An Undersampling Communication System Based on Compressive Sensing and In-Fiber Grating

Abstract

An undersampling free-space optical (FSO) communication system based on compressive sensing (CS) and in-fiber grating is proposed. Our proposal drastically mitigates the data scale unfavorable issue and reduces the great amount of required data. Also, our proposal employs low-bandwidth and low-cost photodetectors (PDs) and analog-to-digital converters (ADCs) and decreases the cost of data detection. Besides, the data transmitted in the free space is encrypted, which enhances the security of the system. The in-fiber grating, which has the advantage of highly efficient thanks to its inherent compatibility with optical fiber links, plays the roles of light emitter, in-fiber polarizer, and light diffraction device, simultaneously. A sinusoidal radio frequency (RF) signal with a frequency of 1 GHz is utilized for a 2.4 m data transmission. And each PD with a bandwidth of 300 MHz is employed for detecting the transmitted signal on each channel. The proposal obtains data compression ratios of 25%, 15%, and 5% both in the temporal domain and frequency domain. Besides, a two-tone signal and a random RF signal are applied in the proposed FSO transmission for data transmission. Our proposal not only has the merits of secure, compact, stable, and highly efficient, but also has great potential in extremely secure situations, such as secured indoor and under water data communication.

Principle and Recent Development in Photonic Time-Stretch Imaging

“…The recent development of PTS imaging systems are mainly in four categories: first, PTS imaging systems combined with shorter wavelength bands for better imaging resolutions and rich applications [68][69][70][71][72][73]; second, PTS imaging systems with faster speed [21,22,[26][27][28][29]57,58,74,79]; third, PTS imaging systems combined with data compression [11][12][13][14][15][16][17][18][75][76][77][78]; and the last, PTS imaging systems combined with deep learning for target classification [23][24][25]32,66].…”

“…Additionally, it can achieve a continuous ultrafast imaging speed of millions of frames per second, which is several magnitudes higher compared to traditional imaging techniques. Moreover, due to its inherent nature, it can combine other several recent optical technologies such as compressive sensing [10][11][12][13][14][15][16][17][18]22,29,75,76], nonlinear processing [76][77][78], amplification [4,79], and deep learning [80,81], which are beyond the capabilities of other imaging techniques.…”