364IEICE TRANS. COMMUN., VOL.E99–B, NO.2 FEBRUARY 2016

PAPER

Analysis of Oversampling Effect on Selected Mapping SchemeUsing CORR Metric

Jun-Young WOO†a), Kee-Hoon KIM†b), Kang-Seok LEE†c), Nonmembers, Jong-Seon NO†d), Member,and Dong-Joon SHIN††e), Nonmember

SUMMARY It is known that in the selected mapping (SLM) scheme fororthogonal frequency division multiplexing (OFDM), correlation (CORR)metric outperforms the peak-to-average power ratio (PAPR) metric in termsof bit error rate (BER) performance. It is also well known that four timesoversampling is used for estimating the PAPR performance of continuousOFDM signal. In this paper, the oversampling effect of OFDM signal isanalyzed when CORR metric is used for the SLM scheme in the presenceof nonlinear high power amplifier. An analysis based on the correlationcoefficients of the oversampled OFDM signals shows that CORR metricof two times oversampling in the SLM scheme is good enough to achievethe same BER performance as four times and 16 times oversampling cases.Simulation results confirm that for the SLM scheme using CORR metric,the BER performance for two times oversampling case is almost the sameas that for four and 16 times oversampling cases.key words: correlation (CORR) metric, high power amplifier (HPA), or-thogonal frequency division multiplexing (OFDM), peak-to-average powerratio (PAPR), selected mapping (SLM)

1. Introduction

Orthogonal frequency division multiplexing (OFDM) hasbeen widely used in various wireless communication sys-tems. Since OFDM signals show high peak-to-averagepower ratio (PAPR), many schemes have been proposed tomitigate the PAPR problem. Among them, partial trans-mit sequence (PTS) and selected mapping (SLM) schemescan be good answers to solve the PAPR problem [1], wheremany alternative OFDM signals are generated and the onewith the minimum PAPR is selected for transmission. Sincethese schemes have high computational complexity due tomultiple inverse fast Fourier transform (IFFT) operations,many low-complexity schemes have been proposed.

While above schemes focus on reducing the PAPR met-ric, many other metrics are proposed to improve the bit errorrate (BER) performance in the presence of nonlinear devicessuch as high power amplifier (HPA). It is known that inter-modulation distortion [2], distortion-to-signal power ratio[3], mean squared error [4], and correlation (CORR) met-

Manuscript received July 31, 2015.†The authors are with the Department of Electrical and Com-

puter Engineering, Institute of New Media and Communications(INMC), Seoul National University, Seoul, Korea.††The author is with the Department of Electronic Engineering,

Hanyang University, Seoul, Korea.a) E-mail: jywoo@ccl.snu.ac.krb) E-mail: kkh@ccl.snu.ac.krc) E-mail: kanseo@ccl.snu.ac.krd) E-mail: jsno@snu.ac.kre) E-mail: djshin@hanyang.ac.kr

DOI: 10.1587/transcom.2015EBP3321

rics [5] outperform the PAPR metric in terms of BER per-formance of OFDM systems. Except for the PAPR metric,CORR metric shows the lowest computational complexitywhile its BER performance is almost the same as those ofother metrics. Although CORR shows relatively high PAPRthan PAPR metric, BER performance of CORR metric canbe enhanced than that of PAPR metric.

In this paper, it is shown that two times oversamplingis enough to be used instead of four times oversampling forSLM scheme using CORR metric. Four times oversamplingcauses high computational complexity due to U 4N-pointIFFT operations and U CORR metric calculations. Notethat one CORR metric calculation consists of LN complexmultiplications. To reduce the computational complexity,two times oversampling instead of four times oversamplingfor SLM scheme using CORR metric is proposed.

Oversampled signal sequences can be obtained by lin-ear combination of Nyquist-rate signal sequences. Based onthis, the resultant sequences after CORR calculation withdifferent oversamplings are obtained. By deriving the Pear-son correlation coefficients of these resultant sequences, itis shown that the Pearson correlation coefficient of two and16 times oversampling cases is relatively high. This impliesthat the BER performance of two times oversampling caseis the same as that of 16 times oversampling case.

The rest of this paper is organized as follows. In Sect. 2,CORR metric is briefly reviewed and it is shown that CORRmetric is near optimal metric in terms of BER performance.In Sect. 3, the oversampling effect is analyzed for the SLMscheme using CORR metric in the presence of nonlinearHPA by computing CORR metric and Pearson correlationcoefficient values. Simulation results and conclusion aregiven in Sect. 4.

