Eric Blow NEC Labs America

Eric Blow

Researcher

Optical Networking & Sensing

Posts

Real-Time Photonic Blind Interference Cancellation

mmWave devices can broadcast multiple spatially-separated data streams simultaneously in order to increase data transfer rates. Data transfer can, however, be compromised by interference. Photonic blind interference cancellation systems offer a power-efficient means of mitigating interference, but previous demonstrations of such systems have been limited by high latencies and the need for regular calibration. Here, we demonstrate real-time photonic blind interference cancellation using an FPGA-photonic system executing a zero-calibration control algorithm. Our system offers a greater than 200-fold reduction in latency compared to previous work, enabling sub-second cancellation weight identification. We further investigate key trade-offs between system latency, power consumption, and success rate, and we validate sub-Nyquist sampling for blind interference cancellation. We estimate that photonic interference cancellation can reduce the power required for digitization and signal recovery by greater than 74 times compared to the digital electronic alternative.

Real-Time Blind Source Separation with Integrated Photonics for Wireless Signals

We demonstrate, for the first time, real-time blind source separation of interfering GHz transmitters using photonic weights controlled by an RF-System-on-Chip FPGA. This analog system achieves multi-antenna signal separation with millisecond execution latency.

Time Series Prediction and Classification using Silicon Photonic Neuron with Self-Connection

Time Series Prediction and Classification using Silicon Photonic Neuron with Self-Connection We experimentally demonstrated real-time operation of a photonic neuron with a self-connection, a pre-requisite for integrated recurrent neural networks (RNNs). After studying two applications we propose a photonics-assisted platform for time series prediction and classification.

A Silicon Photonic-Electronic Neural Network for Fiber Nonlinearity Compensation

A Silicon Photonic-Electronic Neural Network for Fiber Nonlinearity Compensation In optical communication systems, fibre nonlinearity is the major obstacle in increasing the transmission capacity. Typically, digital signal processing techniques and hardware are used to deal with optical communication signals, but increasing speed and computational complexity create challenges for such approaches. Highly parallel, ultrafast neural networks using photonic devices have the potential to ease the requirements placed on digital signal processing circuits by processing the optical signals in the analogue domain. Here we report a silicon photonic–electronic neural network for solving fibre nonlinearity compensation in submarine optical-fibre transmission systems. Our approach uses a photonic neural network based on wavelength-division multiplexing built on a silicon photonic platform compatible with complementary metal–oxide–semiconductor technology. We show that the platform can be used to compensate for optical fibre nonlinearities and improve the quality factor of the signal in a 10,080 km submarine fibre communication system. The Q-factor improvement is comparable to that of a software-based neural network implemented on a workstation assisted with a 32-bit graphic processing unit.

Demonstration of photonic neural network for fiber nonlinearity compensation in long-haul transmission systems

Demonstration of photonic neural network for fiber nonlinearity compensation in long-haul transmission systems We demonstrate the experimental implementation of photonic neural network for fiber nonlinearity compensation over a 10,080 km trans-pacific transmission link. Q-factor improvement of 0.51 dB is achieved with only 0.06 dB lower than numerical simulations.