In this thesis, we discuss two computationally efficient iterative methods from literature. We consider the problem of joint channel estimation and data detection under quasi-static channels now. Here, we consider the problems that channels and noise variance are known or unknown and known or unknown. When channels and noise variance are unknown, we use the method based on space-alternating generalized expectation-maximization (SAGE) algorithms. When channels are unknown and noise variances are known, we use the other method based on expectation-maximization (EM) algorithms. Bit error rate (BER) performance of the Maximum Likelihood (ML) method with perfect channel state information (CSI) is simulated and compared with BER performance of the two iterative methods. The results show that the two methods exhibit near ML performance with a few iterations and we want to know which is better.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0913107-045724 |
| Date | 13 September 2007 |
| Creators | Jiang, Cyue-Jhe |
| Contributors | Yang-fang Chen, Tsang-Yi Wang, Shiann-shiun Jeng, Chih-peng Li |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0913107-045724 |
| Rights | not_available, Copyright information available at source archive |
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