A Hybrid Internet/CATV/5G Fiber-FSO Integrated System With a Triple-Wavelength Polarization Multiplexing Scenario

Abstract

A hybrid Internet/cable television (CATV)/5G fiber-free-space optical (FSO) integrated system with a triple-wavelength polarization multiplexing scenario is attainably demonstrated for the first time. In a triple-wavelength polarization multiplexing scenario with vestigial sideband (VSB) four-level pulse amplitude modulation (PAM4)/CATV/16-quadrature amplitude modulation (QAM)-orthogonal frequencydivision multiplexing (OFDM) modulation, the channel capacity is considerably multiplied and the overall performance is significantly improved. With a tunable optical band-pass filter, polarization-tracking free de-multiplexing is attained by removing other wavelengths with different polarizations, and the optical VSB PAM4/CATV/16-QAM-OFDM signal is created by partially reducing the optical signal's linewidth. Compared with established polarization multiplexing fiber-FSO integrations with complex polarization de-multiplexing mechanism, it demonstrates a notable one with the benefits of simplicity and flexibility. Sufficiently low bit error rate, acceptable PAM4 eye diagrams, clear constellation map, and high composite second-order/composite triple beat values are attained through a 40 km single-mode fiber transport with 200 m free-space link. This demonstrated triple-wavelength polarization multiplexing fiber-FSO integrated system is promising because it not only incorporates fiber backhaul with optical wireless feeder, but it also simplifies system's structure and enhance system's overall performance. INDEX TERMS Fiber-FSO integrated system, optical VSB filter, polarization multiplexing scenario, tunable optical band-pass filter, triple-wavelength.

A Bidirectional FSO Communication Employing Phase Modulation Scheme and Remotely Injection-Locked DFB LD

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Single-Line Bidirectional Optical Add/Drop Multiplexer for Ring Topology Optical Fiber Networks

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Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm With Sigmoid Function Data Preprocessing Method

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