Pilot-Based LMMSE Channel Estimation for OFDM Systems With Power–Delay Profile Approximation

Hung, Kun-Chien; Lin, Dekang

doi:10.1109/tvt.2009.2029862

Cited by 60 publications

(8 citation statements)

References 12 publications

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“…Finally, various interpolation algorithms are employed to estimate the CSI at non-pilot positions (data positions) [22][23][24][25]. Among them, the LS algorithm [26,27], the minimum mean square error (MMSE) algorithm [28,29], and the linear MMSE (LMMSE) algorithm [30,31] are the most classical pilot-based channel estimation methods. While we will introduce these pilot-based estimation methods using the OFDM system as an example, it is essential to note that these methods are not limited to OFDM systems.…”

Section: Pilot-based Channel Estimation Methodsmentioning

confidence: 99%

Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges

Lv,

Luo

2023

Electronics

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With the rapid development of wireless communication technology, intelligent communication has become one of the mainstream research directions after the fifth generation (5G). In particular, deep learning has emerged as a significant artificial intelligence technology widely applied in the physical layer of wireless communication for achieving intelligent receiving processing. Channel estimation, a crucial component of physical layer communication, is essential for further information recovery. As a motivation, this paper aims to review the relevant research on applying deep learning methods in channel estimation. Firstly, this paper briefly introduces the conventional channel estimation methods and then analyzes their respective merits and drawbacks. Subsequently, this paper introduces several common types of neural networks and describes the application of deep learning in channel estimation according to data-driven and model-driven approaches, respectively. Then, this paper extends to emerging communication scenarios and discusses the existing research on channel estimation based on deep learning for reconfigurable intelligent surface (RIS)-aided communication systems. Finally, to meet the demands of next-generation wireless communication, challenges and future research trends in deep-learning-based channel estimation are discussed.

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Section: Pilot-based Channel Estimation Methodsmentioning

confidence: 99%

Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges

Lv,

Luo

2023

Electronics

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“…Note that IEEE 802.11p is a modification of the frequency bandwidth of the IEEE 802.11a standard from 20 MHz to 10 MHz [1]. This means that channel estimation (CE) in IEEE 802.11p V2X communication systems can be a difficult task, usually due to the high mobility and insufficient number of pilots in orthogonal frequency division multiplexing (OFDM) systems [1][2][3][4][5][6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Since the minimum mean square error (MMSE)-CE scheme for OFDM systems shows excellent channel estimation performance, extensive research related to this has been conducted [2][3][4][5][6][7][8]. There are two kinds of MMSE scheme: one is the pilot symbol assisted scheme requiring pilot or training symbols [2][3][4][5][6] and the other is the decision-directed scheme utilizing the constructed data pilots [7,8]. In [2], the maximum likelihood (ML) and the MMSE schemes were compared as the channel estimators based on pilot-aided OFDM systems.…”

Section: Introductionmentioning

confidence: 99%

Power-Delay-Profile-Based MMSE Channel Estimations for OFDM Systems

Lim

Wang

2023

Electronics

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This paper deals with the problem of the channel estimation of orthogonal frequency division multiplexing (OFDM) signals transmitted through a time-varying fading channel. We show that an accurate power delay profile (PDP) estimator can be obtained by utilizing the cyclic redundancy induced by a cyclic prefix (CP), which can be applicable to OFDM systems with insufficient pilot or training symbols (IEEE 802.11p/WAVE system), and then a least mean square error (MMSE) channel estimation scheme can be obtained in a manner based on the estimated PDP. The simulation results highlight the benefit of the proposed methods compared with the state-of-the-art standard and three achievable performance bounds.

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“…We note that the use of PDP information by a receiver to enhance data and channel recovery, particularly as it applies to OFDM, has been previously studied; e.g., [17]- [19]. However, the proposed approach in this work differs from these existing methods in three ways: first, the existing PDP estimators rely on exploiting the particular structure of multicarrier waveforms and do not generalize to single-carrier waveforms.…”

Section: Introductionmentioning

confidence: 99%

A Method of Estimating Sparse and Doubly-Dispersive Channels

Hunt,

Haab,

Moradi

et al. 2023

MILCOM 2023 - 2023 IEEE Military Communications Conference (MILCOM)

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Using pilot-symbol assisted modulation, we develop a method to acquire channel information from signals received in low-SNR environments. First, we estimate the power-delay profile (PDP) of the channel. Then, we apply information from that estimate to obtain the channel coefficients only at points where the PDP is nonzero. Because we estimate a reduced number of channel coefficients, the complexity and estimation error are simultaneously reduced. We present a case study of the proposed estimation method when applied to a wideband skywave high frequency (HF) channel. Through this study, we find that the proposed approach has significantly enhanced performance when compared to an existing method that does incorporate sparsity information into its estimate. The improved low-SNR performance of the proposed estimator makes it particularly well suited for spread-spectrum and underlay waveforms.

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Pilot-Based LMMSE Channel Estimation for OFDM Systems With Power–Delay Profile Approximation

Cited by 60 publications

References 12 publications

Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges

Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges

Power-Delay-Profile-Based MMSE Channel Estimations for OFDM Systems

A Method of Estimating Sparse and Doubly-Dispersive Channels

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