IEICE TRANS. COMMUN., VOL.E93–B, NO.10 OCTOBER 2010

2578

PAPER

Special Section on Advanced Technologies in Antennas and Propagation in Conjunction with Main Topics of ISAP2009

A Stochastic Approach to Design MIMO Antenna with Parasitic
Elements Based on Propagation Characteristics
Naoki HONMA†a) , Member, Kentaro NISHIMORI†† , Senior Member, Riichi KUDO††† , Member,
Yasushi TAKATORI††† , Senior Member, Takefumi HIRAGURI†††† , and Masato MIZOGUCHI††† , Members

SUMMARY
This paper proposes a channel capacity maximization
method for Multiple-Input Multiple-Output (MIMO) antennas with parasitic elements. Reactive terminations are connected to the parasitic elements, and the reactance values are determined to achieve stochasticallyhigh channel capacity for the environment targeted. This method treats the
S-parameter and propagation channel of the antenna, including the parasitic elements, as a combined circuit. The idea of the ‘parasitic channel,’
which is observed at the parasitic antenna, is introduced to simplify the
optimization procedure. This method can significantly reduce the numberof necessary measurements of the channel for designing the antenna. As
a design example, a bidirectional Yagi-Uda array, which has two driven
antennas at both ends of the linear array, is measured in an indoor environment. The resulting design offers enhanced channel capacity mainly due
to its improved signal-to-noise ratio compared to the antenna without theparasitic antennas.
key words: MIMO, parasitic antennas, mobile antennas, Yagi-Uda arrays

1.

Introduction

The spatial multiplexing technique using multi-antennas
known as Multiple-Input Multiple-Output (MIMO) continues to be pursued since it can enhance the spectral efficiencyof wireless communications [1]. To implement MIMO in
wireless communications systems, small antenna configurations suitable for compact terminals have been studied, suchas PC card terminals, [2]–[4], PDAs (Personal Digital Assistants) [5], [6], and cellular phone terminals [7], [8].
However, if further channel capacity enhancement isrequired without increasing the aperture or the number oftransmitters/receivers, the radiation pattern of the antennas
must be considered. Jensen et al. [9] have shown that the
MIMO-optimized aperture distribution can increase channel
capacity. It was also shown that the non-conjugate matchingcondition improves the channel capacity since it gives radiation patterns with low correlation and fairly high signal-tonoise ratios (SNR) [10]. These studies indicate there is stillroom for optimizing the radiation pattern, even for the small
MIMO antennas, and that the optimal radiation pattern deManuscript received January 4, 2010.
Manuscript revised May 5, 2010.
†
The author is with Faculty of Engineering, Iwate University,
Morioka-shi, 020-8551 Japan.
††
The author is with Niigata University, Niigata-shi, 950-2181
Japan.
†††
The authors are with NTT Network Innovation Laboratories,
NTT Corporation, Yokosuka-shi, 239-0847 Japan.
††††
The author is with Faculty of Engineering, Nippon Instituteof Technology, Saitama-ken, 345-8501 Japan.
a) E-mail: honma@m.ieice.org
DOI: 10.1587/transcom.E93.B.2578

pends on the propagation environment in which the terminal
antennas are placed.
The use of parasitic antennas is the one of the easiest
approaches for designing the radiation pattern.
The adaptive array with parasitic antennas have been
well studied [11], [12], and the work [11] treats the antenna
impedance and propagation channel as a combined circuit.
However, it can be difficult to apply the adaptive array withthe parasitic antennas for the compact terminal because of
its limited size and battery power. Hence, such terminals
need the antenna with the fixed radiation pattern, which
gives high channel capacity even without adaptive radiation pattern control. Therefore, for small and simple terminals, designing fixed antenna based on stochastic approach
is more suitable than the adaptive or reconfigurable array antenna. This paper proposes a practical design method for antennas with parasitic elements that yields stochastically highchannel capacity. To find out optimal antenna parameterfrom enormous number of possible antennas, the propagation channel and the antenna characteristics, including thoseof the parasitic antennas, are treated as a combined circuit.
The channel capacity is maximized by changing the electric lengths of the parasitic antennas virtually; this reduces
the number of measurements since only the basic antennaconfiguration needs to be measured. In [13], the Yagi-Uda
array is optimized using reactive termination, but it only focused the gain of the Yagi-Uda array. In order to find out
fixed radiation pattern that maximize the channel capacity,
the channel information and antenna characteristics must be
combined. As an example, we optimize the design of a bidirectional Yagi-Uda array [14]. The bidirectional Yagi-Uda
array is a variant of Yagi-Uda array, and conventionally it isdesigned for obtaining a high antenna gain with a small form
factors. This study optimizes a bidirectional Yagi-Uda arrayto yield high channel capacity in the indoor environment.
In Sect. 2, the multi-antenna system model and proposed optimization method are described. In Sect. 3, theproposed design method is experimentally verified using thebidirectional Yagi-Uda array.
2.

