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IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, VOL. 47, NO. 1, FEBRUARY 2005 45

Performance Analysis of Interference ProblemsInvolving DS-SS WLAN Systems

and Microwave OvensYasushi Matsumoto , Member, IEEE , Morio Takeuchi, Katsumi Fujii , Member, IEEE ,

Akira Sugiura , Senior Member, IEEE , and Yukio Yamanaka

Abstract—Theoretical and experimental investigations are car-ried out on the degradation in the performance of DS-SS wirelessLAN systems (WLANs) (IEEE802.11b) systems caused by electro-magnetic noise radiating from microwave ovens in the2.4-GHz fre-quency band. Based on the time-domain oven-noise model, theo-retical expressions for short-duration bit-error rates (BERs) andpacket-error rate (PERs) are derived. Measurements were doneusing commercially available microwave ovens and a noise simu-lator that consisted of a function generator and an RF synthesizer.

The PERs measured with actual oven noise were found to corre-late well with those derived by simulated noise, and they were inreasonable agreement with the theoretical estimates given by thederived approximate formulae. It is concluded that the noise simu-lator and the derived approximate expressions were very useful inevaluating the BERand PERof WLAN systems that have incurredinterference from microwave ovens.

Index Terms—Ad hoc network, bit-error rate (BER), direct se-quence spread spectrum (DS-SS), electromagnetic interface (EMI),industrial, scientific, and medical (ISM), microwave oven, noisemodel, wireless LAN (WLAN).

I. INTRODUCTION

VARIOUS kinds of wireless LAN (WLAN) systems have

come to be widely used in recent years because of their

flexible operability and cost effectiveness. The IEEE 802.11b

WLAN system, however, shares the 2.4-GHz frequency band

with industrial, scientific, and medical (ISM) equipment, and,

hence, the electromagnetic noise emitted from ISM equipment

may cause interference in wireless links. The most popular

household appliances utilizing this frequency band are mi-

crowave ovens whose noise easily exceeds in magnitude the

signal of WLAN links. Thus, microwave-oven noise is consid-

ered to be a major cause of degraded performance in 2.4-GHz

WLAN systems.

Many theoretical and experimental analyses have beendone on evaluating and reducing the interference in wireless

systems, including WLAN, caused by microwave-oven noise

[1]–[7]. However, these were based on stochastic models of

Manuscript received July 8, 2003; revised May 26, 2003.Y. Matsumoto and Y. Yamanaka are with the National Institute of Infor-

mation and Communications Technology, Tokyo 184-8795, Japan (e-mail:ymatsumoto@ nict.go.jp).

M. Takeuchi was with Tohoku University,Sendai 980-8577, Japan. He is nowwith Murata Manufacturing Company, Ltd., Yasu 520-2393, Japan.

K. Fujii and A. Sugiura are with the Research Institute of Electrical Commu-nication, Tohoku University, Sendai 980-8577, Japan.

Digital Object Identifier 10.1109/TEMC.2004.842114

microwave-oven noise. For example, empirical models were

developed using the noise’s amplitude probability distribution

(APD) [1], [2]. Middleton’s impulse noise model [8] was

employed to evaluate the bit-error rate (BER) in wireless links

[3], [5]. In recent years, stochastic models of microwave-oven

noise have been developed taking into consideration the fact

that actual oven noise has periodic burst envelopes. In addition,

intermittent Gaussian noise chopped by periodic pulses ( -mix-

ture model) [9] has been applied in analyzing the performance

of a direct sequence spread spectrum (DS-SS) system [6].

Mingxin and Ling [7] proposed a method of producing periodic

bursts of random noise with a given Middleton’s class-A APD

to simulate microwave-oven noise.

These stochastic models have, however, been expressed in

terms of statistical parameters such as APD, and, hence, no in-

formation has been provided on time-domain noise waveforms.

Since microwave-oven noise has burst envelopes, which depend

strongly on the observation frequency, APD-based performance

analyses of WLAN systems have had the following drawbacks.

