Efficient compressed sensing based non-sample spaced sparse channel estimation in OFDM system

We present a new class of convex optimization programs, or decoders, coined Basis Pursuit DeQuantizer of moment p (BPDQ p ), that model the quantization distortion more faithfully

This section, thus, presents three extensions of the first-order statistics based MIMO channel estimation model in (35) for the case of sparse multipath channels by incorporating

The main objective of this thesis is to investigate the performance of channel estimation in UWB systems based on the new Compressed Sensing theory [6]-[7] using a frequency

The first approach, consists of estimating the channel over all of the three subbands, using the model (3), similar to [5] while the second scheme is a wavelet domain EM

Since bigger g denotes higher orthogonality in structure for a matrix with a given degree d, LDPC matrices construction algorithms are also available to construct measurement

Looking at the subtracted volume of Fig.3c, we notice that even if our approach yields sparser structures than standard reconstruction, the whole volume is not very sparse, which

We propose exploiting the sparse nature of the channel through the use of a new greedy algorithm named extended OMP (eOMP) in order to reduce the false path detection

Abstract—A superimposed (arithmetically added) Pilot (SiP) sequence based channel estimation method for beamforming assisted multi-antenna High Altitude Platform (HAP) land mobile