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

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1 Throughput Performance Analysis of AMC Based on a New SNR Estimation Algorithm Using Preamble Changwoo Seo, Sherlie Portugal, Saransh Malik, Cheolwoo You, Taejin Jung, Huaping Liu & Intae Hwang Wireless Personal Communications An International Journal ISSN Volume 68 Number 4 Wireless Pers Commun (2013) 68: DOI /s x 1 23

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3 Wireless Pers Commun (2013) 68: DOI /s x Throughput Performance Analysis of AMC Based on 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 Abstract The rapid growth in mobile communication users necessitates the development of reliable communication systems that provide higher data rates. To meet these requirements, techniques such as multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) have been developed in recent years. Current research activity is focused 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 optimized 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 system performance depends on how accurately the feedback-based system obtains the channel state 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 , Republic of Korea hit@chonnam.ac.kr C. Seo apples021@naver.com S. Portugal sherlie.portugal@gmail.com S. Malik saranshmk@gmail.com T. Jung tjjung@chonnam.ac.kr C. You Department of Information and Communications Engineering, Myongji University, San 38-2 Namdong, Cheoingu, Yongin, Gyonggi-Do , Republic of Korea cwyou@mju.ac.kr H. Liu School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR , USA hliu@eecs.oregonstate.edu

4 1226 C. Seo et al. a signal-to-noise ratio (SNR) estimation algorithm in which the preamble is known for both sides of the transceiver. Also, we applied AMC on several channel environments using the parameters of IEEE n and compared throughput performance using each of the different SNR Estimation Algorithm. The results obtained prove that our proposed algorithm is more accurate than traditional algorithms. Keywords MIMO OFDM CSI Preamble SNR 1 Introduction Adaptive modulation and coding (AMC), adaptive subcarrier allocation, and power allocation 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 based on the estimated signal-to-noise ratio (SNR) of the received signal. Therefore, many studies have been conducted to improve system performance by designing a low-complexity SNR estimation algorithm [1 7]. Previous conventional SNR estimation algorithms were based on maximum likelihood (ML) or minimum mean squared error (MMSE) and required an estimation 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 estimating the SNR on the basis of preamble transmission without channel estimation. Our proposal consists of using the preamble principle to diminish the complexity and feedback delay and avoid 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, Ren et al. and Milan, as well as the new proposed SNR estimation algorithm. In Sect. 4,weanalyze 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 and two receiver antennas make up a 2 2 multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) system. At the receiver, the SNR is estimated after the received signal is changed from the time domain to the frequency domain using fast Fourier transform (FFT). The timing of the received signal is assumed to be perfectly synchronized. Fig. 1 Block diagram of the preamble-based 2 2 MIMO-OFDM system

5 New SNR Estimation Algorithm Transmitter Each antenna transmits an OFDM symbol, consisting of a sequence of a predetermined number (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 C i (k, n),where,i = 1, 2 represents the transmit antenna index, k = 1, 2 is the preamble index, and n = 0,...,N 1 is the subcarrier index. For preamble 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 H ij (k, n) C i (k, n) + n j (k, n) (1) i=1 where Y j (k, j) is the signal received at the jth antenna, and n j (k, n) is the additive white Gaussian noise (AWGN) present at the input of the jth receive antenna. H ij (k, n) indicates the channel frequency response between the ith transmission antenna and the jth receiver antenna. It can be expressed according to Eq. (2), H ij (k, n) = L l=1 h l,ij (kt s ) e jπ nτ l,ij NTs (2) where h l,ij (k, T s ) and τ l,ij represent the lth path gain and delay, respectively, between the ith transmission and jth receiver antenna during the kth preamble. T s is the OFDM preamble time plus CP, and L is the number of channel paths. In this paper, the channel is assumed to be constant during a frame period. Therefore, for simplicity, the time k is not taken into account 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 varies slowly in both the frequency and time domains; with this assumption, two identical consecutive preambles are used to estimate the SNR [5]. The signal power is estimated as follows. First, we estimate Ĥ using Eq. (3), which is a function of the two received signals Y (0, n) and Y (1, n), and the transmitted preamble C(n). The star mentioned as `represents the complex conjugation of the transmitted preamble. This preamble is transmitted to estimated the channel with received signals Y (0, n) and Y (1, n). Next, we calculate the average of the squares of the absolute values of Ĥ using Eq. (4). The noise power is estimated using Eq. (5), and finally, the SNR is estimated using Eq. (6). The ρ av,bou represents the value of estimated SNR which is calculated from the average of squares of the absolute values of Ŝ Bou and estimated noise power Ŵ Bou.

