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Wireless Personal CommunicationsAn International Journal ISSN 0929-6212Volume 68Number 4 Wireless Pers Commun (2013)68:1225-1240DOI 10.1007/s11277-012-0505-x

Throughput Performance Analysis of AMCBased on a New SNR Estimation AlgorithmUsing Preamble

Changwoo Seo, Sherlie Portugal,Saransh Malik, Cheolwoo You, TaejinJung, Huaping Liu & Intae Hwang

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Wireless Pers Commun (2013) 68:1225–1240DOI 10.1007/s11277-012-0505-x

Throughput Performance Analysis of AMC Basedon a New SNR Estimation Algorithm Using Preamble

Changwoo Seo · Sherlie Portugal · Saransh Malik ·Cheolwoo You · Taejin Jung · Huaping Liu ·Intae Hwang

Published online: 12 January 2012© Springer Science+Business Media, LLC. 2012

Abstract The rapid growth in mobile communication users necessitates the developmentof reliable communication systems that provide higher data rates. To meet these requirements,techniques such as multiple input multiple output (MIMO) and orthogonal frequency divi-sion multiplexing (OFDM) have been developed in recent years. Current research activity isfocused on developing MIMO-OFDM systems that combine the benefits of both techniques.In addition, for a fast wireless channel environment, the data rate and reliability can be opti-mized by setting the modulation and coding adaptively according to the channel conditions,as well as by using sub-carrier frequency and power allocation techniques. The overall sys-tem performance depends on how accurately the feedback-based system obtains the channelstate information and feeds it back to the transmitter without delay. In this paper, we propose

C. Seo · S. Portugal · S. Malik · T. Jung · I. Hwang (B)Department of Electronics and Computer Engineering, Chonnam National University,300 Yongbong-dong, Buk-gu, Gwangju 500-757, Republic of Koreae-mail: [email protected]

C. Seoe-mail: [email protected]

S. Portugale-mail: [email protected]

S. Malike-mail: [email protected]

T. Junge-mail: [email protected]

C. YouDepartment of Information and Communications Engineering, Myongji University,San 38-2 Namdong, Cheoingu, Yongin, Gyonggi-Do 449-728, Republic of Koreae-mail: [email protected]

H. LiuSchool of Electrical Engineering and Computer Science, Oregon State University, Corvallis,OR 97331-3211, USAe-mail: [email protected]

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a signal-to-noise ratio (SNR) estimation algorithm in which the preamble is known for bothsides of the transceiver. Also, we applied AMC on several channel environments using theparameters of IEEE 802.11n and compared throughput performance using each of the dif-ferent SNR Estimation Algorithm. The results obtained prove that our proposed algorithm ismore accurate than traditional algorithms.

Keywords MIMO · OFDM · CSI · Preamble · SNR

1 Introduction

Adaptive modulation and coding (AMC), adaptive subcarrier allocation, and power allo-cation are used to increase a communication system’s reliability and transmission rate [1].These techniques require feedback of the channel state information (CSI), which is basedon the estimated signal-to-noise ratio (SNR) of the received signal. Therefore, many studieshave been conducted to improve system performance by designing a low-complexity SNRestimation algorithm [1–7]. Previous conventional SNR estimation algorithms were basedon maximum likelihood (ML) or minimum mean squared error (MMSE) and required anestimation of the channel, which entails feedback delay and higher computational costs.Recently, researchers such as Boumard [5], Ren et al. [6], and Milan [7] proposed estimatingthe SNR on the basis of preamble transmission without channel estimation. Our proposalconsists of using the preamble principle to diminish the complexity and feedback delay andavoid channel estimation. Because the preamble is known by both sides of the transceiver,the new algorithm can accurately estimate the SNR without channel estimation.

This paper is organized as follows. In Sect. 2, we present the system model, and in Sect. 3,we briefly explain the conventional SNR estimation algorithms proposed by Boumard, Renet al. and Milan, as well as the new proposed SNR estimation algorithm. In Sect. 4, we ana-lyze and compare the simulated performance of each of the algorithms. Finally, in Sect. 5,we present our conclusions.

2 System Model

In this section, we explain the structure of the communication system. As shown in Fig. 1,only two signals with two respective preambles are transmitted. The two transmission andtwo receiver antennas make up a 2 × 2 multiple-input multiple-output (MIMO)-orthogo-nal frequency division multiplexing (OFDM) system. At the receiver, the SNR is estimatedafter the received signal is changed from the time domain to the frequency domain usingfast Fourier transform (FFT). The timing of the received signal is assumed to be perfectlysynchronized.