Model and Proposed Design Method

2.1 Multi-Antenna System Model Including Parasitic Antennas
Figure 1 shows the system model considered here. Thisc 2010 The Institute of Electronics, Information and Communication Engineers
Copyright HONMA et al.: A STOCHASTIC APPROACH TO DESIGN MIMO ANTENNA WITH PARASITIC ELEMENTS BASED ON PROPAGATION CHARACTERISTICS

2579

Fig. 1 Model of the multi-antenna system including the parasitic
antennas.

model consists of Mt transmitting antennas, Mr receiving
antennas, and M p parasitic antennas. The electric lengths ofthe parasitic antennas are virtually changed by using reactive terminations, {z1 . . . z M p }, at the parasitic antennas for
simplicity. The receiving elements are connected to theloads, z0 , of the receiver through a matching and decoupling
network [15] whose scattering parameter (S-parameter) isrepresented by S M . In the following consideration andmeasurements, the value of the receiving port loads, z0 , is50 Ω. Here, we define the S-parameter matrix, SS , which
is defined for all ports being terminated by the reference
impedance z0 and is partitioned as,⎛⎞⎜⎜⎜ST T ST R ST P ⎟⎟⎟SS = ⎜⎜⎜⎜⎝SRT SRR SRP ⎟⎟⎟⎟⎠ .
(1)
SPT SPR SPP

where the subscripts T , R, and P indicate the transmitting,receiving, and parasitic antennas, respectively. Partitioned
matrices ST T and SRR are the S-parameters for the transmitting and receiving antennas, respectively. Term SPP is the
S-parameter matrix of the parasitic antenna and SPR is thematrix that represents the mutual coupling between the receiving antenna and parasitic antenna. Terms SRT and SPT
denote the channel matrices from the transmitting antenna
to the receiving antenna and parasitic antenna, respectively.
Since the S-parameter matrix is symmetric, ST R = STRT ,ST P = STPT , and SRP = STPR .
Matrix SPT , which corresponds to the channel between
the transmitting antenna and the parasitic antenna at the receiver, can not be measured in an actual situation, but it ispossible to define SPT in this model, and it should be obtained by measurement. In this paper, we define SPT as the‘parasitic channel.’
The termination condition at the parasitic antenna is defined as
⎞
⎛
0 ⎟⎟
⎜⎜⎜Γ1
⎟⎟⎟
⎜⎜⎜
..
⎟⎟⎟ ,
Γ = ⎜⎜⎜
(2)
.
⎟⎟⎠
⎜⎝
0
ΓM p
where Γi is the reflection coefficient of the i-th termination.
It is defined as Γ = ( jzi − z0 )/( jzi + z0 ), where zi can be expressed as zi = ri + jxi . Figure 2 diagrams the S-parametermodel for this multi-antenna system. Here, the parasitic antenna ports are terminated by the reactive loads,{z1 . . . z M p }.

Fig. 2Model of the multi-antenna system including parasitic antennas.

