Next-Generation Passive Optical Network Based on Sparse Code Multiple Access and Graph Neural Networks

Abstract

Sparse code multiple access (SCMA) is a promising technology to provide high throughput and overall improved system performance at affordable cost for next-generation passive optical networks (NG-PONs). Message passing algorithm (MPA) based on a factor graph is usually used for low-complexity multiuser detection in SCMA. However, MPA requires accurate and effective channel estimation due to the interference between the user's signals on the same resource block and suffers uncertain convergence caused by the cycles in the factor graph. In this paper, a graph neural networks (GNN)-based detection method is proposed for SCMA-PON, which performs channel impairment compensation and signal detection in a joint manner. 25.5 Gb/s SCMA-OFDM system over 20/60 km single mode fiber link is simulated to demonstrate the feasibility with 8.5-G class optical devices. The simulation results show that GNN-based detection method outperforms MPA and is more robust to the nonlinear distortion for the same level of computational complexity.

High-Security Multi-Level Joint Encryption Scheme Based on SCMA Chaotic Codebook and OCDM Subcarrier Disturbance

“…A series of SCMA transmission systems [4][5] and multi-user detection algorithms with superior performance have been proposed and implemented. These results are usually applicable to large-scale access scenarios [6]. In such a large optical access network, data hijacking is inevitable and its security deserves consideration.…”

Upscaling Customer Access Network Using Spectrum Conversion–Slicing–Duplication Technique

“…Instead of scalability, many research studies focus on the extension of the fiber length to support users located at distances far from the central offices [17][18][19] and effective bandwidth to support more application to the users [20,21].…”