Estimation of Time-Varying Channels – A Block Approach

Leus, Geert; Tang, Zijian; Banelli, Paolo

doi:10.1016/b978-0-12-374483-8.00004-2

Cited by 12 publications

(13 citation statements)

References 64 publications

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“…As shown in eqn. (5), the received signals in a TDMR system are typically modeled by convolving the data with a 2-D spatial impulse response [21]. The TDMR ISI system is a 2-D extension of a banded linear system studied in the previous sections.…”

Section: B 2-d Near-banded Systemmentioning

confidence: 99%

CNN-Based Signal Detection for Banded Linear Systems

Fan

Yuan

Zhang

2019

IEEE Trans. Wireless Commun.

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Banded linear systems arise in many communication scenarios, e.g., those involving inter-carrier interference and inter-symbol interference. Motivated by recent advances in deep learning, we propose to design a high-accuracy low-complexity signal detector for banded linear systems based on convolutional neural networks (CNNs). We develop a novel CNN-based detector by utilizing the banded structure of the channel matrix. Specifically, the proposed CNN-based detector consists of three modules: the input preprocessing module, the CNN module, and the output postprocessing module. With such an architecture, the proposed CNN-based detector is adaptive to different system sizes, and can overcome the curse of dimensionality, which is a ubiquitous challenge in deep learning. Through extensive numerical experiments, we demonstrate that the proposed CNN-based detector outperforms conventional deep neural networks and existing model-based detectors in both accuracy and computational time. Moreover, we show that CNN is flexible for systems with large sizes or wide bands. We also show that the proposed CNN-based detector can be easily extended to near-banded systems such as doubly selective orthogonal frequency division multiplexing (OFDM) systems and 2-D magnetic recording (TDMR) systems, in which the channel matrices do not have a strictly banded structure.The work in this paper will be partially presented in IEEE Globecom 2018 [1].

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Section: B 2-d Near-banded Systemmentioning

confidence: 99%

CNN-Based Signal Detection for Banded Linear Systems

Fan

Yuan

Zhang

2019

IEEE Trans. Wireless Commun.

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“…Then, the L o × 1 index vector of observed samples correspond to the

sans-serifg

th pilot cluster of the u th UE that is used for channel estimation is represented as

{bold-italicp}_{o}^{false(u, sans-serifg false)} = {([], p_{sans-serifg}^{false(u false)} - Q + 1 + b, …, p_{sans-serifg}^{false(u false)} - 1, p_{sans-serifg}^{false(u false)}, p_{sans-serifg}^{false(u false)} + 1, …, p_{sans-serifg}^{false(u false)} + Q - 1 - b)}^{T}, p_{sans-serifg}^{false(u false)} \in 𝒫_{active}^{false(u false)},

where L o = 2 Q − 2 b − 1 and

b ⩽ Q - 1

is an integer that controls the number of observed samples in

{bold-italicp}_{o}^{false(u, sans-serifg false)}

. In other words, it indicates the amount of ICI from data subcarriers on the adjacent pilot subcarriers that is taken into account in channel estimation …”

Section: Beyond‐sparsity–based Channel Estimationmentioning

confidence: 99%

Beyond‐sparsity–based doubly selective channel estimation for high‐mobility multiuser MIMO‐OFDM systems

Karani

Beheshti

Fazel

2019

Trans Emerging Tel Tech

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Wireless communications in high-mobility situations face with doubly selective (DS) fading. In addition, many practical wireless channels exhibit a sparse multipath structure. This paper investigates the DS multiuser multiple-input-multiple-output (MIMO) channel estimation via compressed sensing. The DS channel is characterized by a general sparse basis expansion model (BEM). This model reduces the channel estimation complexity as the channel estimation problem is reduced to estimating the BEM coefficients instead of numerous channel parameters. In addition, a pilot pattern is proposed for multiuser MIMO orthogonal frequency division multiplexing systems, which includes guard and silent pilots to reduce intercarrier interference and interuser interference, respectively. Then, two beyond-sparsity features (ie, block and joint sparsity) of the BEM coefficients are exploited to propose a beyond-sparsity-based channel estimation method. Moreover, a pilot design algorithm is proposed to optimize the pilot positions and enhance the estimator performance. Simulation results demonstrate that the proposed method significantly improves the estimation accuracy over the existing approaches, with a much smaller pilot overhead.Trans Emerging Tel Tech. 2019;30:e3636.wileyonlinelibrary.com/journal/ett

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“…Extended version of a manuscript submitted to the IEEE Transactions on Signal Processing, August 27, 2018. common approach is to embed pilot symbols into the transmit signal and to perform least-squares or minimum mean-square error channel estimation [10]. More advanced pilot-based channel estimation methods include [11][12][13][14][15][16][17][18]. In particular, compressive channel estimation [17,18] uses CS techniques [2][3][4] to exploit an inherent sparsity of the channel that is related to the fact that doubly selective channels tend to be dominated by a relatively small number of clusters of significant propagation paths [19].…”

Section: Introductionmentioning

confidence: 99%

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

Eiwen

Taubock

Hlawatsch

et al. 2010

2010 IEEE 11th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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We consider channel estimation within pulseshaping multicarrier multiple-input multiple-output (MIMO) systems transmitting over doubly selective MIMO channels. This setup includes MIMO orthogonal frequency-division multiplexing (MIMO-OFDM) systems as a special case. We show that the component channels tend to exhibit an approximate joint group sparsity structure in the delay-Doppler domain. We then develop a compressive channel estimator that exploits this structure for improved performance. The proposed channel estimator uses the methodology of multichannel group sparse compressed sensing, which combines the methodologies of group sparse compressed sensing and multichannel compressed sensing. We derive an upper bound on the channel estimation error and analyze the estimator's computational complexity. The performance of the estimator is further improved by introducing a basis expansion yielding enhanced joint group sparsity, along with a basis optimization algorithm that is able to utilize prior statistical information if available. Simulations using a geometry-based channel simulator demonstrate the performance gains due to leveraging the joint group sparsity and optimizing the basis.Index Terms-Channel estimation, doubly selective channel, group sparse compressed sensing, MIMO-OFDM, multicarrier modulation, multichannel compressed sensing, multiple-input multiple-output (MIMO) communications, orthogonal frequencydivision multiplexing (OFDM), sparse reconstruction.

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Estimation of Time-Varying Channels – A Block Approach

Cited by 12 publications

References 64 publications

CNN-Based Signal Detection for Banded Linear Systems

CNN-Based Signal Detection for Banded Linear Systems

Beyond‐sparsity–based doubly selective channel estimation for high‐mobility multiuser MIMO‐OFDM systems

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

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