Abstract

In this paper, we proposed a simple uniform entropy loading (UEL) algorithm to adaptively load the same entropy for all the subcarriers according to the channel state information. Thanks to the signal-to-noise ratio (SNR) equalization effect of precoding, only one distribution matcher is used for UEL, maximally reducing the complexity of entropy loading. The proposed algorithm is based on the relationship between the optimal information bits per symbol (IBPS) and SNR for probabilistically shaped quadrature amplitude modulation (PS QAM) under the constraint of normalized generalized mutual information (NGMI) considering the implementation of constant composition distribution matching (CCDM) and off-the-shelf forward error correction (FEC) code. Different from generalized mutual information (GMI), IBPS intuitively represents practical performance. Besides, the relationship between IBPS and SNR for conventional QAM and time domain hybrid QAM (TDHQ) are also calculated to perform the well-known bit power loading (BPL) algorithm, Levin-Campello (LC) algorithm, and TDHQ assisted adaptive loading (AL) algorithm, respectively. Based on theoretical analysis, to our best knowledge, we first found that precoding procedure adopted by TDHQ assisted AL and UEL scheme degrades Shannon capacity compared to water-filling algorithm, the upper limit of BPL algorithms. However, benefiting from shaping gain of PS QAM, the proposed UEL algorithm achieves higher net data rate (NDR) and lower peak-to-average power ratio (PAPR) than the LC algorithm, which is demonstrated by the simulations and experiments in single sideband discrete multi-tone (SSB-DMT) systems. According to the experimental results, up to 3.2% NDR improvement and 0.82 dB PAPR reduction are realized by the UEL algorithm compared to the LC algorithm. The UEL algorithm also outperforms TDHQ assisted AL algorithm by the average NDR gain of up to 8.0%. The simple and effective advantages make the proposed UEL scheme suitable for the short-to-medium reach optical fiber communication systems.

© 2019 IEEE

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