Adaptive optical post distortion linearization

Chou, Jason; Boyraz, Özdal; Jalali, Bahram

doi:10.1364/opex.13.005711

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…Fig. 4 System proposed in [10] Invention described in [11] consists of supervisory and control equipment on receiver side that calculates FWM level, compares it with acceptable. If FWM level is unacceptable, output level of the sender amplifier is decreased.…”

Section: Supessionmentioning

confidence: 99%

Bandwidth expansion approach for DWDM deployment in O-band

2008

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Section: Supessionmentioning

confidence: 99%

Bandwidth expansion approach for DWDM deployment in O-band

2008

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“…To solve this problem, we propose an approach to tune and optimize the nonlinear optical transfer function of the kernel. While the nonlinear optical system parameters cannot be tuned, the nonlinear process can be effectively engineered by varying the phase of the input data [7]. This is based on the insight that most nonlinear interactions, such as self-phase modulation, four-wave mixing, etc are coherent processes that depend on the input phase.…”

Section: Introductionmentioning

confidence: 99%

Low latency computing for time stretch instruments

Zhou,

Jalali

2023

J. Phys. Photonics

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Time stretch instruments have been exceptionally successful in discovering single-shot ultrafast phenomena such as optical rogue waves and have led to record-speed microscopy, spectroscopy, lidar, etc. These instruments encode the ultrafast events into the spectrum of a femtosecond pulse and then dilate the time scale of the data using group velocity dispersion. Generating as much as Tbit per second of data, they are ideal partners for deep learning networks which by their inherent complexity, require large datasets for training. However, the inference time scale of neural networks in the millisecond regime is orders of magnitude longer than the data acquisition rate of time stretch instruments. This underscores the need to explore means where some of the lower-level computational tasks can be done while the data is still in the optical domain. The Nonlinear Schrödinger Kernel computing addresses this predicament. It utilizes optical nonlinearities to map the data onto a new domain in which classification accuracy is enhanced, without increasing the data dimensions. One limitation of this technique is the fixed optical transfer function, which prevents training and generalizability. Here we show that the optical kernel can be effectively tuned and trained by utilizing digital phase encoding of the femtosecond laser pulse leading to a reduction of the error rate in data classification.

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“…Among them, particular attention was paid to the following linearization methods: 1) digital pre-distortion (DPD), which consists in applying a pre-distorted electrical signal to the EOC in order to reduce the degradation induced by the EOC+PIN nonlinearity [2], 2) feed forward compensation, where the nonlinearity is suppressed using a feedback loop comprising specific devices and it may be employed only in the optical domain, in the optical and electrical domain, and at the transmitter or at the receiver sides [3], 3) EOC linearized in the optical domain, where the structure used at the transmitter side for electro-optic conversion is designed to provide an extended linearized operation range. These optical transmitters can still be classified in two main groups: transmitters comprising only a single modulator or comprising multiple modulators.…”

Section: Introductionmentioning

confidence: 99%