Adaptive algorithms for sparse system identification

Kalouptsidis, N.; Mileounis, Gerasimos; Babadi, Behtash; Tarokh, Vahid

doi:10.1016/j.sigpro.2011.02.013

Cited by 90 publications

(56 citation statements)

References 15 publications

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“…To better exert the p i to the channel coefficients, the channel coefficients are classified according to their magnitudes. From the measurement and the previous investigations of the sparse channels [2,6,7,[10][11][12][13][14][15][16][21][22][23][24]26,27], we found that few channel coefficients are active non-zero ones, while most of the channel coefficients are inactive zero or near-zero ones. Thus, we propose a threshold to categorize the channel coefficients into two groups.…”

Section: The Proposed Group-constrained Sparse MCC Algorithmsmentioning

confidence: 77%

Group-Constrained Maximum Correntropy Criterion Algorithms for Estimating Sparse Mix-Noised Channels

Wang

Albu

et al. 2017

Entropy

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Abstract:A group-constrained maximum correntropy criterion (GC-MCC) algorithm is proposed on the basis of the compressive sensing (CS) concept and zero attracting (ZA) techniques and its estimating behavior is verified over sparse multi-path channels. The proposed algorithm is implemented by exerting different norm penalties on the two grouped channel coefficients to improve the channel estimation performance in a mixed noise environment. As a result, a zero attraction term is obtained from the expected l 0 and l 1 penalty techniques. Furthermore, a reweighting factor is adopted and incorporated into the zero-attraction term of the GC-MCC algorithm which is denoted as the reweighted GC-MCC (RGC-MMC) algorithm to enhance the estimation performance. Both the GC-MCC and RGC-MCC algorithms are developed to exploit well the inherent sparseness properties of the sparse multi-path channels due to the expected zero-attraction terms in their iterations. The channel estimation behaviors are discussed and analyzed over sparse channels in mixed Gaussian noise environments. The computer simulation results show that the estimated steady-state error is smaller and the convergence is faster than those of the previously reported MCC and sparse MCC algorithms.

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Section: The Proposed Group-constrained Sparse MCC Algorithmsmentioning

confidence: 77%

Group-Constrained Maximum Correntropy Criterion Algorithms for Estimating Sparse Mix-Noised Channels

Wang

Albu

et al. 2017

Entropy

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“…Such an approach is known as LASSO [27]; in the view of the principle of parsimony [36], such sparse model representations must be preferred. These techniques have been used successfully in SISO Volterra basis selection and polynomial models [28,37,38]. The large number of coefficients of the predistorter or equalizer limits its applicability to only low nonlinear orders, short memory depth, and a few carriers.…”

Section: Complexity Reductionmentioning

confidence: 99%

Low complexity predistortion and equalization in nonlinear multicarrier satellite communications

Zenteno

Piazza

Shankar

et al. 2015

EURASIP J. Adv. Signal Process.

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Aiming to reduce the power/mass requirements in satellite transponders and to reduce mission costs, joint amplification of multiple carriers using a single high-power amplifier (HPA) is being considered. In this scenario, a careful investigation of the resulting power efficiency is essential as amplification is nonlinear, and multicarrier signals exhibit enlarged peak-to-average power ratio. Thus, operating the amplifier close to saturation vastly increases signal distortion resulting in a severe degradation of performance, especially for higher order modulations. This paper proposes a reduced-complexity digital predistortion (DPD) scheme at the transmitter and a corresponding equalizer (EQ) at the receiver to mitigate these nonlinear effects. Scenarios include both the forward as well as the return links. In particular, the paper exploits the MIMO Volterra representation and builds on a basis pursuit approach using a LASSO (least absolute shrinkage and selection operator) algorithm to achieve an efficient basis representation, avoiding large computational complexity, to describe the selection of predistorter/equalizer model. The work further compares and contrasts the two mitigation techniques taking various system aspects into consideration. The gains in performance and amplification efficiency demonstrated by the use of DPD/ EQ motivate their inclusion in next-generation satellite systems.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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 .…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Yang

et al. 2017

Entropy

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Abstract:A soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC) algorithm is proposed for sparse system identification. The proposed SPF-NMCC algorithm is derived on the basis of the normalized adaptive filter theory, the maximum correntropy criterion (MCC) algorithm and zero-attracting techniques. A soft parameter function is incorporated into the cost function of the traditional normalized MCC (NMCC) algorithm to exploit the sparsity properties of the sparse signals. The proposed SPF-NMCC algorithm is mathematically derived in detail. As a result, the proposed SPF-NMCC algorithm can provide an efficient zero attractor term to effectively attract the zero taps and near-zero coefficients to zero, and, hence, it can speed up the convergence. Furthermore, the estimation behaviors are obtained by estimating a sparse system and a sparse acoustic echo channel. Computer simulation results indicate that the proposed SPF-NMCC algorithm can achieve a better performance in comparison with the MCC, NMCC, LMS (least mean square) algorithms and their zero attraction forms in terms of both convergence speed and steady-state performance.

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Adaptive algorithms for sparse system identification

Cited by 90 publications

References 15 publications

Group-Constrained Maximum Correntropy Criterion Algorithms for Estimating Sparse Mix-Noised Channels

Group-Constrained Maximum Correntropy Criterion Algorithms for Estimating Sparse Mix-Noised Channels

Low complexity predistortion and equalization in nonlinear multicarrier satellite communications

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

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