At the receiving antenna port, the decoupling and matching
network (DMN) is connected, shown as S M to consider theeffect of the radiation pattern without the matching condition; it is described in the later part of this paper.
2.2 Optimization of Parasitic Antenna
The key idea of this paper is that the radiation pattern of
the array antenna can be designed by appropriately setting
the electric lengths of the parasitic antennas. Changing theelectric length of the antenna is achieved by changing reactive load. This approach reduces the number of measurements needed since antennas with parasitic antennas of various lengths are not necessary.
Connecting a reactive termination to a parasitic antennatransforms the system’s S-parameter as follows
 
S
ST R
SS = T T
SRT SRR⎛⎜⎜ST T + STPT [Γ−1 − SPP ]−1 SPT= ⎜⎝⎜SRT + STPR [Γ−1 − SPP ]−1 SPT⎞
ST R + STPT [Γ−1 − SPP ]−1 SPR ⎟⎟⎟(3)⎠⎟ ,SRR + STPR [Γ−1 − SPP ]−1 SPR

where the partitioned matrix, SRT , is the channel matrix
when the parasitic antennas are connected to the reactive terminations. This includes the effect of miss-matching. Since
we are interested in the radiation pattern, not impedance
matching, the decoupling and matching network (DMN)
is introduced to verify the channel matrix properties underideal matching conditions. The S-parameter matrix of thelossless DMN is easily obtained from reference [15], and isexpressed as

S
S M12
.
(4)
S M = M11
S M21 S M22

By connecting two circuits, SS and S M , the observed channel matrix, H, can be written as,
H = S M21 · (I − SRR S M11 )−1 · SRT
= S M21· I − SRR + STPR (Γ−1 − SPP )−1 SPR S M11· SRT + STPR (Γ−1 − SPP )−1 SPT .

−1

(5)

IEICE TRANS. COMMUN., VOL.E93–B, NO.10 OCTOBER 2010

2580

Equation (5) shows that the channel matrix with an arbitrary
termination condition can be easily estimated without further measurement if the ‘parasitic channel,’ SPT , is known.
Here, the S-parameter of the receiver antennas, described as
SRR , SPR , and SPP , must also be known in advance.
The followings are the actual procedure for proposed
optimization method. In the first step, the antenna configuration with the parasitic antennas must be determined, andthis will determine the upper bound of the channel capacity.
To obtain high channel capacity within the limited antenna
aperture, the parasitic antenna must be coupled strongly, andplacing the driven antennas around the edge of the apertureis desirable [9]. The S -parameter matrices, SRR , SPR , and
SPP , need to be measured. Here, the array antenna with theparasitic antennas is used at the receiver side in this consideration. In the second step, the transmitting and receiving
antennas are placed in typical environments, where the array antenna is supposed to be used. The channel matrices
with several locations and orientations of the array antenna
are measured. Here, the parasitic channel, SPT , must be included in these measurements. In the third step, the termination condition is optimized so as to maximize the channel
capacity, which can be calculated byγ
C = log2 det I +
(6)
HH H
MT
γ is the SNR observed at the receiver. The channel matrix, H, with arbitrary termination conditions can be calculated by using (5). Since the proposed method is for designing the MIMO antenna with stochastically high channel
capacity, the termination impedances should be determined
to maximize the outage capacity by giving various termination impedances, {z1 . . . z M p } at the parasitic antennas. The
advantage of the proposed method is that the channel with
arbitrary terminations can be estimated without actually attaching them to the parasitic antennas. This mean the process of changing the terminations in the measurement can beomitted. The several optimization methods, such as Genetic
Algorithm (GA) [22], Particle Swarm Optimization (PSO)[23], steepest gradient method, etc. have been proposed, butthese issues are out of scope. Based on the procedure described above, the optimal value of zi is determined.
3.

Experimental Demonstration Using Bidirectional
Yagi-Uda Array

3.1 Antenna Configuration
As a demonstration, some results of parasitic antenna optimization are shown and discussed. Figure 3 shows the conceptual sketch of the bidirectional Yagi-Uda array antenna.
A Yagi-Uda array is well-known; it uses parasitic antennas
to form the radiation pattern. When two or more Yagi-Uda
arrays are used for MIMO communication, the overall sizeof the antenna becomes too large to suit compact terminals.
In the bidirectional Yagi-Uda array, two opposed Yagi-Uda
arrays share director elements, and this configuration offers

