Multi-target ranging using optical reservoir computing approach in the laterally coupled semiconductor lasers with self-feedback

2021 ◽  
Author(s):  
Dong-Zhou Zhong ◽  
Zhe Xu ◽  
Ya-Lan Hu ◽  
Ke-Ke Zhao ◽  
Jin-Bo Zhang ◽  
...  

Abstract In this work, we utilize three parallel reservoir computers using semiconductor lasers with optical feedback and light injection to model radar probe signals with delays. Three radar probe signals are generated by driving lasers constructed by a three-element lase array with self-feedback. The response lasers are implemented also by a three-element lase array with both delay-time feedback and optical injection, which are utilized as nonlinear nodes to realize the reservoirs. We show that each delayed radar probe signal can well be predicted and to synchronize with its corresponding trained reservoir, even when there exist parameter mismatches between the response laser array and the driving laser array. Based on this, the three synchronous probe signals are utilized for ranging to three targets, respectively, using Hilbert transform. It is demonstrated that the relative errors for ranging can be very small and less than 0.6%. Our findings show that optical reservoir computing provides an effective way for applications of target ranging.

Photonics ◽  
2019 ◽  
Vol 6 (4) ◽  
pp. 124 ◽  
Author(s):  
Krishan Harkhoe ◽  
Guy Van der Sande

Reservoir computing has rekindled neuromorphic computing in photonics. One of the simplest technological implementations of reservoir computing consists of a semiconductor laser with delayed optical feedback. In this delay-based scheme, virtual nodes are distributed in time with a certain node distance and form a time-multiplexed network. The information processing performance of a semiconductor laser-based reservoir computing (RC) system is usually analysed by way of testing the laser-based reservoir computer on specific benchmark tasks. In this work, we will illustrate the optimal performance of the system on a chaotic time-series prediction benchmark. However, the goal is to analyse the reservoir’s performance in a task-independent way. This is done by calculating the computational capacity, a measure for the total number of independent calculations that the system can handle. We focus on the dependence of the computational capacity on the specifics of the masking procedure. We find that the computational capacity depends strongly on the virtual node distance with an optimal node spacing of 30 ps. In addition, we show that the computational capacity can be further increased by allowing for a well chosen mismatch between delay and input data sample time.


2001 ◽  
Vol 63 (3) ◽  
Author(s):  
Y. Liu ◽  
H. F. Chen ◽  
J. M. Liu ◽  
P. Davis ◽  
T. Aida

2018 ◽  
pp. 96-101 ◽  
Author(s):  
Bruno Garbin ◽  
Giovanna Tissoni ◽  
Stephane Barland

Semiconductor lasers with optical injection may be brought to an “excitable” regime, in which they respond to external perturbations in a neuron-like way. When submitted to delayed optical feedback this system can host stable optical localized states. We characterize experimentally the excitable response of a semiconductor laser with optical injection to external perturbations for different parameter values and show that localized states may diffuse in presence of noise.


Nanophotonics ◽  
2020 ◽  
Vol 9 (13) ◽  
pp. 4163-4171 ◽  
Author(s):  
Irene Estébanez ◽  
Janek Schwind ◽  
Ingo Fischer ◽  
Apostolos Argyris

AbstractSemiconductor lasers (SLs) that are subject to delayed optical feedback and external optical injection have been demonstrated to perform information processing using the photonic reservoir computing paradigm. Optical injection or optical feedback can under some conditions induce bandwidth-enhanced operation, expanding their modulation response up to several tens of GHz. However, these conditions may not always result in the best performance for computational tasks, since the dynamical and nonlinear properties of the reservoir might change as well. Here we show that by using strong optical injection we can obtain an increased frequency response and a significant acceleration in the information processing capability of this nonlinear system, without loss of performance. Specifically, we demonstrate numerically that the sampling time of the photonic reservoir can be as small as 12 ps while preserving the same computational performance when compared to a much slower sampling rate. We also show that strong optical injection expands the reservoir’s operating conditions for which we obtain improved task performance. The latter is validated experimentally for larger sampling times of 100 ps. The above attributes are demonstrated in a coherent optical communication decoding task.


2017 ◽  
Vol 25 (3) ◽  
pp. 2401 ◽  
Author(s):  
Julián Bueno ◽  
Daniel Brunner ◽  
Miguel C. Soriano ◽  
Ingo Fischer

2018 ◽  
Vol 26 (8) ◽  
pp. 10211 ◽  
Author(s):  
YuShuang Hou ◽  
GuangQiong Xia ◽  
WenYan Yang ◽  
Dan Wang ◽  
Elumalai Jayaprasath ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document