Two Are Better Than One: Adaptive Sparse System Identification Using Affine Combination of Two Sparse Adaptive Filters

Abstract

Abstract-Sparse system identification problems often exist in many applications, such as echo interference cancellation, sparse channel estimation, and adaptive beamforming. One of popular adaptive sparse system identification (ASSI) methods is adopting only one sparse least mean square (LMS) filter. However, the adoption of only one sparse LMS filter cannot simultaneously achieve fast convergence speed and small steady-state mean state deviation (MSD). Unlike the conventional method, we propose an improved ASSI method using affine combination of two sparse LMS filters to simultaneously achieving fast convergence and low steady-state MSD. First, problem formulation and standard affine combination of LMS filters are introduced. Then an approximate optimum affine combiner is adopted for the proposed filter according to stochastic gradient search method. Later, to verify the proposed filter for ASSI, computer simulations are provided to confirm effectiveness of the proposed filter which can achieve better estimation performance than the conventional one and standard affine combination of LMS filters.

An affine combination of adaptive filters for channels with different sparsity levels

“…A number of authors have opted for the convex combination as in [8], [9], some suggest using an affine combination as in [7], [10].…”

“…Bershad et al [7] describe an optimal affine combiner but are focusing their work on a traditional approach for improving both convergence rate and steadystate error. Gui et al [10] are also using an affine combination but only for sparse channel identification and are not considering the case with the dispersive impulse response. In this paper we are proposing an affine combination algorithm for channels with different sparsity levels.…”

Affine combination of two adaptive sparse filters for estimating large scale MIMO channels

“…However, L0LMS filter adopts only one step-size which cannot tradeoff estimation performance and convergence speed. Based on this background, affine combination of two L0LMS filters has been proposed [14] to estimation single-input single-output (SISO) sparse channels. To the best of our knowledge, no paper has been reported the combined structure of two sparse LMS filters for estimating large scale MIMO channels.…”

A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

“…In nature, the impulse response of most unknown systems can be regarded as sparse, which consists of only a few dominant coefficients [1][2][3][4]. The prior known sparse information can be used for improving the estimation performance in signal processing.…”

“…The prior known sparse information can be used for improving the estimation performance in signal processing. Thus, sparse signal processing has been garnering significant attention in recent decades [1][2][3][4][5]. In particular, the developed sparse signal processing techniques have been used in wireless communications, speech signal processing and imaging processing, which include compressed sensing (CS) [6][7][8] and sparse adaptive filtering .…”