2. Overview of SLM Scheme Using CORR Metric

2.1 Overview of CORR Metric

Binary data sequences are modulated by M-ary quadratureamplitude modulation (QAM) to generate an input sym-bol sequence [X0, X1, · · ·, XN−1]. Then, (L − 1)N zeros arepadded to the end (or middle) of an input symbol sequence,which is called L times oversampling, as

X = [X0, X1, · · ·, XN−1, 0, · · ·, 0︸��︷︷��︸(L−1)N 0′s

] (1)

Copyright c© 2016 The Institute of Electronics, Information and Communication Engineers

WOO et al.: ANALYSIS OF OVERSAMPLING EFFECT ON SELECTED MAPPING SCHEME USING CORR METRIC365

Fig. 1 A block diagram of the SLM scheme using CORR metric.

where N is the number of subcarriers and L is the oversam-pling factor. In general, it is known that four times oversam-pling (L = 4) is good enough for estimating the PAPR ofcontinuous OFDM signal [6]. Then, X in (1) performs IFFToperation and the nth element of the resulting OFDM signalis expressed as

xn =1√LN

N−1∑k=0

Xke j 2πknLN , 0 ≤ n ≤ LN − 1. (2)

Figure 1 shows a block diagram of the SLM schemeusing CORR metric in the presence of nonlinear HPA. Thisscheme generates U alternative symbol sequences X(u) bycomponentwisely multiplying each of U different phase se-quences to an input symbol sequence [X0, X1, · · ·, XN−1]. LetP(u) = [P(u)

0 , P(u)1 , · · ·, P(u)

N−1] be the uth phase sequence with

P(u)k = e jφ(u)

k , where φ(u)k ∈ [0, 2π), 0 ≤ k ≤ N − 1, and

0 ≤ u ≤ U − 1. It is customary to use P(u)k ∈ {1,−1} or

P(u)k ∈ {±1,± j}.

Each alternative symbol sequence is zero padded andperforms IFFT operation to generate x(u) = [x(u)

0 , x(u)1 , · · ·,

x(u)LN−1] and it passes through HPA such as solid state power

amplifier (SSPA) which is commonly used in mobile com-munications. It is known that the polynomial model for esti-mating the real SSPA to calculate the CORR metric is suffi-ciently accurate and has relatively low computational com-plexity [3]. The polynomial model is usually expressed asa third order nonlinearity such that the nth element of theoutput of SSPA can be expressed as

y(u)n ≈ α1x(u)

n + α3x(u)n |x(u)

n |2 (3)

where x(u)n and y(u)

n are the nth elements of the uth alterna-tive input and output OFDM signals of SSPA, respectively[3]. As the polynomial coefficients in (3), α1 = 1 andα3 = −0.1769 are used to match the practical SSPA usedin WiMAX [5]. The CORR metric [5] for the uth alternativeOFDM signal is obtained as

R(u)xy =

LN−1∑n=0

x(u)n y

(u)∗n = α1

LN−1∑n=0

|x(u)n |2 + α3

LN−1∑n=0

|x(u)n |4 (4)

where (·)∗ indicates the complex conjugation. Then, thisscheme calculates U CORR metrics between the input sig-nal x(u) and the output signal y(u) of SSPA. Among U CORR

metrics R(u)xy , the signal x(u) with the maximum CORR is se-

lected for transmission. Also, the side information u shouldbe transmitted to the receiver.

As an HPA to amplify the selected alternative OFDMsignal at the transmitter, Rapp model [5] is used becauseit is also widely used in mobile communications and moreaccurate one than the polynomial model in (3). The outputof Rapp model is given as

yn = A(|xn|)e j[arg(xn)+φ(|xn |)] (5)

where xn is the input to Rapp model and A(·) and φ(·) denoteamplitude to amplitude (AM/AM) and amplitude to phase(AM/PM) conversions of the nonlinear amplifier, respec-tively. Since SSPA is used in this paper, AM/PM conversionis assumed flat, i.e., φ(|xn|) = 0, and the AM/AM conversionis given as

A(|xn|) = |xn|⎡⎢⎢⎢⎢⎢⎣1 +

( |xn|A0

)2p⎤⎥⎥⎥⎥⎥⎦− 1

2p

(6)

where A0 is the maximum amplifier output and p is thesmoothness factor [7] with p = 3.286 as given in [5].

To determine the operating point of HPA, we use outputback-off (OBO) defined as

OBO = 10 log10

A20

Pout(7)

where Pout denotes the average power of OFDM signal atthe output of HPA.

