Neural Network Equalizers and Successive Interference Cancellation for Bandlimited Channels with a Nonlinearity

2024/08/28 by Plabst, Daniel, Prinz, Tobias, Diedolo, Francesca +4
#FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2408.15767

Abstract

Neural networks (NNs) inspired by the forward-backward algorithm (FBA) are used as equalizers for bandlimited channels with a memoryless nonlinearity. The NN-equalizers are combined with successive interference cancellation (SIC) to approach the information rates of joint detection and decoding (JDD) with considerably less complexity than JDD and other existing equalizers. Simulations for short-haul optical fiber links with square-law detection illustrate the gains.

Related

• Neural Network-Based Successive Interference Cancellation for Non-Linear Bandlimited Channels
• Joint Neural Network Equalizer and Decoder
• Experimental Study of Deep Neural Network Equalizers Performance in Optical Links
• Neural Networks-based Equalizers for Coherent Optical Transmission: Caveats and Pitfalls
• Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation