Paper proposes a graph model for optimal AP deployment in indoor optical wireless networks.
problem Challenges in deploying optical wireless networks due to LoS requirement and limited range.
method Graph modeling approach to identify minimum number of APs and their optimal locations.
result Optimal deployment of APs ensures connectivity and minimizes interference in indoor environments.
Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space optical networks. This paper investigates the optimal design of power allocation for Wavelength Division Multiplexing (WDM) transmission in Ro…
New ML models improve VVLC channel characterization for vehicular OWC.
problem Inaccurate channel models for VVLC due to mobility effects.
method Machine learning (ML) models incorporating ambient light, turbulence, and reflection effects.
result ML models predict VVLC channel loss and CFR more accurately than existing methods.
ViWi dataset framework tackles wireless communication problems with visual data.
problem Leveraging visual sensory information in wireless communications.
method Developed a parametric, systematic, and scalable data generation framework using 3D modeling and ray-tracing.
result Offers a way to generate training and testing datasets for assessing machine learning solutions.
Multi-layer optical film has been found to afford important applications in optical communication, optical absorbers, optical filters, etc. Different algorithms of multi-layer optical film design has been developed, as simplex method, colony algorithm, genetic algorithm. These algorithms rapidly promote the design and …
Open problems in billiards and optics from a workshop.
problem Open problems in billiards and geometric optics.
method Collection of open problems from a workshop.
result No specific key result mentioned; collection of problems.
New method generates optical vortices in any knot shape.
problem Generating optical vortices in complex shapes beyond simple knots.
method Mathematical construction and experimental verification.
result Complex optical fields can form any knot shape.
The paper discusses scalable learning for wireless data-driven systems.
problem Expanding data volume and model complexity limit centralized learning solutions.
method Discusses scalable architecture and local learning strategies.
result Promising research directions in scalable data-driven wireless communications.
Optical flow refers to the visual motion observed between two consecutive images. Since the degree of freedom is typically much larger than the constraints imposed by the image observations, the straightforward formulation of optical flow as an inverse problem is ill-posed. Standard approaches to determine optical flow…
Method detects and locates eavesdropping in optical links.
problem Detect and locate eavesdropping in optical links with small power losses.
method Cluster-based approach using OPM data at receiver and in-line OPM data for localization.
result Subtle eavesdropping losses can be detected and localized using OPM data.
Optical ESNs enable flexible, efficient machine learning with reduced energy.
problem Implementing universal computational capabilities in machine learning.
method Optical implementation of ESNs leveraging stimulated Brillouin scattering.
result Efficient, scalable, and memory-capable optical reservoir computing.
Paper develops a deforestation detection system using optical and SAR data.
problem Detecting tree-loss in dense forests using satellite data.
method Combines optical and SAR data, uses KL expansion for anomaly detection, and Hidden Markov Model for classification.
result Hybrid method achieves high accuracy and robustness in sparse optical data.
Study of optical geometries with intrinsic torsion in general relativity.
problem Understanding null line distributions and their properties in Lorentzian manifolds.
method Investigation of intrinsic torsion and congruences of null curves, extending to generalized optical geometries.
result Characterization of conformal properties of null line distributions and congruences.
Optical DNNet boosts accuracy with multiple frequency-channels.
problem Improving the accuracy of optical neural networks.
method Developed a novel optical diffractive deep neural network with multiple frequency-channels.
result Multiple frequency-channels significantly increase network accuracy.
Generative model learns wireless channel distributions efficiently.
problem Learning precise wireless channel distributions for optimal communication.
method Physics-informed sparse Bayesian generative modeling (SBGM) with compressed data.
result Model learns channel parameters from compressed AP observations, is physically interpretable, and generalizes across different systems.
Wi-GATr learns to simulate wireless signals with high accuracy and speed.
problem Inaccurate wireless signal propagation models limit modern communication system design.
method Wi-GATr uses a Geometric Algebra Transformer to learn from scene primitives.
result Wi-GATr achieves more accurate predictions than existing methods.
Glaucoma is the second leading cause of blindness all over the world, with approximately 60 million cases reported worldwide in 2010. If undiagnosed in time, glaucoma causes irreversible damage to the optic nerve leading to blindness. The optic nerve head examination, which involves measurement of cup-to-disc ratio, is…
We present a deep learning framework for wide-field, content-aware estimation of absorption and scattering coefficients of tissues, called Generative Adversarial Network Prediction of Optical Properties (GANPOP). Spatial frequency domain imaging is used to obtain ground-truth optical properties from in vivo human hands…
We describe two applications of machine learning in the context of IP/Optical networks. The first one allows agile management of resources at a core IP/Optical network by using machine learning for short-term and long-term prediction of traffic flows and joint global optimization of IP and optical layers using colorles…
Express Wavenet reduces neural network parameters to 1% of standard networks.
problem Optical neural networks with high parameter count.
method Wavelet modulation, random shift wavelets, expressway structure.
result Express Wavenet achieves high accuracy with significantly fewer parameters.
