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| Doubly Selective Channel Estimation and Symbol Detection with Data-dependent Superimposed Training for OFDM Systems |
| Qinjuan Zhang, Muqing Wu, Qilin Guo, Tingting Zhang, Rui Zhang |
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Abstract A channel estimation using Data-dependent Superimposed Training
(DDST) over doubly selective channel is proposed for OFDM systems.
Instead of using traditional pilot-assisted scheme, this scheme uses
training superimposed to data sequence in time domain to assist the
estimation, which consumes no extra bandwidth. The superimposed
training is designed data-dependent to eliminate the interference of
unknown data on estimation thoroughly. Meanwhile, an iterative
symbol detection method is presented for the DDST based time varying
channel. Simulations show that the proposed estimator can approach
the real channel more accurately and achieve better Bit Error Ratio
(BER) performance.
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