol. 3 No. 9 Sep. 2005

0
–30

–20

–10
0
Eigenvalue (dB)1020

Fig. 4. Cumulative probabilities of eigenvalues.

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Selected Papers

each AP and L is the number of APs. As this figure
shows, the proposed scheme improved the magnitudeof the third and fourth eigenvalues (i.e., the third andfourth spatial channels), while the first eigenvalue
had almost the same magnitude. It indicates that the
proposed method improves the spatial separation performance and enables the use of the third and fourth
eigenvalues.
The cumulative probability of the channel capacitywith proposed scheme is compared with the conventional MIMO systems in Fig. 5. In the conventional

MIMO systems, one AP is selected to achieve themaximum channel capacity. As this figure shows, theproposed scheme doubles the channel capacity of theconventional MIMO systems with the same antennaconfiguration. Moreover, it indicates that the proposed scheme outperforms the conventional systemeven if each conventional AP uses three times as
many antenna branches as the proposed method.
The distribution of the ergodic channel capacity inthe evaluation area is shown in Figs. 6, 7, and 8. The
white and dark areas indicate high and low ergodic
25

100
12

Conventional(M = 4)

8

y (m)

60

40

Proposed(M = 4, K = 3)

6
4
15
2

20

0

20

0
–2
–2
10

15
20
25
Channel capacity (bit/s/Hz)

0

2

4

30

6

8

10

12

x (m)

Fig. 7. Ergodic channel capacity distribution with aconventional scheme where 12 antenna branches
are assumed at one AP.

Fig. 5. Cumulative probabilities of channel capacity.

25
12

10

10

6
4
15
2
0
–2
–2

20

6
4
15
2
0

0

2

4

6

8

10

12

10

x (m)

Fig. 6. Ergodic channel capacity distribution with aconventional scheme with four antenna branches
at each AP.

46

8

y (m)

20

Channel capacity (bit/s/Hz)

y (m)

25

12

8

10

Channel capacity (bit/s/Hz)

Cumulative probability (%)

Conventional(M = 12)Channel capacity (bit/s/Hz)

10

80

–2
–2

0

2

4

6

8

10

12

10

x (m)

Fig. 8. Ergodic channel capacity distribution with theproposed scheme with 4 antenna branches at
each AP.

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Selected Papers

channel capacity, respectively. In Fig. 6, perfect synchronization among APs was assumed in the conventional data transmission scheme and no interference
was considered. The best AP was selected to achieve
the highest performance. Since only four antennabranches were used at APs, high channel capacity
could not be achieved. In Fig. 7, one AP with twelve
antenna branches was assumed as a conventional AP
while three APs were assumed in other scenarios. As
Fig. 7 shows, although high channel capacity wasachieved around the AP area, the performance wasdegraded as the distance between APs increases. On
the other hand, as Fig. 8 shows, the proposed method
achieved very high channel capacity and outperformed both the conventional methods at any point.
And higher channel capacity was achieved around thearea between two APs. This is because the distance
between each AP and WT became small and APs
could cooperate with each other. These results confirm the effectiveness of the proposed method.
3.3 Simulation results for multi-user case
Figure 9 shows the cumulative probability of theaverage total throughput achievable with the proposed scheme compared with that of the conventional MIMO systems where each AP works independently. In this figure, d indicates the distance between
AP and WT. As this figure shows, higher throughput
was achieved in the proposed scheme regardless ofthe distance between AP and WT, but the improvement decreased as the distance increased. For d = 50
100

Cumulative probability (%)

80

d = 10 m
d = 50 m
60
Conventional
methodd = 50 m
d = 10 m

40

20

0
5

Proposedmethod10
15
20
Achievable total throughput (bit/s/Hz)

Fig. 9. Cumulative probability of the achievablethroughput.