6 1228 C. Seo et al. Ĥ (n) = C (n) (Y (0, n) + Y (1, n)) 2 (3) Ŝ Bou = 1 N 1 Ĥ N 2 (4) Ŵ Bou = 1 N n=0 N 1 n=1 C (n 1)(Y (0, n) + Y (1, n)) C (n 1)(Y (0, n 1) + Y (1, n 1)) ˆρ av,bou = ŜBou (6) Ŵ Bou Unlike ML or MMSE-based SNR estimation, Boumard s algorithm does not require channel 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 selective channels by using the same subcarrier in the noise power estimation (Eq. (7)) [6]. The signal power is estimated by Eq. (8), where the estimated noise power is removed from the total received signal power. As in Boumard s algorithm, Ĥ is estimated by Eq. (3), and finally, we calculate the SNR with Eq. (9). Ŵ Ren = 4 N Ŝ Ren = 1 N ˆρ av,ren = N 1 n=0 N 1 n=0 Im Y (0, n) C (0, n) Ĥ (n) Ĥ (n) 2 2 (5) (7) Y (0, n) 2 Ŵ Ren (8) ŜRen Ŵ Ren (9) 3.3 Milan s SNR Estimation Algorithm The preamble used in Milan s SNR estimation algorithm contains periodic identical parts in 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 in the 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 is FFT modulated (with an FFT size equal to the total preamble duration, i.e., 128), the signal power is contained in the Q signal subcarriers, and the noise power is contained in the null subcarriers of the received signal. As we can see in reference [2], Milan s algorithm provides more accurate estimations by reducing the interval period; however, the preamble structure becomes more complicated. In our system, we transmit two equal OFDM symbols of size N = 64, which is the preamble length corresponding to Milan s algorithm for the case of N = 128 and Q = 2. However, in our algorithm we need an FFT size of only 64 at the receiver, whereas Milan s algorithm requires an FFT size of 128.

7 New SNR Estimation Algorithm 1229 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. The mechanism 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 can estimate the correlation in the original and distorted values that how difference of original signal and distorted signal occurs. We have tried some efforts to briefly describe the system computation by simulation results and comparing various values of original reference signal and the distorted signal. Figure 3 shows the structure of the transmission frame, including the 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. ρ av,new = 1 ( 1N (10) Nn=1 Y (0, n) Y (1, n) 2) According to Eq. (10), the signal power is considered to be the total power carried by the preambles, i.e., the noise power is calculated by the average of the square of the absolute values of the received preambles. 4 Performance Analysis of the Proposed and Conventional SNR Estimation Algorithms In this section, we present the performance analysis of the proposed and conventional SNR estimation algorithms. Tables 1 and 2 contain the simulation and channel parameters, respectively. The simulation parameters are based on IEEE Standard n; they include 20 MHz of bandwidth and MIMO-OFDM as the simulation platform. The SNR is estimated by considering only two consecutive preambles with the OFDM symbol size and BPSK or QPSK modulation. We performed simulations over three different channels: the Rayleigh flat

8 0 C. Seo et al. Table 1 Simulation parameters Parameters Value System bandwidth (BW) 20 MHz 1 OFDM symbol time 4 µs(3.2 µs: FFT length+0.8 µs: CP length) Number of data symbols per space stream (SS) 468 Number of subcarriers per preamble 64 Subcarrier spacing KHz MIMO Layered 2 2 Noise AWGN FFT length 64 point GI(CP) length 16 point 1 OFDM symbol samples 80 SNR estimation algorithm Boumard, Milan, Ren, new Preamble 2 OFDM symbols: 2 equal sequences of QPSK or BPSK symbols Transmission packets 25,000 Table 2 Channel parameter Channel Delay path(samples) Rayleigh power Rayleigh selective fading channel A 3Path: [ ] [ 1.92, 5.92, 9.92 ] Rayleigh selective fading channel B 4Path: [ ] [ 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 fading channel A, where the maximum delay of the samples is shorter than the CP; and Rayleigh selective 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 to obtain. Our proposal based on the use of a preamble and does not require channel estimation to make an accurate estimation of the SNR. In our algorithm, the signal power is considered to be the entire sequence of two preambles with OFDM size and composed of BPSK or QPSK symbols; therefore, we consider the signal power to be 1. The relative noise power is calculated by the square of the absolute value of the two received preambles. By dividing the signal 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 t ( ) ˆρav,i ρ 2 av NMSE av = (11) i=1 N t is the number of transmitted packets (25,000), ˆρ av,i is the estimated SNR value corresponding to the received preamble from the ith package, and ρ av represents the actual SNR value. Figure 4 compares the actual SNR values and those estimated by each algorithm over the Rayleigh flat fading channel. At low SNRs, the Ren algorithm has a higher SNR estimation error than the other algorithms, which return values almost identical to the ρ av

