Performance Enhancement of OMP Algorithm for Compressed Sensing Based Sparse Channel Estimation in OFDM Systems

Vahidi, Vahid; Saberinia, E.

doi:10.1007/978-3-319-54978-1_1

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…The knowledge about the non‐zero channel tap positions of the sparse downlink channel model can increase the accuracy of the DSF estimation as it is discussed in the next two steps. We proposed a CS‐based CE method in [29] which is called orthogonal matching pursuit‐compressive sampling matching pursuit (OMP‐CoSaMP). Although the OMP‐CoSaMP method is proposed for zero DSF conditions, it offers a very accurate information of non‐zero channel tap positions even in high DSF and low SNR conditions.…”

Section: Methodsmentioning

confidence: 99%

“…Assuming

P N = (][, ρ_{1}, ρ_{2}, \dots, ρ_{G})

, where each

ρ_{i}

can be randomly −1 or +1, and by the assumption that the DSFs are zero, the last G ‐ L received signals at the receiver can be expressed as

(][, \begin{matrix} 1em4pt \\ y_{t} ()(, L + 1) \\ y_{t} ()(, 2) \\ \begin{matrix} 1em4pt \\ ⋮ \\ y_{t} ()(, G) \end{matrix} \end{matrix}) = (][, \begin{matrix} 1em4pt \\ ρ_{L + 1} & ρ_{L} & \dots & ρ_{2} \\ ρ_{L + 2} & ⋱ & ρ_{3} \\ ⋮ & ⋮ \\ ρ_{G} & \dots & ρ_{G - L + 1} \end{matrix}) (][, \begin{matrix} 1em4pt \\ a_{1} \\ a_{2} \\ \begin{matrix} 1em4pt \\ ⋮ \\ a_{L} \end{matrix} \end{matrix}) + bold-italicZ

where

bold-italicZ

is the noise vector. Considering (20), the columns of the coefficient matrix are orthogonal to each other and can be used as the measurement matrix for the CS based method of [29]. Afterwards, steps 2 and 3 of the DSF estimation method are done using the TS 1 block similar to what was discussed for the TS‐DSFE method.…”

Section: Methodsmentioning

confidence: 99%

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