For channel estimation in the relay-assisted multiple-input multiple-output and orthogonal frequency division multiplexing indoor visible light communication (VLC) system affected by light source, there is a problem that the received signal cannot be separated by simple filtering. To solve this problem, a channel estimation by PARATUCK2-PARAFAC-based noise compensation which introduces tensors to construct the suitable model of VLC is proposed. First, the transmitting pilots for channel estimation and noise estimation are constructed separately with the Hermitian symmetry structure. Then the tensor-based transmission model of the indoor VLC system is established, and the signals for channel and noise estimations are received. Finally, an asymptotic estimation for separating signals from different sources and noise is proposed by PARATUCK2-PARAFAC. Simulation results show that the proposed algorithm provides a more effective scheme for channel estimation comparing with the state-of-the-art algorithms.
Existing image compression and encryption methods have several shortcomings: they have low reconstruction accuracy and are unsuitable for three-dimensional (3D) images. To overcome these limitations, this paper proposes a tensor-based approach adopting tensor compressive sensing and tensor discrete fractional random transform (TDFRT). The source video images are measured by three key-controlled sensing matrices. Subsequently, the resulting tensor image is further encrypted using 3D cat map and the proposed TDFRT, which is based on higher-order singular value decomposition. A multiway projection algorithm is designed to reconstruct the video images. The proposed algorithm can greatly reduce the data volume and improve the efficiency of the data transmission and key distribution. The simulation results validate the good compression performance, efficiency, and security of the proposed algorithm.
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