Deblocking Joint Photographic Experts Group Compressed Images via Self-learning Sparse Representation

Amiri, S. Asadi; Hassanpour, Hamid

doi:10.5829/idosi.ije.2016.29.12c.07

Cited by 1 publication

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nowadays sprase representation is widely used on those problems that there is an underdetermined relation between observed paramters and the desired answer has a sparse features [11,12]. In OFDM channel estimation problem, as wireless channels are considered as a sparse signal because their taps are related to scattering objects, and these objects are sparsely located [13], and there is an underdetermined system of linear equations between the CIR of the channel and the observation vector, sparse representation is applicable to estimate the OFDM channel.…”

Section: Pp Hmentioning

confidence: 99%

A General Framework for Estimating Channel of Orthogonal Frequency Division Multiplexing Systems by Utilizing Sparse Representation

Hashemi-Rafsanjani

Ghazi‐Maghrebi

2021

IJE

View full text Add to dashboard Cite

Channel estimation is a crucial task for orthogonal frequency division multiplexing (OFDM) modulation-based systems since this estimation is used for compensating impacts of a wireless channel. Recently, sparse representation (SR) is proposed for this task as wireless channels are considered as a sparse signal. However, SR considers sparse as the main feature and omit other features of the channel while estimating the channel. In this paper, we propose a general framework for utilizing other features of the channel in sparse channel estimation for OFDM systems, while these features are omitted in conventional sparse methods. In this regard, by utilizing maximum a posterior (MAP) estimation and defining new parameters, these features are conveyed into sparse channel estimation process to improve channel estimation. The simulation results indicate that our proposed framework not only improves the estimated parameter, but also reduces the number of resources such as the number of estimation pilots or transmitted power.

show abstract

Section: Pp Hmentioning

confidence: 99%