Abstract
A new reweighted proportionate affine projection algorithm (RPAPA) with memory and row action projection (MRAP) is proposed in this paper. The reweighted PAPA is derived from a family of sparseness measures, which demonstrate performance similar to mu-law and the l 0 norm PAPA but with lower computational complexity. The sparseness of the channel is taken into account to improve the performance for dispersive system identification. Meanwhile, the memory of the filter’s coefficients is combined with row action projections (RAP) to significantly reduce computational complexity. Simulation results demonstrate that the proposed RPAPA MRAP algorithm outperforms both the affine projection algorithm (APA) and PAPA, and has performance similar to l 0 PAPA and mu-law PAPA, in terms of convergence speed and tracking ability. Meanwhile, the proposed RPAPA MRAP has much lower computational complexity than PAPA, mu-law PAPA, and l 0 PAPA, etc., which makes it very appealing for real-time implementation.
Highlights
Adaptive filtering has been studied for decades and has found wide areas of application
In this paper, considering the computational complexity, we propose using the following reweighted proportionate APA (PAPA): F hl hl
The traditional PAPA requires M × L multiplications to calculate P(n), and in order to further reduce the computational complexity, we propose to apply the memory of the proportionate coefficients [17] into SC-reweighted proportionate affine projection algorithm (RPAPA)
Summary
Adaptive filtering has been studied for decades and has found wide areas of application. 3.3 The proposed SC-RPAPA with MRAP the main computational complexity of the family of PAPA algorithm is the matrix inversion in (5). The traditional PAPA requires M × L multiplications to calculate P(n), and in order to further reduce the computational complexity, we propose to apply the memory of the proportionate coefficients [17] into SC-RPAPA. What’s more important is that, both the PAPA and the MPAPA algorithms require a M × M direct matrix inversion, which is especially expensive for high projection orders. The combination of the memory and the iterative RAP structure, avoids the M × M direct matrix inversion, and reduces the computational complexity required for the calculation of both GX and XT GX. In order to demonstrate the tracking ability, an echo path change was incurred through switching the impulse
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