1) It is generally difficult to evaluate short-duration BER

characteristics, which are necessary in investigating the

packet-error rate (PER) performance of WLAN systems

with interference.

2) To analyze the BER in spread spectrum systems, we must

assume that successive amplitudes of received oven noise

sampled at the chip rate take independent random values

having a given APD. However, with actual oven noise,

there is sometimes a correlation with chip duration, which

affects BER performance, as will be discussed in the fol-

lowing sections.

To resolve these problems, we proposed a time-domain model

for microwave-oven noise and demonstrated its validity in [10],

[11]. Using the same model, we investigate degradation in theBER and PER of a DS-SS WLAN system caused by microwave-

oven noise. The time-domain noise model is briefly explained

in the next section. In Section III, we discuss theoretical anal-

ysis and numerical simulations on the BER in a DS-SS WLAN

system using this model. Useful approximate formulae are also

derived. Section IV discusses measurements of PER in a WLAN

system obtained using commercially available transceivers and

microwave ovens. Measurements were also done using simu-

lated noise that was produced by the set of a function generator

and an RF synthesizer based on the time-domain noise model.

The experimental results corroborate the validity and usefulness

of our theoretical analyses.

0018-9375/$20.00 © 2005 IEEE



46 IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY, VOL. 47, NO. 1, FEBRUARY 2005

II. MICROWAVE-OVEN NOISE MODEL [11]

A. Time-Domain Noise Model

Microwave-oven noise is caused by the leakage of elec-

tromagnetic waves generated by a magnetron in the oven

at around 2.4 GHz. RF wave generation only occurs during

the time interval when the magnetron driving voltage (anodevoltage) exceeds a threshold for oscillation. A high driving

voltage is produced by directly supplying ac-mains voltage

to a step-up transformer (so called transformer-type oven) or

through an inverter (inverter-type oven). As a result, microwave

ovens generate RF pulses in the 2.4-GHz band at the frequency

of the ac mains (50 or 60 Hz) or at the switching frequency of

the inverter (typically 30 to 60 kHz). The RF pulse produced

is a pulsed sinusoidal wave whose instantaneous amplitude

and frequency vary widely with the instantaneous magnetron

driving voltage. Considering this mechanism of RF pulse

generation, We made the following assumptions in developing

their noise model [10], [11].

a) A noise pulse has a width equal to the time interval during

which the driving voltage exceeds threshold voltage

.

b) The instantaneous amplitude of the pulse envelope varies

linearly with the driving voltage (amplitude modulation).

c) The instantaneous frequency also changes linearly with

the driving voltage (frequency modulation).

Thus, microwave-oven noise can be expressed with the fol-

lowing noise model:

(1)

where denotes the carrier frequency (around 2.45 GHz) for

frequency modulation and is the maximum frequency de-

viation (typically, from 10 to 50 MHz). The driving voltage

is normalized by its maximum value. The maximum amplitude

of the envelope is given by , and the phase of is assumed

to be uniformly distributed within . The amplitude mod-

ulation with a threshold voltage is given by

for

for

(2)

This model can be easily realized by a combination of FM and

AM modulators as is described in Section IV. The magnetron

driving voltage is represented as

for transformer type (3a)

for inverter type (3b)

The ac-mains frequency and inverter switching frequency are

denoted by and in (3). The instantaneous frequency

of this noise model is given by

(4)

The instantaneous frequency changes in accordance with

in the frequency range of , as repre-

sented by (4).

The proposed noise model has six independent parameters,

, and . All of these can be determined

by measuring the spectrum and waveform of actual noise,

as detailed in [11]. Since magnetron oscillation is stronglyaffected by the volume, position, and temperature of food

materials inside the oven, the actual values of noise parameters

, and vary considerably over time due to the

changes in these load conditions. However, the fluctuations of

the noise parameters in symbol duration or in packet duration

of WLAN system are practically negligible. Therefore, the

noise parameters can be considered constant in evaluating

short-duration BER or PER. The PER for longer durations is

given by averaging short-duration PER weighting it with the

probability density of noise parameters.

