 Research Article
 Open Access
Bit Error Rate Analysis for an OFDM System with Channel Estimation in a Nonlinear and FrequencySelective Fading Channel
 Amir Ligata^{1}Email author,
 Haris Gacanin^{2, 3},
 Fumiyuki Adachi^{4},
 Miha Smolnikar^{5} and
 Mihael Mohorcic^{5}
https://doi.org/10.1155/2011/670637
Â© Amir Ligata et al. 2011
 Received: 27 December 2010
 Accepted: 21 February 2011
 Published: 13 March 2011
Abstract
Orthogonal frequency division multiplexing (OFDM) is an effective technique for highspeed digital transmission over timedispersive channels. However, for coherent detection, a reliable channel estimation (CE) is required. OFDM is characterized by its high peaktoaverage power ratio (PAPR), which makes it very sensitive to nonlinear distortions that may affect the channel estimation accuracy leading to a bit error rate (BER) performance degradation. In this paper, we present closedform BER expression for OFDM with a pilotassisted CE in a nonlinear and frequencyselective fading channel. We discuss how, and to what extent, the nonlinear degradation affects the BER performance with the CE based on a time/frequency divisionmultiplexed (TDM/FDM) pilot. The analysis is based on a Gaussian approximation of the nonlinear noise due to both HPA amplitude limitation and quantization. We also evaluate the estimator's mean square error (MSE) with both TDM and FDM pilots. Our results show that pilotassisted CE using FDM pilot is more sensitive to nonlinear distortions than the CE using a TDM pilot, since its pilot subcarriers are affected by nonlinear noise due to both the HPA and the quantization.
Keywords
 Mean Square Error
 Orthogonal Frequency Division Multiplex
 Channel Estimation
 Orthogonal Frequency Division Multiplex System
 Orthogonal Frequency Division Multiplex Signal
1. Introduction
In a terrestrial radio channel, the transmitted signal reaches the receiver through multiple propagation paths, which all have a different relative delay and gain. This produces intersymbol interference (ISI) and degrades the system's performance [1]. Orthogonal frequency division multiplexing (OFDM) can be used to overcome the channel frequency selectivity, but it requires an accurate channel estimation (CE) for coherent detection. Various CE schemes have been proposed for OFDM [2â€“5], where the pilot signals are multiplexed either in the time (TDM pilot) or in the frequency domain (FDM pilot). In a fading channel, the performance of an OFDM system with CE using a TDM pilot gets rapidly degraded whenever the channel has a significant time variance. On the other hand, the CE with an FDM pilot improves the tracking against a fast fading, but the performance degrades, since the noise is spread over all subcarriers due to interpolation.
The main drawback of OFDM is its high peaktoaverage power ratio (PAPR), which makes the system very sensitive to nonlinear distortions caused by analog components, such as a highpower amplifier (HPA) as well as digitaltoanalog (DA) and analogtodigital (AD) converters. Usually, DA and AD converters are assumed to have a large number of quantization levels and an optimally exploited dynamic range. Because of such assumptions, the quantization noise (representing the quantizer granularity) and the noise due to amplitude limitation (corresponding to the overloading distortion) can be neglected [6â€“8]. However, in a real implementation, in order to keep the system complexity and the power consumption low, it is desirable to keep the resolution of the DA/AD converters as low as possible [9, 10]. It was shown, in [11], that the quantization requirements are higher at the receiver end, particularly for severely frequencyselective channels. The analysis of the nonlinear distortions due to amplitude clipping in an OFDMA system is presented in [12], where it was shown that users with less allocated power are subject to stronger nonlinear interference. In [13], the impact of the nonlinear degradation due to amplitude clipping on an OFDM system's transmission performance was investigated with a computer simulation. To the best of the authors' knowledge, closedform BER expressions for an OFDM system with CE in a nonlinear and frequencyselective fading channel has not been presented.
