Low complexity equalization for doubly selective channels modeled by a basis expansion

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 subcarriers, each iteration of GMRES or LSQR requires only flops and memory. Additionally, we further accelerate convergence of both GMRES and LSQR by using the single-ta...

• Type: Journal
• Year: 2010
• Where: TSP
• Authors: Tomasz Hrycak, Saptarshi Das, Gerald Matz, Hans G. Feichtinger
• Keywords: Artificial Intelligence, Channel Matrix, GMRES, TSP 2010, Wireless Channel