Adaptive Signal Detection for Statistical Signal Transmission in Fast Time-Varying Channels

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 a mobility adaptive detection method 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.