Probabilistic Shaping Design Based on Reduced-Exponentiation Subset Indexing and Honeycomb-Structured Constellation Optimization for 5G Fronthaul Network

Xiangyu Wu,Lijia Zhang,Bo Liu,Xiangjun Xin,Lei Jiang,Yaya Mao,Jianxin Ren,Ying Zhang,Xing Xu

doi:10.1109/access.2019.2942316

Abstract

A probabilistic shaping method based on reduced-exponentiation subset indexing and honeycomb-structured constellation optimization is proposed to compress the number of signal points in the constellation, so that the total number doesn't fit the traditional pattern of multiples of power exponents of 2. The proposed scheme can significantly reduce the average signal power, enhance the space utilization in view of (identifying signals in) the judgement area of the constellation, as well as improve the mutual information, owing to the combination of probabilistic shaping and constellation optimization. Moreover, an experiment of a 25 km intensity-modulation and direct-detection system (IM/DD) transmission system is successfully demonstrated to present the superiority of our proposed scheme. It is shown that the proposed probabilistic shaping 64-to-31 carrier-less amplitude and phase modulation (CAP) based on honeycomb constellation can achieve the gains of 1.5 dB and 3 dB over receiver sensitivity when compared with uniform 32-CAP and uniform 64-CAP at the bit error rate (BER) of 1*10 -3 , respectively. The experiment suggests the proposed scheme a promising technique for future 5G fronthaul network.

Highlights

The massive application and ever-growing development of the emerging internet technologies has driven the underlying network infrastructure to evolve in a more advanced fashion, which is able to accommodate the trends of exponential increase in network bandwidth and resources requirements
To our best knowledge, we for the first time propose an optical probabilistically shaped carrier-less amplitude and phase modulation (CAP) modulation based on reduced-exponentiation subset indexing and honeycomb-structured constellation optimization for 5G fronthaul network
In a bid to reduce and minimize this side effects, this paper proposes a probabilistic shaping scheme based on reducedexponentiation subset indexing to fully make use of the completion of code word set

Summary

Introduction

The massive application and ever-growing development of the emerging internet technologies (e.g., big data, cloud computing, virtual reality, Internet of Things) has driven the underlying network infrastructure to evolve in a more advanced fashion, which is able to accommodate the trends of exponential increase in network bandwidth and resources requirements. To our best knowledge, we for the first time propose an optical probabilistically shaped CAP modulation based on reduced-exponentiation subset indexing and honeycomb-structured constellation optimization for 5G fronthaul network. PRINCIPLE OF PROBABILISTIC SHAPING DESIGN BASED ON REDUCED-EXPONENTIATION SUBSET INDEXING AND HONEYCOMB-STRUCTURED CONSTELLATION OPTIMIZATION A.

Results

Conclusion