Application of compressive sensing to sparse channel estimation

Berger, Christian; Wang, Zhaohui; Huang, Jianzhong; Zhou, Shengli

doi:10.1109/mcom.2010.5621984

Cited by 528 publications

(318 citation statements)

References 16 publications

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“…In recent years, the field has seen a flurry of research involving the use of sparse solution techniques in fields as diverse as underwater communication and networks [18], [116], [153], room acoustics [187], sparse inverse covariance matrix estimation [27], [66], [114], and source localization [105].…”

Section: Subset Selection and Lasso Regressionmentioning

confidence: 99%

“…A sparse-constrained LS algorithm. While it has been proposed that the underwater communication channel has sparse properties (e.g., [9], [18], [30], [161]) the precise sparsity of the channel under consideration is not known a-priori. Thus, rather than the LASSO problem posed in (4.38), the sparse constrained solution is posed using the Basis-Pursuit De-Noise problem aŝ 40) where the constraint is chosen as the mean value of R x (N )h 0 −r xy (N ) , with a factor of 1.05 to add a little buffer space.…”

Section: Algorithms Consideredmentioning

confidence: 99%

“…Thus, the RAGS-RLS solution over each clique can be written aŝ 18) which is the same as (5.15), except over a single clique. In this case, make the directaveraging assumption over each clique and separator, so thatĴ xc (n)…”

Section: Performance Analysismentioning

confidence: 99%

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Graphical model driven methods in adaptive system identification

Yellepeddi¹

2016

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Identifying and tracking an unknown linear system from observations of its inputs and outputs is a problem at the heart of many different applications. Due to the complexity and rapid variability of modern systems, there is extensive interest in solving the problem with as little data and computation as possible.This thesis introduces the novel approach of reducing problem dimension by exploiting statistical structure on the input. By modeling the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler.Algorithms that can leverage this property in order to either improve the performance or reduce the computational complexity of the estimation problem are developed. The first of these, termed the graphical expectation-maximization least squares (GEM-LS) algorithm, can utilize the reduced dimensional problems induced by the structure to improve the accuracy of the system identification problem in the low sample regime over conventional methods for linear learning with limited data, including regularized least squares methods.Next, a relaxation of the GEM-LS algorithm termed the relaxed approximate graph structured least squares (RAGS-LS) algorithm is obtained that exploits structure to perform highly efficient estimation. The RAGS-LS algorithm is then recast into a recursive framework termed the relaxed approximate graph structured recursive least squares (RAGS-RLS) algorithm, which can be used to track time-varying linear systems with low complexity while achieving tracking performance comparable to much more computationally intensive methods.The performance of the algorithms developed in the thesis in applications such as channel identification, echo cancellation and adaptive equalization demonstrate that the gains admitted by the graph framework are realizable in practice. The methods have wide applicability, and in particular show promise as the estimation and adaptation algorithms for a new breed of fast, accurate underwater acoustic modems.The contributions of the thesis illustrate the power of graphical model structure in simplifying difficult learning problems, even when the target system is not directly structured.Thesis Supervisor: James C. Preisig Title: Scientist Emeritus, Woods Hole Oceanographic Institution AcknowledgmentsPiglet noticed that even though he had a Very Small Heart, it could hold a rather large amount of Gratitude. Winnie the PoohA.A.MilneMy years at MIT have been about so much more than a degree. In the past six years you have introduced to more worlds than I ever knew existed. As this long and wonderful journey ends, I find that my heart can, too, hold so much gratitude, for everything that you have given.Of course, I haven't said who "you" is, so here goes.It is impossible to write any list of acknowledgements without having at the very top my thesis advisor Dr. James Preisig, who has truly been a wide-angle lens f...

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Section: Subset Selection and Lasso Regressionmentioning

confidence: 99%

Section: Algorithms Consideredmentioning

confidence: 99%

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Graphical model driven methods in adaptive system identification

Yellepeddi¹

2016

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“…To solve this problem, some recent works have exploited the sparse nature of the multipath channel and used compressive sensing (CS) based estimators to reconstruct the channel perfectly with relatively less pilots [8] [9]. Some CS algorithms, e.g., orthogonal matching pursuit (OMP) and basis pursuit (BP), have been already 45 used in channel estimation for MIMO systems [9,10,11,12]. However, neither of these algorithms can attain accurate estimation performance and low complexity at the same time.…”

Section: Introductionmentioning

confidence: 99%

Joint channel estimation algorithm via weighted Homotopy for massive MIMO OFDM system

Nan

Sun

Zhang

2016

Digital Signal Processing

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Massive (or large-scale) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is widely acknowledged as a key technology for future communication. One main challenge to implement this system in practice is the high dimensional channel estimation, where the large number of channel matrix entries requires prohibitively high computational complexity. To solve this problem efficiently, a channel estimation approach using few number of pilots is necessary. In this paper, we propose a weighted Homotopy based channel estimation approach which utilizes the sparse nature in MIMO channels to achieve a decent channel estimation performance with much less pilot overhead. Moreover, inspired by the fact that MIMO channels are observed to have approximately common support in a neighborhood, an information exchange strategy based on the proposed approach is developed to further improve the estimation accuracy and reduce the required number of pilots through joint channel estimation. Compared with the traditional sparse channel estimation methods, the proposed approach can achieve more than 2dB gain in terms of mean square error (MSE) with the same number of pilots, or * Corresponding author

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“…CS theory shows that reliable signal reconstruction far below the Nyquist sampling rate is possible provided that the signal is sparse. In communications, CS theory has been applied for instance to system parameter estimation [3] and channel estimation [4].…”

Section: Introductionmentioning

confidence: 99%

Sparse Multi-User Detection for CDMA transmission using greedy algorithms

Schepker

Dekorsy

2011

2011 8th International Symposium on Wireless Communication Systems

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Abstract-A possible future application in communications is the wireless uplink transmission in sensor networks. This application is mainly characterized by sporadic transmission over a random access channel. Since each sensor has a low activity probability, the signal for Multi-User Detection (MUD) is sparse. Compressive Sensing (CS) theory introduces detectors that are able to recover sparse signals reliably. When applied to MUD, CS detectors perform a joint detection of both data and user activity. This allows for less control signaling, since information about the activity state of each user no longer need to be signaled. Additionally, CS detectors are able to reliably detect sparse signals even in under-determined systems. In the context of transmission with Code Division Multiple Access (CDMA) this property can be exploited by reducing the length of spreading sequences, which increases the symbol-rate for a given bandwidth, and making the CDMA system overloaded. In this paper we introduce the application of greedy CS detection algorithms to detect CDMA spread multi-user data.

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Application of compressive sensing to sparse channel estimation

Cited by 528 publications

References 16 publications

Graphical model driven methods in adaptive system identification

Graphical model driven methods in adaptive system identification

Joint channel estimation algorithm via weighted Homotopy for massive MIMO OFDM system

Sparse Multi-User Detection for CDMA transmission using greedy algorithms

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