'Semi-blind Sparse Channel Estimation and Data Detection by Successive Convex Approximation
| Autor: | Marius Pesavento, Ouahbi Rekik, Karim Abed-Meraim, Anissa Mokraoui |
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| Přispěvatelé: | Abed-Meraim, Karim, Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (PRISME), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Signal processing
Line search Channel (digital image) business.industry Computer science [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing Approximation algorithm 020206 networking & telecommunications 02 engineering and technology Stationary point [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing Convex optimization Convergence (routing) 0202 electrical engineering electronic engineering information engineering Wireless business Algorithm ComputingMilieux_MISCELLANEOUS |
| Zdroj: | IEEE SPAWC IEEE SPAWC, 2020, Virtual, France SPAWC |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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