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Statistical signal transmission (SST) is a developing technology, which can carry additional data over conventional signal streams without extra bandwidth sacrifice. A latest research provided an automatic repeat transmission technique for SST, which can largely strengthen SST's detection performance by identifying and handling deep fading situations. In this paper, we discover a novel negative effect, i.e., high divergence of channel coefficients (HDCC) effect for SST. Such an effect brings in a performance bottleneck for SST while it cannot be well tackled by the latest repeat transmission technique. Motivated by this circumstance, we analyze the essential cause of HDCC effect, and design an enhanced repeat transmission technique for SST accordingly. The proposed method can not only well address the impact of HDCC effect, but also absorb the advantages of the latest repeat transmission technique. Numerical results demonstrate that the proposed method obviously improve the detection performance upon existing techniques. [ABSTRACT FROM AUTHOR]

Cognitive radio technology, which helps to alleviate spectrum scarcity problem, plays an important role in future wireless communication systems. Spectrum sensing is the key technique of cognitive radio. The upcoming fifth generation (5G) communications need to deal with fast time-varying channels (i.e., channel state is non-static within a sensing window), while existing sensing methods are based mainly on conventional quasi-static channels. Consequently, traditional sensing methods may not work well in 5G communications. This paper aims to propose a sliced sensing technique, which can support 5G cognitive radio applications working in fast time-varying channels. We analyze the impact of non-static channels on the performance of traditional sensing methods and disclose a negative factor, i.e., out-of-phase feature distortion effect. Accordingly, we propose a sliced spectrum sensing scheme and probe its feasibility via several relevant trials. Based on the aforementioned works, a complete version of the sliced sensing technique is designed, which can adapt to channel variation and choose the optimal slicing strategy. Simulation results manifest that the sliced sensing technique outperforms traditional sensing methods in both Rayleigh fading and 3GPP spatial channel models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Based on the bijective mapping between cyclostationary features and the cyclic delays in orthogonal frequency division multiplexing (OFDM) systems, cyclostationary signatures can be used for signal representation in statistical spectrum domain. However, the conventional detection method for this kind of statistical signals is hindered by huge computational complexity. In this letter, we propose a novel detection method for such statistical signals with cyclostationarity, which only extract local features on the cyclic autocorrelation function (CAF) coordinate plane. Analytical and simulation results show that the proposed method achieves significant performance gain over the conventional detection method under practical scenarios, even with the reduced computational complexity. [ABSTRACT FROM PUBLISHER]