Fig. 1 Block diagram of the preamble-based 2 × 2 MIMO-OFDM system

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New SNR Estimation Algorithm 1227

2.1 Transmitter

Each antenna transmits an OFDM symbol, consisting of a sequence of a predeterminednumber (OFDM size) of binary phase shift keying (BPSK) or quadrature phase shift keying(QPSK) symbols. The preamble is thus composed of these two identical OFDM symbols.In Fig. 1, the preamble is given by Ci (k, n), where, i = 1, 2 represents the transmit antennaindex, k = 1, 2 is the preamble index, and n = 0, . . . , N − 1 is the subcarrier index. Forpreamble transmission, we used a cyclic prefix (CP) of length N/4 as the guard interval.

2.2 Receiver

The received signal after FFT processing is described by Eq. (1):

Y j (k, n) =2∑

i=1

Hi j (k, n) Ci (k, n) + n j (k, n) (1)

where Y j (k, j) is the signal received at the j th antenna, and n j (k, n) is the additive whiteGaussian noise (AWGN) present at the input of the j th receive antenna. Hi j (k, n) indicatesthe channel frequency response between the i th transmission antenna and the j th receiverantenna. It can be expressed according to Eq. (2),

Hi j (k, n) =L∑

l=1

hl,i j (kTs) e− jπnτl,i jN Ts (2)

where hl,i j (k, Ts) and τl,i j represent the lth path gain and delay, respectively, between thei th transmission and j th receiver antenna during the kth preamble. Ts is the OFDM preambletime plus CP, and L is the number of channel paths. In this paper, the channel is assumedto be constant during a frame period. Therefore, for simplicity, the time k is not taken intoaccount for SNR estimation.

3 Conventional SNR Estimation Algorithms

3.1 Boumard’s SNR Estimation Algorithm

According to the Boumard algorithm, in a 2 × 2 MIMO-OFDM system, the channel variesslowly in both the frequency and time domains; with this assumption, two identical consec-utive preambles are used to estimate the SNR [5]. The signal power is estimated as follows.First, we estimate H using Eq. (3), which is a function of the two received signals Y (0, n) andY (1, n), and the transmitted preamble C(n). The star mentioned as represents the complexconjugation of the transmitted preamble. This preamble is transmitted to estimated the chan-nel with received signals Y (0, n) and Y (1, n). Next, we calculate the average of the squaresof the absolute values of H using Eq. (4). The noise power is estimated using Eq. (5), and

finally, the SNR is estimated using Eq. (6). The∧ρ

av,Bourepresents the value of estimated SNR

which is calculated from the average of squares of the absolute values of SBou and estimatednoise power WBou .

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H (n) = C∗ (n)

2(Y (0, n) + Y (1, n)) (3)

SBou = 1

N

N−1∑

n=0

∣∣∣H (n)

∣∣∣2

(4)

WBou = 1

N

N−1∑

n=1

∣∣∣∣C (n − 1) (Y (0, n) + Y (1, n))

−C (n − 1) (Y (0, n − 1) + Y (1, n − 1))

∣∣∣∣2

(5)

ρav,Bou = SBou

WBou(6)

Unlike ML or MMSE-based SNR estimation, Boumard’s algorithm does not require chan-nel estimation, but large changes in the channel can lead to errors in the SNR estimate.

3.2 Ren’s SNR Estimation Algorithm

Ren’s SNR estimation overcomes the weakness of Boumard’s regarding frequency selectivechannels by using the same subcarrier in the noise power estimation (Eq. (7)) [6]. The signalpower is estimated by Eq. (8), where the estimated noise power is removed from the totalreceived signal power. As in Boumard’s algorithm, H is estimated by Eq. (3), and finally, wecalculate the SNR with Eq. (9).

