ICI Mitigation for Pilot-Aided OFDM Mobile Systems
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ICI Mitigation for Pilot-Aided OFDM Mobile Systems Yasamin Mostofi, Member, IEEE and Donald C. Cox, Fellow, IEEE IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 4, NO.2, MARCH 2005. 老師:高永安 學生:蔡育修. Outline. Introduction System model Piece-Wise Linear Approximation Method I

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ICI Mitigation for Pilot-Aided OFDM Mobile SystemsYasamin Mostofi, Member, IEEE and Donald C. Cox, Fellow, IEEEIEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 4, NO.2, MARCH 2005





  • Introduction

  • System model

  • Piece-Wise Linear Approximation

    Method I

    Method II

  • Mathematical Analysis and Simulation Result

  • Noise/Interference Reduction

  • Simulation Results and Conclusion



  • Transmission in a mobile communication environment is impaired by both delay and Doppler spread.

  • As delay spread increases, symbol duration should also increase.

    reasons---1.near-constant channel in each frequency subband. 2.prevent ISI.

  • OFDM system become more susceptible to time-variations as symbol length increases. Time-variations introduce ICI. be mitigated to improve the performance.


  • We introduce two new methods to mitigate ICI. Both methods use a piece-wise linear model to approximate channel time-variations.

System model

Assume perfect timing synchronizaton

System model


The channel output y


The FFT of sequence y



Pilot extraction

An estimate of Hi,0 can then be acquired at pilot:

Pilot Extraction


  • In the absence of mobility, L pilots would have been enough to estimate the channel.

  • However, in the presence of Doppler, due to the ICI term,

    using them for data detection results in poor perfor-mance.

  • This motivates the need to mitigate the resultant ICI.

Piece wise linear approximation

Piece-Wise Linear Approximation

  • We approximate channel time-variations with a piece-wise linear model with a constant slope over the time duration T.


For normalized Doppler of up to 20%, linear approxi-

mation is a good estimate of channel time-variations.

We will derive the frequency domain relationship.

Therefore, we approximate



we will have




An FFT of y:


  • To solve for X, both Hmid and Hslope should be estimated.

  • Matrix C is fixed matrix and Hmid is readily available.

  • So we show how to estimate Hslope with our two methods.

Method i ici mitigation using cyclic prefix

The output prefix vector

Method I:ICI Mitigation Using Cyclic Prefix




  • Equations (9) and (11) provide enough information to solve for X.

  • We use a simpler iterative approach to solve for X.

Method ii ici mitigation utilizing adjacent symbols

Method II:ICI Mitigation Utilizing Adjacent Symbols

  • This can be done by utilizing either the previous symbol

    or both adjacent symbols.

  • A constant slope is assumed over the time duration of

    T+(N/2)*Ts for the former and T for the latter.


Estimate of the slopes in region 2:


Utilizing two slopes introduces a minor change in (8).


It can be easily shown the frequency domain relationship


  • Method I and Method II can handle considerably higher

    delay and Doppler spread at the price of higher compu-

    tation complexity.

Mathematical analysis and simulation result

Mathematical Analysis and Simulation Result

  • We define SIRave as the ratio of average signal power

    to the average interference power.

  • Our goal is to calculate SIRave when ICI is mitigated and

    compare it to the that of the “no mitigation” case.

Noise interference reduction

Estimated channel taps are compared with a Threshold.

Let MAV represent the tap with maximum absolute value.

All the estimated taps with absolute values smaller than

MAV/γ for some γ>=1 will be zeros.

Noise/Interference Reduction

Simulation results

Simulation Results

  • System parameters


  • The power-delay profile of channel#1 has two main taps

    that are separated by 20μs.

  • The power-delay profile of channel#2 has two main clus-

    ters with total delay of 36.5μs.


  • Each channel tap is generated as Jakes model.

  • To see how ICI mitigation methods reduce the error floor.

    in the absence of

    noise for both channels.


  • To see the effect of noise for fd,norm = 6.5%


  • To see how ICI mitigation methods reduce the required

    received SNR for achieving a Pb = 0.2.



  • Both methods used a piece-wise linear approximation to

    estimate channel time-variations in each OFDM symbol.

  • These methods would reduce average Pb or the required

    received SNR to a value close to that of the case with no


  • The power savings become considerable as fd,norm incre-


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