IEICE TRANSACTIONS on Communications
Evolutional Algorithm Based Learning of Time Varying Multipath Fading Channels for Software Defined Radio
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.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E89-B No.12 pp.3269-3273
- Publication Date
- 2006/12/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1093/ietcom/e89-b.12.3269
- Type of Manuscript
- Special Section LETTER (Special Section on Software Defined Radio Technology and Its Applications)
- Category
Authors
Keyword
Latest Issue
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Gagik MKRTCHYAN, Katsuhiro NAITO, Kazuo MORI, Hideo KOBAYASHI, "Evolutional Algorithm Based Learning of Time Varying Multipath Fading Channels for Software Defined Radio" in IEICE TRANSACTIONS on Communications,
vol. E89-B, no. 12, pp. 3269-3273, December 2006, doi: 10.1093/ietcom/e89-b.12.3269.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e89-b.12.3269/_p
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@ARTICLE{e89-b_12_3269,
author={Gagik MKRTCHYAN, Katsuhiro NAITO, Kazuo MORI, Hideo KOBAYASHI, },
journal={IEICE TRANSACTIONS on Communications},
title={Evolutional Algorithm Based Learning of Time Varying Multipath Fading Channels for Software Defined Radio},
year={2006},
volume={E89-B},
number={12},
pages={3269-3273},
abstract={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.},
keywords={},
doi={10.1093/ietcom/e89-b.12.3269},
ISSN={1745-1345},
month={December},}
Copy
TY - JOUR
TI - Evolutional Algorithm Based Learning of Time Varying Multipath Fading Channels for Software Defined Radio
T2 - IEICE TRANSACTIONS on Communications
SP - 3269
EP - 3273
AU - Gagik MKRTCHYAN
AU - Katsuhiro NAITO
AU - Kazuo MORI
AU - Hideo KOBAYASHI
PY - 2006
DO - 10.1093/ietcom/e89-b.12.3269
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E89-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2006
AB - 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.
ER -
English