B. Band-Limited Noises

Since received oven noise is band limited by the RF and IF

filters of the receiver, the band-limited noise waveform is neces-

sary to analyze the BER in a WLAN system with interference.

If the bandwidth of the receiving filter is wider than the cri-

terion bandwidth defined by

(5)

band-limited noise can be approximated as

(6)

In (6), denotes the group delay of the filter and rep-

resents the Fourier transform of time-shifted impulse responseof the filter . Equation (6) means that the envelope am-

plitude of band-limited noise is modulated with the frequency

response of the filter at the instantaneous frequency of noise.

From (4) and (6), the amplitude of band-limited noise is related

to its instantaneous frequency, as follows:

(7)

Using (3), the criterion bandwidth in (5) is given by the

following:

for transformer type (8a)

for inverter type (8b)

From typical values of noise parameters, bandwidth is

estimated to be around 0.1 MHz for the transformer-type oven

and from 2 to 3 MHz for inverter-type ovens.

III. THEORETICAL ANALYSIS OF WLAN PERFORMANCE

A. Formulation of BER and PER

IEEE802.11b WLAN systems employ a DS-SS scheme with

phase-shift keying (PSK) modulation. Table I summarizes the

specifications for this system. There is a schematic model of

the WLAN receiver we used in analysis in Fig. 1. Received SS

signal , receiver noise , and microwave-oven noise

are band limited by a band pass filter (BPF) with center fre-

quency (channel frequency) and bandwidth (chip rate),



MATSUMOTO et al.: PERFORMANCE ANALYSIS OF INTERFERENCE PROBLEMS 47

TABLE IMAJOR SPECIFICATIONS OF IEEE 802.11b WLAN [12]

Fig. 1. Schematic model of WLAN receiver.

and then down converted to baseband components ,

and . The output of the down converter is sampled at chip

rate and input to a matched filter, which outputs the corre-

lation with the spreading code . Finally, the matched filter’s

output is sampled at symbol rate and bit decision is made ac-cording to the phase of the sampled value.

In this paper, we focused on the most basic case, i.e., BPSK

modulation spread by the Barker code, and we made the fol-

lowing assumptions to simplify analysis.

1) Carrier frequency and chip clock are ideally recovered in

the receiver.

2) Intersymbol interference is negligible.

3) Receiver noise is white Gaussian with single-sided power

spectral density .

4) The occurrence of transmitted symbols “0” and “1” are

equally probable.

Matched-filter input sampled at time ( ; integer) isa sum of the signal , Gaussian noise with variance

, and oven noise

(9)

Since the receiving BPF bandwidth ( MHz) is much

wider than the criterion bandwidth given by (5) and (8), the

approximation (6) can be applied to band-limited oven noise.

Hence, oven noise can be represented as

(10)

where denotes frequency response of the receiving

BPF and represents channel frequency. Note that time

in (6) has been redefined as in (10).

The output of the matched filter is

(11)

where ( or to ) represents the

Barker code sequence and denotes code length. In (11),

represents the desired matched pulse. Let have a positive or

negative peak value, or , corresponding to transmitted

symbol “1” or “0”, at ( ; integer, ).

Gaussian noise has variance .

Oven noise at the matched filter’s output is given by

(12)

To evaluate oven noise , the following approximations may

be applicable according to the frequency variation rate of the

oven noise.

a) CW Approximation (Symbol Rate ): If symbol

rate is larger than the defined by (5), the received oven

noise can be assumed to be a CW in symbol duration, and the

approximation (6) can again be applied to oven noise

(13)

In (13), the instantaneous frequency of oven noise is

defined by (4), and represents the frequency response of

the matched filter, i.e., the Fourier transform of the impulse

response of the matched-filter

(14)

Fig. 2 shows the frequency response of the matched-filter

using the B arker code . Note that amplitude

is a periodic function with a period of (11 MHz)

and that has minimal value if frequency is an

integer multiple of . Amplitude is approximately

except for the neighborhood of the above-mentioned

minima. From (13), the amplitude of oven noise at the

matched-filter outputcan easilybe obtained by weightingthe ini-

tial amplitude with frequency responses and

at theinstantaneous frequency of oven noise.




