The efficacy of data decoding in contemporary ultrafast fiber transmission systems is greatly determined by the capabilities of the signal processing tools that are used. The received signal must not exceed a certain level of complexity, beyond which the applied signal processing solutions become insufficient or slow. Moreover, the required signal-to-noise ratio (SNR) of the received signal can be challenging, especially when adopting modulation formats with multi-level encoding. Lately, photonic reservoir computing (RC)-a hardware machine learning technique with recurrent connectivity-has been proposed as a post-processing tool that deals with deterministic distortions from fiber transmission. Here, we show that RC post-processing is remar...
We propose a direct detection scheme for pulse amplitude modulation (PAM) signals over long-reach fi...
\u3cp\u3ePhotonic reservoir computing uses recent advances in machine learning, and in particular th...
International audienceReservoir computing, originally referred to as an echo state network or a liqu...
The efficacy of data decoding in contemporary ultrafast fiber transmission systems is greatly determ...
Analog photonic computing has been proposed and tested in recent years as an alternative approach fo...
Machine learning techniques have proven very efficient in assorted classification tasks. Nevertheles...
Nonlinearity mitigation in optical fiber networks is typically handled by electronic Digital Signal ...
Here we propose a novel design of fiber-optic reservoir computing (FORC) and demonstrate it applicab...
In recent years, various methods, architectures, and implementations have been proposed to realize h...
Optical reservoir computing is a machine learning technique in which a photonic chip can be trained ...
In the current development of coherent optical communication systems, nonlinear noise is considered ...
Reservoir computing is a brain-inspired approach for information processing, well suited to analog i...
Reservoir computers (RCs) are randomized recurrent neural networks well adapted to process time seri...
We investigate methods for experimental performance enhancement of auto-encoders based on a recurren...
Improving bit error rates in optical communication systems is a difficult and important problem. The...
We propose a direct detection scheme for pulse amplitude modulation (PAM) signals over long-reach fi...
\u3cp\u3ePhotonic reservoir computing uses recent advances in machine learning, and in particular th...
International audienceReservoir computing, originally referred to as an echo state network or a liqu...
The efficacy of data decoding in contemporary ultrafast fiber transmission systems is greatly determ...
Analog photonic computing has been proposed and tested in recent years as an alternative approach fo...
Machine learning techniques have proven very efficient in assorted classification tasks. Nevertheles...
Nonlinearity mitigation in optical fiber networks is typically handled by electronic Digital Signal ...
Here we propose a novel design of fiber-optic reservoir computing (FORC) and demonstrate it applicab...
In recent years, various methods, architectures, and implementations have been proposed to realize h...
Optical reservoir computing is a machine learning technique in which a photonic chip can be trained ...
In the current development of coherent optical communication systems, nonlinear noise is considered ...
Reservoir computing is a brain-inspired approach for information processing, well suited to analog i...
Reservoir computers (RCs) are randomized recurrent neural networks well adapted to process time seri...
We investigate methods for experimental performance enhancement of auto-encoders based on a recurren...
Improving bit error rates in optical communication systems is a difficult and important problem. The...
We propose a direct detection scheme for pulse amplitude modulation (PAM) signals over long-reach fi...
\u3cp\u3ePhotonic reservoir computing uses recent advances in machine learning, and in particular th...
International audienceReservoir computing, originally referred to as an echo state network or a liqu...