GMRES: a generalized minimal residual algorithm for solving nonsymmetric linear systems
SIAM Journal on Scientific and Statistical Computing
Matrix computations (3rd ed.)
LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
ACM Transactions on Mathematical Software (TOMS)
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Low-complexity banded equalizers for OFDM systems in Doppler spread channels
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Wireless Communications
Low-Complexity Block Turbo Equalization for OFDM Systems in Time-Varying Channels
IEEE Transactions on Signal Processing
Maximum likelihood blind channel estimation in the presence ofDoppler shifts
IEEE Transactions on Signal Processing
Low-complexity equalization of OFDM in doubly selective channels
IEEE Transactions on Signal Processing
Time-Variant Channel Estimation Using Discrete Prolate Spheroidal Sequences
IEEE Transactions on Signal Processing
Equalization for OFDM over doubly selective channels
IEEE Transactions on Signal Processing
Pilot-Assisted Time-Varying Channel Estimation for OFDM Systems
IEEE Transactions on Signal Processing
An Efficient Design of Doubly Selective Channel Estimation for OFDM Systems
IEEE Transactions on Wireless Communications
Wireless Personal Communications: An International Journal
Hi-index | 35.68 |
We propose a novel equalization method for doubly selective wireless channels, whose taps are represented by an arbitrary Basis Expansion Model (BEM). We view such a channel in the time domain as a sum of product-convolution operators created from the basis functions and the BEM coefficients. Equivalently, a frequency-domain channel can be represented as a sum of convolution-products. The product-convolution representation provides a low-complexity, memory efficient way to apply the channel matrix to a vector. We compute a regularized solution of a linear system involving the channel matrix by means of the GMRES and the LSQR algorithms, which utilize the product-convolution structure without ever explicitly creating the channel matrix. Our method applies to all cyclic-prefix transmissions. In an OFDM transmission with K subcarriers, each iteration of GMRES or LSQR requires only O(K log K) flops and O(K) memory. Additionally, we further accelerate convergence of both GMRES and LSQR by using the single-tap equalizer as a preconditioner. We validate our method with numerical simulations of a WiMAX-like system (IEEE 802.16e) in channels with significant delay and Doppler spreads. The proposed equalizer achieves BERs comparable to those of MMSE equalization, and noticeably outperforms low-complexity equalizers using an approximation by a banded matrix in the frequency domain. With preconditioning, the lowest BERs are obtained within 3-16 iterations. Our approach does not use any statistical information about the wireless channel.