9 New SNR Estimation Algorithm 1 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 that Boumard s and the new SNR algorithm provide the most accurate estimations, with NMSE values close to 0, followed by Milan and Ren. As mentioned in the description above, for Boumard s algorithm the channel is considered to be almost stationary. The performance results, confirm that the most suitable algorithms for these conditions are Boumard s and the new SNR estimation algorithm. Figure 6 compares the actual and estimated SNR values over Rayleigh selective fading channel A. The estimation error of Boumard s algorithm increases for a selective channel. Figure 7 shows the NMSE performance on the same channel. For Boumard s algorithm, the NMSE increases with the SNR, whereas Milan s and Ren s

10 2 C. Seo et al. 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. The NMSE of the new algorithm is very close to 0, which means that for a frequency selective multi-path channel, the new algorithm provides the most reliable estimation of the real SNR value. Figures 8 and 9 show the result of the same simulations over Rayleigh selective fading channel 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 a higher estimation error than in the previous simulation of channel A. In Fig. 8, the values estimated by Boumard s algorithm are very far from the actual SNR values, whereas those estimated by the new algorithm, as well as by those of Milan and Ren, remain very close to the

11 New SNR Estimation Algorithm 3 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 approximately 38 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 n and analyze the throughput performance. For each algorithm we assume the feedback of the channel information.

12 4 C. Seo et al. Table 3 Selected MCS levels for the AMC scheme MCS Modulation Code rate Data rate (data subcarrier = 64, CP = 16) Stream 1 Stream 2 N SS N STS 1 BPSK BPSK 1/2 16 Mbps QPSK BPSK 1/2 24 Mbps QPSK QPSK 1/2 32 Mbps QPSK QPSK 3/4 48 Mbps QAM 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 n parameters corresponding to each MCS (Modulation and Coding Scheme) level. The higher the estimated SNR (good channel conditions), the higher the order of modulation and channel coding used for the transmission. Figure 10 showsthe throughputperformanceof the independentmcs levels andfig. 11, the average throughput for each SNR estimation algorithm when we apply AMC. The simulations shown in both figures are performed over the Rayleigh flat fading channel. The AMC scheme sets specific SNR thresholds and assigns a specific MCS level for a determined SNR range. Thus, for a given SNR range, AMC selects the MCS level with the best throughput performance. As we see in Fig. 11, the New and Boumard SNR estimation algorithms exhibit an overall higher throughput performance compared to the Ren and Milan algorithms. We can conclude that for flat fading channels, since the New and Boumard algorithms produce the lowest NMSE values and, hence, produce a more accurate SNR estimation, they also exhibit better average throughput compared to the Ren and Milan algorithms. Figure 12 shows the throughput performance of each MCS level described in Table 3 over the Rayleigh selective fading channel A. It can be seen that in a multi-path fading channel the SNR necessary to reach the maximum throughput in each MCS level is relatively higher. Figure 13 shows the average throughput performance of each SNR estimation algorithm when AMC scheme is applied over the Rayleigh selective fading channel A. When the actual SNR values are higher than 10 db, the SNR values estimated by Boumard are inaccurate.

13 New SNR Estimation Algorithm 5 Fig. 11 Throughput comparison of the AMC scheme according to each SNR estimation algorithm in the Rayleigh 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 the maximum 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 the proper selection of the MCS levels according to the actual SNR. In addition, since the proposed algorithm presents the best NMSE performance for this channel, its average throughput is higher than that of the Ren and Milan algorithms. Figure 14 shows the throughput performance of each MCS level over the Rayleigh selective fading channel B. In channel B, the maximum 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 algorithm when we apply the AMC scheme over the selective channel B. As we can see, after actual

14 6 C. Seo et al. Fig. 13 Throughput comparison of the AMC scheme according to each SNR estimation algorithm in the Rayleigh 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 due to his low accuracy in estimating the SNR in selective channels. The rest of the algorithms keep a good level of accuracy in the estimation of the SNR and achieve the proper transition of 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 preamble and does not require channel estimation to make an accurate estimation of the SNR. In our

