Sequential convex combinations of multiple adaptive lattice filters in cognitive radio channel identification

Abstract

Sequential convex combinations of multiple adaptive lattice filters using different exponential weighting factors in cognitive radio (CR) channel identification framework have been considered in this presentation. First, the sequential processing multichannel lattice stages (SPMLSs) are modified so as to be used in filter combination task. Then, two different combination schemes, i.e., regular combination of multiple lattice filters (R-CMLF) and decoupled combination of multiple lattice filters (D-CMLF), that utilize modified SPMLSs as filter structure have been proposed. A modified Gram-Schmidt orthogonalization of multiple channels of data, which is constituted in multiple filter combination task, is accomplished. A highly modular, regular, and reconfigurable filter structure, which is suitable for cognitive radios, is achieved with the combination processing implemented in an order-recursive fashion. The mean square deviation (MSD) performances of the schemes under stationary and nonstationary conditions have been presented and compared with the performances of multiple combination of least mean square (M-CLMS), decoupled combination of least mean square (D-CLMS) schemes, and component filters. It has also been shown that the fault tolerances of the proposed schemes are better than those of the component filters due to the redundancy introduced with combination processing, and that the proposed schemes bring together the desired adaptive filter features such as fast convergence and low steady-state MSD levels, which do not normally coexist.