Fig. 2. Frequency response of matched-filter j H ( f ) j with Barker code(length 11).

On the other hand, phase in (13) can be regarded

as a random variable having a uniform distribution within

because the initial phase of oven noise is distributed

uniformly within .

Recallingthatmatched pulse has peak value or

at and that Gaussian noise has variance in

(11), the bit-error probability at for a given phase is

obtained as follows:

(15)

If the transmitted binary data is differentially encoded, the

bit-error probability is given by

(16)

Fig. 3. Bit-error probability calculated from (17a) as a function of N A = ( 2 ) and N j I j = ( 2 ) .

Averaging or over , the time-dependent

bit-error probability is obtained

(17a)

differential encoding (17b)

Bit-error probability is plotted in Fig. 3 as a function of

and . It should be noted that the

above two parameters correspond to the interference-to-noise

ratio (INR) and one half of the signal-to-noise ratio (SNR) at

the matched filter’s output, respectively. The dependence of bit-

error probability on INR is found to be small when the INR

is less than dB.

Since criterion bandwidth [given by (8)] is typically

0.1 MHz for transformer-type oven noise, as mentioned in

Section II, and is much lower than symbol rate (1 Mbps),

(17) is considered to be a good approximation yielding the

probability of bit error caused by transformer-type oven noise.

b) Gaussian Approximation (Symbol Rate ): If

frequency variations of oven noise in symbol duration are

much greater than the symbol rate, i.e., , the phase

of sampled noise in (12) may be distributed




uniformly within . According to the central limit the-

orem, oven noise sampled at matched-filter output can

be considered to be a mutually-independent Gaussian random

variable with variance given by

(18)

This equation yields a generalized form for the intermittent

Gaussian noise model [9]. Time-dependent bit-error probability

can be obtained as follows:

(19a)

differential encoding (19b)

The typical value of criterion bandwidth for inverter-type

ovens is from 2 to 3 MHz, which is not much greater than

symbol rate (1 MHz). However, even though the conditions

for the above Gaussian approximation are not fully satisfied,

(19) can still be applied to predicting the BER degradation

caused by inverter-type oven noise, as will be discussed in the

next section.

Using the time-dependent bit-error probability given by (17)or (19), we can evaluate the average BER and PER. The average

BER is given by taking an average of or over a

period from to , i.e.

(20)

Since WLAN packets employ differential encoding,

symbols are used to recover information bits in the packet.

A packet error occurs if there are one or more error bits in the

packet because no error correction coding is applied in WLAN

packet. Hence, time-dependent PER can be evaluated as

CW approximation (21a)

Gaussian approximation (21b)

Fig. 4. Comparison of PERs calculated from (21a) and those from (22). Datalength = 1 0 0 0 bits. Solid lines: calculated from (21a). Dotted lines: calculatedfrom (22).

In practice, it is time consuming to numerically calculate (21a),

especially for a large , and an approximated form can be ob-tained by replacing the in (21b) with the de-

fined by (17a)

(22)

To verify the validity of this approximation, we compared the

PERs calculated from (22) with those from (21a) for various

at the matched filter’s output. As can

be seen from Fig. 4, (22) provides a very good approximationof (21a) if the PER is less than 0.1. It should also be noted that

the last terms in (21a), (21b), and (22) are practically negligible.

Since packet transmissions occur randomly, the average PER is

given by averaging the time-dependent PER

(23)

B. Calculated Results

Average BER and PER were calculated using the aforemen-

tioned approximations with the oven noise parameters listed in

Table II. Although, in general, the actual values of the param-

eters, , and , gradually change over time, as de-

scribed in Section II, they were assumed to be constant for the

calculations to allow the discussion of fundamental BER char-

acteristics. The receiving BPF was approximated as an ideal

Gaussian filter with a bandwidth of 11 MHz. The average BER

calculated from (20) is plotted in Fig. 5 as a function of

CNR (defined as ). In the figures, the peak power of

oven noise normalized by the Gaussian noise power

is 20 dB. To verify the validity of the derived approximations,

we conducted Monte Carlo simulations employing the exact ex-

pression (11) for the matched-filter output.




