Improving channel estimation in OFDM systems sparse multipath channels

We describe an algorithm for sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithm uses least squares (LS) technique for channel estimation and a generalized Akaike information criterion to estimate the channel length and tap positions. This effectively reduces the signal space of the LS estimator, and hence improves the estimation performance as demonstrated using computer simulations. For example, the proposed modified LS with sparse channel estimation algorithm has a 5 dB lower mean square error in channel estimation when compared to the conventional LS approach which translates to approximately 0.5 dB improvement in SNR at receiver.