In this paper, we present a theoretical analysis of an OFDM system with a pilotassisted CE based on TDM and FDM pilots in a nonlinear and frequencyselective fading channel. We derive a closedform BER and mean square error (MSE) expressions and discuss the sensitivity of both CE schemes to the nonlinear and channel impairments. Unlike previous papers, where nonlinear noise due to the HPA and the quantization is treated separately, we take into consideration the effects of both. Our analysis is based on a Gaussian approximation of the nonlinearity due to the HPA amplitude saturation and the insufficient resolution of the quantization. The results show that the BER performance with pilotassisted CE based on an FDM pilot is more sensitive to the nonlinear distortion then a TDM pilot, since its pilot subcarriers are affected by nonlinear noise due to both the HPA and the quantization.
The rest of the paper is organized as follows. Section 2 gives an overview of the system model. A performance analysis is given in Section 3, while the numerical results and discussions are presented in Section 4. The conclusion of the paper is set out in Section 5.
2. System Model
2.1. Mathematical Signal Representation
The th ( ) frame of the datamodulated symbols with is transmitted during one signaling interval. The datamodulated symbol sequence is fed to an point inverse FFT (IFFT) to obtain the timedomain OFDM signal .
for , where denotes the HPA amplitude saturation level. We note that the relation between and the input backoff is given as , where denotes the average input power. We emphasize that we introduce the clipping effect through an HPA, for which the inputoutput softlimiter characteristic is approximated by (2). Thus, in this paper we refer to amplitude clipping as the amplitude saturation of the HPA. We also note that the PAPR at the output of the HPA is affected by , irrespective of the CE scheme; a lower will give a lower PAPR and vice versa [15].
An sample guard interval (GI) is inserted at the beginning of each OFDM frame, and the signal is multiplied by the power coefficient , where denotes the datamodulated symbol energy.
where and , respectively, denote the attenuation constant and noise due to the nonlinearity. The attenuation constant is chosen so as to minimize the MSE [17]. It is shown in [17] that for the amplitude saturation level â€‰dB, . For lower , can be well approximated as [18], where erfc denotes the complementary error function. The nonlinear noise after quantization and HPA can be expressed as for , where and , respectively, denote the noise due to HPA amplitude saturation, and the quantization. We assume that is approximated as a zeromean random variable with the variance [17]. Furthermore, we assume that the quantization noise is a zeromean random variable with the variance given by [7], where denotes the quantization step as illustrated in Figure 3.
2.2. Channel Estimation Overview [2â€“5]
In this section, we first give a short overview of pilotassisted CE with a TDM pilot, and then pilotassisted CE with a FDM pilot is presented.
2.2.1. CE with TDM Pilot
2.2.2. CE with FDM Pilot
In pilotassisted CE using the FDM pilot the frequencydomain interpolation is used over equallyspaced pilot subcarriers as a subset of subcarriers. In [22], it was shown that the optimum FDMpilot scheme is the one with equally spaced inserted pilots. We also note here that the amplitude saturation level has no effect on the optimal distribution of the pilot subcarriers, since the nonlinear noise is a random variable that is equally distributed over all the subcarriers in the frequency domain by the receiver's FFT.
for . In (9), and , respectively, denote the th frame's distorted part of the channel gain estimates due to the HPA saturation and quantization at the th subcarrier.
3. Performance Analysis
In this section, first the closedform BER expressions for pilotassisted CE with both TDM and FDM pilots in a nonlinear and frequencyselective fading channel are presented, and then the estimator's MSE under the same conditions is evaluated.
3.1. BER
where denotes the normalized covariance given by . Here, , and denote the second moments of the random variables , , and , respectively. Next, we derive the closedform BER expression in a nonlinear and frequencyselective fading channel with both TDM and FDM pilots.
3.1.1. TDM Pilot
Finally, the average BER is obtained by (11).
3.1.2. FDM Pilot
where and are defined in Section 3.1.1. Finally, the average BER is obtained by (11). We note that in the case of CE with the FDM pilot .
3.1.3. Ideal CE
where and are defined in (15). Finally, the average BER is obtained by (11).
3.2. MSE
We define the MSE of the th frame at the th subcarrier as and assume that the HPA amplitude saturation level is known at the receiver.
