FPGA Implementation of Rate-Adaptable Prefix-Free Code Distribution Matching for Probabilistic Constellation Shaping

Yu, Qinyang; Corteselli, Steve; Cho, Junho

doi:10.1109/jlt.2020.3035039

Cited by 27 publications

(14 citation statements)

References 31 publications

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“…However, it is not yet commonly implemented in commercial systems. One of the reasons for this is that the approach is not yet mature with virtually no hardware prototypes demonstrating constellation shaping and its performance in realistic transmission systems [3][4][5]. In addition, the existing literature is focused on the implementation of the DSP chain components related to constellation shaping, such as constellation demappers and distribution matchers.…”

Section: Innovationmentioning

confidence: 99%

Adaptive Geometric Constellation Shaping in a Transmission System with a Real-time Optimisation Loop

Jarmolovičius,

Vasylchenkova,

Sillekens

2023

2023 Optical Fiber Communications Conference and Exhibition (OFC)

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Section: Innovationmentioning

confidence: 99%

Adaptive Geometric Constellation Shaping in a Transmission System with a Real-time Optimisation Loop

Jarmolovičius,

Vasylchenkova,

Sillekens

2023

2023 Optical Fiber Communications Conference and Exhibition (OFC)

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“…Fig. 6 shows an example of a (𝑌 ℓ 𝐴 , 𝑍 ℓ 𝐴 ) combination for (𝐴, 𝐵) = (8,12), where there are 65536 candidates (entire region) and 4096 used cases (blue markers). Fig.…”

Section: Principle Of Lower-complexity Hidmmentioning

confidence: 99%

“…Several DM techniques have been proposed, such as constantcomposition DM [6], enumerative sphere shaping [7], hierarchical DM (HiDM) [8], and prefix-free code DM [9]. Among these, only HiDM and prefixfree code DM have been implemented on a fieldprogrammable gate array (FPGA) [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient implementation of probabilistic shaping

Yoshida,

Karlsson,

Agrell

2023

IET Conf. Proc.

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“…The accuracy of the power consumption estimation is improved by introducing a dynamic simulation. Even if we consider shaping encoding only, there have been very few other reports on FPGA implementations [34], [35]. Furthermore, there are neither any reports on static/dynamic power consumption estimates nor any real-time evaluations of FPGA implementations including both shaping encoding and decoding, except [32].…”

Section: Introductionmentioning

confidence: 99%

Compressed Shaping: Concept and FPGA Demonstration

Yoshida,

Igarashi,

Karlsson

et al. 2021

Preprint

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Probabilistic shaping (PS) has been widely studied and applied to optical fiber communications. The encoder of PS expends the number of bit slots and controls the probability distribution of channel input symbols. Not only studies focused on PS but also most works on optical fiber communications have assumed source uniformity (i.e. equal probability of marks and spaces) so far. On the other hand, the source information is in general nonuniform, unless bit-scrambling or other source coding techniques to balance the bit probability is performed. Interestingly, one can exploit the source nonuniformity to reduce the entropy of the channel input symbols with the PS encoder, which leads to smaller required signal-to-noise ratio at a given input logic rate. This benefit is equivalent to a combination of data compression and PS, and thus we call this technique compressed shaping. In this work, we explain its theoretical background in detail, and verify the concept by both numerical simulation and a field programmable gate array (FPGA) implementation of such a system. In particular, we find that compressed shaping can reduce power consumption in forward error correction decoding by up to 90% in nonuniform source cases. The additional hardware resources required for compressed shaping are not significant compared with forward error correction coding, and a real-time back-to-back test is successfully demonstrated.

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FPGA Implementation of Rate-Adaptable Prefix-Free Code Distribution Matching for Probabilistic Constellation Shaping

Cited by 27 publications

References 31 publications

Adaptive Geometric Constellation Shaping in a Transmission System with a Real-time Optimisation Loop

Adaptive Geometric Constellation Shaping in a Transmission System with a Real-time Optimisation Loop

Energy-efficient implementation of probabilistic shaping

Compressed Shaping: Concept and FPGA Demonstration

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