Optimization of Deterministic Pilot Pattern Placement Based on Quantum Genetic Algorithm for Sparse Channel Estimation in OFDM Systems
Summary :
This paper proposes a deterministic pilot pattern placement optimization scheme based on the quantum genetic algorithm (QGA) which aims to improve the performance of sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By minimizing the mutual incoherence property (MIP) of the sensing matrix, the pilot pattern placement optimization is modeled as the solution of a combinatorial optimization problem. QGA is used to solve the optimization problem and generate optimized pilot pattern that can effectively avoid local optima traps. The simulation results demonstrate that the proposed method can generate a sensing matrix with a smaller MIP than a random search or the genetic algorithm (GA), and the optimized pilot pattern performs well for sparse channel estimation in OFDM systems.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E103-B No.10 pp.1164-1171
- Publication Date
- 2020/10/01
- Publicized
- 2020/04/21
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2019EBP3200
- Type of Manuscript
- PAPER
- Category
- Wireless Communication Technologies
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Keyword
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Yang NIE, Xinle YU, "Optimization of Deterministic Pilot Pattern Placement Based on Quantum Genetic Algorithm for Sparse Channel Estimation in OFDM Systems" in IEICE TRANSACTIONS on Communications,
vol. E103-B, no. 10, pp. 1164-1171, October 2020, doi: 10.1587/transcom.2019EBP3200.
Abstract: This paper proposes a deterministic pilot pattern placement optimization scheme based on the quantum genetic algorithm (QGA) which aims to improve the performance of sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By minimizing the mutual incoherence property (MIP) of the sensing matrix, the pilot pattern placement optimization is modeled as the solution of a combinatorial optimization problem. QGA is used to solve the optimization problem and generate optimized pilot pattern that can effectively avoid local optima traps. The simulation results demonstrate that the proposed method can generate a sensing matrix with a smaller MIP than a random search or the genetic algorithm (GA), and the optimized pilot pattern performs well for sparse channel estimation in OFDM systems.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2019EBP3200/_p
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@ARTICLE{e103-b_10_1164,
author={Yang NIE, Xinle YU, },
journal={IEICE TRANSACTIONS on Communications},
title={Optimization of Deterministic Pilot Pattern Placement Based on Quantum Genetic Algorithm for Sparse Channel Estimation in OFDM Systems},
year={2020},
volume={E103-B},
number={10},
pages={1164-1171},
abstract={This paper proposes a deterministic pilot pattern placement optimization scheme based on the quantum genetic algorithm (QGA) which aims to improve the performance of sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By minimizing the mutual incoherence property (MIP) of the sensing matrix, the pilot pattern placement optimization is modeled as the solution of a combinatorial optimization problem. QGA is used to solve the optimization problem and generate optimized pilot pattern that can effectively avoid local optima traps. The simulation results demonstrate that the proposed method can generate a sensing matrix with a smaller MIP than a random search or the genetic algorithm (GA), and the optimized pilot pattern performs well for sparse channel estimation in OFDM systems.},
keywords={},
doi={10.1587/transcom.2019EBP3200},
ISSN={1745-1345},
month={October},}
Copy
TY - JOUR
TI - Optimization of Deterministic Pilot Pattern Placement Based on Quantum Genetic Algorithm for Sparse Channel Estimation in OFDM Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 1164
EP - 1171
AU - Yang NIE
AU - Xinle YU
PY - 2020
DO - 10.1587/transcom.2019EBP3200
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E103-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2020
AB - This paper proposes a deterministic pilot pattern placement optimization scheme based on the quantum genetic algorithm (QGA) which aims to improve the performance of sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By minimizing the mutual incoherence property (MIP) of the sensing matrix, the pilot pattern placement optimization is modeled as the solution of a combinatorial optimization problem. QGA is used to solve the optimization problem and generate optimized pilot pattern that can effectively avoid local optima traps. The simulation results demonstrate that the proposed method can generate a sensing matrix with a smaller MIP than a random search or the genetic algorithm (GA), and the optimized pilot pattern performs well for sparse channel estimation in OFDM systems.
ER -
English