WRen = 4

N

N−1∑

n=0

⎧⎨

⎩I m

⎡

⎣Y (0, n) C∗ (0, n) H∗ (n)∣∣∣H∗ (n)

∣∣∣

⎤

⎦

⎫⎬

⎭

2

(7)

SRen = 1

N

N−1∑

n=0

|Y (0, n)|2 − WRen (8)

ρav,Ren = SRen

WRen(9)

3.3 Milan’s SNR Estimation Algorithm

The preamble used in Milan’s SNR estimation algorithm contains periodic identical partsin the time domain [7]. Figure 2a shows the structure of the preamble in the time domain.N subcarriers are divided into Q identical parts. Figure 2b shows the preamble structure inthe frequency domain. Q signal subcarriers appear periodically between the null subcarriers.Milan’s algorithm uses these characteristics to estimate the SNR. After the received signal isFFT modulated (with an FFT size equal to the total preamble duration, i.e., 128), the signalpower is contained in the Q signal subcarriers, and the noise power is contained in the nullsubcarriers of the received signal. As we can see in reference [2], Milan’s algorithm providesmore accurate estimations by reducing the interval period; however, the preamble structurebecomes more complicated. In our system, we transmit two equal OFDM symbols of sizeN = 64, which is the preamble length corresponding to Milan’s algorithm for the case ofN = 128 and Q = 2. However, in our algorithm we need an FFT size of only 64 at thereceiver, whereas Milan’s algorithm requires an FFT size of 128.

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Fig. 2 Preamble structure of Milan’s algorithm. a Time domain. b Frequency domain

Fig. 3 Transmission preamble structure in the new SNR estimation algorithm

3.4 New SNR Estimation Algorithm

The newly Proposed SNR estimator deals with complexity and computational cost. Themechanism is explained as—If we use specific reference signal that is known to both side(Rx, Tx), so by comparing original reference signal with distorted reference signal, we canestimate the correlation in the original and distorted values that how difference of originalsignal and distorted signal occurs. We have tried some efforts to briefly describe the systemcomputation by simulation results and comparing various values of original reference signaland the distorted signal. Figure 3 shows the structure of the transmission frame, includingthe preamble. Equation (10) is the new expression for estimating the SNR, where Y (0, n)

and Y (1, n) represent the consecutive receive preambles after FFT.

ρav,New = 1(1N

∑Nn=1 |Y (0, n) − Y (1, n)|2

) (10)

According to Eq. (10), the signal power is considered to be the total power carried by thepreambles, i.e., the noise power is calculated by the average of the square of the absolutevalues of the received preambles.

4 Performance Analysis of the Proposed and Conventional SNR EstimationAlgorithms

In this section, we present the performance analysis of the proposed and conventionalSNR estimation algorithms. Tables 1 and 2 contain the simulation and channel parameters,respectively. The simulation parameters are based on IEEE Standard 802.11n; they include20 MHz of bandwidth and MIMO-OFDM as the simulation platform. The SNR is estimatedby considering only two consecutive preambles with the OFDM symbol size and BPSK orQPSK modulation. We performed simulations over three different channels: the Rayleigh flat

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Table 1 Simulation parameters

Parameters Value

System bandwidth (BW) 20 MHz1 OFDM symbol time 4µs(3.2µs: FFT length+0.8µs: CP length)Number of data symbols per space stream (SS) 468Number of subcarriers per preamble 64Subcarrier spacing 312.5 KHzMIMO Layered 2 × 2Noise AWGNFFT length 64 pointGI(CP) length 16 point1 OFDM symbol samples 80SNR estimation algorithm Boumard, Milan, Ren, newPreamble 2 OFDM symbols: 2 equal sequences of QPSK or BPSK

symbolsTransmission packets 25,000

Table 2 Channel parameter

Channel Delay path(samples) Rayleigh power

Rayleigh selective fading channel A 3Path: [0 12 15] [ −1.92,−5.92,−9.92 ]Rayleigh selective fading channel B 4Path: [0 12 15 18] [ −1.92,−5.92,−9.92,−12.92]Rayleigh flat fading channel No delay –

fading channel, where the channel conditions change only slightly; Rayleigh selective fadingchannel A, where the maximum delay of the samples is shorter than the CP; and Rayleighselective fading channel B, where the maximum delay of the samples is longer than the CP.

In our system we actually didn’t use channel estimation, so the results were simpler toobtain. Our proposal based on the use of a preamble and does not require channel estimationto make an accurate estimation of the SNR. In our algorithm, the signal power is consideredto be the entire sequence of two preambles with OFDM size and composed of BPSK orQPSK symbols; therefore, we consider the signal power to be 1. The relative noise power iscalculated by the square of the absolute value of the two received preambles. By dividing thesignal between the noise powers, we obtain the SNR estimation.

