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Channel Estimation in OFDM System over Fading Channels: A Compressive Sensing based Approach


  • School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara – 144411, Punjab, India


As Least Square (LS) and Minimum Mean Square Error (MMSE) do not give the satisfactory result to justify the features of the wireless channel, Compressed Sensing (CS) based on Discrete Sine Transform (DST) for channel impulse response has been studied which gained much more importance in digital signal processing. We have used Compressed Sensing to estimate the channel coefficients of the fading channel; then we have performed the CS recovery algorithm to estimate the channel and to nullify the fading effect, the thus much better result are obtained in the simulation which satisfies the better performance of the system as compared to the traditional method. Simulation results show a considerable improvement in Bit Error Rate (BER) performance on employing the Compressed Sensing approach in comparison to LS and MMSE. The improvement of the order of 2-9 dB of SNR is for a particular value of BER.


Channel Estimation, Compressed Sensing, DST, LS, MMSE, OFDM.

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  • Cheng P, Chen Z, Rui Y. Channel estimation for OFDM systems over doubly selective channels: A distributed compressive sensing based approach. IEEE Transactions on Communications. 2013; 61:4173–85.
  • Xiong X, Jiang B, Gao X, You X. DFT-based channel estimator for OFDM systems with leakage estimation. IEEE Communications Letters. 2013; 17:1592–5.
  • Chen JC, Wen CK, Ting P. An efficient pilot design scheme for sparse channel estimation in OFDM systems. IEEE Communications Letters. 2013; 17:1352–5.
  • Tropp A, Gilbert AC. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on Information Theory. 2007 Dec; 53(12):4655–66.
  • Cotter SF, Sparse channel estimation via matching pursuit with application to equalization. IEEE Communication Letter. 2002; 50:374–7.
  • Carbonelli C. Sparse channel estimation with zero tap detection. IEEE Transaction on Wireless Communication. 2007; 6:1743–63.
  • Bajwa WU. Compressed channel sensing: A new approach to estimating sparse multipath channels. IEEE Transaction on Vehicular Technology. 2010; 98:1058–76.
  • Qi C. Pilot design schemes for sparse channel estimation in OFDM systems. IEEE Transaction on Vehicular Technology. 2015; 64:1493–505.
  • Donoho DL. Uncertainty principles and ideal atomic decomposition. IEEE Transaction in Field Theory. 2001; 47:2845–62.


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