high gain characteristics even if there are driven antennas atboth ends of the array. Note the parasitic antennas couple
more strongly to the driven antennas when they are placedin the center of the array than when they are placed at theends of the array [16]. In terms of aperture efficiency, placing the driven antennas at the end of the array is effective inobtaining high MIMO channel capacity [9]. In this consideration, the reactive terminations, z1 = jx1 , and z2 = jx2 aregiven equally, i.e., z1 = z2 = jx1 , since the arrival directionof the signal can be random in the indoor situation and theantenna should have symmetrical radiation pattern.
Table 1 shows the antenna configurations measured
here. The transmitting antenna is an 8-element circular array antenna, and the antenna spacing is set to a λ0 . In configuration (A), a 4-element dipole array is used as the receiving antenna. The central two antennas are assumed tobe the parasitic antenna, nevertheless, the channel including
the parasitic antennas must be measured, and the channel
response with the reactive loads at the parasitic antennas are
estimated as described in 2. For comparison, the antenna
without the parasitic antennas, which is shown in Table 1(B), is also evaluated.
3.2 Measurement Setup
The detail measurement conditions are listed in Table 2. The
frequency was 4.85 GHz, and the receiving antenna, a 4element dipole array, was placed at 9 measurement points,
each separated by 3 m; the channels created by setting thereceiving antenna at 8 different horizontal orientations were
measured at each point. Therefore, the total number of thechannel sample is 53 × 9 × 8 = 3816. The height of thetransmitting antenna was set to 1.5 m, slightly higher thanFig. 3

Conceptual sketch of bidirectional Yagi-Uda array.
Table 1

Antenna combinations.

HONMA et al.: A STOCHASTIC APPROACH TO DESIGN MIMO ANTENNA WITH PARASITIC ELEMENTS BASED ON PROPAGATION CHARACTERISTICS

2581

Table 2 Measurement conditions.
Frequency
4.85 GHz
Bandwidth
20 MHzNumber of subcarriers
53
Rx. antenna direction
0 ∼ 360 deg. (45 deg. step)Rx. antenna positions
9 (see Fig. 4)
Rx. antenna height
1.2 m
Tx. antenna position
1 (see also Fig. 4)
Tx. antenna height
1.5 m

Fig. 4

Indoor environment.

the receiving antenna. The SNR in this study is evaluatedby measuring the noise power before the signals are transmitted. Figure 4 is a sketch of the measurement environment. The height, width, and depth of the room were 4,
20, and 10 m, respectively. The transmitting antenna wasplaced at the left side of the room, and the receiving antenna was placed at the measurement points indicated in the
figure. The NLOS situation was created by placing a partition board in front of the transmitting antenna. To prevent
diffraction from the edge of the board, it was covered withan anechoic material.
A photo of the antennas and environment is shown in
Fig. 5. The 4-element dipole array was placed on a fixture that could rotate the direction of antenna horizontally.
Fig. 5(b) shows the LOS situation; the transmitting antenna
is also shown on top of the pole. The testbed [19] can beseen behind the receiving array antenna; four receivers of
the MIMO testbed were used in this measurement.

Fig. 5Photo of antennas and measurement setup.

3.3 Results
Figure 6 plots median capacity versus reactance, x1 . Thisgraph was generated from the results at the various positions
and directions of receiving antenna. It is found that the channel capacity at the range of x1 < −200 Ω slightly varies, andthe maximum channel capacity occurs around x1 = −290 Ω
in both LOS(a) and NLOS(b) environments. The channel
capacity improvement compared with two dipoles in the
NLOS environment is greater than that in the LOS environment. It is also found that a drop in channel capacity
is observed at x1 = −47 Ω in both LOS(a) and NLOS(b)environments. From these results, the values of x1 that give
maximum and minimum capacity are independent of the existence of the direct path in this environment. In order to findFig. 6 Median capacity versus the reactance value, x1 .: (a) LOS
environment, (b) NLOS environment.

what is happening at these x1 values, the SNR and the spatial correlation is investigated as in the following Figs. 7 and
8.
Figure 7 plots SNR versus reactance. Similarly to theIEICE TRANS. COMMUN., VOL.E93–B, NO.10 OCTOBER 2010

2582

Fig. 7 Median SNR versus the reactance value, x1 .: (a) LOS
environment, (b) NLOS environment.

