Adaptive Filters: Theory and Applications
Adaptive Filters: Theory and Applications
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Matrix Analysis For Scientists And Engineers
Matrix Analysis For Scientists And Engineers
Adaptive algorithms for sparse echo cancellation
Signal Processing
Acoustic MIMO Signal Processing (Signals and Communication Technology)
Acoustic MIMO Signal Processing (Signals and Communication Technology)
Set-membership proportionate affine projection algorithms
EURASIP Journal on Audio, Speech, and Music Processing
A low delay and fast converging improved proportionate algorithm for sparse system identification
EURASIP Journal on Audio, Speech, and Music Processing
On the constrained stochastic gradient algorithm: model, performance, and improved version
IEEE Transactions on Signal Processing
Underwater acoustic communication channels: propagation models and statistical characterization
IEEE Communications Magazine
IEEE Transactions on Signal Processing
A PNLMS algorithm with individual activation factors
IEEE Transactions on Signal Processing
Stochastic model for the mean weight evolution of the IAF-PNLMS algorithm
IEEE Transactions on Signal Processing
Adaptive algorithms for sparse system identification
Signal Processing
The behavior of LMS and NLMS algorithms in the presence ofspherically invariant processes
IEEE Transactions on Signal Processing
Exploiting sparsity in adaptive filters
IEEE Transactions on Signal Processing
Proportionate adaptive algorithms for network echo cancellation
IEEE Transactions on Signal Processing
Stochastic Modeling of the Transform-Domain Algorithm
IEEE Transactions on Signal Processing
RAKE Receiver for Channels with a Sparse Impulse Response
IEEE Transactions on Wireless Communications
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This paper presents a stochastic model for the individual-activation-factor proportionate normalized least-mean-square (IAF-PNLMS) adaptive algorithm operating under correlated Gaussian input data. The proposed approach uses the contragredient transformation to obtain an analytical solution for the normalized autocorrelation-like matrices arising from the model development. Model expressions describing the learning curve and the second-order moment of the weight-error vector for the IAF-PNLMS algorithm are derived taking into account the time-varying characteristic of the gain distribution matrix. As a consequence, the obtained model predicts very well the algorithm behavior for both transient and steady-state phases. Through simulation results, considering different operating scenarios, the accuracy of the proposed model is attested (via learning curve) for both complex- and real-valued input data.