SNR optimization of multi-span fiber optic communication systems employing EDFAs with non-flat gain and noise figure

• URL: http://arxiv.org/abs/2106.03639v1
• Date: Mon, 7 Jun 2021 14:09:53 GMT
• Title: SNR optimization of multi-span fiber optic communication systems employing EDFAs with non-flat gain and noise figure
• Authors: Metodi Plamenov Yankov, Pawel Marcin Kaminski, Henrik Enggaard Hansen, Francesco Da Ros
• Abstract summary: We propose a component wise model of a multi-span transmission system for signal-to-noise (SNR) optimization. A machine-learning based model is trained for the gain and noise figure spectral profile of a C-band amplifier without a GFF. An SNR flatness down to 1.2 dB is simultaneously achieved.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Throughput optimization of optical communication systems is a key challenge for current optical networks. The use of gain-flattening filters (GFFs) simplifies the problem at the cost of insertion loss, higher power consumption and potentially poorer performance. In this work, we propose a component wise model of a multi-span transmission system for signal-to-noise (SNR) optimization. A machine-learning based model is trained for the gain and noise figure spectral profile of a C-band amplifier without a GFF. The model is combined with the Gaussian noise model for nonlinearities in optical fibers including stimulated Raman scattering and the implementation penalty spectral profile measured in back-to-back in order to predict the SNR in each channel of a multi-span wavelength division multiplexed system. All basic components in the system model are differentiable and allow for the gradient descent-based optimization of a system of arbitrary configuration in terms of number of spans and length per span. When the input power profile is optimized for flat and maximized received SNR per channel, the minimum performance in an arbitrary 3-span experimental system is improved by up to 8 dB w.r.t. a system with flat input power profile. An SNR flatness down to 1.2 dB is simultaneously achieved. The model and optimization methods are used to optimize the performance of an example core network, and 0.2 dB of gain is shown w.r.t. solutions that do not take into account nonlinearities. The method is also shown to be beneficial for systems with ideal gain flattening, achieving up to 0.3 dB of gain w.r.t. a flat input power profile.

Related papers

• A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems [63.213207442368294]
  We consider a novel optimization design for multi-waveguide pinching-antenna systems.<n>The proposed GML-JO algorithm is robust to different choices and better performance compared with the existing optimization methods.
  arXiv  Detail & Related papers  (2025-06-14T17:35:27Z)
• Programmable Photonic Unitary Processor Enables Parametrized Differentiable Long-Haul Spatial Division Multiplexed Transmission [2.602614049977146]
  Spatial division multiplexing (SDM) using multicore or multimode fibers is a promising solution to overcome the capacity limit of single-mode fibers.<n>Long-haul SDM transmission faces significant challenges due to modal dispersion.<n>We propose parameterized SDM transmission, where programmable photonic unitary processors are installed at intermediate nodes.
  arXiv  Detail & Related papers  (2025-05-23T01:35:41Z)
• Joint Transmit and Pinching Beamforming for Pinching Antenna Systems (PASS): Optimization-Based or Learning-Based? [89.05848771674773]
  A novel antenna system ()-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed. It consists of multiple waveguides, which equip numerous low-cost antennas, named (PAs) The positions of PAs can be reconfigured to both spanning large-scale path and space.
  arXiv  Detail & Related papers  (2025-02-12T18:54:10Z)
• Graph Neural Network Based Hybrid Beamforming Design in Wideband Terahertz MIMO-OFDM Systems [11.887537452826622]
  6G wireless technology is projected to adopt higher and wider frequency bands, enabled by highly directional beamforming. The vast bandwidths available also make the impact of beam squint in massive multiple input and multiple output (MIMO) systems non-negligible. Traditional approaches such as adding a true-time-delay line (TTD) on each antenna are costly due to the massive antenna arrays required. This paper puts forth a signal processing alternative, specifically adapted to the multicarrier structure of OFDM systems, through an innovative application of Graph Neural Networks (GNNs) to optimize hybrid beamforming.
  arXiv  Detail & Related papers  (2025-01-27T18:45:54Z)
• Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors [52.195637608631955]
  Non-line-of-sight (NLOS) imaging has attracted increasing attention due to its potential applications. Existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors. We introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF)
  arXiv  Detail & Related papers  (2024-09-21T04:39:45Z)
• Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework [70.83511997272457]
  We propose a semi-federated learning (SemiFL) paradigm to leverage both the base station (BS) and devices for a hybrid implementation of centralized learning (CL) and FL. We propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers.
  arXiv  Detail & Related papers  (2023-10-04T03:32:39Z)
• A comparison between black-, grey- and white-box modeling for the bidirectional Raman amplifier optimization [0.8098985611919018]
  offline optimization of optical amplifiers relies on models ranging from white-box models deeply rooted in physics to black-box data-driven and physics-agnostic models. We show that any of the studied methods can achieve similar frequency and distance flatness of between 1 and 3.6 dB over the C-band in an 80-km span.
  arXiv  Detail & Related papers  (2023-09-11T08:39:57Z)
• One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers [57.441926088870325]
  Deep Image Prior (DIP) is a technique that optimized the weights of a randomly-d convolutional neural network to fit a signal from noisy or under-determined measurements. Relative to publicly available implementations of Vector Fitting (VF), our method shows superior performance on nearly all test examples.
  arXiv  Detail & Related papers  (2023-06-06T20:28:37Z)
• Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks [103.22651843174471]
  This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which aggregates them into a quantized global model and synchronizes the devices. We show that the FL training process can be described as a Markov decision process and propose a model-based reinforcement learning (RL) method to optimize action selection over iterations.
  arXiv  Detail & Related papers  (2022-09-21T08:52:51Z)
• Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach [79.59560136273917]
  limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL) We first devise a novel FL framework with partial model aggregation (PMA) The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-04-20T19:09:52Z)
• A neural network-supported two-stage algorithm for lightweight dereverberation on hearing devices [13.49645012479288]
  A two-stage lightweight online dereverberation algorithm for hearing devices is presented in this paper. The approach combines a multi-channel multi-frame linear filter with a single-channel single-frame post-filter. Both components rely on power spectral density (PSD) estimates provided by deep neural networks (DNNs)
  arXiv  Detail & Related papers  (2022-04-06T11:08:28Z)
• Fast and accurate waveform modeling of long-haul multi-channel optical fiber transmission using a hybrid model-data driven scheme [3.771681732160885]
  The proposed scheme is demonstrated to have high accuracy, high computing speeds, and robust abilities for different optical launch powers, modulation formats, channel numbers and transmission distances. The results represent a remarkable improvement in nonlinear fiber modeling and open up novel perspectives for solution of NLSE-like partial differential equations and optical fiber physics problems.
  arXiv  Detail & Related papers  (2022-01-12T06:10:30Z)
• Learning OFDM Waveforms with PAPR and ACLR Constraints [15.423422040627331]
  We propose a learning-based method to design OFDM-based waveforms that satisfy selected constraints while maximizing an achievable information rate. We show that the end-to-end system is able to satisfy target PAPR and ACLR constraints and allows significant throughput gains.
  arXiv  Detail & Related papers  (2021-10-21T08:58:59Z)
• Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation [10.775558382613077]
  Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. We propose a deep learning-based approach that directly optimize the beamformers at the base station according to the received uplink pilots. A neural calibration method is proposed to improve the scalability of the end-to-end design.
  arXiv  Detail & Related papers  (2021-08-03T14:26:14Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.