Vol. 3 No. 9 Sep. 2005

25m, the highest throughput with the conventional
scheme was almost the same as that with the proposed scheme because high throughput could beachieved if each WT was close to the AP being
accessed and the influence of interference was negligible. However, the performance of the conventional
method degraded as the distance between AP and WT
decreased. This is because the interference increased
as the distance decreased, while the channel capacityof the proposed scheme improved as a result of cooperation among multiple APs. These results confirm
that our proposed scheme is very attractive for future
wireless hotspot scenarios where a high density of
APs is expected.
4. Reduction of calculation complexity using a
suboptimal approach
Since a virtual array of antennas has a large numberof antenna branches, the calculation complexity ofthe spatial channel decomposition with the SVD (singular value decomposition) algorithm is very high. To
decrease the calculation complexity of our proposedsystem, we introduce a new suboptimal orthogonal
beamforming method in this section. In the following, MWT is the number of antenna branches at each
WT, MAP is the number of antenna branches at eachAP, and N is the number of APs connected to thesame AC. We also assume the condition N MAP >MWT because the calculation complexity is significant for this condition.
The aim of the spatial channel decomposition is togenerate multiple orthogonal beams and find thedesired signal subspace. To do this, the proposed
method uses the combination of the power method
and Gram–Schmidt orthogonalization procedure toenhance the desired signal power while keeping
orthogonality among multiple beams. In the power
method, the beam pattern is updated using a linear
transformation. Since this procedure does not require
SVD calculation, it can greatly decrease the calculation complexity. A trial system is shown in Fig. 10. It
can evaluate two-AP systems. The measured beam
pattern of the proposed suboptimal approach at a WT
is shown in Fig. 11. In this evaluation, two antennabranches were set at both a WT and at APs and thenumber of APs was two. As this figure shows, thefirst beam captured the signal from AP1 while the second beam generated a null toward AP1. It confirmsthat the suboptimal approach can find the signal
space accurately. The output signal-to-noise-plusinterference ratio (SINR) for the location of AP2 is47

Selected Papers

Transmitter and receiver(WT with two antennabranches)

Monitor (WT signals)

Fading simulator
for uplink

Fading simulator
for downlink

Monitor (two APs)

Transmitter and receiver(APs)

Fig. 10. Trial system.
AP1

AP2
20
Iteration = 30

2nd beam

1st beam

10Output SINR (dB)

Relative level (dB)

0

–10

–20

0
Ideal
–10

–30

–40
–90

Iteration = 10
Iteration = 3

–60

–30

0

30

60

90

θ (deg)

–20
–90

–60

–30

0

θ 2 (deg)

Fig. 11. Suboptimal beam patterns at a WT.

Fig. 12. SINR when a suboptimal approach is used.

shown in Fig. 12. In this evaluation, the direction to
AP1 was set to 25°. When the difference in directionsof two APs was 20° or more, then 30 iterations produced a good approximation of the ideal performance. Moreover, the performance degradation withthree iterations was less than 2 dB when the differences in directions among APs were significant.
Thus, the suboptimal approach is suitable for the proposed method.

that the proposed method outperforms the conventional MIMO system in strongly correlated environments. We evaluated the proposed scheme with conventional MIMO systems in multipath environments
and showed its potential. The simulation results confirm that the proposed scheme improved the higher
eigenvalues of the channel transfer matrices andenabled us to double the channel capacity for the conventional MIMO systems in the one-user case. Moreover, evaluating the total throughput performance inthe multi-user case, we found that the proposed
scheme improved the performance through cooperation among multiple APs as the distance between AP
and wireless terminal decreased, while the performance in the conventional method was significantly
deteriorated by interference from adjacent APs.
These results confirm that our proposed scheme isvery attractive for future wireless hotspot scenarios

5. Conclusion
In this paper, we proposed a multiple access point
MIMO data transmission scheme that combines asingle-frequency-network with TDD-OFDM-MIMO
using array antennas at access points (APs). We
derived the channel capacity of the proposed scheme
in the direct path environment. The results confirm
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where a high density of APs is expected.
In the proposed method, the calculation complexity
is a problem. To reduce the calculation cost, we introduced a suboptimal approach and evaluated the performance of a trial system. The results confirmed thatthe suboptimal beamforming approach is suitable forthe proposed method.
6. Acknowledgment
Authors would like to thank Prof. Ramjee Prasad of
director of CTIF in Aalborg University, Denmark,and Prof. Frank Fitzek of Aalborg University, Denmark, for their useful comments.
References[1]

