Adaptive regularization for BEM channel estimation in multicarrier systems

Zakharov, Yuriy; Zheng, Daixu

doi:10.1109/icassp.2016.7472363

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…In this paper, we address the CE challenges of the SC-FDMA with application to the LTE-V system. For the pilot symbols, each ray (i.e., path) is characterized by the basis expansion model (BEM) [10]. In the BEM, the band-limited Slepian sequences [11], are employed to express the timevarying fading channel in a low-dimensional space.…”

Section: Introductionmentioning

confidence: 99%

Time-Domain Channel Estimation for the LTE-V System Over High-Speed Mobile Channels

Qu¹,

Liu²,

Wang³

et al. 2018

EasyChair Preprints

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Developed from the LTE uplink, the LTE-V2X standard, widely used as LTE-V, increases the overhead of pilot symbols in order to acquire channel information robustly. In this paper, we propose to estimate time-varying pieces of channel rays for pilot symbols based on the basis expansion model (BEM), and subsequently to reconstruct time-domain channel response for data symbols by utilizing Slepian sequences based piece-wise interpolation (SS-PWI). To demonstrate the precision of channel estimation (CE), the Cramer Rao lower bound (CRLB) is derived. The simulations are implemented on the LTE-V platform, on which we consider the extended vehicular A model (EVA) channel with 2800 Hz maximum Doppler shift. The proposed time-domain CE schemes are well capable of tracking the channel state information (CSI), and the mean squared error (MSE) of CE nearly reaches the theoretically derived CRLB.

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Section: Introductionmentioning

confidence: 99%

Time-Domain Channel Estimation for the LTE-V System Over High-Speed Mobile Channels

Qu¹,

Liu²,

Wang³

et al. 2018

EasyChair Preprints

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“…In this work, we develop a basis expansion model (BEM) approach for estimation of the channel parameters [6], [5], [7] in OFDM and GFDM systems. We first consider classical least-squares (LS) and linear minimum mean-square error (LMMSE), and then examine BEM-type channel estimation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Study of BEM-Type Channel Estimation Techniques for 5G Multicarrier Systems

Dias,

de Lamare,

Zakharov

2018

Preprint

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In this paper, we investigate channel estimation techniques for 5G multicarrier systems. Due to the characteristics of the 5G application scenarios, channel estimation techniques have been tested in Orthogonal Frequency Division Multiplexing (OFDM) and Generalized Frequency Division Multiplexing (GFDM) systems. The orthogonality between subcarriers in OFDM systems permits inserting and extracting pilots without interference. However, due to pulse shaping, subcarriers in GFDM are no longer orthogonal and interfere with each other. Due to such interference, the channel estimation for GFDM is not trivial. A robust and low-complexity channel estimator can be obtained by combining a minimum mean-square error (MMSE) regularization and the basis expansion model (BEM) approach. In this work, we develop a BEM-type channel estimator along with a strategy to obtain the covariance matrix of the BEM coefficients. Simulations show that the BEM-type channel estimation shows performance close to that of the linear MMSE (LMMSE), even though there is no need to know the channel power delay profile, and its complexity is low.

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Time-Domain Channel Estimation for the LTE-V System Over High-Speed Mobile Channels

Guang-hui

Wang

et al. 2018

2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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Adaptive regularization for BEM channel estimation in multicarrier systems

Cited by 5 publications

References 22 publications

Time-Domain Channel Estimation for the LTE-V System Over High-Speed Mobile Channels

Time-Domain Channel Estimation for the LTE-V System Over High-Speed Mobile Channels

Study of BEM-Type Channel Estimation Techniques for 5G Multicarrier Systems

Time-Domain Channel Estimation for the LTE-V System Over High-Speed Mobile Channels

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