Wavelength reconfigurability for next generation optical access networks

Next generation optical access networks should not only increase the capacity but also be able to redistribute the capacity on the fly in order to manage larger variations in traffic patterns. Wavelength reconfigurability is the instrument to enable such capability of network-wide bandwidth redistribution since it allows dynamic sharing of both wavelengths and timeslots in WDM-TDM optical access networks. However, reconfigurability typically requires tunable lasers and tunable filters at the user side, resulting in cost-prohibitive optical network units (ONU). In this dissertation, I propose a novel concept named cyclic-linked flexibility to address the cost-prohibitive problem. By using the cyclic-linked flexibility, the ONU needs to switch only within a subset of two pre-planned wavelengths, however, the cyclic-linked structure of wavelengths allows free bandwidth to be shifted to any wavelength by a rearrangement process. Rearrangement algorithm are developed to demonstrate that the cyclic-linked flexibility performs close to the fully flexible network in terms of blocking probability, packet delay, and packet loss. Furthermore, the evaluation shows that the rearrangement process has a minimum impact to in-service ONUs. To realize the cyclic-linked flexibility, a family of four physical architectures is proposed. PRO-Access architecture is suitable for new deployments and disruptive upgrades in which the network reach is not longer than 20 km. WCL-Access architecture is suitable for metro-access merger with the reach up to 100 km. PSB-Access architecture is suitable to implement directly on power-splitter-based PON deployments, which allows coexistence with current technologies. The cyclically-linked protection architecture can be used with current and future PON standards when network protection is required

TRANSMISSION PERFORMANCE OPTIMIZATION IN FIBER-WIRELESS ACCESS NETWORKS USING MACHINE LEARNING TECHNIQUES

The objective of this dissertation is to enhance the transmission performance in the fiber-wireless access network through mitigating the vital system limitations of both analog radio over fiber (A-RoF) and digital radio over fiber (D-RoF), with machine learning techniques being systematically implemented. The first thrust is improving the spectral efficiency for the optical transmission in the D-RoF to support the delivery of the massive number of bits from digitized radio signals. Advanced digital modulation schemes like PAM8, discrete multi-tone (DMT), and probabilistic shaping are investigated and implemented, while they may introduce severe nonlinear impairments on the low-cost optical intensity-modulation-direct-detection (IMDD) based D-RoF link with a limited dynamic range. An efficient deep neural network (DNN) equalizer/decoder to mitigate the nonlinear degradation is therefore designed and experimentally verified. Besides, we design a neural network based digital predistortion (DPD) to mitigate the nonlinear impairments from the whole link, which can be integrated into a transmitter with more processing resources and power than a receiver in an access network. Another thrust is to proactively mitigate the complex interferences in radio access networks (RANs). The composition of signals from different licensed systems and unlicensed transmitters creates an unprecedently complex interference environment that cannot be solved by conventional pre-defined network planning. In response to the challenges, a proactive interference avoidance scheme using reinforcement learning is proposed and experimentally verified in a mmWave-over-fiber platform. Except for the external sources, the interference may arise internally from a local transmitter as the self-interference (SI) that occupies the same time and frequency block as the signal of interest (SOI). Different from the conventional subtraction-based SI cancellation scheme, we design an efficient dual-inputs DNN (DI-DNN) based canceller which simultaneously cancels the SI and recovers the SOI.Ph.D