activity
20182022
most citedDoes Probabilistic Constellation Shaping Benefit IM-DD Systems without Optical Amplifiers?

66 citations · 111 across the 9 of their papers we have counts for

collaborators

9 papers

eess.SP2022

On Digital Subcarrier Multiplexing under A Bandwidth Limitation and ASE Noise

Junho Cho, Xi Chen, Greg Raybon +1

We show that digital subcarrier multiplexing (DSM) systems require much greater complexity for Nyquist pulse shaping than single-carrier (SC) systems, and it is a misconception tha…

eess.SP2021★ 17 cited

On the Kurtosis of Modulation Formats for Characterizing the Nonlinear Fiber Propagation

Junho Cho, Robert Tkach

Knowing only two high-order statistical moments of modulation symbols, often represented by the fourth moment called "kurtosis", the overestimation of nonlinear interference (NLI)…

eess.SP2021

Digital Interference Mitigation in Space Division Multiplexing Self-Homodyne Coherent Detection

Hanzi Huang, Yetian Huang, Haoshuo Chen +7

We propose a digital interference mitigation scheme to reduce the impact of mode coupling in space division multiplexing self-homodyne coherent detection and experimentally verify…

eess.SP2021★ 66 cited

Does Probabilistic Constellation Shaping Benefit IM-DD Systems without Optical Amplifiers?

Di Che, Junho Cho, Xi Chen

Probabilistic constellation shaping (PCS) has been widely applied to amplified coherent optical transmissions owing to its shaping gain over the uniform signaling and fine-grained…

eess.SP2020★ 3 cited

Full C-Band WDM Transmission of Nonlinearity-Tolerant Probabilistically Shaped QAM over 2824-km Dispersion-Managed Fiber

Junho Cho, Xi Chen, Greg Raybon +3

By tailoring probabilistic constellation shaping (PCS) for nonlinearity tolerance, we experimentally demonstrate up to 1.1 dB increase in signal-to-noise ratio (SNR) and 6.4% incre…

eess.SP2020★ 18 cited

Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks

Junho Cho, Sethumadhavan Chandrasekhar, Erixhen Sula +9

We experimentally solve the problem of maximizing capacity under a total supply power constraint in a massively parallel submarine cable context, i.e., for a spatially uncoupled sy…

eess.SP2020★ 2 cited

Prefix-Free Code Distribution Matching for 5G New Radio

Junho Cho, Ori Shental

We use prefix-free code distribution matching (PCDM) for rate matching (RM) in some 5G New Radio (NR) deployment scenarios, realizing a wide range of information rates from 1.4 to…

eess.SP2019★ 1 cited

Supply-Power-Constrained Cable Capacity Maximization Using Deep Neural Networks

Junho Cho, Sethumadhavan Chandrasekhar, Erixhen Sula +9

We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using machin…

cs.IT2018★ 4 cited

Prefix-Free Code Distribution Matching for Probabilistic Constellation Shaping

Junho Cho

In this work, we construct energy-efficient variable-to-fixed length (V2F), fixed-to-variable length (F2V), and variable-to-variable length (V2V) prefix-free codes, which are optim…