Research Article
Superimposed Training for PTS-PAPR Reduction in OFDM: A Side Information Free Data Recovery Scheme
@INPROCEEDINGS{10.1109/ChinaCom.2013.6694682, author={Tan Tian and Li Yi and Zhang Han and Su Jinli}, title={Superimposed Training for PTS-PAPR Reduction in OFDM: A Side Information Free Data Recovery Scheme}, proceedings={8th International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2013}, month={11}, keywords={ofdm peak-to-average power ratio superimposed training}, doi={10.1109/ChinaCom.2013.6694682} }- Tan Tian
Li Yi
Zhang Han
Su Jinli
Year: 2013
Superimposed Training for PTS-PAPR Reduction in OFDM: A Side Information Free Data Recovery Scheme
CHINACOM
IEEE
DOI: 10.1109/ChinaCom.2013.6694682
Abstract
In this paper, a side information free partial trans-mit sequence (PTS) technique is proposed to reduce the peak-to-average power ratio (PAPR) for orthogonal frequency division multiplexing (OFDM) signals. Based on superimposed training (ST), the combined channel response with the phase factors of PTS can be estimated within each interleaved PTS sub-block, and thus enables data recovery without the side information of PTS. To enhance the performance of bit error rate (BER), phase equalization is provided across the interleaved PTS sublocks at receiver to enable an improvement on the combined estimation over the whole sub-blocks, and the optimal ratio of the ST power to the total transmission power is theoretically analyzed by maxi-mizing the signal-to-interference plus noise ratio. Both theoretical analysis and simulation results demonstrate the advantages of the proposed method over the existing schemes