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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.
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

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.
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.

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 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-