Abstract
In this paper, we propose an adaptive channel estimation methodology for Space-Time Block-Coded (STBC) OFDM systems, aided by nature-inspired evolutionary optimization strategies, namely: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The use of GA and PSO allows at increasing the convergence of adaptive channel estimation to the optimal MMSE solution with respect to state-of-the-art optimization methodologies based on the concept of deterministic gradient. As a result, system performances are greatly improved, with a clear advantage taken by PSO, both in terms of channel estimation accuracy, implementation ease, and reduced computational effort.
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D’Orazio, L., Sacchi, C., Donelli, M. (2010). Adaptive Channel Estimation for STBC-OFDM Systems Based on Nature-Inspired Optimization Strategies. In: Vinel, A., Bellalta, B., Sacchi, C., Lyakhov, A., Telek, M., Oliver, M. (eds) Multiple Access Communications. MACOM 2010. Lecture Notes in Computer Science, vol 6235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15428-7_19
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DOI: https://doi.org/10.1007/978-3-642-15428-7_19
Publisher Name: Springer, Berlin, Heidelberg
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