TABLE IINOISE PARAMETERS FOR CALCULATIONS AND NUMERICAL SIMULATIONS

With respect to interference caused by transformer-type oven

noise, Fig. 5(a) shows that the BERs yielded by simulation

correlate extremely well with the ones calculated by CW ap-

proximation. In contrast to this, the simulation for inverter-type

oven noise yielded intermediate BER values between thoseobtained by CW and Gaussian approximations as can be seen

from Fig. 5(b). It should be noted that channel frequency

chosen for Fig. 5(a) and (b) is nearly equal to , around

which oven noise reaches maximum amplitude and minimum

frequency variation rate. Fig. 5(c) has the results we calculated

and simulated for lower channel frequency, where the BER

is generally lower than that in Fig. 5(b) because oven noise

has a smaller peak amplitude at the input of the matched

filter. Fig. 5(c) also proves that simulation yields results that are

closer to those given by the Gaussian approximation compared

with the case in Fig. 5(b). This can be understood as follows.

From (3) and (4), the frequency variation rate of inverter-type

oven noise is given by

(24)

Since receiving BPF is tuned to channel frequency , the en-

velope amplitude of band-limited oven noise has pulse-

like waveform around instants that satisfy )= (note that

). From (24), the frequency

variation rate at one such instant is

. This means that band-limited oven noise

has a larger frequency variation rate if a lower channel

frequency is selected. Hence, the successive values of sam-pled oven noise , etc., have less correlation, and

the Gaussian approximation for (12) improves. Considering the

above, we can conclude that Gaussian approximation yields a

good estimate of worst-case BER for WLAN links that incur

interference from inverter-type oven noise.

The average PERs calculated from (22) and (23) and are

plotted in Fig. 6 for the same noise parameters and channel

frequency used in Fig. 5(a). The horizontal axis represents

, i.e., the ratio of signal power to peak power of oven

noise. The PER exhibits a steep increase at a threshold value

of dB, where peak oven-noise amplitude equals

signal amplitude at the matched filter’s output. Since the CNR

is considerably high (20 dB) in this example, BER and PER

increase very rapidly as oven-noise amplitude exceeds signal

Fig. 5. Comparison of BER characteristics calculated for noise parametersin Table II. Bit rate

= 1

Mbps. (a) Transformer-type oven,f = 2 4 6 2

MHz.(b) Inverter-type oven,

f = 2 4 6 2

MHz. (c) Inverter-type oven,f =

2 4 4 2 MHz.

amplitude. Since the repetition rate of oven noise is 50 Hz

and the pulse width of band-limited oven noise is about

4 ms in this example, a packet whose length is longer than

16 ms must overlap in part with an oven noise burst, and PER

approaches 100% as power ratio decreases. For a

shorter packet, there is some possibility of packet transmission

without overlap with oven noise bursts, and the upper bound of

PER for a low is limited by packet length and CNR.




Fig. 6. Calculated PER with the same noise parameters and channel frequencyas those in Fig. 5(a). CNR = 2 0 dB. Bit rate = 1 Mbps.

Fig. 7. Measurement system simulating WLAN link with interference fromoven noise.

IV. COMPARISON WITH EXPERIMENTAL RESULTS

We measured the PER of an actual WLAN link employing

two types of microwave ovens that are available for domestic

use. The layout for the measurement system is in Fig. 7.

