Self-Coherent Receiver Based on a Recurrent Optical Spectrum Slicing Neuromorphic Accelerator

Sozos, Kostas; Deligiannidis, Stavros; Mesaritakis, Charis; Bogris, Adonis

doi:10.1109/jlt.2023.3235278

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…In addition, the transfer learning [93][94][95] technique has been employed to realize fast remodeling in SSB system, which could be scalable to other DD systems. Finally, we briefly introduce the photonics neuromorphic computing [96] used in SCD systems to extract the phase information and demodulate the quadrature amplitude modulation (QAM) formats.…”

Section: Techniques In Scd Systemmentioning

confidence: 99%

“…In the case of a photonic RC, the reservoir implemented using photonic components and principles. In [96], a recurrent optical spectrum slicing (ROSS) neuromorphic accelerator was proposed to realize an SCD receiver.…”

Section: Optical Signal Processingmentioning

confidence: 99%

“…In the case of a photonic RC, the reservoir is implemented using photonic components and principles. In [96], a recurrent optical spectrum slicing (ROSS) neuromorphic accelerator was proposed to realize an SCD receiver. This network aimed to extract phase information and demodulate QAM formats while simultaneously mitigating CD.…”

Section: Optical Signal Processingmentioning

confidence: 99%

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Machine Learning for Self-Coherent Detection Short-Reach Optical Communications

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Xu,

Zhu

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Driven by emerging technologies such as the Internet of Things, 4K/8K video applications, virtual reality, and the metaverse, global internet protocol traffic has experienced an explosive growth in recent years. The surge in traffic imposes higher requirements for the data rate, spectral efficiency, cost, and power consumption of optical transceivers in short-reach optical networks, including data-center interconnects, passive optical networks, and 5G front-haul networks. Recently, a number of self-coherent detection (SCD) systems have been proposed and gained considerable attention due to their spectral efficiency and low cost. Compared with coherent detection, the narrow-linewidth and high-stable local oscillator can be saved at the receiver, significantly reducing the hardware complexity and cost of optical modules. At the same time, machine learning (ML) algorithms have demonstrated a remarkable performance in various types of optical communication applications, including channel equalization, constellation optimization, and optical performance monitoring. ML can also find its place in SCD systems in these scenarios. In this paper, we provide a comprehensive review of the recent progress in SCD systems designed for high-speed optical short- to medium-reach transmission links. We discuss the diverse applications and the future perspectives of ML for these SCD systems.

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