2.2 BER Performance of SLM Scheme under HPA

The performance of SLM scheme was analyzed in the pres-ence of nonlinear HPA [8]. First, we review the analysis in[8] as follows. The output of nonlinear HPA can be formu-lated as

yn = K0xn + dn, 0 ≤ n ≤ N − 1 (8)

where xn is the input to the nonlinear HPA, dn is the uncor-related distortion, and K0 is the linear scaling factor definedas

K0 =E[xny∗n]

E[∣∣∣xn

∣∣∣2] . (9)

Also, signal-to-distortion-plus-noise ratio (SDNR) mea-sured at the receiver after FFT is defined as

SDNR =E[|K0Xk |2]

E[|Dk +Wk|2](10)

where Dk = FFT{dn} and Wk is additive white Gaussiannoise (AWGN) at the kth subcarrier. Assume that Xk, Dk,and Wk are mutually uncorrelated. Then, the average BER[8] is calculated as

Pb =4

log2 M

(1 − 1√

M

)Q

⎛⎜⎜⎜⎜⎜⎝√

3M − 1

SDNR

⎞⎟⎟⎟⎟⎟⎠ (11)

366IEICE TRANS. COMMUN., VOL.E99–B, NO.2 FEBRUARY 2016

where M is the constellation order and Q(·) denotes the Q-function.

According to (11), to achieve good BER performance,SDNR should be large. That is, the numerator in (10) shouldbe large because Dk and Wk cannot be controlled. SinceE[|K0Xk |2] = K2

0 E[|xk|2] and E[|xk|2] is constant, for goodBER performance, E

[x(u)

n y(u)∗n

]in (9) should be large, which

is identical to the CORR metric in (4). Therefore, the CORRmetric can be regarded as near optimal in terms of BER per-formance of the SLM scheme for OFDM signals.

From this reason, we will focus on the CORR metricamong various metrics targeting the BER performance andanalyze the oversampling effect on the SLM scheme usingCORR metric in the presence of nonlinear HPA.

3. Oversampling Effect on SLM Scheme Using CORRMetric

3.1 Expression of Oversampled Signal and CORR Metric

Oversampled signal can be expressed by linear combinationof Nyquist-rate samples [10]. The oversampling operator iscalled interpolator and the impulse response of an interpola-tor for L times oversampling is defined as

hL[n] =sin(πn/L)πn/L

. (12)

Since an ideal interpolator cannot be implemented, afinite-length filter of length I is used in practice. Then, theoutput of finite-length interpolator can be expressed as

xL[nL] =(nL+LI)/L∑

k=�(nL−LI)/L�x[k]hL[nL − Lk] (13)

where xL[nL] is the estimated nLth element of L times over-sampled signal and x[k] denotes the kth element of Nyquist-rate signal. In this paper, we assume I = 2 for simplicityand for I > 2, a similar analysis can be applied.

To represent the continuous signal, it is known that 16times oversampling (L = 16) is good enough. By substitut-ing L = 16 and I = 2 to (13), 16 times oversampled signalx16[16m + s] can be obtained as

x16[16m + s]

= h16[16 + s]x[m − 1] + h16[s]x[m]

+ h16[−16 + s]x[m + 1] + h16[−32 + s]x[m + 2].(14)

where 0 ≤ m ≤ N − 1 denotes the indices of Nyquist-ratesamples and 0 ≤ s ≤ 15. We can also obtain y16[16m+ s] inthe similar manner.

Note that the purpose of this analysis is investigatinghow frequently the same phase sequences are selected whenL times oversamplings are used compared to the case of 16times oversampling. Therefore, we first investigate the re-sult of CORR metric when 16 times oversampling is used

and then compare the result from L times oversamplingcases. In order to do this, we only need to consider 16 sam-ples for CORR metric computation in (4) instead of total16N samples. Since the same filter coefficients h16[n] in(12) are used for all m (see Eq. (14)), the resulting coeffi-cients of CORR metric for 16N samples is just multiple ofresulting coefficients of CORR metric for 16 samples.

Then, a partial CORR metric of 16 samples within twoadjacent Nyquist-rate samples can be given as

R(u)xy (m)=

15∑s=0

x16[16m+s](u)y16[16m+s](u)∗=15∑s=0

vm(s).

