Semi-blind Sparse Channel Estimation and Data Detection by Successive Convex Approximation
Ouahbi Rekik, Karim Abed-Meraim, Marius Pesavento, Anissa Mokraoui
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28 May 2020
The aim of this paper is to propose a semi-blind
solution, for joint sparse channel estimation and data detection,
based on the successive convex approximation approach. The
optimization is performed on an approximate convex problem,
rather than the original nonconvex one. By exploiting available
data and system structure, an iterative procedure is proposed
where the channel coefficients and data symbols are updated
simultaneously at each iteration. Also an optimized step size,
introduced according to line search procedure, is used for convergence
improvement with guaranteed convergence to a stationary
point. Simulation results show that the proposed solution exhibits
fast convergence with very attractive channel and data estimation
performance.
solution, for joint sparse channel estimation and data detection,
based on the successive convex approximation approach. The
optimization is performed on an approximate convex problem,
rather than the original nonconvex one. By exploiting available
data and system structure, an iterative procedure is proposed
where the channel coefficients and data symbols are updated
simultaneously at each iteration. Also an optimized step size,
introduced according to line search procedure, is used for convergence
improvement with guaranteed convergence to a stationary
point. Simulation results show that the proposed solution exhibits
fast convergence with very attractive channel and data estimation
performance.
