Channel Estimation for OFDM Systems over Doubly Selective Channels: A Distributed Compressive Sensing Based Approach

Cheng, Peng; Chen, Zhuo; Rui, Yun; Guo, Yi; Gui, Lin; Tao, Meixia; Zhang, Q. T.

doi:10.1109/tcomm.2013.072813.120758

Cited by 108 publications

(106 citation statements)

References 34 publications

Supporting

Mentioning

105

Contrasting

Order By: Relevance

“…The MP algorithm sequentially selects the dominant BEM coefficient index that maximizes the projection of the residual vector onto the corresponding column vector of the so-called dictionary matrix. While applying the MP algorithm, in order to obtain the dictionary matrix A (P) using (15), (16), and (17), we use the pilot symbols in their respective positions and set the unknown data symbols to zero [10]. As a first step in the MP algorithm, the column in the matrix A (P) = [a ρDL cp ] which is best aligned with the residue vector, R 0 = y, is found and denoted as a (P)…”

Section: Initialization Of the Algorithmmentioning

confidence: 99%

“…More recently, compressive sensing (CS) techniques have been applied to sparse channel estimation [10][11][12][13][14][15][16][17]. The CS-based channel estimators in [11][12][13][14][15][16] assume that the channel is sparse in the equivalent discrete-time baseband representation.…”

mentioning

confidence: 99%

“…The CS-based channel estimators in [11][12][13][14][15][16] assume that the channel is sparse in the equivalent discrete-time baseband representation. However, in practice, the sparsity assumption does not always hold due to the non-integer normalized path delays in the equivalent discrete-time baseband representation of the channel.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Joint Channel Estimation and Symbol Detection for OFDM Systems in Rapidly Time-Varying Sparse Multipath Channels

Şenol

2015

Wireless Pers Commun

View full text Add to dashboard Cite

In this paper, we propose a space-alternating generalized expectation maximization (SAGE) based joint channel estimation and data detection algorithm in compressive sensing (CS) framework for orthogonal frequency-division multiplexing (OFDM) systems in rapidly time-varying sparse multipath channels. Using dynamic parametric channel model, the sparse multipath channel is parameterized by a small number of distinct paths, each represented by the path delays and path gains. In our model, we assume that the path gains rapidly vary within the OFDM symbol duration while the number of paths and path delays vary symbol by symbol. Since the convergency of the SAGE algorithm needs statistically independent parameter set of interest to be estimated, we specifically choose the discrete orthonormal Karhunen-Loeve basis expansion model (DKL-BEM) to provide statistically independent BEM coefficients within one OFDM symbol duration and use just a few significant BEM coefficients to represent the rapidly time-varying path gains. The resulting SAGE algorithm that also incorporates inter-channel interference cancellation updates the data sequences and the channel parameters serially. The computer simulations show that our proposed algorithm has better channel estimation and symbol error rate performance than that of the orthogonal matching pursuit algorithm that is commonly proposed in the CS literature.

show abstract

Section: Initialization Of the Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Joint Channel Estimation and Symbol Detection for OFDM Systems in Rapidly Time-Varying Sparse Multipath Channels

Şenol

2015

Wireless Pers Commun

View full text Add to dashboard Cite

show abstract

“…In other words, the CS technology can reduce channel estimation error at the same number of pilots. To further improve channel estimation accuracy, distributed compressed sensing based algorithm is proposed in [14]. However, the above channel estimation methods is still difficult to meet the demand for Quality of Service under highspeed mobile environments.…”

Section: Introductionmentioning

confidence: 99%

Structured Compressed Sensing for Channel Estimation under High-Speed Mobile Environment

Yang¹,

Zhou²

2017

dtcse

View full text Add to dashboard Cite

Abstract. In the fourth generation wireless communication systems, channel estimation technology is very important for orthogonal frequency division multiplexing (OFDM). However, the existing schemes hardly meet the demand for Quality of Service in high-speed mobile environment. To handle this problem, in this paper, the channel sparsity characteristic and temporal correlations are exploited and displayed by the block-sparse system model. Then, the block-based orthogonal matching pursuit (OMP) algorithm is used to obtain the channel state information. Simulation results illustrate the performance of our proposed algorithm superiority.

show abstract

“…The increase of the utilized bandwidth in practical systems and the introduction of compressed sensing (CS) brought a significant interest for pilot aided sparse channel estimation in OFDM and, thus, many new estimation algorithms with suitable pilot allocation schemes were proposed [3][4][5][6][7][8][9][10][11][12]. Those works confirm the gain from using CS but they don't consider the capacity increase or the optimal parameter choice in the system.…”

Section: Introductionmentioning

confidence: 99%

Improved Asymptotic Capacity Lower Bound for OFDM System with Compressed Sensing Channel Estimation for Bernoulli Gaussian Channel

Pejoski

Kafedziski

2016

RADIOENGINEERING

View full text Add to dashboard Cite

Channel Estimation for OFDM Systems over Doubly Selective Channels: A Distributed Compressive Sensing Based Approach

Cited by 108 publications

References 34 publications

Joint Channel Estimation and Symbol Detection for OFDM Systems in Rapidly Time-Varying Sparse Multipath Channels

Joint Channel Estimation and Symbol Detection for OFDM Systems in Rapidly Time-Varying Sparse Multipath Channels

Structured Compressed Sensing for Channel Estimation under High-Speed Mobile Environment

Improved Asymptotic Capacity Lower Bound for OFDM System with Compressed Sensing Channel Estimation for Bernoulli Gaussian Channel

Contact Info

Product

Resources

About