DSP-based Coherent Optical Systems : Receiver Sensitivity and Coding Aspects

Abstract: User demand for faster access to more data is at a historic high and rising. One of the enabling technologies that makes the information age possible is fiber-optic communications, where light is used to carry information from one place to another over optical fiber. Since the technology was first shown to be feasible in the 1970s, it has been constantly evolving with each new generation of fiber-optic systems achieving higher data rates than its predecessor.Today, the most promising approach for further increasing data rates is digital signal processing (DSP)-based coherent optical transmission with multi-level modulation. As multi-level modulation formats are very susceptible to noise and distortions, forward error correction (FEC) is typically used in such systems. However, FEC has traditionally been designed for additive white Gaussian noise (AWGN) channels, whereas fiber-optic systems also have other impairments. For example, there is relatively high phase noise (PN) from the transmitter and local oscillator (LO) lasers.The contributions of this thesis are in two areas. First, we use a unified approach to analyze theoretical performance limits of coherent optical receivers and microwave receivers, in terms of signal-to-noise ratio (SNR) and bit error rate (BER). By using our general framework, we directly compare the performance of ten coherent optical receiver architectures and five microwave receiver architectures. In addition, we put previous publications into context, and identify areas of agreement and disagreement between them. Second, we propose straightforward methods to select codes for systems with PN. We focus on Bose-Chaudhuri-Hocquenghem (BCH) codes with simple implementations, which correct pre-FEC BERs around 10−3. Our methods are semi-analytical, and need only short pre-FEC simulations to estimate error statistics. We propose statistical models that can be parameterized based on those estimates. Codes can be selected analytically based on our models.