The transceiver was a commercially available WLAN card of

PCMCIA type, which was installed in a notebook PC. Theantenna port of the WLAN card was connected to the measure-

ment system with a coaxial cable to form a wired link. WLAN1

and WLAN2 in the figure were the transmitter and the receiver

for data packets. WLAN2 returned an ACK (acknowledgment)

packet when it received a data packet correctly. The transmitting

power of the ACK packet from WLAN2 was set to 15 dBm,

which was higher than that of the data packet, to ensure correct

reception of the ACK packet by WLAN1. The microwave-oven

noise was received with a double ridged guide horn antenna

(DRGA) 1 m from the oven in an anechoic chamber and was

then injected into the WLAN link through a variable attenuator.

The oven was operated with a load of 1.5 L of water.

During PER measurement, we measured the noise spectrum

with a spectrum analyzer to determine the noise parameters

Fig. 8. PER measured with Gaussian noise. Bit rate= 1

Mbps. Data length=

1 1 9 0 4

bits.

to calculate the theoretical values of PER. To allow quantita-

tive discussion, it is important to stabilize oven noise parame-

ters, such as amplitude and frequency, because it takes a few

minutes to measure PER. Domestic microwave ovens usually

have a mechanism that rotates the food to ensure uniform ex-

posure to microwaves and since this is a major cause of varia-

tions in oven-noise parameters, the turntable was mechanically

fixed during measurement. In addition, to ensure further repro-

ducible measurement, we developed an oven noise simulator

using a set made up of a function generator and an RF synthe-

sizer (Fig. 7). The simulator could generate pseudo oven noise

with noise parameters determined from measurements of actual

oven noise. Table II lists the noise parameters determined during

the experiment.As we can see from Table I, the data packet of a WLAN

consists of a preamble, a physical layer convergence protocol

(PLCP) header segment and a medium access control (MAC)

data segment. The header and the MAC data segments are inde-

pendently coded by the cyclic redundancy check (CRC), which

can detect (but cannot correct) bit errors in a corresponding seg-

ment. If one or more bit errors are found, the receiver detects

packet error. The WLAN transceivers were operated in link-test

mode, and we measured the number of received packets in-

cluding bit errors in the data segment. For comparison, we cal-

culated theoretical values of PER using the noise parameters in

Table II and (21)–(23). We set the data length of a data packetto 1450 bytes in the experiment. The total length of the MAC

data segment was bits, including the MAC

header (30 bytes) and the CRC (8 bytes).

We fixed the number of transmitted packets to 1000 when

simulated oven noise was used for PER measurement. We re-

duced this to 100 using actual oven noises to shorten the mea-

suring time and avoid the adverse effects of time variation in

oven-noise characteristics.

The PER measured without applying oven noise is in Fig. 8

with the theoretical values. The measured PER differs from cal-

culated values by 1 to 5 dB. One possible cause is the phase

error in the carrier or the timing clock recovered in the actual re-

ceiver, because theoretical analysis assumed an ideally synchro-

nized system. Measured PERs for two types of oven noises are




Fig. 9. PER measured with actual and simulated oven noises. Thick lines:with actual oven noise. Broken lines: with simulated noise. CNR

= 2 3 : 3

dB.

Bit rate = 1 Mbps. Data length = 1 1 9 0 4 bits. (a) Transformer-type oven,f = 2 4 7 2

MHz. (b) Inverter-type oven,f = 2 4 6 2

MHz.

in Fig. 9. The horizontal axis indicates the signal to peak ovennoise power ratio shown in Fig. 6. We found that the

PERs measured with real oven noise generally agreed well with

those obtained through simulated noise, which demonstrates the

validity of the noise model given by (1) to (3). Considering the

difference in PER between theoretical and experimental results

obtained without oven noise in Fig. 8, we can conclude that the

measured PER characteristics in Fig. 9 have reasonable agree-

ment with the theoretical values.

V. CONCLUSION

Since IEEE 802.11b WLANs share a frequency band of

2.4 GHz with microwave ovens, we conducted theoretical

and experimental investigations to develop useful numerical

expressions that would explain performance degradation in

DS-SS WLAN links that was due to oven noise. We used the

time-domain model for oven noise as the theoretical basis of

our investigations.