3.2.1. TDM Pilot
where . The first term in (23) denotes the negative effect of the nonlinearity due to the quantization, while the second term denotes the influence of AWGN.
3.2.2. FDM Pilot
where the first, the second, and the last term denote the negative effect of the nonlinearity due to the HPA amplitude saturation, quantization, and AWGN, respectively.
4. Numerical Results and Discussions
Numerical parameters.
Data modulation  QPSK  

Transmitter  IFFT/FFT size 

GI 
 
Channel  path frequencyselective  
block Rayleigh fading  
FDE  MRC  
Receiver  Channel estimation  TDM and FDM pilots 
4.1. BER
4.2. MSE
Next, we investigated the effect of nonlinearity on the channel estimator with both the TDM and FDM pilots by numerically evaluating its MSE. For the CE with the TDM pilot, we select , while for the CE with the FDM pilot, and consequently, the same transmission data rate is maintained.
The impact of the HPA amplitude saturation level is presented in Figure 9(b) for value â€‰dB in order to better observe the effect of parameter . It is evident that the quantization noise represented through quantization step affects more the MSE of the channel estimator for the FDMbased pilotassisted CE. Finally, in Figure 9(c), the impact of is plotted. In comparison with pilotassisted CE with the TDM pilot, the MSE of the channel estimator with the pilotassisted CE based on the FDM pilot is more affected by nonlinear noise. This is because for the CE with the TDM pilot the Chu pilot sequence is used with a constant (practically a very low) amplitudes in both the time and frequency domains, and consequently, the channel estimator's performance using the TDM pilot is not affected by the HPA. The nonlinear degradations in this case are only due to the quantization. This is because of the fact that for pilotassisted CE using the FDM pilot, the pilot signals are inserted onto dedicated (i.e., pilot) subcarriers in frequency domain within the OFDM signal. Consequently, the corresponding OFDM signal in the time domain may have a large PAPR causing a signal degradation due to nonlinear noise coming from both the HPA and quantization. After the FFT at the receiver, the nonlinear noise will spread over all the subcarriers and affect the pilot subcarriers. Naturally, this will have a negative effect on the channel estimator's performance in comparison with the pilotassisted CE using the TDM pilot.
5. Conclusions
In this paper, we have presented closedform BER expressions for OFDM with CE based on both TDM and FDM pilots in a nonlinear and frequencyselective fading channel. In our analysis the nonlinear noise is approximated with a Gaussian random variable, where unlike previous studies, we consider the impact of both the DA converter and the HPA. The results show that the pilotassisted CE with the FDM pilot is affected by the nonlinear noise due to both the quantization and the HPA, while the pilotassisted CE with the TDM pilot is only affected by nonlinear degradation due to quantization because of the pilotsequence with low PAPR. Thus, the higher BER with FDM pilot in comparison with TDM pilot is observed. Furthermore, numerical results have confirmed the validity of the analytical derivations in terms of closedform BER expressions, since a fairly good agreement between the simulation and the analytical results is observed.
Appendix
Declarations
Acknowledgment
This work was supported in part by Ministry of Civil Affairs of Bosnia and Herzegovina with support grant for preparation of EU FP7related research projects.
