112-Gb/s/λ Downstream Transmission for TDM-PON with 31-dB Power Budget using 25-Gb/s Optics and Simple DSP in ONU

Abstract

We experimentally demonstrate 112-Gb/s/λ PAM-4 transmission based on 25-Gb/s optics. Over 31-dB power budget is achieved by using OLT-side pre-equalization, amplification and only simple FFE in ONU.

Overview of high-speed TDM-PON beyond 50 Gbps per wavelength using digital signal processing [Invited Tutorial]

“…The use of SOA has not only been proposed at the receiver side, but also as booster amplifier integrated with an electroabsorptionmodulated laser (EML), to produce a high-power transmitter [30]. Currently, output powers of 10 dBm [31] and 11 dBm [32] can be achieved using this transmitter structure.…”

100G PAM-4 PON with 34 dB Power Budget Using Joint Nonlinear Tomlinson-Harashima Precoding and Volterra Equalization

“…However, the false hard decision symbols may induce error propagation. To address this issue, the feedback structure of DFE is moved to the transmitter side (Tx), namely linear THP (LTHP) [8] . LTHP-FFE can only compensate linear ISI.…”

“…To cope with the ever-increasing bandwidth demand, the international standardization organizations IEEE 802.3ac and ITU-T Q2/SG15 groups are going to release the solutions for 25 Gbps/λ and 50 Gbps/λ passive optical networks (PONs) [1]- [2] , while the single channel 100G PON is currently being investigated in research community [3][4][5][6][7][8] . Directly modulated later (DML) with intensity modulation and direct detection (IMDD) is often considered more cost-effective for PON compared to external modulation and coherent detection [3] .…”

Simplified SVM Equalization Algorithm Based on Single Hyperplane Training Enabled 50Gb/s PAM-4/8 With 10-G Optics in NG-PON System

“…To resolve this problem mentioned above, some equalization schemes [9][10][11][12][13][14][15][16][17][18][19] based on digital signal processing (DSP) technology have been presented in high-speed limitedbandwidth NG-PON system. Owing to its simple implementation, strong adaptability, and significant performance improvement [14], the equalization schemes have become the mainstream ISI compensation methods.…”

“…Despite that, these common equalizers have unsatisfactory performances in severely distorted channel transmission systems. To handle this, much more taps configurated with these common equalizers were proposed [16], which would greatly increase the computational complexity. Therefore, to further improve system performance, some advanced equalization algorithms based on machine learning (ML) are proposed, such as neural network (NN) [20], support vector machine (SVM) [22], and K-nearest neighbor (KNN) [21].…”