On the use of EMD based adaptive filtering for OFDM channel estimation

Hilbert Huang transform (HHT) based data driven empirical mode decomposition (EMD) in conjunction with adaptive filter (AF) is proposed for estimation of communication channel in OFDM system. EMD can be viewed as alike of wavelet decomposition which decomposes the signal of interest to intrinsic mode functions (IMF), whose basis function is derived from signal itself. In this method, the length of channel impulse response (CIR), is approximated using Akaike information criterion (AIC). Then the estimation of CIR is performed using adaptive filter with EMD decomposed IMF of the received OFDM symbol. Conventional AF uses random initial weight vector. The novelty of the proposed method lies in the fact that it uses decimated version of one of the decomposed IMFs of received OFDM symbol as initial weight vector. The selection of useful IMF component is done based on correlation and kurtosis measures. This makes the proposed EMD based AF method converge to minimum mean square error (MMSE) in less number of iterations resulting in almost 50% saving of computations. Bit error rate (BER), mean square error (MSE) and normalized root mean square error (NRMSE) are computed. The simulation studies established the efficacy of proposed method; and comparative studies under different modulation schemes and fading conditions revealed improved performance. Simulations have shown an average improvement of 3 dB in BER performance for proposed EMD based AF as compared to conventional AF.