Authorsâ€™ Affiliations
References
 Proakis JG: Digital Communications. 3rd edition. McGrawHill, New York, NY, USA; 1995.MATHGoogle Scholar
 Coleri S, Ergen M, Puri A, Bahai A: Channel estimation techniques based on pilot arrangement in OFDM systems. IEEE Transactions on Broadcasting 2002, 48(3):223229. 10.1109/TBC.2002.804034View ArticleGoogle Scholar
 Hsieh MH, Wei CH: Channel estimation for OFDM systems based on combtype pilot arrangement in frequency selective fading channels. IEEE Transactions on Consumer Electronics 1998, 44(1):217225. 10.1109/30.663750View ArticleGoogle Scholar
 Zhang W, Xia XG, Ching PC: Optimal training and pilot pattern design for OFDM systems in Rayleigh fading. IEEE Transactions on Broadcasting 2006, 52(4):505514.View ArticleGoogle Scholar
 Choi YS, Voltz PJ, Cassara FA: On channel estimation and detection for multicarrier signals in fast and selective Rayleigh fading channels. IEEE Transactions on Communications 2001, 49(8):13751387. 10.1109/26.939860View ArticleMATHGoogle Scholar
 Schmidt H, Kammeyer KD: Quantization and its effects on OFDM concepts for wireless indoor applications. Proceedings of the 4th International OFDMWorkshop (InOWo '99), 1999, Hamburg, GermanyGoogle Scholar
 Xiaoying S, Slump CH: Quantization effects in OFDM system. Proceedings of the 29th Symposium on Information Theory in the Benelux, May 2008, Leuven, Belgium 93103.Google Scholar
 Dardari D: Joint clip and quantization effects characterization in OFDM receivers. IEEE Transactions on Circuits and Systems I 2006, 53(8):17411748.View ArticleGoogle Scholar
 Yang H, Schenk TCW, Smulders PFM, Fledderus ER: Joint impact of quantization and clipping on single and multicarrier block transmission systems. Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC '08), 2008, Las Vegas, Nev, USA 548553.Google Scholar
 Sawada M, Okada H, Yamazato T, Katayama M: Influence of ADC nonlinearity on the performance of an OFDM receiver. IEICE Transactions on Communications 2006, 89(12):32503256.View ArticleGoogle Scholar
 Araujo T, Dinis R: Performance evaluation of quantization effects on multicarrier modulated signals. IEEE Transactions on Vehicular Technology 2007, 56(5):29222930.View ArticleGoogle Scholar
 Araujo T, Dinis R: Analytical evaluation of nonlinear effects on OFDMA signals. IEEE Transactions on Wireless Communications 2010, 9(11):34723479.View ArticleGoogle Scholar
 Ryu HG, Hoa TP, Hieu NT, Jianxue J: BER analysis of clipping process in the forward link of the OFDMFDMA communication system. IEEE Transactions on Consumer Electronics 2004, 50(4):10581064. 10.1109/TCE.2004.1362499View ArticleGoogle Scholar
 White GP, Burr AG, Javornik T: Modelling of nonlinear distortion in broadband fixed wireless access systems. Electronics Letters 2003, 39(8):686687. 10.1049/el:20030462View ArticleGoogle Scholar
 Li X, Cimini LJ: Effects of clipping and filtering on the performance of OFDM. IEEE Communications Letters 1998, 2(5):131133. 10.1109/4234.673657View ArticleGoogle Scholar
 Papoulis A: Probability, Random Variables, and Stochastic Processes. 3rd edition. McGrawHill, New York, NY, USA; 1991.MATHGoogle Scholar
 Dardari D, Tralli V, Vaccari A: A theoretical characterization of nonlinear distortion effects in ofdm systems. IEEE Transactions on Communications 2000, 48(10):17551764. 10.1109/26.871400View ArticleGoogle Scholar
 Banelli P, Cacopardi S: Theoretical analysis and performance of OFDM signals in nonlinear AWGN channels. IEEE Transactions on Communications 2000, 48(3):430441. 10.1109/26.837046View ArticleGoogle Scholar
 Hara S, Prasad R: Overview of multicarrier CDMA. IEEE Communications Magazine 1997, 35(12):126144. 10.1109/35.642841View ArticleGoogle Scholar
 Chu DC: Polyphase codes with good periodic correlation properties. IEEE Transactions on Information Theory 1972, 18(4):531532. 10.1109/TIT.1972.1054840View ArticleMATHGoogle Scholar
 Edfors O, Sandell M, Van Beek JJD, Wilson SK, Borjesson PO: OFDM channel estimation by singular value decomposition. IEEE Transactions on Communications 1998, 46(7):931939. 10.1109/26.701321View ArticleGoogle Scholar
 Negi R, Cioffi J: Pilot tone selection for channel estimation in a mobile ofdm system. IEEE Transactions on Consumer Electronics 1998, 44(3):11221128. 10.1109/30.713244View ArticleGoogle Scholar
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