Group sparsity methods for compressive channel estimation in doubly dispersive multicarrier systems

Eiwen, Daniel; Taubock, Georg; Hlawatsch, Franz; Feichtinger, Hans G.

doi:10.1109/spawc.2010.5670986

Cited by 16 publications

(11 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It leads to various applications such as multiple kernel learning [2], microarray data analysis [3], channel estimation in doubly dispersive multicarrier systems [4], etc. The group sparse reconstruction problem has been well studied recently.…”

mentioning

confidence: 99%

Group sparse optimization by alternating direction method

2013

View full text Add to dashboard Cite

Abstract. This paper proposes efficient algorithms for group sparse optimization with mixed 2,1 -regularization, which arises from the reconstruction of group sparse signals in compressive sensing, and the group Lasso problem in statistics and machine learning. It is known that encoding the group information in addition to sparsity will lead to better signal recovery/feature selection. The 2,1 -regularization promotes group sparsity, but the resulting problem, due to the mixed-norm structure and possible grouping irregularity, is considered more difficult to solve than the conventional 1 -regularized problem. Our approach is based on a variable splitting strategy and the classic alternating direction method (ADM). Two algorithms are presented, one derived from the primal and the other from the dual of the 2,1 -regularized problem. The convergence of the proposed algorithms is guaranteed by the existing ADM theory. General group configurations such as overlapping groups and incomplete covers can be easily handled by our approach. Computational results show that on random problems the proposed ADM algorithms exhibit good efficiency, and strong stability and robustness.

show abstract

mentioning

confidence: 99%

Group sparse optimization by alternating direction method

2013

View full text Add to dashboard Cite

show abstract

“…Specifically, CS based simultaneous Orthogonal Matching Pursuit (OMP) [14], Modified OMP [15], Simultaneous Basis Pursuit Denoising and Simultaneous OMP [16] have been proposed for pilot-assisted ga-sparse channel estimation in MIMO-OFDM systems. Further, CS based Block OMP (BOMP) has been proposed for pilot-assisted gac-sparse MIMO-OFDM channel estimation [17].…”

Section: Conventional Pilot-based Interpolation Techniques Using On Fmentioning

confidence: 99%

Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

Prasad

Murthy

Rao

2015

IEEE Trans. Signal Process.

117

View full text Add to dashboard Cite

The impulse response of wireless channels between the N t transmit and N r receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., the N t N r channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this work, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the Sparse Bayesian Learning (SBL) framework, and propose a bouquet of novel algorithms for pilotbased channel estimation and joint channel estimation and data detection in MIMO-OFDM systems. The proposed joint channel estimation and data detection schemes are capable of recovering ga-sparse and gac-sparse channels even when the measurement matrix is only partially known. Further, we employ a first order autoregressive modeling of the temporal variation of the wireless ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking and data detection. The KFS framework exploits the correlation structure in the time-varying , IEEE Transactions on Signal Processing 2 channel. We also propose novel, parallel-implementation based, low complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the efficacy of proposed techniques in terms of mean square error (MSE) and coded bit error rate (BER) performance. In particular, we demonstrate the performance benefits offered by algorithms that exploit the gac-sparse structure in the wireless channel.

show abstract

“…It is becoming a hot research topic to find sparse solutions of underdetermined linear systems in the last few years in various fields, for example, compressive sensing (CS), signal processing, statistics, and machine learning [1], for example, multiple kernel learning [2], microarray data analysis [3], and channel estimations in doubly dispersive multicarrier systems [4]. For example, in machine learning, the high dimensionality poses significant challenges for us to build interpretable models with high prediction accuracy, and many sparsity related regularization techniques have been commonly utilized to obtain more stable and interpretable models.…”

Section: Group Sparse Reconstruction and Related Workmentioning

confidence: 99%

A Greedy Multistage Convex Relaxation Algorithm Applied to Structured Group Sparse Reconstruction Problems Based on Iterative Support Detection

Wang

2014

Mathematical Problems in Engineering

View full text Add to dashboard Cite

We propose a new effective algorithm for recovering a group sparse signal from very limited observations or measured data. As we know that a better reconstruction quality can be achieved when encoding more structural information besides sparsity, the commonly employedl2,1-regularization incorporating the prior grouping information has a better performance than the plainl1-regularized models as expected. In this paper we make a further use of the prior grouping information as well as possibly other prior information by considering a weightedl2,1model. Specifically, we propose a multistage convex relaxation procedure to alternatively estimate weights and solve the resulted weighted problem. The procedure of estimating weights makes better use of the prior grouping information and is implemented based on the iterative support detection (Wang and Yin, 2010). Comprehensive numerical experiments show that our approach brings significant recovery enhancements compared with the plainl2,1model, solved via the alternating direction method (ADM) (Deng et al., 2013), either in noiseless or in noisy environments.

show abstract

Group sparsity methods for compressive channel estimation in doubly dispersive multicarrier systems

Cited by 16 publications

References 59 publications

Group sparse optimization by alternating direction method

Group sparse optimization by alternating direction method

Joint Channel Estimation and Data Detection in MIMO-OFDM Systems: A Sparse Bayesian Learning Approach

A Greedy Multistage Convex Relaxation Algorithm Applied to Structured Group Sparse Reconstruction Problems Based on Iterative Support Detection

Contact Info

Product

Resources

About