Fig. 8 Median spatial correlation versus the reactance value, x1 .: (a)LOS environment, (b) NLOS environment.

capacity, the parasitic antennas offer better improvement in
the NLOS environment than in the LOS environment.
Figure 8 shows the characteristics of the spatial correlation versus x1 . The spatial correlation rises when x1 isaround -47, which corresponds to the minimum point in thechannel capacity as shown in Fig. 6. The difference in thespatial correlation between the bidirectional Yagi-Uda arrayand two dipoles is very small. Here, the spatial correlation isFig. 9 Cumulative distribution function of channel capacity with the
MIMO-optimal condition (x1 = −290 Ω), (a) LOS environment, (b) NLOS
environment.

larger than 0 even with DMN is used. This is because DMN
only orthogonalizes the radiation patterns of the array antenna but does not orthogonalize the channel [20]. In [21],it is described the channel capacity is hardly affected by thespatial correlation when it is lower than 0.9. From the reason
discussed here and the results of the measurement shown in
Fig. 6∼8, it is found that the parasitic antennas can improvethe channel capacity by better collecting the power delivered
to the aperture.
The cumulative distribution function (CDF) of thechannel capacity is indicated in Fig. 9. x1 is set to −290 Ω,
which maximizes the median channel capacity. It can beseen that the overall channel capacity is improved evenwhen the optimization is performed only for median capacity. Also, the channel capacity is improved more in the
NLOS environment than in the LOS environment.
Figure 10 shows the CDF of the SNR. The distributionof the SNR is very similar to that of the capacity. In the
LOS environment the improvement is remarkable in higher
percentage range, and this means that the SNR tends to beenhanced for the receiving antennas in not bad environment.
Figure 11 shows the CDF of the spatial correlation. In
the LOS situation, the spatial correlation with optimal x1 ishigher than that achieved with two dipoles. The radiation
pattern that provides the stochastically highest channel capacity enhances the capacity only by improving the SNR
stochastically. On the other hand, the spatial correlation of
the bidirectional Yagi-Uda array in the NLOS environment
is very similar to that with the two dipoles. In the NLOS situation, the optimized pattern only slightly impacts the spatial correlation.

HONMA et al.: A STOCHASTIC APPROACH TO DESIGN MIMO ANTENNA WITH PARASITIC ELEMENTS BASED ON PROPAGATION CHARACTERISTICS

2583

Fig. 12Fig. 10 Cumulative distribution function of SNR with the MIMOoptimal condition (x1 = −290 Ω), (a) LOS environment, (b) NLOS environment.

Fig. 11 Cumulative distribution function of spatial correlation with the
MIMO-optimal condition (x1 = −290 Ω), (a) LOS environment, (b) NLOS
environment.

Figure 12 plots antenna gain versus x1 . The antenna
gains at the forward direction (+x) and backward direction(−x) are indicated. The radiation characteristics were obtained by exciting antenna #1; antenna #2 was terminatedRadiation pattern characteristics versus reactance value, x1 .

using a 50 Ω resistor. It is found that the direction of thebeam changes greatly around −50 ≤ x1 ≤ 50. The highest antenna gain at forward direction (+x) is observed whenx1 = −47 Ω. This can be recognized as a director mode
since the beam extends away from the driven antenna in the
direction of the parasitic antennas. In [13], the optimizationis performed for this gain maximum, and this mode has beenused conventionally to enhance the antenna gain, however it
corresponds to the minimum capacity point shown in Fig. 6.
This means the optimal design for MIMO communicationdiffers from that of the conventional Yagi-Uda array. In theregion of x1 > 0, the parasitic antennas work as reflectors
since the gain at −x is higher than that at +x. In this mode,the spatial correlation is slightly lower than in the director
mode, see Fig. 8, however, the low SNR affects the channel
capacity.
Figure 13 show the measured radiation patterns; thespecific termination conditions are shown. The patterns in
the horizontal plane were measured (xy-plane) when antenna # 1 was excited. The radiation pattern at the capacity
maximum is almost symmetrical with respect to yz-plane,that is, the radiation patterns of #1 and #2 are very similar.
It will cause a relatively high spatial correlation in the observed channel. However, the increase in the spatial correlation is very slight, and the radiation pattern formed offers
high SNR. Since the most of the arriving paths distribute inhorizontal direction [18], the improvement in SNR is causedby the average gain enhancement in the horizontal plane,
which is provided by the parasitic antennas. The director
mode, which offers the highest antenna gain but the lowest
capacity, forms the mainlobe in the direction of φ = 0(+x).
However, a high backlobe is seen at φ = 180◦ (−x) becauseof the other driven antenna at the end of the array. The condition, x1 = 18 Ω, gives the reflector mode, and the highest
gain is observed at φ = 180◦ (−x). The maximum ratio of thegain at φ = 180◦ to that at φ = 0 is obtained in this mode,IEICE TRANS. COMMUN., VOL.E93–B, NO.10 OCTOBER 2010

