Enabling Technologies for High Spectral‐Efficiency Coherent Optical Communication Networks

Abstract

Forward error correction (FEC) in optical communications has been first demonstrated in 1988 [1]. Since then, coding technology has evolved significantly. This pertains not only to the codes but also to encoder and decoder architectures. Modern high-speed optical communication systems require high-performing FEC engines that support throughputs of 100 GBit/s or multiples thereof, that have low power consumption, that realize net coding gains (NCGs) close to the theoretical limits at a target bit error rate (BER) of below 10 −15 , and that are preferably adapted to the peculiarities of the optical channel.Forward error correction coding is based on deterministically adding redundant bits to a source information bit sequence. After transmission over a noisy channel, a decoding system tries to exploit the redundant information for fully recovering the source information. Several methods for generating the redundant bit sequence from the source information bits are known. Transmission systems with 100 GBit/s and 400 GBit/s today typically use one of two coding schemes to generate the redundant information: Block-Turbo Codes (BTCs) or Low-Density Parity-Check (LDPC) codes. In coherent systems, so-called soft information is usually ready available and can be used in high performing systems within a soft-decision decoder architecture. Soft-decision information means that no binary 0/1 decision is made before entering the forward error correction decoder. Instead, the (quantized) samples are used together with their statistics to get improved estimates of the original bit sequence. This chapter will focus on soft-decision decoding of LDPC codes and the evolving spatially coupled LDPC codes.

Accurate Closed-Form Real-Time EGN Model Formula Leveraging Machine-Learning Over 8500 Thoroughly Randomized Full C-Band Systems

“…The parameters Φ n ch and Φ CUT are the constant Φ used by the EGN model, which depends on the modulation format of a channel [4], [20]. The exact values are shown in Table I, for the modulation formats used in this paper and for PM-BPSK.…”

“…12. Plot of the quantity R defined in Eq (20),. representing the ratio between span-by-span increments of NLI as predicted by the EGN model and as predicted by the closed-form model 1 (CFM1).…”

Constructive Methods for the Design and Labeling of Four-Dimensional Modulations

“…As an example, consider for instance the graphical representation of the entire 14PolSK-8PSK constellation shown in Figure (4). Using the static performance metrics presented in Table (I) [ 31], where M is the number of constellation points, N is the dimension of the modulation, d is the minimum (Euclidean) distance between symbols, E b is the energy per bit and E is the average energy per symbol. We have found that for the…”