(15)

For better understanding, we can rewrite vm(8) as (16) at thetop of next page by substituting (14) and s = 8 to (15). Wecan similarly rewrite all the other values vm(s). Note thatin (16), there are 16 coefficients, i.e., {0.04,−0.13,−0.13,· · ·,−0.13, 0.04} and we will call the coefficient set as a co-efficient sequence.

Now, suppose that arbitrary L ∈ {1, 2, 4, 16} times over-samplings are used for CORR metric computation. Then,from (4), we only need vm(0) in (15) for Nyquist-rate sam-pling. Also, note that vm(0) + vm(8), vm(0) + vm(4) + vm(8) +vm(12), and

∑15s=0 vm(s) are needed to compute R(u)

xy (m) in (15)when two times, four times, and 16 times oversamplings areused, respectively.

3.2 Correlation Coefficients between Coefficient Se-quences Derived from CORR Metric Computation

In this subsection, we will investigate which oversamplingrate is required to show the similar performance as the con-tinuous OFDM signal case. Note that BER performance ofSLM scheme using CORR metric is determined by the se-lected phase sequence P(u). Therefore, to find the properoversampling rate, we have to investigate which oversam-pling case selects the same phase sequences as the contin-uous OFDM signal case, i.e., 16 times oversampling case.Since the selection of phase sequences is determined by (15)for all m, we need the resultant coefficient sequences aftercomputing (15) when L ∈ {1, 2, 4, 16}. Then, by investi-gating the correlation coefficients of the resultant coefficientsequences, we can find the proper oversampling rate.

Table 1 shows the resultant coefficient sequences af-ter computing (15) when L = 1, 2, 4, and 16. Note that in-dices are arranged from left to right and top to bottom (1 to16) in a coefficient sequence as given in (16). For example,when L = 2, we need vm(0) + vm(8) to compute (15). Sincevm(0) = x16[16m](u)y16[16m](u)∗ = 1 · x[m](u)y[m](u)∗, thereis only one nonzero coefficient 1 at index 6. Therefore, thecoefficient 1 should be added to 0.4 at index 6 in vm(8), i.e.,1 + 0.4 = 1.4 (see Table 1 when L = 2 and Eq. (16)). Therests can also be computed in the same manner.

Now, we want to investigate the similarity between co-efficient sequences in {vm(0), vm(0) + vm(8), vm(0) + vm(4) +vm(8) + vm(12),

∑15s=0 vm(s)}. In this paper, we use the Pear-

WOO et al.: ANALYSIS OF OVERSAMPLING EFFECT ON SELECTED MAPPING SCHEME USING CORR METRIC367

Table 1 Resultant coefficient sequences after computing (15) when L times oversampling is used.

Index 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

L = 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0L = 2 0.04 −0.13 −0.13 0.04 −0.13 1.4 0.4 −0.13 −0.13 0.4 0.4 −0.13 0.04 −0.13 −0.13 0.04L = 4 0.09 −0.33 −0.3 0.09 −0.33 2.3 0.94 −0.3 −0.3 0.94 1.3 −0.33 0.09 −0.3 −0.33 0.09

L = 16 0.37 −1.42 −1.25 0.36 −1.42 7.72 3.94 −1.25 −1.25 3.94 6.72 −1.42 0.36 −1.25 −1.42 0.37

vm(8) = x16[16m+8](u)y16[16m+8](u)∗

= (−0.21x[m − 1](u) + 0.63x[m](u) + 0.63x[m + 1](u) − 0.21x[m + 2](u))

(−0.21y[m − 1](u) + 0.63y[m](u) + 0.63y[m + 1](u) − 0.21y[m + 2](u))∗

= 0.04x[m − 1](u)y[m − 1](u)∗ − 0.13x[m − 1](u)y[m](u)∗ − 0.13x[m − 1](u)y[m + 1](u)∗ + 0.04x[m − 1](u)y[m + 2](u)∗

− 0.13x[m](u)y[m − 1](u)∗ + 0.4x[m](u)y[m](u)∗ + 0.4x[m](u)y[m + 1](u)∗ − 0.13x[m](u)y[m + 2](u)∗

− 0.13x[m + 1](u)y[m − 1](u)∗ + 0.4x[m + 1](u)y[m](u)∗ + 0.4x[m + 1](u)y[m + 1](u)∗ − 0.13x[m + 1](u)y[m + 2](u)∗

+ 0.04x[m + 2](u)y[m − 1](u)∗ − 0.13x[m + 2](u)y[m](u)∗ − 0.13x[m + 2](u)y[m + 1](u)∗ + 0.04x[m + 2](u)y[m + 2](u)∗.(16)

Table 2 Values of Pearson correlation coefficients between coefficientsequences with different L.