Commercially available microwave ovens can be categorized

into two types according to the magnetron driving mechanism;

i.e., they are either transformer or inverter types. In transformer-type ovens, variations in noise frequency in the symbol dura-

tion of WLANs are not so rapid compared with the symbol rate.

Thus, assuming that noise behaves like a CW, we derived ap-

proximate formulae for the BER and the PER. In contrast, in-

verter-type microwave ovens change noise frequency at a faster

rate and we employed the Gaussian approximation in devel-

oping a useful formulae for the BER and PER. Measurements

were also carried out to validate these theoretical formulae using

WLAN transceivers and microwave ovens. Additional measure-

ments were done on simulated oven noise generated by a set

made up of a function generator and an RF synthesizer.

We found that the PERs measured with actual oven noise cor-

related well with those derived with simulated noise, and these

were in reasonable agreement with the theoretical estimates

given by the derived approximate formulae. From these results,

we concluded that the noise simulator and the derived approx-

imate expressions were very useful in evaluating the adverse

effects of oven noise on WLAN systems. We intend to conduct

further studies on performance in wireless systems, taking into

account the time variations in oven-noise parameters.

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[12] Wireless LANMedium AccessControl (MAC) and Physical Layer (PHY)

Specifications: High-speed Physical Layer Extension in the 2.4 GHz

Band . IEEE Standard 802.11b (Supplement to ANSI/IEEE Std 802.11,1999 Edition).

Yasushi Matsumoto (M’99)receivedthe B.E., M.E.,and Ph.D. degrees from Tohoku University, Sendai,Japan, in 1983, 1985, and 1998, respectively.

From 1985 to 1999, he was with the Communi-cations Research Laboratory (currently the NationalInstitute of Information and Communications Tech-nology), Tokyo, Japan, where he was engaged in

research and development of space communicationsand satellite antennas. From 1990 to 1994, he was

with the National Space Development Agency of Japan, Tokyo (currently the Japan Aerospace Ex-

ploration Agency). Since 1999, he has been an Associate Professor at Tohoku

University. His research interests include electromagnetic compatibility andwireless communications.

Morio Takeuchi received the B.E. and M.E. degreesin communication engineering from Tohoku Univer-sity, Sendai, Japan, in 2000 and 2002, respectively.

In 2003, he joined Murata Manufacturing Com-pany, Ltd., Yasu, Japan.His research interests includewireless communications.

Katsumi Fujii (M’03) received the B.E., M.E., andPh.D. degrees in electronic engineering fromthe Uni-versity of Electro-Communications, Tokyo, Japan, in1996, 1998, and 2001, respectively.

In 2001, he became a Research Associate at theResearch Institute of Electrical Communication,Tohoku University, Sendai, Japan. His researchinterests include EMC antennas and measurements.

Dr. Fujii is a Member of IEICE.

Akira Sugiura (M’90–SM’99) received the B.S. de-

greein appliedphysics fromFukui University, Fukui,Japan,the M.S. degreein applied physics from OsakaUniversity, Osaka, Japan, and the Ph.D. degree fromthe Tokyo Institute of Technology, Tokyo, Japan, in1966, 1968, and 1996, respectively.

After graduation, he joined Communications Re-search Laboratory (currently the National Instituteof Information and Communications Technology),Tokyo. He became a Professor at the ResearchInstitute of Electrical Communication, Tohoku

University, Sendai, Japan, in 1999. He has been engaged in research work on

EMC measurement technology and involved in CISPR activities.

Yukio Yamanaka received the B.S. and M.S.degrees in electrical engineering from NagoyaUniversity, Nagoya, Japan, in 1980 and 1983,respectively.

In 1983, he joined the Radio Research Laboratory

[renamed the Communications Research Laboratoryin 1988 and currently the National Institute of Infor-

mation and Communications Technology (NICT)],Ministry of Posts and Telecommunications, Tokyo,Japan. He is currently a Group Leader of the EMCMeasurement Group at the NICT. He has been

engaged in the study of statistical characteristics of man-made noise and EMCmeasurement.

He is a Member of IEICE and IEEJ.

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