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Techniques for Suppression of IntercarrierInterference in OFDM Systems

Tiejun (Ronald) Wang, John G. Proakis, and James R. ZeidlerCenter for Wireless CommunicationsUniversity of California, San Diego

La Jolla, CA 92093-04047

Abstract This paper considers an orthogonal frequency divi-sion multiplexing (OFDM) system over frequency selective time-varying fading channels. The time variations of the channelduring one OFDM frame destroy the orthogonality of differentsubcarriers and results in power leakage among the subcarriers,known as Intercarrier Interference (ICI), which results in adegradation of system performance.

In this paper, channel state information is used to minimize theperformance degradation caused by ICI. A simple and efficientpolynomial surface channel estimation technique is proposed toobtain the necessary channel state information. Based on theestimated channel information, we describe a minimal meansquare error (MMSE) based OFDM detection technique thatreduces the performance degradation caused by ICI distortion.Performance comparisons between conventional OFDM and theproposed MMSE-based OFDM receiver structures under thesame channel conditions are provided in this article. Simulationresults of the system performance further confirm the effective-ness of the new technique over the conventional OFDM receiverin suppressing ICI in OFDM systems.

I. INTRODUCTION

Orthogonal Frequency Division Multiplexing (OFDM) isa widely known modulation scheme in which a serial datastream is split into parallel streams that modulate a groupof orthogonal subcarriers [1]. OFDM is widely used andconsidered a promising technique for high speed data trans-mission in digital broadcasting, wireless LANs, HDTV, andnext generation mobile communications.

OFDM symbols are designed to have a relatively long timeduration, but a narrow bandwidth. Hence OFDM is robustto channel multipath dispersion and results in a decrease inthe complexity of equalizers for high delay spread channelsor high data rates. However, the increased symbol durationmakes an OFDM system more sensitive to the time varia-tions of mobile radio channels. In particular, the effect ofDoppler spreading destroys the orthogonality of the subcar-riers, resulting in intercarrier interference (ICI) due to powerleakage among OFDM subcarriers. In paper [2], the carrier tointerference (C/I) ratio has been introduced to demonstrate theeffect of the ICI under different maximum Doppler spreads anddifferent Doppler spectra. Performance degradation of OFDMsystems due to Dopper spreading is also analyzed in [3].

This work was supported by the Center for Wireless Communications underthe CoRe research grant core 00-10071.

In this paper, the channel state information is assumed to beunavailable at the receiver and has to be estimated in the firstplace. In [4], a time-frequency polynomial model for channelestimation in OFDM systems is proposed, which does not haveto estimate channel statistics such as the channel correlationmatrix and average SNR per bit. However, in practice suchknowledge is usually not available and the channel statisticsmay vary by time. But a large polynomial order is requiredin order to represent the 2-D frequency channel response withsufficient accuracy, since the frequency selectivity makes thechannel changes relatively fast over the frequency domain.Therefore, we need to design a channel estimation methodunder the frequency selective and time-varying fading channelwith low complexity. In this paper, we propose a new modifiedpolynomial channel estimator with better performance. Incontrast to the estimator which estimates the channel responsein the frequency domain, the modified estimation algorithmdirectly estimates the time domain response (has relativelyslower variation and requires lower order to polynomial func-tion representations), and hence achieves better estimationquality. Another point is that in [4], the fading channel ismodeled as constant within one OFDM frame, but changesfrom frame to frame, which is inaccurate for most cases,especially when ICI is to be shown and analyzed. The modifiedpolynomial channel estimator in our paper not only estimatesthe variations frame by frame, but also within one OFDMframe.

We also propose in this paper a minimum mean square error(MMSE) criterion-based OFDM receiver structure that takesinto account both additive noise and the ICI disturbance. Thenumerical simulation results of the system performance thatare provided under various channel conditions confirm thesuperior performance of the MMSE-OFDM receiver over theconventional OFDM receiver.

The rest of the paper is organized as follows: In Section IIwe describe the OFDM system model as well as the frequencyselective time varying fading channel model considered inthis paper. In Section III, the polynomial model is describedfor the channel and an estimation algorithm is provided andapplied to perform the OFDM channel estimation. In SectionIV, two different OFDM receiver structures, the conventionalOFDM receiver and the MMSE-based receiver are descibed. InSection V, the numerical system performance of these differentdetection techniques are presented and compared. Finally, ourconclusions are contained in Section VI.