L = 1 and L = 16 L = 2 and L = 16 L = 4 and L = 16r (I = 2) 0.6023 0.9133 0.9839r (I = 3) 0.5980 0.9145 0.9835

Table 3 Probability of choosing different phase sequences comparedwith 16 times oversampling case when N = 256.

U = 4 U = 8 U = 16 U = 32L = 1 0.343 0.443 0.501 0.604L = 2 0 0 0 0L = 4 0 0 0 0

son correlation coefficient r because it is a measure of cor-relation between two sequences. The Pearson correlationcoefficient between two sequences a and b is defined as

r =∑

i(ai − a)(bi − b)√∑i(ai − a)2

∑i(bi − b)2

(17)

where a denotes the sample mean of a and ai denotes theith elements of the sequence a. Clearly, r takes a value in[−1, 1], where 0 implies no correlation and 1 or − 1 impliespositive or negative correlation, respectively.

Table 2 shows r between two coefficient sequences ob-tained from Table 1 when different L’s are used. When thecases of L = 1 and L = 16 are considered, the value of ris 0.6023, which shows relatively low correlation. On theother hand, when the cases of L = 2 and L = 16 and thecases of L = 4 and L = 16 are considered, the values of rare 0.9133 and 0.9839, respectively, which show relativelyhigh correlation. Also, I = 3 case is also derived for com-parison. As seen in Table 2, the values of r look almost thesame for I = 2 and 3. These results imply that when twotimes or four times oversamplings are used, the probabilityof choosing the same phase sequences as the 16 times over-sampling case in the SLM scheme using CORR metric is

very high.Table 3 shows the probabilities of choosing different

phase sequences from 16 times oversampling case for vari-ous values of L when N = 256. 104 OFDM signals are ran-domly generated and the unequal cases are counted. WhenL = 1 and U = 4, the probability is 0.343 and it increasesas U increases. However, when L = 2 and 4, the probabili-ties are all zero. That is, they always choose the same phasesequence as the case of L = 16. This result confirms thatanalysis using Pearson correlation coefficient is reasonable.

4. Simulation Results and Conclusion

In this section, the BER performances of the SLM schemeusing CORR metric for different oversampling factor L arecompared when N = 256, U = 4, and OBO = 3, 3.5, 4, 4.5,and 5 dB. A0 in (7) is calculated by multiple simulations fora given OBO. Also, 16-QAM is used for data modulation.We only consider the AWGN channel since the BER perfor-mance of the SLM scheme using CORR in selective fadingchannel is shown in [5]. For simulation, nonlinear HPA isused and 16 times oversampling is used for the Rapp modelto estimate the performance of continuous OFDM signalcase. We assume the perfect knowledge of side information.

In Fig. 2, Original, CL, and No HPA indicate the orig-inal OFDM signal with nonlinear HPA, OFDM signal us-ing CORR metric under nonlinear HPA where L denotes theoversampling factor for computing CORR metrics in (4),and the original OFDM signal without nonlinear HPA, re-spectively. Note that C2, C4, and C16 show almost the sameBER performances for various OBO values, while C1 is de-graded compared to them.

Figure 3 shows the BER performance of the SLMschemes using CORR and PAPR metrics. Since it is al-ready shown that C2 shows the same BER performance asC4 and C16 and thus, C4 and C16 cases are excluded. PL

368IEICE TRANS. COMMUN., VOL.E99–B, NO.2 FEBRUARY 2016

Fig. 2 BER performance of the SLM schemes using CORR metric whenN = 256, U = 4, and L = 1, 2, 4, and 16 for various OBOs.

Fig. 3 BER performance of the SLM schemes using CORR and PAPRmetrics when N = 256, U = 4, L = 1, 2, 4, and 16, and OBO = 4 dB.

denotes the OFDM signal using PAPR metric under nonlin-ear HPA, where L denotes the oversampling factor for com-puting CORR metrics in (4). The BER performance of theSLM scheme using PAPR metric is degraded compared tothat using CORR metric.

Figure 4 shows the PAPR reduction performances ofthe SLM schemes using CORR and PAPR metrics whenN = 64, 256, and 1024, U = 4, and L = 4. The PAPRreduction performances of SLM scheme using CORR met-ric are severely degraded compared to that using PAPR met-ric. However, Fig. 3 shows that the BER performance of theSLM scheme using CORR metric is better than that of theSLM scheme using PAPR metric.

