Research Article
Conditional probability density function based signal detection for OFDM-based transform domain communication systems
@INPROCEEDINGS{10.4108/crnet.2010.3, author={Biao Huang and Jun Wang and Wanbin Tang and Shaoqian Li}, title={Conditional probability density function based signal detection for OFDM-based transform domain communication systems}, proceedings={The 2nd International ICST Workshop on Cognitive Radio Network}, publisher={IEEE}, proceedings_a={CRNET}, year={2011}, month={1}, keywords={Bandwidth High definition video}, doi={10.4108/crnet.2010.3} }- Biao Huang
Jun Wang
Wanbin Tang
Shaoqian Li
Year: 2011
Conditional probability density function based signal detection for OFDM-based transform domain communication systems
CRNET
IEEE
DOI: 10.4108/crnet.2010.3
Abstract
The orthogonal frequency division multiplexing-based transform domain communication system (OFDM-TDCS) is a promising candidate for signaling transmission in Cognitive Radio (CR) networks. An important issue of OFDM-TDCS system is the effective signal detection scheme design when channel coding is applied. In this paper, a class of new hard-demodulation (HD) and soft-demodulation (SD) algorithms are proposed in a unified signal detection framework. Although HD is based on the maximum likelihood (ML) estimation while SD is to calculate the log-likelihood ratio (LLR) of each coded bit, both of them are derived from an identical conditional probability density function. To further improve the implementation efficiency of SD detector, a code table is established to facilitate the needed searching operation. Finally, simulations under IEEE 802.22 Profile C channel validate the proposed signal detection detectors in terms of bit error rate (BER).
