Open AccessConvolutional neural network-based signal demodulation method for NOMA-PON
Author(s) -
Bangjiang Lin,
Hui Yang,
Rui Wang,
Zabih Ghassemlooy,
Xuan Tang
Publication year - 2020
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.392535
Subject(s) - computer science , demodulation , bit error rate , noma , convolutional neural network , passive optical network , interference (communication) , electronic engineering , distortion (music) , channel (broadcasting) , telecommunications , optics , artificial intelligence , wavelength division multiplexing , bandwidth (computing) , physics , wavelength , telecommunications link , engineering , amplifier
Non-orthogonal multiple access (NOMA) is a promising scheme for flexible passive optical networks (PONs), which provides high throughput and overall improved system performance. NOMA with the successive interference cancellation (SIC)-based receiver, which is used to detect the multiplexed signal in a sequential fashion, requires perfect channel state information and suffers from the error propagation problem. In this paper, we propose a convolutional neural network (CNN) based signal demodulation method for NOMA-PON, which performs channel estimation and signal detection in a joint manner. The CNN is first trained offline using the captured data for a given received optical power and then used to recover the data stream directly in the online mode. We show by experimental demonstration that, the proposed CNN-based receiver (Rx) outperforms the conventional SIC-based Rx and is more robust to the nonlinear distortion. We show that for the CNN-based system with 20 km optical fiber, the required received optical power levels at a bit error rate (BER) of 1×10 -3 are lower by 4, 3 and 2.5 dB for power allocation ratios of 0.16, 0.25, 0.36, respectively compared with SIC-based system. In addition, the BER performance of CNN deteriorates considerably less with non-linear distortion compared with SIC.