2584

Acknowledgements
The authors thank Mr. Masaaki Ida and Mr. Yoshinobu
Makise of NTT Advanced Technology Corporation for offering a great help in the measurement.
References

Fig. 13

Measured radiation pattern.

however, this cannot reduce the spatial correlation as shownin Fig. 8. This means that the simple combination of orthogonal radiation patterns cannot enhance the channel capacity,and the propagation characteristics should be taken into account in designing MIMO-optimal antennas.
The discussion above validates the proposed design
method for the MIMO antennas using fixed parasitic antennas, and the demonstrated design achieved the highest median capacity at both LOS and NLOS situation in this environment even with the common reactance value. To obtainthe universally optimal design for most indoor or other environments, more measurements at various situations can beneeded. By this process, the antenna design parameter fortypical environments can be extracted by proposed method.
This is our future work.
4.

Conclusion

A practical design method for antennas with the parasitic
elements, which gives stochastically high channel capacity,
has been proposed. In this method, the antenna characteristics and the propagation channel, which includes the parasitic antennas, are treated as a combined circuit, and the termination conditions of the parasitic antennas are optimized.
A bidirectional Yagi-Uda array has been optimized and measured based on real channel distributions. The results of themeasurements have indicated that the antenna designed by
the proposed method can offer enhanced channel capacity
mainly by its improved the signal-to-noise ratios. It was also
found the mode for the maximum channel capacity differsfrom the gain maximum mode for the conventional YagiUda array, that is, MIMO-optimal design is needed. These
results supports that a high capacity MIMO antenna withparasitic antennas can be designed with very small numbersof the channel measurement.

[1] I.E. Teletar, “Capacity of multi-antenna Gaussian channels,” Tech.
Rep., AT&T-Bell Labs, June 1995.
[2] D.W. Browne, M. Mateghi, M.P. Fitz, and Y. Rahmat-Samii, “Experiments with compact antenna arrays for MIMO radio communications,” IEEE Trans. Antennas Propag., vol.54, no.11, pp.3239–
3250, Nov. 2006.
[3] Y. Okano and K. Cho, “Monopole antenna array arrangement forcard-type mobile terminal,” 2004 IEEE Radio and Wireless Conference, pp.415–418, Sept. 2004.
[4] N. Honma, K. Nishimori, T. Seki, K. Nishikawa, and K. Tsunekawa,“Triple polarization antenna employing capacitor loaded monopole
antenna and notch antenna for MIMO systems,” 2005 International
Symposium on Antennas and Propagation (ISAP 2005), vol.2, TB24, pp.367–370, Aug. 2005.
[5] C.C. Chiau, X. Chen, and C.G. Parini, “A compact four-element
diversity-antenna array for PDA terminals in a MIMO system,” Microwave and Optical Technology Letters, vol.44, no.5, pp.408–412,
Jan. 2005.
[6] L. Schulteis, C. Kuhnert, and W. Wiesbeck, “Three dual-band miniaturized inverted F antennas integrated in a PDA for MIMO applications,” 2006 IEEE Antennas and Propagation Society International
Symposium, pp.3593–3596, July 2006.
[7] P. Hongli, L. Qizhong, and S. Tang, “The implementation of compact multiple antennas for WCDMA MIMO handset,” 2003 IEEE
Antennas and Propagation Society International Symposium, vol.1,
pp.479–482, June 2003.
[8] Y. Gao, C.C. Chiau, X. Chen, and C.G. Parini, “Modified PIFA
and its array for MIMO terminals,” IEE Proc. Microw., Antennas
Propag., vol.152, no.4, pp.255–259, Aug. 2005.
[9] M.A. Jensen, B. Quist, and N. Bikhazi, “Antenna design for mobile MIMO systems,” IEICE Trans. Commun., vol.E91-B, no.6,
pp.1705–1712, June 2008.
[10] B.K. Lau, J.B. Andersen, G. Kristensson, and A.F. Molisch, “Impact of matching network on bandwidth of compact antenna arrays,”
IEEE Trans. Antennas Propag., vol.54, no.11, pp.3225–3238, Nov.
2006.
[11] D. Pinchera, J.W. Wallace, M.D. Migliore, and M.A. Jensen, “Experimental analysis of a wideband adaptive-MIMO antenna,” IEEE
Trans. Antennas Propag., vol.56, no.3, pp.908–913, March 2008.
[12] T. Ohira, and K. Gyoda,“Electronically steerable passive array radiator antennas for low-cost analog adaptive beamforming,” 2000
IEEE International Conference on Phased Array Systems and Tech.,
pp.101–104, May 2000.
[13] J.H. Bojsen, H. Schjaer-Jacobsen, E. Nilsson, and J. Bach Anderson,“Maximum gain of Yagi-Uda arrays,” Electron. Lett., vol.7,
no.18, pp.531–532, Sept. 1971.
[14] N. Honma, T. Seki, T.K. Nishikawa, K. Tsunekawa, and K. Sawaya,“Compact six-sector antenna employing three intersecting bidirectional microstrip Yagi-Uda arrays with common director,” IEEE
Trans. Antennas Propag., vol.54, no.11, pp.3055–3062, Nov. 2006.
[15] J. Weber, C. Volmer, K. Blau, R. Stephan, and M.A. Hein, “Miniaturized antenna arrays using decoupling networks with realistic elements,” IEEE Trans. Microw. Theory Tech., vol.54, no.6, pp.2733–
2740, June 2006.
[16] M.D. Migliore, D. Pinchera, and F. Schettino, “Improving channel
capacity using adaptive MIMO antennas,” IEEE Trans. Antennas
Propag., vol.54, no.11, pp.3481–3489, Nov. 2006.

