skip to main content


Title: Probabilistic spectrum Gaussian noise estimate for random bandwidth traffic
A probabilistic spectrum Gaussian noise (PSGN) model is proposed to predict the nonlinear noise for random bandwidth traffic in long-haul elastic optical networks. The model reduces the noise estimate 9.1% on average compared to the standard Gaussian noise model applied to the maximum bandwidth.  more » « less
Award ID(s):
1718130
NSF-PAR ID:
10166451
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 45th European Conference on Optical Communication
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Flexible grid networks need rigorous resource planning to avoid network over-dimensioning and resource over-provisioning. The network must provision the hardware and spectrum resources statically, even for dynamic random bandwidth demands, due to the infrastructure of flexible grid networks, hardware limitations, and reconfiguration speed of the control plane. We propose a flexible online–offline probabilistic (FOOP) algorithm for the static spectrum assignment of random bandwidth demands. The FOOP algorithm considers the probabilistic nature of random bandwidth demands and balances hardware and control plane pressures with spectrum assignment efficiency. The FOOP algorithm uses the probabilistic spectrum Gaussian noise (PSGN) model to estimate the physical-layer impairment (PLI) for random bandwidth traffic. Compared to a benchmark spectrum assignment algorithm and a widely applied PLI estimation model, the proposed FOOP algorithm using the PSGN model saves up to 49% of network resources.

     
    more » « less
  2. Time-frequency (TF) filtering of analog signals has played a crucial role in the development of radio-frequency communications and is currently being recognized as an essential capability for communications, both classical and quantum, in the optical frequency domain. How best to design optical time-frequency (TF) filters to pass a targeted temporal mode (TM), and to reject background (noise) photons in the TF detection window? The solution for ‘coherent’ TF filtering is known—the quantum pulse gate—whereas the conventional, more common method is implemented by a sequence of incoherent spectral filtering and temporal gating operations. To compare these two methods, we derive a general formalism for two-stage incoherent time-frequency filtering, finding expressions for signal pulse transmission efficiency, and for the ability to discriminate TMs, which allows the blocking of unwanted background light. We derive the tradeoff between efficiency and TM discrimination ability, and find a remarkably concise relation between these two quantities and the time-bandwidth product of the combined filters. We apply the formalism to two examples—rectangular filters or Gaussian filters—both of which have known orthogonal-function decompositions. The formalism can be applied to any state of light occupying the input temporal mode, e.g., ‘classical’ coherent-state signals or pulsed single-photon states of light. In contrast to the radio-frequency domain, where coherent detection is standard and one can use coherent matched filtering to reject noise, in the optical domain direct detection is optimal in a number of scenarios where the signal flux is extremely small. Our analysis shows how the insertion loss and SNR change when one uses incoherent optical filters to reject background noise, followed by direct detection, e.g. photon counting. We point out implications in classical and quantum optical communications. As an example, we study quantum key distribution, wherein strong rejection of background noise is necessary to maintain a high quality of entanglement, while high signal transmission is needed to ensure a useful key generation rate.

     
    more » « less
  3. The combination of photonic integrated circuits and free-space metaoptics has the ability to untie technological knots that require advanced light manipulation due to their conjoined ability to achieve strong light–matter interaction via wave-guiding light over a long distance and shape them via large space-bandwidth product. Rapid prototyping of such a compound system requires component interchangeability. This represents a functional challenge in terms of fabrication and alignment of high-performance optical systems. Here, we report a flexible and interchangeable interface between a photonic integrated circuit and the free space using an array of low-loss metaoptics and demonstrate multifunctional beam shaping at a wavelength of 780 nm. We show that robust and high-fidelity operation of the designed optical functions can be achieved without prior precise characterization of the free-space input nor stringent alignment between the photonic integrated chip and the metaoptics chip. A diffraction limited spot of ∼3 μm for a hyperboloid metalens of numerical aperture 0.15 is achieved despite an input Gaussian elliptical deformation of up to 35% and misalignments of the components of up to 20 μm. A holographic image with a peak signal-to-noise ratio of >10 dB is also reported. 
    more » « less
  4. The radio frequency spectral shaper is an essential component in emerging multi-service mobile communications, multiband satellite and radar systems, and future 5G/6G radio frequency systems for equalizing spectral unevenness, removing out-of-band noise and interference, and manipulating multi-band signal simultaneously. While it is easy to achieve simple spectral functions using either conventional microwave photonic filters or the optical spectrum to microwave spectra mapping techniques, it is challenging to enable complex spectral shaping functions over tens of GHz bandwidth as well as to achieve point-by-point shaping capability to fulfill the needs in dynamic wireless communications. In this paper, we proposed and demonstrated a novel spectral shaping system, which utilizes a two-section algorithm to automatically decompose the target RF response into a series of Gaussian functions and to reconstruct the desired RF response by microwave photonic techniques. The devised spectral shaping system is capable of manipulating the spectral function in various bands (S, C, and X) simultaneously with step resolution of as fine as tens of MHz. The resolution limitation in optical spectral processing is mitigated using the discrete convolution technique. Over 10 dynamic and independently adjustable spectral control points are experimentally achieved based on the proposed spectral shaper.

     
    more » « less
  5. In this thesis, I present a decentralized sparse Gaussian process regression (DSGPR) model with event-triggered, adaptive inducing points. This DSGPR model brings the advantages of sparse Gaussian process regression to a decentralized implementation. Being decentralized and sparse provides advantages that are ideal for multi-agent systems (MASs) performing environmental modeling. In this case, MASs need to model large amounts of information while having potential intermittent communication connections. Additionally, the model needs to correctly perform uncertainty propagation between autonomous agents and ensure high accuracy on the prediction. For the model to meet these requirements, a bounded and efficient real-time sparse Gaussian process regression (SGPR) model is needed. I improve real-time SGPR models in these regards by introducing an adaptation of the mean shift and fixed-width clustering algorithms called radial clustering. Radial clustering enables real-time SGPR models to have an adaptive number of inducing points through an efficient inducing point selection process. I show how this clustering approach scales better than other seminal Gaussian process regression (GPR) and SGPR models for real-time purposes while attaining similar prediction accuracy and uncertainty reduction performance. Furthermore, this thesis addresses common issues inherent in decentralized frameworks such as high computation costs, inter-agent message bandwidth restrictions, and data fusion integrity. These challenges are addressed in part through performing maximum consensus between local agent models which enables the MAS to gain the advantages of decentralization while keeping data fusion integrity. The inter-agent communication restrictions are addressed through the contribution of two message passing heuristics called the covariance reduction heuristic and the Bhattacharyya distance heuristic. These heuristics enable user to reduce message passing frequency and message size through the Bhattacharyya distance and properties of spatial kernels. The entire DSGPR framework is evaluated on multiple simulated random vector fields. The results show that this framework effectively estimates vector fields using multiple autonomous agents. This vector field is assumed to be a wind field; however, this framework may be applied to the estimation of other scalar or vector fields (e.g., fluids, magnetic fields, electricity, etc.). Keywords: Sparse Gaussian process regression, clustering, event-triggered, decentralized, sensor fusion, uncertainty propagation, inducing points 
    more » « less