A DFT-based Low Complexity LMMSE Channel Estimation Technique for OFDM Systems

Abstract

The linear minimum mean square error (LMMSE) channel estimation technique is often employed in orthogonal frequency division multiplexing (OFDM) systems because of its optimal performance in the mean square error (MSE) performance. However, the LMMSE method requires cubic complexity of order O(N 3 p ), where Np is the number of pilot subcarriers. To reduce the computational complexity, a discrete Fourier transform (DFT) based LMMSE method is proposed in this paper for OFDM systems in the frequency selective channel. To validate the proposed method, the closed form mean square error (MSE) expression is also derived. Finally, a computer simulation is carried out to compare the performance of the proposed LMMSE method with the classical LS and LMMSE methods in terms of bit error rate (BER) and computational complexity. Results of the simulation show that the proposed LMMSE method achieves exactly the same performance as the conventional LMMSE method, with much lower computational complexity.

Subspace Blind Channel Estimation Methods in OFDM Systems under Multi-path Channels

“…Channel estimation at pilot frequencies can be performed not only using LS but also using minimum mean square error (MMSE) or least mean square (LMS), where MMSE has been shown to work much better than LS [21,22]. Another method that is widely used in OFDM channel estimation is linear MMSE (LMMSE) [23,24], since it is optimal for minimizing the root-mean-square error of channel estimates in the presence of Rayleigh. Blind channel estimation methods avoid the use of pilot symbols that consume valuable channel bandwidth, but instead the channel is estimated using the internal information contained in the received signals, as well as the structural properties of the transmitted signals.…”