Abstract
We experimentally demonstrate the transmission performance of the bit-loading discrete multi-tone (DMT) signal in intensity modulation and direct detection (IM-DD) system with a transmission distance of 2km and a net data rate of 108-Gb/s by two different shaping methods. A detailed comparison between the application of geometric shaping (GS) and probabilistic shaping (PS) in bit-loading DMT is presented. With continuous entropy loading for better channel fitting, probabilistically shaped bit-loading DMT achieves 1-dB receiver sensitivity gain compared to the conventional bit-loading DMT and outperforms geometrically shaped bit-loading DMT in the transmission experiment.
Highlights
The explosive growth of cloud computing, artificial intelligence, interactive games and so on attributes to the development of high-speed short-reach communication
We present a detailed comparison among the application of geometric shaping (GS)-QAM, probabilistic shaping (PS)-QAM and uniform QAM in bit-loading Discrete multi-tone (DMT)
Based on the measured SNR response after 2-km fiber transmission, bit-loading processes are performed on 400 subcarriers
Summary
The explosive growth of cloud computing, artificial intelligence, interactive games and so on attributes to the development of high-speed short-reach communication. The intensity modulation and direct detection (IM-DD) optical interconnect system is considered as a cost-effective solution with low power consumption and simple system configuration [1]–[5]. The frequency response is usually non-flat with power fading at high frequency due to the bandwidth limitation of optical and electrical devices which severely reduces the system capacity. Constellation shaping techniques have been a hot topic and applied to the IM-DD system to enhance the system capacity and transmission distance [11], [12]. Geometric shaping (GS) technique exploits the non-uniformly spaced constellation distribution with a larger minimum Euclidean distance and achieves performance gain with low computational complexity [15]–[18]
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