A MAP-Based Order Estimation Procedure for Sparse Channel Estimation

  title={A MAP-Based Order Estimation Procedure for Sparse Channel Estimation},
  author={Sajad Daei and Massoud Babaie-Zadeh and Christian Jutten},
Recently, there has been a growing interest in estimation of sparse channels as they are observed in underwater acoustic and ultrawideband channels. In this paper we present a new Bayesian sparse channel estimation SCE algorithm that, unlike traditional SCE methods, exploits noise statistical information to improve the estimates. The proposed method uses approximate maximum a posteriori probability MAP to detect the non-zero channel tap locations while least square estimation is used to… Expand
Sparse Channel Estimation for OFDM Systems Based on Sparse Reconstruction by Separable Approximation
  • Xiaolin Shi, Yixin Yang
  • Computer Science
  • 2016 International Conference on Information System and Artificial Intelligence (ISAI)
  • 2016
A novel scheme based on the typical least square and sparse reconstruction by separable approximation for the sparse channel estimation is presented to improve the poor performance of the LS and ℓ2-norm channel estimations and can reach the global optimal solution. Expand


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A modified iterative detector/estimator (IDE) algorithm is proposed by replacing the parallel thresholding approach in a previously developed IDE algorithm by an approach wherein a single threshold is varied for each iteration of the algorithm. Expand
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The performance of decision feedback equalizers based on the channel estimates obtained by using the MP and OMP algorithms are compared, verifying that the OMP outperforms the MP, with a comparable computational complexity. Expand
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In many communication systems the channel impulse response can be characterized with a parametric form, though the channel estimation is often performed using an equivalent discrete-time linearExpand
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It is shown how an estimate of the channel may be obtained using a matching pursuit (MP) algorithm and this estimate is compared to thresholded variants of the least squares channel estimate. Expand
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A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. Expand
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A structured thresholding algorithm for sparse underwater channel estimation using compressed sensing shows some improvements over standard algorithms for sparse channel estimation such as matching pursuit, iterative detection and least squares. Expand
Ultrawideband propagation channels-theory, measurement, and modeling
  • A. Molisch
  • Engineering, Computer Science
  • IEEE Transactions on Vehicular Technology
  • 2005
It is demonstrated how the frequency selectivity of propagation processes causes fundamental differences between UWB channels and "conventional" (narrowband) channels. Expand
The state of the art in underwater acoustic telemetry
Progress in underwater acoustic telemetry since 1982 is reviewed within a framework of six current research areas: (1) underwater channel physics, channel simulations, and measurements; (2) receiverExpand
Robust Modeling With Erratic Data
An attractive alternative to least‐squares data modeling techniques is the use of absolute value error criteria. Unlike the least‐squares techniques the inclusion of some infinite blunders along withExpand