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Yves Jaouen

Publications and source records attributed to Yves Jaouen.

5 recordsLinked to original sources
arXiv2024

2 $μ$m Watt-level Fiber Amplifiers, Lasers, and ASE Sources Pumped by Broadband ASE Pumps

We report the design and demonstration of novel 2 $μ$m band Watt-level fiber amplifiers, fiber lasers, and wideband ASE sources that are pumped with broad spectrum Watt-level ASE sources instead of conventional fiber laser pumps. We show good agreement between the simulations and experimental results for the performance of a single-stage Ho-doped fiber amplifier at 2050nm wavelength pumped by a 50 nm broadband Tm-doped ASE source centered at 1860 nm. Next, we show that the new ASE pumping approach works effectively for a two -stage Ho-doped fiber amplifier as for a single stage Ho-doped fiber amplifier. Then, we demonstrate successfully the pumping of a Tm-doped fiber laser at 2039 nm using an ASE source centered at 1550 nm. Finally, we produce a novel 265 nm wide broadband ASE source at 2 $μ$m by concatenating Tm and Ho ASE sources. Our ASE-based pumping approach is simple and versatile compared to the standard laser based pumping means, and leads to similar device performance.

physics.optics↗
arXiv2023

Quantization of Neural Network Equalizers in Optical Fiber Transmission Experiments

The quantization of neural networks for the mitigation of the nonlinear and components' distortions in dual-polarization optical fiber transmission is studied. Two low-complexity neural network equalizers are applied in three 16-QAM 34.4 GBaud transmission experiments with different representative fibers. A number of post-training quantization and quantization-aware training algorithms are compared for casting the weights and activations of the neural network in few bits, combined with the uniform, additive power-of-two, and companding quantization. For quantization in the large bit-width regime of $\geq 5$ bits, the quantization-aware training with the straight-through estimation incurs a Q-factor penalty of less than 0.5 dB compared to the unquantized neural network. For quantization in the low bit-width regime, an algorithm dubbed companding successive alpha-blending quantization is suggested. This method compensates for the quantization error aggressively by successive grouping and retraining of the parameters, as well as an incremental transition from the floating-point representations to the quantized values within each group. The activations can be quantized at 8 bits and the weights on average at 1.75 bits, with a penalty of $\leq 0.5$~dB. If the activations are quantized at 6 bits, the weights can be quantized at 3.75 bits with minimal penalty. The computational complexity and required storage of the neural networks are drastically reduced, typically by over 90\%. The results indicate that low-complexity neural networks can mitigate nonlinearities in optical fiber transmission.

eess.SP↗
arXiv2022

Complexity Reduction over Bi-RNN-Based Nonlinearity Mitigation in Dual-Pol Fiber-Optic Communications via a CRNN-Based Approach

Bidirectional recurrent neural networks (bi-RNNs), in particular, bidirectional long short term memory (bi-LSTM), bidirectional gated recurrent unit, and convolutional bi-LSTM models have recently attracted attention for nonlinearity mitigation in fiber-optic communication. The recently adopted approaches based on these models, however, incur a high computational complexity which may impede their real-time functioning. In this paper, by addressing the sources of complexity in these methods, we propose a more efficient network architecture, where a convolutional neural network encoder and a unidirectional many-to-one vanilla RNN operate in tandem, each best capturing one set of channel impairments while compensating for the shortcomings of the other. We deploy this model in two different receiver configurations. In one, the neural network is placed after a linear equalization chain and is merely responsible for nonlinearity mitigation; in the other, the neural network is directly placed after the chromatic dispersion compensation and is responsible for joint nonlinearity and polarization mode dispersion compensation. For a 16-QAM 64 GBd dual-polarization optical transmission over 14x80 km standard single-mode fiber, we demonstrate that the proposed hybrid model achieves the bit error probability of the state-of-the-art bi-RNN-based methods with greater than 50% lower complexity, in both receiver configurations.

eess.SP↗
arXiv2011

Evidence of thermal effects in high power Er3+-Yb3+ fiber laser

We analyse the influence of heat generation by non radiative transitions in high power 1.55$μ$m double cladding Erbium-Ytterbium fiber laser. At strong pumping rates, 1$μ$m lasing can start due to parasitic reflections. We present a model including heat generation and its effect on the Stark level population using the MacCumber relation. Heat generation plays then a significant role and improves the 1.5$μ$m laser efficiency by increasing the 1$μ$m threshold.

physics.optics↗