TY - JOUR
T1 - Adaptive signal detection for statistical signal transmission in fast time-varying channels
AU - Xu, Tianheng
AU - Zhang, Mengying
AU - Yao, Sha
AU - Hu, Honglin
AU - Chen, Hsiao Hwa
N1 - Funding Information:
The authors would like to acknowledge the valuable support from the Chinese Academy of Sciences. The authors would also like to thank Prof. Z. Ding, the University of California at Davis, USA, for his valuable suggestions.
Funding Information:
Manuscript received January 29, 2017; revised January 3, 2017 and May 21, 2017; accepted July 13, 2017. Date of publication July 18, 2017; date of current version December 14, 2017. This work was supported in parts by the Special Foundation of Zhangjiang Hi-Tech Park under Grant 2016-14, by the Shanghai Young Talent Sailing Program under Grant 17YF1428500 and 17YF1418700, by the NSFC Projects under Grant 61671437 and 61461136001, and by the Taiwan Ministry of Science and Technology (104-2221-E-006-081-MY2). This paper was presented in part at the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, April 2015 [1]. The review of this paper was coordinated by Dr. S. K. Jayaweera. (Corresponding author: Hsiao-Hwa Chen.) T. Xu and H. Hu are with the Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China (e-mail: xuth@sari. ac.cn; [email protected]).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/12
Y1 - 2017/12
N2 - Statistical signal transmission (SST) is an emerging orthogonal frequency-division multiplexing (OFDM) technology that can carry additional information over conventional OFDM signal streams without extra bandwidth cost. This additional information can be exploited to carry control information, location/speed data, or cooperative schedule signals, etc., to improve the performance of wireless communications. However, existing detection methods used in this technique work-based mainly on time-invariant channels (i.e., channel state is quasi-static within a detection period), and cannot work well in diverse channel variations caused by different user velocities. In this paper, we propose amobility adaptive detectionmethod for SST. First, we analyze the influence of nonstatic channels on SST detection and identify a negative factor, called cyclic autocorrelation function whitening effect. Correspondingly, we probe the feasibility of antiwhitening concept and carry out relevant experiments for verification purpose. Based on this, a complete version of mobility adaptive detection method is proposed. Numerical results manifest that the proposed method outperforms the existing ones in high velocity scenarios. In addition, the properties of the proposed method are investigated, based on which we show a specific application for SST to reveal the potential for SST to be used in 5G wireless communications.
AB - Statistical signal transmission (SST) is an emerging orthogonal frequency-division multiplexing (OFDM) technology that can carry additional information over conventional OFDM signal streams without extra bandwidth cost. This additional information can be exploited to carry control information, location/speed data, or cooperative schedule signals, etc., to improve the performance of wireless communications. However, existing detection methods used in this technique work-based mainly on time-invariant channels (i.e., channel state is quasi-static within a detection period), and cannot work well in diverse channel variations caused by different user velocities. In this paper, we propose amobility adaptive detectionmethod for SST. First, we analyze the influence of nonstatic channels on SST detection and identify a negative factor, called cyclic autocorrelation function whitening effect. Correspondingly, we probe the feasibility of antiwhitening concept and carry out relevant experiments for verification purpose. Based on this, a complete version of mobility adaptive detection method is proposed. Numerical results manifest that the proposed method outperforms the existing ones in high velocity scenarios. In addition, the properties of the proposed method are investigated, based on which we show a specific application for SST to reveal the potential for SST to be used in 5G wireless communications.
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U2 - 10.1109/TVT.2017.2728721
DO - 10.1109/TVT.2017.2728721
M3 - Article
AN - SCOPUS:85100752987
SN - 0018-9545
VL - 66
SP - 11070
EP - 11085
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 12
M1 - 2728721
ER -