Channel Condition Aware Detection in Statistical Signal Transmission

Abstract

Heterogeneous network (HetNet) plays an important role in the upcoming 5G communications. A HetNet in futuristic communications is envisaged to provide full access data services, such that users will always have the best service with their available radio resources. To realize a full access HetNet, statistical signal transmission (SST) can be an important enabling technique. SST exploits statistical features of ordinary signals as a data transmission scheme to carry information data. Due to its inherent unique properties, SST signal is more robust than ordinary signals in conventional quasi-static channels. However, detection of SST signals in fast time-varying channels will face challenges. Motivated by this fact, this paper aims to analyze the impact of fast time-varying channels to SST, and reveals negative effects of burst of deep fading channel-state (BDFC) and feature dissipation with out-of-phase (FDOP). Accordingly, we design an automatic re-transmission scheme and a fragmental feature capturing method to mitigate the aforementioned issues, respectively. Based on these studies, we propose a channel condition aware detection scheme for SST, aiming to enhance SST detection performance in fast time-varying channels. Numerical results show that with the proposed scheme, a maximum gain of more than 10 dB can be obtained under either BDFC or FDOP condition. Furthermore, it is also demonstrated that the proposed scheme is robust with imperfect channel coherence time information, which is attractive for practical applications.