TY - GEN
T1 - A Fast Iterative Bayesian Inference Algorithm for Sparse Channel Estimation
AU - Pedersen, Niels Lovmand
AU - Manchón, Carles Navarro
AU - Fleury, Bernard Henri
PY - 2013
Y1 - 2013
N2 - In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited by modeling the prior distribution of multipath components' gains with a hierarchical representation of the Bessel K probability density function; a highly efficient, fast iterative Bayesian inference method is then applied to the proposed model. The resulting estimator outperforms other state-of-the-art Bayesian and non-Bayesian estimators, either by yielding lower mean squared estimation error or by attaining the same accuracy with improved convergence rate, as shown in our numerical evaluation.
AB - In this paper, we present a Bayesian channel estimation algorithm for multicarrier receivers based on pilot symbol observations. The inherent sparse nature of wireless multipath channels is exploited by modeling the prior distribution of multipath components' gains with a hierarchical representation of the Bessel K probability density function; a highly efficient, fast iterative Bayesian inference method is then applied to the proposed model. The resulting estimator outperforms other state-of-the-art Bayesian and non-Bayesian estimators, either by yielding lower mean squared estimation error or by attaining the same accuracy with improved convergence rate, as shown in our numerical evaluation.
U2 - 10.1109/ICC.2013.6655294
DO - 10.1109/ICC.2013.6655294
M3 - Article in proceeding
T3 - I E E E International Conference on Communications
SP - 4591
EP - 4596
BT - Communications (ICC), 2013 IEEE International Conference on
T2 - I E E E International Conference on Communications
Y2 - 9 June 2013 through 13 June 2013
ER -