| Project/Area Number |
18560381
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Communication/Network engineering
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
YAMASHITA Katsumi Osaka Prefecture University, Graduate School of Engineering, Professor (60158152)
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| Co-Investigator(Kenkyū-buntansha) |
OHTA Masaya Osaka Prefecture University, Graduate School of Engineering, Lecturer (70288786)
LIN Hai Osaka Prefecture University, Graduate School of Engineering, Assistant Professor (40336805)
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| Project Period (FY) |
2006 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥3,950,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2007: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2006: ¥2,000,000 (Direct Cost: ¥2,000,000)
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| Keywords | OFDM / UWB / MB-OFDM / PAPR reduction / ICI reduction / CFO estimation |
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| Research Abstract |
Since each subcarrier is modulated independently, an MB-OFDM signal has a large instantaneous peak power, compared with single-carrier signal. The peak power is usually measured in terms of peak-to-average power ratio (PAPR), and an MB-OFDM signal with large PAPR will be distorted by the nonlinear amplifier. Meanwhile, due, to the overlapped spectrum, MB-OFDM is very vulnerable to carrier frequency offset (CFO), which mainly arises from the frequency mismatch between the transmitter and receiver local oscillators. The CFO causes inter-carrier interference (ICI), which severely degrades the performance of MB-OFDM systems.
We have proposed the PAPR reduction method, in which the PAPR reducing problem is formulated as a combinatorial optimization problem and Hopfield neural nertwork (HNN) is applied to solving the optimization. Also, by extending the MPP-based CFO estimation, we have proposed an efficient compensation scheme for the IEEE 802.11a WLAN. In this scheme, the CFO estimation is enhanced by a CFO sign detection and an autocorrelation-based estimator to avoid the ill-condition of cost function.
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