Mitigation of Intercarrier Interference in OFDM System over underwater Acoustic Channels

1. Mitigation of Intercarrier Interference in OFDM System over underwater Acoustic Channels Su Je Lee

2. Contents • Introduction • System Model • Channel Estimation And Data Detection • Numerical Results

3. Introduction (1/2) • UWA(Under-Water Acousic) channels are challenging communication media. • Multiple propagation • Frequency/time-selectivity • Doppler effect • OFDM(orthogonal frequency division multiplexing) emerge as an attractive solution because of their robustness for time-dispersive channel.

4. Introduction (2/2) • In time invariant channel, modulation symbols do not interfere with each other. • But, when time variant channels are considered, ICI(intercarrier interference) arises, and this property no longer holds • Time variation is slow  we can neglect ICI problem • Time variation is fast  we should have account for ICI problem in the receiver design • This paper shows some methods to mitigate ICI.

5. System model (1/4) • An OFDM word consists of K symbols (K is an integer power of two) • A subset of the K symbols are typically used as pilot symbols, for use in channel estimation. • We can derived x(n) by using IFFT(inverse fourier transform)

6. System model (2/4) • In delay dispersive channel, we should insert a cyclic prefix to prevent possible intersymbol interference.

7. System model (3/4) • In time varying channel, received sample presented like this • c(n;l) is the channel impulse response • is AWGN • After suitable synchronization and removal cyclic prefix • we obtain output by using FFT

8. System model (4/4) • In the case of time invariant channels, c(n;l) does not depend on the time index n, the coefficients C(k;m) are non-zero only for k=m so Y(K) is simplified to • So we can see that C(k;m) describes the ICI due to the m-th subcarrier on the k-th subcarriers

9. Channel Estimation And Data Detection(1/5) • In this paper, author presents three different approaches for channel estimation and data detection • A. Standard approach • Consist of neglecting the ICI assuming • Typically, a subset of the subcarriers is reserved for pilot symbols, which are used at the receiver side for channel estimation • The coefficient C(k;k) is estimated as • This simple approach provides good performance when channel variation is slow w.r.t OFDM duration

10. Channel Estimation And Data Detection(2/5) B. First proposed approach • We assume that the ICI between two subcarriers becomes weaker as their separation increases. So ICI occurred only by closest subcarrier • so the received signal presented like this • In such case an effective way to estimate the channel coefficients is given by a the closed-loop tracking based on the gradient algorithm

11. Channel Estimation And Data Detection(3/5) • Let us define the “error term” E(k) • We assume that all symbols X(k) are known pilots • The channel coefficients at the subcarrier index k+1 are estimated as • is the step sizes of the closed loop update rules • After estimation , standard MMSE detection is performed • So we call this method FD-MMSE(frequency domain MMSE)

12. Channel Estimation And Data Detection(4/5) C. Second proposed approach • Unlike previous one , this method does not require an explicit estimation of the ICI coefficients • adaptive-equalization concept • We will consider DFE(Decision Feedback Equalization) • briefly referred to as FD-DFE(frequency-domain DFE)

13. Channel Estimation And Data Detection(5/5) • a(k) is feed-forward filter • b(k) is feedback filter • p(k) is output of feed-forward filter and q(k) is output of feedback filter.

14. Numerical Results(1/2) • Uncoded BPSK • 2048-carrier OFDM • One-pilot symbol/4-subcarrier • Time variation of channel B is much faster than those in channel A -BER-floors are unavoidable when ICI is neglected while proposed ICI can remove. On the other hand at the low SNR classical approach is basically the same as that of FD-DFE and better than that of the FD-MMSE -At the high SNR, FD-DFE outperforms FD-MMSE in channel A, But the opposite happens in channel B Channel estimation achieved by the FD-MMSE technique is accurate only if ,the SNR is very large

15. Numerical Results(2/2) Tracking of the main tap (ICI index 0) and the coefficient describing the ICI due to the closest following subcarrier (ICI index 1). • Refer to channel A with an SNR of 20dB • Estimate is very good for the main coefficients but is relatively noisy for the coefficient with ICI index 1

16. Conclusion • This paper present two ICI-mitigation schemes for OFDM transmissions over time-varying channels • Both proposed techniques provide a significant performance improvement with respect to the standard OFDM receivers • Particularly, FD-DFE turns out to be more robust than FD-MMSE

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