4.1 NMSE Performance Analysis of New Algorithm and Conventional Algorithms

To evaluate each estimation algorithm, we used the Normalized Mean Square Error (NMSE)[NMSE, Eq. (11)] to calculate the error between the actual SNR and the estimated SNRs.

N M SEav =Nt∑

i=1

(ρav,i − ρav

ρav

)2

(11)

Nt is the number of transmitted packets (25,000), ρav,i is the estimated SNR valuecorresponding to the received preamble from the i th package, and ρav represents the actualSNR value. Figure 4 compares the actual SNR values and those estimated by each algo-rithm over the Rayleigh flat fading channel. At low SNRs, the Ren algorithm has a higherSNR estimation error than the other algorithms, which return values almost identical to the

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Fig. 4 Actual and estimated SNR values over the Rayleigh flat fading channel

Fig. 5 NMSE performance over Rayleigh flat fading channel

actual SNR. Figure 5 shows the NMSE performance for each algorithm. We can verify thatBoumard’s and the new SNR algorithm provide the most accurate estimations, with NMSEvalues close to 0, followed by Milan and Ren. As mentioned in the description above, forBoumard’s algorithm the channel is considered to be almost stationary. The performanceresults, confirm that the most suitable algorithms for these conditions are Boumard’s andthe new SNR estimation algorithm. Figure 6 compares the actual and estimated SNR val-ues over Rayleigh selective fading channel A. The estimation error of Boumard’s algorithmincreases for a selective channel. Figure 7 shows the NMSE performance on the same chan-nel. For Boumard’s algorithm, the NMSE increases with the SNR, whereas Milan’s and Ren’s

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Fig. 6 Actual and estimated SNR values over Rayleigh selective fading channel A

Fig. 7 NMSE performance over Rayleigh selective fading channel A

algorithms maintain a constant NMSE value of about 0.3 starting at approximately 0 dB. TheNMSE of the new algorithm is very close to 0, which means that for a frequency selectivemulti-path channel, the new algorithm provides the most reliable estimation of the real SNRvalue. Figures 8 and 9 show the result of the same simulations over Rayleigh selective fadingchannel B, where the maximum delay is larger than the CP length, using four multiple paths.This channel environment is more difficult than channel A; therefore, each algorithm has ahigher estimation error than in the previous simulation of channel A. In Fig. 8, the valuesestimated by Boumard’s algorithm are very far from the actual SNR values, whereas thoseestimated by the new algorithm, as well as by those of Milan and Ren, remain very close to the

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Fig. 8 Actual and estimated SNR values over Rayleigh selective fading channel B

Fig. 9 NMSE performance over Rayleigh selective fading channel B

actual value until approximately 26 dB. Furthermore, Fig. 9 shows that until approximately38 dB, the new algorithm has the lowest estimation error.

4.2 Throughput Performance Analysis of AMC Scheme with SNR Estimation Algorithm

In this section we apply the AMC scheme to the system IEEE 802.11n and analyze thethroughput performance. For each algorithm we assume the feedback of the channel infor-mation.

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Table 3 Selected MCS levels for the AMC scheme

MCS Modulation Code rate Data rate (datasubcarrier = 64,CP = 16)

NSS NSTS

Stream 1 Stream 2

1 BPSK BPSK 1/2 16 Mbps 2 22 QPSK BPSK 1/2 24 Mbps 2 23 QPSK QPSK 1/2 32 Mbps 2 24 QPSK QPSK 3/4 48 Mbps 2 25 16QAM 16QAM 1/2 64 Mbps 2 2

Fig. 10 Throughput of each MCS level in the Rayleigh flat fading channel

Table 3 shows the IEEE 802.11n parameters corresponding to each MCS (Modulation andCoding Scheme) level. The higher the estimated SNR (good channel conditions), the higherthe order of modulation and channel coding used for the transmission.

Figure 10 shows the throughput performance of the independent MCS levels and Fig. 11,the average throughput for each SNR estimation algorithm when we apply AMC. Thesimulations shown in both figures are performed over the Rayleigh flat fading channel. TheAMC scheme sets specific SNR thresholds and assigns a specific MCS level for a determinedSNR range. Thus, for a given SNR range, AMC selects the MCS level with the best throughputperformance. As we see in Fig. 11, the New and Boumard SNR estimation algorithms exhibitan overall higher throughput performance compared to the Ren and Milan algorithms. We canconclude that for flat fading channels, since the New and Boumard algorithms produce thelowest NMSE values and, hence, produce a more accurate SNR estimation, they also exhibitbetter average throughput compared to the Ren and Milan algorithms. Figure 12 shows thethroughput performance of each MCS level described in Table 3 over the Rayleigh selectivefading channel A. It can be seen that in a multi-path fading channel the SNR necessary toreach the maximum throughput in each MCS level is relatively higher.