HONMA et al.: A STOCHASTIC APPROACH TO DESIGN MIMO ANTENNA WITH PARASITIC ELEMENTS BASED ON PROPAGATION CHARACTERISTICS

2585[17] A.F. Molisch, “A generic model for MIMO wireless propagation
channels in macro- and microcells,” IEEE Trans. Signal Process.,
vol.52, no.1, pp.61–71, Jan. 2004.
[18] K. Nishimori, N. Tachikawa, Y. Takatori, R. Kudo, and K.
Tsunekawa, “Frequency correlation characteristics due to antennaconfigurations in broadband MIMO transmission,” IEICE Trans.
Commun., vol.E88-B, no.6, pp.2438–2445, June 2005.
[19] K. Nishimori, R. Kudo, N. Honma, Y. Takatori, A. Ohta, and K.
Okada, “Experimental evaluation using 16 × 16 multiuser MIMO
testbed in an actual indoor scenario,” 2008 IEEE Antennas andPropagation Society International Symposium, vol.2, no.204.2, July
2008.
[20] J.W. Wallace and M.A. Jensen, “Mutual coupling in MIMO wirelesssystems: A rigorous network theory analysis,” IEEE Trans. Wirel.
Commun., vol.3, no 4, pp.1317–1325, July 2004.
[21] S. Loyka and J. Mosig, “Channel capacity of N-antenna BLAST
architecture,” Electron. Lett., vol.36, no.7, pp.660–661, March 2000.
[22] R.L. Haupt,“An introduction to genetic algorithms for electromagnetics,” IEEE Antennas Propag. Mag., vol.37, no.2, pp.7–15, April
1995.
[23] J. Robinson and Y. Rahmat-Samii, “Particle swarm optimizationin electromagnetics,” IEEE Trans. Antennas Propag., vol.52, no.2,
pp.397–407, Feb. 2004.

Naoki Honma
received the B.E., M.E., andPh.D. degrees in electrical engineering from Tohoku University, Sendai, Japan in 1996, 1998,
and 2005, respectively. In 1998, he joined the
NTT Radio Communication Systems Laboratories, Nippon Telegraph and Telephone Corporation (NTT), in Japan. He is now working for
Iwate University. He received the Young Engineers Award from the IEICE of Japan in 2003,
the APMC Best Paper Award in 2003, and theBest Paper Award of IEICE Communication Society in 2006, respectively. His current research interest is planar antennas
for high-speed wireless communication systems. He is a member of IEEE.

