IEICE TRANSACTIONS on Communications
Adaptive PSP-MLSE Using State-Space Based RLS for Multi-Path Fading Channels
Summary :
An adaptive per-survivor processing maximum likelihood sequence estimation (PSP-MLSE) using state-space based recursive least-squares (RLS) is proposed for rapidly time varying multi-path fading channels. Unlike PSP-MLSE using Kalman filtering, it does not require the knowledge of model statistics, and with an aid of state-space modeling, it has a robust performance to the fade rate, compared to PSP-MLSE using conventional RLS.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E91-B No.12 pp.4024-4026
- Publication Date
- 2008/12/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1093/ietcom/e91-b.12.4024
- Type of Manuscript
- LETTER
- Category
- Wireless Communication Technologies
Authors
Keyword
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Jung Suk JOO, "Adaptive PSP-MLSE Using State-Space Based RLS for Multi-Path Fading Channels" in IEICE TRANSACTIONS on Communications,
vol. E91-B, no. 12, pp. 4024-4026, December 2008, doi: 10.1093/ietcom/e91-b.12.4024.
Abstract: An adaptive per-survivor processing maximum likelihood sequence estimation (PSP-MLSE) using state-space based recursive least-squares (RLS) is proposed for rapidly time varying multi-path fading channels. Unlike PSP-MLSE using Kalman filtering, it does not require the knowledge of model statistics, and with an aid of state-space modeling, it has a robust performance to the fade rate, compared to PSP-MLSE using conventional RLS.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.12.4024/_p
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@ARTICLE{e91-b_12_4024,
author={Jung Suk JOO, },
journal={IEICE TRANSACTIONS on Communications},
title={Adaptive PSP-MLSE Using State-Space Based RLS for Multi-Path Fading Channels},
year={2008},
volume={E91-B},
number={12},
pages={4024-4026},
abstract={An adaptive per-survivor processing maximum likelihood sequence estimation (PSP-MLSE) using state-space based recursive least-squares (RLS) is proposed for rapidly time varying multi-path fading channels. Unlike PSP-MLSE using Kalman filtering, it does not require the knowledge of model statistics, and with an aid of state-space modeling, it has a robust performance to the fade rate, compared to PSP-MLSE using conventional RLS.},
keywords={},
doi={10.1093/ietcom/e91-b.12.4024},
ISSN={1745-1345},
month={December},}
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TY - JOUR
TI - Adaptive PSP-MLSE Using State-Space Based RLS for Multi-Path Fading Channels
T2 - IEICE TRANSACTIONS on Communications
SP - 4024
EP - 4026
AU - Jung Suk JOO
PY - 2008
DO - 10.1093/ietcom/e91-b.12.4024
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2008
AB - An adaptive per-survivor processing maximum likelihood sequence estimation (PSP-MLSE) using state-space based recursive least-squares (RLS) is proposed for rapidly time varying multi-path fading channels. Unlike PSP-MLSE using Kalman filtering, it does not require the knowledge of model statistics, and with an aid of state-space modeling, it has a robust performance to the fade rate, compared to PSP-MLSE using conventional RLS.
ER -
English
