Fiber-Wireless Integration with Enhanced Adaptability for Next Generation Radio Access Networks

Abstract

5G mobile communications marks the beginning of supporting various usage scenarios with diverse requirements and this trend will continue, with more applications of even more divergent targeted capabilities to be covered. This calls for enhanced adaptability in the fiber-wireless integrated system. In this dissertation, this problem is approached from three aspects. Firstly, a hybrid analog/digital radio-over-fiber (RoF) transmission system is studied so that the network can accommodate both RoF formats and they can be utilized for different applications. Secondly, advanced techniques applied to digital RoF are investigated, aiming at being adaptable to device impairments. For band-limited intensity-modulation and direct-detection (IM/DD) systems, a correlated training sequence with non-white spectrum is proposed to accelerate equalizer training. For coherent communications, laser phase noise tolerance is improved through probabilistic shaping (PS) and a novel distribution is proposed. Thirdly, high accuracy quality of transmission (QoT) estimation in analog RoF systems is studied. Artificial neural network (ANN) is applied to improve estimation accuracy and the data efficiency of ANN training is promoted through active learning.