Kentaro Nishimori
received the B.E., M.E.
and Ph.D. degrees in electrical and computer engineering form Nagoya Institute of Technology,
Nagoya, Japan in 1994, 1996 and 2002, respectively. In 1996, he joined the NTT Wireless Systems Laboratories, Nippon Telegraph and Telephone Corporation (NTT), in Japan. He was senior research engineer on NTT Network Innovation Laboratories. He is now associate professor in Niigata University. He was a visiting
researcher at the Center for Teleinfrastructure(CTIF), Aalborg University, Aalborg, Denmark from Feb. 2006 to Jan.
2007. He was an editor for the Transactions on Communications for the
IEICE Communications Society from May 2007 to May 2010 and Assistant
Secretary of Technical Committee on Antennas and Propagation of IEICE
from June 2009 to May 2010. He received the Young Engineers Award
from the IEICE of Japan in 2001, Young Engineer Award from IEEE APS Japan Chapter in 2001, Best Paper Award of Software Radio Society in2007 and Distinguished Service Award from the IEICE Communications
Society in 2005 and 2008. His main interests are spatial signal processing including MIMO systems and interference management techniques onheterogeneous networks. He is a member of IEEE.

Riichi Kudo
received the B.S. and M.S.
degree in geophysics from Tohoku University,
Japan, in 2001 and 2003, respectively. In 2003,
he joined NTT Network Innovation Laboratories, Yokosuka, Japan. He has been engaged
in the MIMO communication systems and the
beamforming method. He received the Young
Engineer Award from the Institute of Electronics, Information and Communication Engineers(IEICE) Japan in 2006. His current researchinterest is digital signal processing for wirelessand optical communication systems. He is a member of IEEE.

Yasushi Takatori
was born in Tokyo, Japan,
1971. He received his B.E. degree in electrical and communication engineering and his
M.E. degree in system information engineeringfrom Tohoku University, Sendai, Japan in 1993
and 1995, respectively. He received his Ph.D.
degree in wireless communication engineeringfrom Aalborg University, Denmark in 2005. In
1995, he joined the Wireless Systems Laboratories, Nippon Telegraph and Telephone Corporation (NTT), in Japan. He is currently a seniorresearch engineer in the Wireless Systems Innovation Laboratory of NTT
Network Innovation Laboratories. He was a visiting researcher at the Center for TeleInFrastructure (CTIF), Aalborg University, Aalborg, Denmark
from 2004 to 2005. He received the Young Engineers Award from the
IEICE of Japan in 2000, the Excellent Paper Award of WPMC in 2004 and
YRP Award in 2005. His current research interest is MIMO systems, spatial
signal processing techniques and ultra-high speed optical communication
systems. He is a member of the IEEE.

Takefumi Hiraguri
received the M.E. and
Ph.D. degrees from the University of Tsukuba,
Ibaraki, Japan, in 1999 and 2008, respectively.
In 1999, he joined the NTT Access Network
Service Systems Laboratories, Nippon Telegraph and Telephone Corporation (NTT) in
Japan. He has been engaged in research and development of MAC protocol for the high speed
and the high communication quality in wirelessLANs systems. He is now associate professor in
Nippon Institute of Technology (NIT). He is amember of IEEE.

Masato Mizoguchi
received the B.E. and
M.E. degrees in electrical engineering from Tokyo University of Science, Japan in 1989 and1991, respectively. In 1991, he joined Nippon
Telegraph and Telephone Corporation (NTT)and was mainly engaged in research and development of personal communication systems
and high data rate wireless LANs including theIEEE 802.11a systems. He is currently a Senior Research Engineer, Supervisor in the department of Wireless Systems Innovation Laboratory at NTT Network Innovation Laboratories, and is working on researchand development of next generation wireless home network. He received
the Young Researcher’s Award in 1998, the Best Paper Award in 2000 and
the Achievement Award in 2006 from IEICE. He is a member of IEEE.