Figure 13 shows the average throughput performance of each SNR estimation algorithmwhen AMC scheme is applied over the Rayleigh selective fading channel A. When the actualSNR values are higher than 10 dB, the SNR values estimated by Boumard are inaccurate.

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Fig. 11 Throughput comparison of the AMC scheme according to each SNR estimation algorithm in theRayleigh flat fading channel

Fig. 12 Throughput of each MCS level in Rayleigh selective fading channel A

This causes the system to choose the wrong MCS level at high SNR and as a result themaximum average throughput achievable by the Boumard method is very low. In contrast,the accuracy of the New, Ren, and Milan SNR estimation algorithms is enough to make theproper selection of the MCS levels according to the actual SNR. In addition, since the pro-posed algorithm presents the best NMSE performance for this channel, its average throughputis higher than that of the Ren and Milan algorithms. Figure 14 shows the throughput perfor-mance of each MCS level over the Rayleigh selective fading channel B. In channel B, themaximum delay of the channel is larger than CP, which produces Inter Symbol Interference(ISI) and yields a lower maximum throughput in the highest MCS levels (MCS1 and MCS2).Figure 15 shows the average throughput performance of each SNR estimation algorithmwhen we apply the AMC scheme over the selective channel B. As we can see, after actual

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Fig. 13 Throughput comparison of the AMC scheme according to each SNR estimation algorithm in theRayleigh selective fading channel A

Fig. 14 Data rate of each MCS level in Rayleigh selective fading channel B

SNR of 10 dB, the maximum throughput achieved with the Boumard method is very low dueto his low accuracy in estimating the SNR in selective channels. The rest of the algorithmskeep a good level of accuracy in the estimation of the SNR and achieve the proper transitionof the MCS levels, although the maximum throughput is affected by the ISI.

5 Conclusions

In this paper, we proposed a new SNR estimation algorithm based on the use of a preambleand does not require channel estimation to make an accurate estimation of the SNR. In our

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Fig. 15 Data rate comparison of AMC scheme according to each SNR estimation algorithm in the Rayleighselective fading channel B

algorithm, the signal power is considered to be the entire sequence of two preambles withOFDM size and composed of BPSK or QPSK symbols; therefore, we consider the signalpower to be 1. The relative noise power is calculated by the square of the absolute value ofthe two received preambles. By dividing the signal between the noise powers, we obtain theSNR estimation. Simulations performed in several channels prove that the proposed algorithmproduces the lowest estimation error. Also, we applied the AMC on several channel envi-ronments using the parameters of IEEE 802.11n, and compared the throughput performancewhen using each of the different SNR estimation algorithms. The results obtained in the sim-ulation confirm that the proposed algorithm produces the highest throughput performance.

Acknowledgments This research was supported by the MKE (The Ministry of Knowledge Economy),Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA(National IT Industry Promotion Agency) (NIPA-2011-C1090-1111-0008). This study was financially sup-ported by Chonnam National University, 2011.

References

1. Keller, T., & Hanzo, L. (2000). Adaptive multicarrier modulation: A convenient framework fortime-frequency processing in wireless communications. Proceedings of the IEEE, 88, 611–640.

2. Pauluzzi, D. R., & Beaulieu, N. C. (2000). A comparison of SNR estimation techniques for the AWGNchannel. IEEE Transactions on Communications, 48, 1681–1691.

3. Xu, H., Wei, G., & Zhu, J. (2005). Novel SNR estimation algorithm for OFDM. Proceedings of theIEEE VTC, 5, 3068–3071.

4. Jiao, F., Ren, G., & Zhang, Z. (2008). New noise variance and post detection SNR estimation methodfor MIMO OFDM systems. In Proceedings of the IEEE Conference ICCT, (pp. 179–182). Nov. 2008.

5. Boumard, S. (2003). Novel noise variance and SNR estimation algorithm for wireless MIMO OFDMsystems. Proceedings of the IEEE Global Telecommunications Conference (Globecom), 3, 1330–1334.

