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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaolin Hou Shubo Li Changchuan Yin Guangxin Yue |
| Copyright Year | 2005 |
| Description | Author affiliation: Sch. of Telecommun. Eng., Beijing Univ. of Posts & Telecommun., China (Xiaolin Hou; Shubo Li; Changchuan Yin; Guangxin Yue) |
| Abstract | In orthogonal frequency division multiplexing (OFDM) system, time- and frequency-domain two-dimensional minimum mean square error (2D-MMSE) channel estimation is optimum. However, accurate channel statistics, which are often time-varying and unavailable in practice, are required to realize it. In this paper, we propose two-dimensional recursive least square (2D-RLS) adaptive channel estimation for OFDM system. 2D-RLS adaptive channel estimation does not require accurate channel statistics, and at the same time can make full use of time-and frequency-domain correlations of the frequency response of time-varying wireless channels. With properly chosen parameters, 2D-RLS adaptive channel estimation can converge into the steady state in only several OFDM symbols' time. Computer simulations demonstrate that 2D-RLS adaptive channel estimation is very effective and suitable for a broad range of channel conditions. Furthermore, with proper training sequences design on transmitter antennas, 2D-RLS adaptive channel estimation can be applied to multiple inputs multiple outputs (MIMO) OFDM system. |
| Starting Page | 232 |
| Ending Page | 236 |
| File Size | 266597 |
| Page Count | 5 |
| File Format | |
| ISBN | 078039335X |
| DOI | 10.1109/WCNM.2005.1544024 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-09-26 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Adaptive systems Time varying systems OFDM Channel estimation Mean square error methods MIMO Frequency response Steady-state Least squares approximation Statistics |
| Content Type | Text |
| Resource Type | Article |
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