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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Anding Wang Yuyang Qiu Lili Lin Shiju Li |
| Copyright Year | 2008 |
| Description | Author affiliation: Coll. of Stat. & Math., Zhejiang Gongshang Univ., Hangzhou (Yuyang Qiu) || Dept. of Inf. & Electron., Zhejiang Univ., Hangzhou (Anding Wang; Shiju Li) || Coll. of Inf. & Electron., Zhejiang Gongshang Univ., Hangzhou (Lili Lin) |
| Abstract | This paper proposes a blind carrier frequency offset (CFO) estimation algorithm based on improved MUSIC algorithm for the interleaved orthogonal frequency division multiple access (OFDMA) uplinks. The main advantages of this method are that it obtains the CFOs of all users simultaneously within only one OFDMA block and has high accuracy due to the application of data reconstruction and the special periodic characteristic of the received signal. Unlike other existing CFO algorithms, this algorithm doesnpsilat need any precondition knowledge of the channel, the training sequence or even pilot signal, therefore it saves frequency band effectively and degrades the computational complexity greatly. Because of estimation quickly it is suitable for the rapid change channel especially. Simulation results show that this algorithm has good performance even under low SNR condition and improves the variance performance a lot. |
| Starting Page | 145 |
| Ending Page | 149 |
| File Size | 441473 |
| Page Count | 5 |
| File Format | |
| ISBN | 9780769533049 |
| DOI | 10.1109/ICNC.2008.272 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-10-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | OFDM Computational modeling Educational institutions Frequency conversion Frequency estimation Mathematics Multiple signal classification MUSIC algorithm Statistics Computational complexity Degradation OFDMA CFO estimation |
| Content Type | Text |
| Resource Type | Article |
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