15 New SNR Estimation Algorithm 7 Fig. 15 Data rate comparison of AMC scheme according to each SNR estimation algorithm in the Rayleigh selective fading channel B algorithm, the signal power is considered to be the entire sequence of two preambles with OFDM size and composed of BPSK or QPSK symbols; therefore, we consider the signal power to be 1. The relative noise power is calculated by the square of the absolute value of the two received preambles. By dividing the signal between the noise powers, we obtain the SNR estimation. Simulations performed in several channels prove that the proposed algorithm produces the lowest estimation error. Also, we applied the AMC on several channel environments using the parameters of IEEE n, and compared the throughput performance when using each of the different SNR estimation algorithms. The results obtained in the simulation 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-C ). This study was financially supported by Chonnam National University, References 1. Keller, T., & Hanzo, L. (2000). Adaptive multicarrier modulation: A convenient framework for time-frequency processing in wireless communications. Proceedings of the IEEE, 88, Pauluzzi, D. R., & Beaulieu, N. C. (2000). A comparison of SNR estimation techniques for the AWGN channel. IEEE Transactions on Communications, 48, Xu, H., Wei, G., & Zhu, J. (2005). Novel SNR estimation algorithm for OFDM. Proceedings of the IEEE VTC, 5, Jiao, F., Ren, G., & Zhang, Z. (2008). New noise variance and post detection SNR estimation method for MIMO OFDM systems. In Proceedings of the IEEE Conference ICCT, (pp ). Nov Boumard, S. (2003). Novel noise variance and SNR estimation algorithm for wireless MIMO OFDM systems. Proceedings of the IEEE Global Telecommunications Conference (Globecom), 3, Ren, G., Zhang, H., & Chang, Y. (2009). SNR estimation algorithm based on the preamble for OFDM systems in frequency selective channels. IEEE Transactions on Communications, 57(8), Zivkovic, M., & Mathar, R. (2009). Preamble-based SNR estimation in frequency selective channels for wireless OFDM systems. In Proceedings of the IEEE VTC Spring (pp. 1 5).

16 8 C. Seo et al. Author Biographies Changwoo Seo received a BS degree in Information and Communication Engineering from Sangmyung University, Cheonan, Korea in 2009 and an MS degree in Electronics Engineering from Chonnam National University, Gwangju, Korea in He is currently a PhD student at Chonnam National University, Gwangju, Korea from 2011 in the School of Electronics & Computer Engineering. His research interests include MIMO and OFDM systems. Sherlie Portugal received a BS in Electronic and Telecommunication Engineering from the Technological University of Panama, Panama, in She worked for the School of Electrical Engineering of the Technological University of Panama and is currently a master s student in the School of Electronics & Computer Engineering at Chonnam National University, South Korea. Her research fields include MIMO, OFDM, encoding/decoding algorithms for STC and MIMO spatial multiplexing schemes, and general wireless communications. Saransh Malik received a BS in Information Technology from Rajiv Gandhi Technical University, India in He is currently a master s student in the School of Electronics & Computer Engineering at Chonnam National University, Gwangju, Korea from His research interests include mobile and wireless communication system.

17 New SNR Estimation Algorithm 9 Cheolwoo You received BS, MS, and PhD degrees in electronics engineering from Yonsei University, Seoul, Korea, in 1993, 1995, and 1999, respectively. From Jan to April 2003, he worked as a Senior Research Engineer with LG Electronics, Gyeonggi, Korea. During , he was a Senior Research Engineer at EoNex, Songnam, Korea. From August 2004 to July 2006, he was with Samsung Electronics, Suwon, Korea. Since September 2006, he has been with the Department of Information and Communications Engineering, Myongji University, Gyeonggi, Korea. His research areas are BS/MS modem design, communication theory, signal processing, and advanced channel codes for mobile/nomadic communication systems. He is currently interested in new Multiple Access schemes, Adaptive Resource Allocation, AMC, MIMO systems, advanced FEC, and relay schemes for 4G communication systems. Taejin Jung received BS, MS, and PhD degrees in electronic and electrical 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 Member and worked on the development of efficient receiving algorithms for High Definition TV (HDTV). In 2004, he joined the school of electronics and computer engineering, Chonnam National University (CNU), Korea. His research interests are efficient encoding/decoding algorithms for STC, MIMO and MIMO-OFDM, and modem designs for broadband wireless communication systems. Huaping Liu received his BS and MS degrees from Nanjing University of Posts and Telecommunications, Nanjing, China, in 1987 and 1990, respectively, and his PhD degree from New Jersey Institute of Technology, Newark, NJ, in 1997, all in electrical engineering. From July 1997 to August 2001 he was with Lucent Technologies, New Jersey. In September 2001, he joined the School of Electrical and Computer Engineering at Oregon State University, where he has been an associate professor since June He currently serves as an Associate Editor for the IEEE Transactions on Vehicular Technology and IEEE Communications Letters.

18 1240 C. Seo et al. Intae Hwang received a BS degree in Electronics Engineering from Chonnam National University, Gwangju, Korea in 1990 and a MS degree in Electronics Engineering from Yonsei University, Seoul, Korea in 1992, and a PhD degree in Electrical & Electronics Engineering from Yonsei University, Seoul, Korea in He was a senior engineer at LG Electronics from 1992 to He is currently a Professor in the School of Electronics & Computer Engineering at Chonnam National University, Gwangju, Korea from His current research activities are in digital & wireless communication systems, mobile terminal system for next generation applications; physical layer software for mobile terminals, efficient algorithms for AMC, MIMO, MIMO- OFDM, ICIM, and relaying schemes for wireless communication.