An Adaptive Greedy Algorithm with Application to Sparse Narma Identification

Greedy algorithms form an essential tool for compressed sensing. However, their inherent batch mode discourages their use in time-varying environments due to significant complexity and storage requirements. In this paper a powerful greedy scheme developed in [1, 2] is converted into an adaptive algorithm which is applied to estimation of nonlinear channels. Performance is assessed via computer simulations on a variety of linear and nonlinear channels; all confirm significant improvements over conventional methods.