R. Prasad and S. Hara, “Multicarrier Techniques for 4G Mobile Communications,” Artech House, pp. 1-12, 2003.
[2] K. Cho and T. Hori, “Smart antenna systems actualizing SDMA for
future wireless communication,” 2000 International Symposium
Antenna and Propagation, Vol. 4, pp. 1477-1480, Aug. 2000.
[3] A. Paulraj, R. Nadar, and D. Gore, “Introduction to space-time wireless communications,” Cambridge university press, pp. 1-10, pp. 6385, 2003.
[4] G. J. Foschini and M. J. Gans, “On limits of wireless communications
in a fading environment when using multiple antennas,” Wireless Personal Commun., Vol. 6, No. 3, pp. 311-335, Mar. 1998.
[5] I. E. Telatar, “Capacity of multi-antenna Gaussian channels,” Eur.
Trans. Tel., Vol. 10, No. 6, pp. 585-595, Nov./Dec. 1999.
[6] K. Miyashita, T. Nishimura, T. Ohgane, Y. Ogawa, Y. Takatori, and
K. Cho, “High data-rate transmission with eigenbeam space divisionmultiplexing (E-SDM) in a MIMO channel,” IEEE 56th VTC 2002Fall, Vol. 3, pp. 1302-1306, Sep. 2002.
[7] T. Sugiyama, S. Kurosaki, Y. Asai, D. Uchida, and M. Umehira,“Development of a novel SDM-COFDM prototype for broadband
wireless access systems,” WCNC 2003, Vol. 1, pp. 55-60, Mar. 2003.
[8] K. Nishimori, K. Cho, Y. Takatori, and T. Hori, IEEE Trans. Veh.
Tech., Vol. 50, No. 6, pp. 1636-1640, Nov. 2001.
[9] D. S. Shiu, G. J. Foschini, and J. M. Kahn, “Fading correlation and its
effect on the capacity of multi-element antenna systems,” IEEE Trans.
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[10] T. K. Y. Lo, “Maximum ratio transmission,” IEEE Trans. Commun.,Vol. 47, pp. 1458-1461, Oct. 1999.
[11] R. Rebhan and J. Zander, “On the outage probability in single frequency networks for digital broadcasting,” IEEE Trans. broadcasting,Vol. 39, pp. 395-401, Dec. 1993.
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128-137, 1987.

Vol. 3 No. 9 Sep. 2005

Yasushi Takatori
Research Engineer, Wireless Systems Innovation Laboratory, NTT Network Innovation Laboratories.
He received the B.E. degree in electrical andcommunication engineering and the M.E. degree
in system information engineering from Tohoku
University, Sendai, Miyagi in 1993 and 1995,
respectively. He joined NTT Wireless Systems
Laboratories in 1995. He was a visiting researcher
at the Center for TeleInFrastruktur (CTIF), Aalborg University, Aalborg, Denmark from 2004 to
2005. He received the Young Engineers Award
from the Institute of Electronics, Information andCommunication Engineers (IEICE) of Japan in
2000, the Excellent Paper Award of WPMC
(International Symposium on Wireless Personal
Multimedia Communications) in 2004 and the
YRP (Yokosuka Research Park) Award in 2005.
His current research interests are smart antennas,
MIMO systems, and spatial signal processing
techniques. He is an associate editor of theSpringer Journal of Wireless Personal Communications. He is a member of IEEE and IEICE.

Riichi Kudo
Engineer, Wireless Systems Innovation Laboratory, NTT Network Innovation Laboratories.
He received the B.E. and M.E. degrees in geophysics from Tohoku University, Sendai, Miyagi
in 2001 and 2003, respectively. He joined NTT
Network Innovation Laboratories in 2003. His
current research interest is MIMO communication systems. He is a member of IEICE.

Kentaro Nishimori
Research Engineer, Wireless Systems Innovation Laboratory, NTT Network Innovation Laboratories.
He received the B.E., M.E., and D.Eng.
degrees in electrical and computer engineeringfrom Nagoya Institute of Technology, Nagoya,
Aichi in 1994, 1996, and 2002, respectively. He
joined NTT Wireless Systems Laboratories in
1996. He received the Young Engineers Award
from IEICE in 2001 and the Young Engineer
Award from IEEE AP-S Japan Chapter in 2001.
His current research interests are smart antennas
for mobile communications and MIMO systems.
He is a member of IEICE and IEEE.

Koichi Tsunekawa
Senior Manager, Executive Research Engineer, Wireless Systems Innovation Laboratory,
NTT Network Innovation Laboratories.
He received the B.S., M.S., and Ph.D. degrees
in engineering science from the University ofTsukuba, Tsukuba, Ibaraki in 1981, 1983, and1992, respectively. He joined the ElectricalCommunications Laboratories of Nippon Telegraph and Telephone Public Corporation (nowNTT), Tokyo in 1983. Since 1984, he has been
engaged in R&D of portable telephone antennas
in land mobile communication systems. From1993 to 2003, he was with NTT DoCoMo, Inc.
where he worked on radio propagation research,
intelligent antenna systems for wireless communications, and the development of IMT-2000
antenna systems. His research interests are antenna systems for MIMO transmission, millimeterwave broadband access, and ubiquitous wirelesssystems. He is a member of IEICE and IEEE.

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