This paper focuses on improving the detection performance of spectrum sensing in cognitive radio(CR) networks under complicated electromagnetic environment. Some existing fast spectrum sensing algorithms cannot get specific features of the licensed users'(LUs') signal, thus they cannot be applied in this situation without knowing the power of noise. On the other hand some algorithms that yield specific features are too complicated. In this paper, an algorithm based on the cyclostationary feature detection and theory of Hilbert transformation is proposed. Comparing with the conventional cyclostationary feature detection algorithm, this approach is more flexible i.e. it can flexibly change the computational complexity according to current electromagnetic environment by changing its sampling times and the step size of cyclic frequency. Results of simulation indicate that this approach can flexibly detect the feature of received signal and provide satisfactory detection performance compared to existing approaches in low Signal-to-noise Ratio(SNR) situations.
Orthogonal frequency division multiplexing(OFDM) is an attractive technology to provide immense improvement in wireless transmission capacity but high peak-to-average power ratio(PAPR) is a major drawback of OFDM system.Selected mapping(SLM) scheme has good performance for PAPR reduction.It requires the transmitting data to be multiplied by random phase sequences.However,the sequences are pseudo-random which will decrease the method effectiveness.Exhaustive entropy is introduced in this paper which can identify the strength of random phase sequences property.Then an exhaustive entropy based on SLM method is proposed.The scheme improves the effectiveness of random phase sequences by selecting the larger exhaustive entropy of them.The simulation results show that the PAPR reduction performance is better than that of conventional SLM through this method.
In order to investigate the benefit of multiple-input multiple-output(MIMO) technique applying to the high altitude platform(HAP), a 2×2 MIMO statistical model, which can accurately describe the channel between HAP and high-speed train, is presented. And dual polarization diversity is particularly considered. Based on first-order three-state Markov chain, the single-input single-output(SISO) channel, a subset of the MIMO channel is first established. The ray tracing approach applied to the digital relief model(DRM) which covers the railway between Xi'an and Zhengzhou is used to obtain the state probability vector and matrix of the state transition probability. The proposed model considers both Doppler shift and temporal correlation, and the polarization correlation and spatial correlation statistical properties of large-scale fading and smallscale fading are analyzed. Moreover, useful numerical results on the MIMO HAP channel outage capacity are provided based on which, significant capacity gains with respect to the conventional SISO case are illustrated. Such statistical channel model can be applied to the future wireless communication system between HAP and high-speed train.
