For hypersonic flight, high-dynamic plasma sheaths surrounding vehicles deteriorate communication quality. In this work, hypersonic vehicle downlink integrated channels that cascade the plasma sheath channel and Rice fading channel are first established, and the probability density functions (PDFs) of the integrated channel amplitude and phase are derived theoretically. Then, a downlink SIMO system over the integrated channel is considered, including a single antenna transmitter and a single ground receiver equipped with a large number of antennas. For this system, a noncoherent telemetry scheme is proposed, a transmitter that modulates information only in the power of the symbols, and an adaptive maximum likelihood (ML) receiver which demodulates symbols using the PDF of the average received power (ARP) across the antennas. Motivated by the efficient power utilization, an asymptotically optimal constellation design in terms of symbol error rate (SER) with an increasing number of antennas is proposed, under an assumption that CSI statistics are available. The PDF of ARP is well estimated by a reduced reversible jump markov chain monte carlo (RRJ-MCMC) algorithm and used for symbol demodulation and achievable rate derivation. The numerical results demonstrate that the optimal constellation scheme outperforms the amplitude shift keying (ASK) constellation scheme and is robust against the plasma sheath attenuation with moderate antennas or a large line-of-sight (LOS) component.

Existing remote sensing images of ground objects are difficult to annotate, and building a hyperspectral dataset requires huge resources. To tackle these problems, this paper proposes a new method with low requirements for the scale of the dataset that involves correcting the inter-class differences of hyperspectral images and eliminating the redundant information of spatial–spectral features. Firstly, the algorithm introduces the spatial information of hyperspectral images into the classification task through the superpixel definition based on the entropy rate, which not only reduces the spatial redundancy information of hyperspectral images but also uses the similarity of spatial neighbors to increase the differences between classes and alleviate the differences within classes. Secondly, under the theoretical guidance of similar spectral fluctuation trends of similar objects, a feature extraction method based on fast Fourier transform is proposed to alleviate spectral feature redundancy and further eliminate the inter-class differences of the algorithm. Finally, to verify the improvement effect of the proposed idea on the traditional classification method, the idea was applied to the traditional SVM algorithm, and experiments were carried out based on the PaviaU and Indian Pines hyperspectral datasets. The simulation results show that the proposed (ERS–FFT–SVM) algorithm shows a significant improvement in classification accuracy when compared with the traditional classification algorithm and is able to perform the small sample classification of hyperspectral images.