Comparison of Probabilistically Shaped 64QAM With Lower Cardinality Uniform Constellations in Long-Haul Optical Systems

Pilori, Dario; Bertignono, Luca; Nespola, A.; Forghieri, F.; Bosco, Gabriella

doi:10.1109/jlt.2017.2752842

Cited by 46 publications

(13 citation statements)

References 39 publications

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“…For PSK signals, all PSK modulation formats were identified precisely when the OSNR value was higher than 9 dB, which was lower than the FEC threshold of 28 GBaud BPSK (9.73 dB). According to [28]- [30], the required OSNRs under the FEC threshold for PS-16QAM, PS-32QAM, PS-64QAM are approximately 17 dB, 19.7 dB and 22.5 dB, respectively, which are also lower than the It can be seen in Fig. 9 (b) that six image combinations of US-16QAM were misclassified as US-64QAM.…”

Section: Methodsmentioning

confidence: 92%

Identifying Probabilistically Shaped Modulation Formats Through 2D Stokes Planes With Two-Stage Deep Neural Networks

et al. 2020

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A lightweight two-stage convolutional (deep) neural network (CNN) based modulation format identification (MFI) scheme is proposed and demonstrated for the polarization domain multiplexing (PDM) fiber communication system with probabilistically shaped (PS) modulation formats. The scheme is tested on a PDM system at a symbol rate of 28 GBaud. Six probabilistically shaped (PS) modulation formats (of 3 bit/symbol PS-16QAM, PS-32QAM, and PS-64QAM, of 4 bit/symbol PS-32QAM and PS-64QAM, and of 5 bit/symbol PS-64QAM) along with six standard modulation formats (BPSK, QPSK, 8PSK and three uniformly shaped (US) QAM: US-16QAM, US-32QAM and US-64QAM) are identified by the trained CNN. By taking advantage of computer vision, the results show that the proposed scheme can provide very high accuracy and significantly improve the identification performance over the existing techniques. The influences of the learning rate of the CNN are also discussed. INDEX TERMS Optical fiber communication, modulation format identification, convolutional neural networks.

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Section: Methodsmentioning

confidence: 92%

Identifying Probabilistically Shaped Modulation Formats Through 2D Stokes Planes With Two-Stage Deep Neural Networks

et al. 2020

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“…Subsequently, the accumulated nonlinear phase noise (NLPN) of PS signals will be weaker than that of conventional QAM signals in long‐haul transmission. In [5 ], the performance of PS‐64 QAM is compared with those of uniform 16 QAM and 32 QAM in coherent optical communication systems, where the net data rate is kept unchanged. For the transmission in pure silica‐core fibre (PSCF), the maximum reach can be improved by 15.5% to 34% by applying the PS.…”

Section: Comparison Between Ps and Conventional Qam Signalsmentioning

confidence: 99%

Probabilistic shaping: a step closer to the Shannon limit in channel capacity for optical communications

Shu

Zhang

Shatin³

2020

Electron. lett.

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“…will further increase the compound annual growth rate (CAGR) of Internet traffic, which is now ∼26%, with peaks of ∼46% in metro [6]. To cope with the current rapid traffic evolution, several options are available, among them: (I) a more efficient management of the resources based on elastic and cognitive optical networks [7]; (II) usage of highorder modulation formats and high symbol rate [8], [9]; (III) implementation of advanced DSP and forward error correction (FEC) algorithms [10]; (IV) transmission with advanced digital communication techniques such as probabilistic shaping (PS) and digital sub-carrier multiplexing (DSCM) [11], [12]; (V) enabling beyond C-band transmission [13]; and finally (VI) development of new fiber types such as multi-mode andcore [14], [15]. Choices (I) -(V) are currently being exploited and the selection criteria mainly depends on system requirements such as reach, cost, footprint, performance, and power consumption.…”

Section: Introductionmentioning

confidence: 99%

800G DSP ASIC Design Using Probabilistic Shaping and Digital Sub-Carrier Multiplexing

Sun

Torbatian

Karimi

et al. 2020

J. Lightwave Technol.

213

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The design of application-specific integrated circuit (ASIC) is at the core of modern ultra-high-speed transponders employing advanced digital signal processing (DSP) algorithms. This manuscript discusses the motivations for jointly utilizing transmission techniques such as probabilistic shaping and digital sub-carrier multiplexing in digital coherent optical transmissions systems. We firstly report the key-building blocks of high-speed modern DSP-based transponders working up to 800G per wave. Secondly, we show the benefits of these transmission methods in terms of system level performance. Finally, we report, to the best of our knowledge, the first long-haul experimental transmission -e.g., over 1000 km -with a real-time 7 nm DSP ASIC and digital coherent optics (DCO) capable of data rates up to 1.6 Tb/s using two waves (2×800G).

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Comparison of Probabilistically Shaped 64QAM With Lower Cardinality Uniform Constellations in Long-Haul Optical Systems

Cited by 46 publications

References 39 publications

Identifying Probabilistically Shaped Modulation Formats Through 2D Stokes Planes With Two-Stage Deep Neural Networks

Identifying Probabilistically Shaped Modulation Formats Through 2D Stokes Planes With Two-Stage Deep Neural Networks

Probabilistic shaping: a step closer to the Shannon limit in channel capacity for optical communications

800G DSP ASIC Design Using Probabilistic Shaping and Digital Sub-Carrier Multiplexing

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