Reduced-rank adaptive filtering using Krylov subspace
EURASIP Journal on Applied Signal Processing
Krylov-proportionate adaptive filtering techniques not limited to sparse systems
IEEE Transactions on Signal Processing
Adaptive conjugate gradient DFEs for wideband MIMO systems
IEEE Transactions on Signal Processing
ISWCS'09 Proceedings of the 6th international conference on Symposium on Wireless Communication Systems
Robust reduced-rank adaptive algorithm based on parallel subgradient projection and Krylov subspace
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Computers and Electrical Engineering
A stochastic conjugate gradient method for the approximation of functions
Journal of Computational and Applied Mathematics
Modified partial update EDS algorithms for adaptive filtering
Analog Integrated Circuits and Signal Processing
| Hi-index | 35.70 |
The paper presents and analyzes two approaches to the implementation of the conjugate gradient (CG) algorithm for filtering where several modifications to the original CG method are proposed. The convergence rates and misadjustments for the two approaches are compared. An analysis in the z-domain is used in order to find the asymptotic performance, and stability bounds are established. The behavior of the algorithms in finite word-length computation are described, and dynamic range considerations are discussed. It is shown that in finite word-length computation and close to steady state, the algorithms' behaviors are similar to the steepest descent algorithm, where the stalling phenomenon is observed. Using 16-bit fixed-point number representation, our simulations show that the algorithms are numerically stable