6. Ren, G., Zhang, H., & Chang, Y. (2009). SNR estimation algorithm based on the preamble for OFDMsystems in frequency selective channels. IEEE Transactions on Communications, 57(8), 2230–2234.

7. Zivkovic, M., & Mathar, R. (2009). Preamble-based SNR estimation in frequency selective channelsfor wireless OFDM systems. In Proceedings of the IEEE VTC Spring (pp. 1–5).

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Author Biographies

Changwoo Seo received a BS degree in Information and Communica-tion Engineering from Sangmyung University, Cheonan, Korea in 2009and an MS degree in Electronics Engineering from Chonnam NationalUniversity, Gwangju, Korea in 2011. He is currently a PhD studentat Chonnam National University, Gwangju, Korea from 2011 in theSchool of Electronics & Computer Engineering. His research interestsinclude MIMO and OFDM systems.

Sherlie Portugal received a BS in Electronic and TelecommunicationEngineering from the Technological University of Panama, Panama,in 2006. She worked for the School of Electrical Engineering of theTechnological University of Panama and is currently a master’s studentin the School of Electronics & Computer Engineering at ChonnamNational University, South Korea. Her research fields include MIMO,OFDM, encoding/decoding algorithms for STC and MIMO spatialmultiplexing schemes, and general wireless communications.

Saransh Malik received a BS in Information Technology from RajivGandhi Technical University, India in 2010. He is currently a master’sstudent in the School of Electronics & Computer Engineering at Chon-nam National University, Gwangju, Korea from 2011. His researchinterests include mobile and wireless communication system.

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Cheolwoo You received BS, MS, and PhD degrees in electronicsengineering from Yonsei University, Seoul, Korea, in 1993, 1995,and 1999, respectively. From Jan. 1999 to April 2003, he worked asa Senior Research Engineer with LG Electronics, Gyeonggi, Korea.During 2003–2004, he was a Senior Research Engineer at EoNex,Songnam, Korea. From August 2004 to July 2006, he was with Sam-sung Electronics, Suwon, Korea. Since September 2006, he has beenwith the Department of Information and Communications Engineer-ing, Myongji University, Gyeonggi, Korea. His research areas areBS/MS modem design, communication theory, signal processing, andadvanced channel codes for mobile/nomadic communication systems.He is currently interested in new Multiple Access schemes, AdaptiveResource Allocation, AMC, MIMO systems, advanced FEC, and relayschemes for 4G communication systems.

Taejin Jung received BS, MS, and PhD degrees in electronic and elec-trical engineering from Pohang University of Science and Technology(POSTECH), Pohang, Korea, in 1996, 1998, and 2003, respectively.In 2003, he was with ETRI, Korea, as a Senior Research Staff Mem-ber and worked on the development of efficient receiving algorithmsfor High Definition TV (HDTV). In 2004, he joined the school ofelectronics and computer engineering, Chonnam National University(CNU), Korea. His research interests are efficient encoding/decodingalgorithms for STC, MIMO and MIMO-OFDM, and modem designsfor broadband wireless communication systems.

Huaping Liu received his BS and MS degrees from Nanjing Univer-sity of Posts and Telecommunications, Nanjing, China, in 1987 and1990, respectively, and his PhD degree from New Jersey Institute ofTechnology, Newark, NJ, in 1997, all in electrical engineering. FromJuly 1997 to August 2001 he was with Lucent Technologies, NewJersey. In September 2001, he joined the School of Electrical and Com-puter Engineering at Oregon State University, where he has been anassociate professor since June 2006. He currently serves as an Asso-ciate Editor for the IEEE Transactions on Vehicular Technology andIEEE Communications Letters.

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Intae Hwang received a BS degree in Electronics Engineering fromChonnam National University, Gwangju, Korea in 1990 and a MSdegree in Electronics Engineering from Yonsei University, Seoul,Korea in 1992, and a PhD degree in Electrical & Electronics Engineer-ing from Yonsei University, Seoul, Korea in 2004. He was a seniorengineer at LG Electronics from 1992 to 2005. He is currently a Profes-sor in the School of Electronics & Computer Engineering at ChonnamNational University, Gwangju, Korea from 2006. His current researchactivities are in digital & wireless communication systems, mobile ter-minal system for next generation applications; physical layer softwarefor mobile terminals, efficient algorithms for AMC, MIMO, MIMO-OFDM, ICIM, and relaying schemes for wireless communication.

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