IEEE Communications Society / WCNC 2005 39 0-7803-8966-2/05/$20.00 2005 IEEE

II. CHANNEL MODEL

Converter

P/S

ts [n]d [k]t

th [n]l

S/P

Converter

S/P

Converter

Guard

Interval

Insertion

IFFT

Mary QAM

Modulator

Binary

Source

Channel

Noise

AWGN

Guard

Interval

Removal

FFT

Channel Estimation

Mary QAM

Detection

Symbol

Data

Output

ttR [k]

r [n]

Fig. 1. Baseband model of the OFDM system

An OFDM system with N subcarriers is considered in thispaper. A block of N log2 M bits of data is first mappedinto a sequence {d[k]} of M -ary complex symbols of lengthN , each modulating an orthonormal exponential functionexp(j 2knN ), k = 0, , N 1. Each data symbol d[k] isnormalized to have unit average signal power E[|d[k]|2] =1. As demonstrated in Fig.1, information bearing sequences{d[k]} is first serial to parallel converted and processed by anIFFT operation, given by

st[n] =1N

N1k=0

dt[k] exp(j 2knN

), 0 n, k N 1 ,(1)

where the subscript t represents the tth OFDM frame. Acyclic prefix is inserted into the transmitted signal to preventpossible intersymbol interference (ISI) between successiveOFDM frames. After parallel to serial conversion, the signalsare transmitted through a frequency selective time varyingfading channel. At the receiver end, after removing the cyclicguard interval, the sampled received signal is characterized inthe following format, by applying the tapped-delay-line model[5]

rt[n] =L1l=0

hlt[n] st[|n l|N ]+nt[n], 0 n N 1 , (2)

where hlt[n] represents the channel response of the lth path

during the tth OFDM frame, L represents the total numberof paths of the frequency-selective fading channel, nt[n]represents the additive Gaussian noise with zero mean andvariance E[|nt[n]|2] = 2 = N0/Es (Es/N0 represents thesystem signal to noise ratio), and | |N represents the modularN operation.

The fading channel coefficients hlt[n] are modeled as zeromean complex Gaussian random variables. Based on the WideSense Stationary Uncorrelated Scattering (WSSUS) assump-tion, the fading channel coefficients in different delay taps are

statistically independent. We also assume that they have anexponential power delay profile, which is given by

E[hlt[n]2] = exp( l), = 1 exp()1 exp(L ) . (3)

The number of fading taps L is given by max/Ts, where maxis the maximum multipath delay, and Ts = 1/W , where Wis the channel (OFDM signal) bandwidth. In the time domain,the fading coefficients hlt[n] are correlated and have a Dopplerpower spectrum density modeled as in Jakes [6], given by

D(f) =

1

Fd

1(

fFd

)2 |f | Fd0 otherwise

, (4)

where Fd is the Doppler bandwidth. Hence hlt[n] has anautocorrelation function given by

E[hlt[n]hlt[m]] = exp( l)J0

(2(nm)FdTs

). (5)

where J0() is the first kind Bessel function of zero order.Written in a concise matrix form, we can represent (2) as,

rt = Ht st + nt , (6)where rt and nt are vectors of size N 1, and the channelmatrix Ht is given by

Ht =[h[0]H ,h[1]H , ,h[N 1]H

]H, (7)

where h[n]H is the right cyclic shift by n + 1 positions of azero padded vector given by

h[n] =[0, 0, , 0

NL

, hL1t [n], hL2t [n], , h0t [n]

]shiftn+1

.

(8)Hence the received signal rt is related to the data vector dt

as,rt = HtW dt + nt, (9)

where W is the inverse Fourier transformation matrix givenby

W =[wn,m

]NN , wn,m = exp(j

2nmN

)/

N . (10)

From another point of view, the received signal can beexpressed in terms of the equivalent frequency domain channelmodel as

rt[n] =1N

N1k=0

dt[k] htk[n] exp(j2kn

N

)+ nt[n], (11)

where htk[n] is the frequency domain channel response for thekth subcarrier during the tth OFDM frame. We incorporatethe result given by (2) into (11), then we have the frequencydomain channel response htk[n] as

htk[n] =L1l=0

hlt[n] exp( j 2lk

N

), n = 0, 1, , N 1 .

(12)

IEEE Communications Society / WCNC 2005 40 0-7803-8966-2/05/$20.00 2005 IEEE

At the receiver side, the FFT operation is performed on eachblock

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