In this paper, the oversampling effect for the SLM

Fig. 4 PAPR reduction performance of the SLM schemes using CORRand PAPR metrics when N = 64, 256, and 1024, U = 4, and L = 4.

scheme using CORR metric is analyzed in the presence ofnonlinear HPA. The oversampled signals for CORR met-ric computation for L = 1, 2, 4, and 16 are obtained bylinear combination of Nyquist-rate samples. As a result,two and four times oversampling cases show relatively highcorrelation with 16 times oversampling case, but Nyquist-rate sampling case shows relatively low correlation with 16times oversampling case. These results imply that, whentwo times oversampling is used for CORR metric computa-tion, the probability of choosing the same phase sequence asthe 16 times oversampling case is very high.

Simulation results show that BER performance of twotimes oversampling for CORR metric calculation is almostthe same as that of four or 16 times oversampling cases.On the other hand, the BER performance for Nyquist-ratesampling case is degraded.

Consequently, two times oversampling for CORR met-ric computation is good enough to achieve the same BERperformance as those of the four or 16 times oversamplingcase in the SLM scheme. By using two times oversamplingfor CORR metric computation, the computational complex-ity can be reduced to 1/2 as that of the four times oversam-pling case.

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Jun-Young Woo received the B.S. degree inelectrical and computer engineering from Uni-versity of Seoul, Seoul, Korea, in 2009 and theM.S. degree in electrical engineering and com-puter science from Seoul National University,Seoul, Korea, in 2011. Currently, he is pursuingthe Ph.D. degree in electrical and computer en-gineering from Seoul National University. Hisresearch interests include orthogonal frequencydivision multiplexing, cryptography, and error-correcting codes.

Kee-Hoon Kim received the B.S. andM.S. degrees in electrical engineering and com-puter engineering from Seoul National Univer-sity, Seoul, Korea, in 2008 and 2010, respec-tively. Currently, he is pursuing the Ph.D.degree in electrical and computer engineeringfrom Seoul National University. His researchinterests include orthogonal frequency divisionmultiplexing, compressed sensing, and commu-nication theory.

Kang-Seok Lee received the B.S. degreein electrical engineering from Yonsei University,Seoul, Korea, in 2012. Currently, he is pur-suing the Ph.D. degree in electrical and com-puter engineering from Seoul National Univer-sity. His research interests include orthogo-nal frequency division multiplexing, and error-correcting codes.

Jong-Seon No received the B.S. andM.S.E.E. degrees in electronics engineeringfrom Seoul National University, Seoul, Korea,in 1981 and 1984, respectively and the Ph.D.degree in electrical engineering from the Uni-versity of Southern California, Los Angeles, in1988. He was a Senior MTS at Hughes NetworkSystems from February 1988 to July 1990. Hewas also an Associate Professor in the Depart-ment of Electronic Engineering, Konkuk Uni-versity, Seoul, Korea, from September 1990 to

July 1999. He joined the faculty of the Department of Electrical and Com-puter Engineering, Seoul National University, in August 1999, where he iscurrently a Professor. From 1996 to 2008, he served as a Founding Chairof Seoul Chapter, IEEE Information Theory Society. He was a GeneralChair for Sequence and Their Applications 2004 (SETA2004) in Seoul,Korea. He also served as a General Co-Chair for International Symposiumon Information Theory and Its Applications 2006 (ISITA 2006) and Inter-national Symposium on Information Theory 2009 (ISIT 2009) in Seoul,Korea. He was a recipient of IEEE Information Theory Society Chapter ofthe Year Award in 2007. He is elevated to IEEE Fellow in Research Engi-neer/Scientist through IEEE Information Theory Society, November, 2011.He was Co-Editor-in-Chief of Journal of Communications and Networks,January, 2012. His area of research interests includes error-correctingcodes, sequences, cryptography, LDPC codes, interference alignment andwireless communication systems.

Dong-Joon Shin received the B.S. de-gree in electronics engineering from Seoul Na-tional University, Seoul, Korea, the M.S. de-gree in electrical engineering from Northwest-ern University, Evanston, USA, and the Ph.D.degree in electrical engineering from Univer-sity of Southern California, Los Angeles, USA.From 1999 to 2000, he was a member of techni-cal staff in Wireless Network Division and Satel-lite Network Division, Hughes Network Sys-tems, Maryland, USA. Since September 2000,

he has been an Associate Professor in the Division of ECE at HanyangUniversity, Seoul, Korea. His current research interests include error-correcting codes, sequences, and discrete mathematics.