IEICE Trans - Evolutional Algorithm Based Learning of Time Varying Multipath Fading Channels for Software Defined Radio

Evolutional Algorithm Based Learning of Time Varying Multipath Fading Channels for Software Defined Radio

Gagik MKRTCHYAN
Katsuhiro NAITO
Kazuo MORI
Hideo KOBAYASHI

Publication
IEICE TRANSACTIONS on Communications Vol.E89-B No.12 pp.3269-3273
Publication Date: 2006/12/01
Online ISSN: 1745-1345
DOI: 10.1093/ietcom/e89-b.12.3269
Print ISSN: 0916-8516
Type of Manuscript: Special Section LETTER (Special Section on Software Defined Radio Technology and Its Applications)
Category:
Keyword:
evolutional algorithms, software-defined radio, time-varying multipath fading channel, dynamic learning systems,

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Summary:
Software defined radio, which uses reconfigurable signal processing devices, requires the determination of multiple unknown parameters to realize the potential capabilities of adaptive communication. Evolutional algorithms are optimal multi dimensional search techniques, and are well known to be effective for parameter determination. This letter proposes an evolutional algorithm for learning the mobile time-varying channel parameters without any specific assumption of scattering distribution. The proposed method is very simple to realize, but can provide precise channel estimation results. Simulations of an OFDM system show that for an example of OFDM communication under the time-varying fading channel, the proposed learning method can achieve the better BER performance.