Optimizes wireless power control using graph neural networks and counterfactual optimization.
problem Mitigating interference in wireless networks with multiple transmitter-receiver pairs.
method Graph neural network architecture combined with unsupervised primal-dual counterfactual optimization.
result Guarantees a minimum rate constraint that adapts to network size, balancing user rates.
A novel method for efficient CDRL over wireless networks.
problem Challenges in collaborative deep reinforcement learning over wireless networks.
method Semantic-aware heterogeneous federated deep reinforcement learning (HFDRL) algorithm.
result Superior performance compared to state-of-the-art baselines.
In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station …
Edge devices learn a global model collaboratively over wireless channels.
problem Learning a global model from edge devices with imperfect channel state information.
method Proposed analog aggregation scheme, receive beamforming at PS, and convergence analysis.
result Performance improvement with more PS antennas, even with imperfect CSI.
Derives Levi-Civita connection formulas for specific geometries.
problem Determining geometric invariants of Lorentzian manifolds.
method Explicitly derives Christoffel symbols in terms of adapted frame fields.
result Formulas for geometric invariants of Lorentzian manifolds.
Paper develops deep neural networks for wireless tasks with reduced complexity.
problem Reducing training complexity for deep neural networks in wireless systems.
method Develops permutation invariant DNNs (PINNs) leveraging wireless task properties.
result Demonstrates dramatic reduction in training complexity for PINNs.
CHOOSE enhances shallow Transformers for wireless symbol detection.
problem Improving wireless symbol detection with shallow Transformers.
method Introducing autoregressive latent reasoning steps within hidden space.
result Lightweight Transformers achieve comparable performance to deep models.
Study compares different levels of supervision for training graph embeddings in wireless networks.
problem Improving power control in wireless interference networks.
method Training graph neural networks (GNNs) with different levels of supervision (supervised, unsupervised, self-supervised).
result Different levels of supervision impact system-level throughput, convergence, and generalization.
Designs chiral photonic structures using machine learning for efficient optical properties.
problem Optimizing chiral photonic nanostructures for light-matter interactions.
method Evolutionary algorithm and neural network approach for rapid optimization.
result Frequency-dependent modification in reflected light's degree of circular polarization.
State-augmented algorithm optimizes wireless network resource management.
problem Optimizing resource allocation in multi-user wireless networks.
method Proposes a state-augmented algorithm using dual variables.
result Feasible and near-optimal resource decisions achieved.
In the last decade, there has been a great technological advance in the infrastructure of mobile technologies. The increase in the use of wireless local area networks and the use of satellite services are also noticed. The high utilization rate of mobile devices for various purposes makes clear the need to track wirele…
Paper proposes DRL for unsupervised IoT localization.
problem Challenges in unsupervised localization of IoT devices.
method Modeling localization as MDP, using DRL with reward-setting and RSS measurements.
result Demonstrated effectiveness of DRL in wireless localization.
New methods for constructing null fluid metrics and solving optical lift conjectures.
problem Constructing null fluid metrics and solving optical lift conjectures.
method Explicit parameterization of null fluid metrics under Kerr type optical structures.
result New explicit metrics, including Kerr black holes and Ricci flat examples.
The paper develops a learning algorithm for distributed training and inference in wireless networks.
problem Challenges of leveraging machine learning in highly distributed wireless networks.
method Developed a learning algorithm and architecture for distributed training and inference.
result Inference propagates and fuses across a network, with benefits over state-of-the-art techniques.
Survey on ML for wireless network optimization across PHY, MAC, and network layers.
problem Improving wireless network performance using machine learning.
method Comprehensive review of ML-based techniques for wireless network optimization.
result Machine learning can significantly enhance wireless network QoS and QoE across all layers.
There is a growing interest in the wireless communications community to complement the traditional model-based design approaches with data-driven machine learning (ML)-based solutions. While conventional ML approaches rely on the assumption of having the data and processing heads in a central entity, this is not always…
Paper presents a WiFi-based indoor sensor localization technique.
problem Indoor localization in wireless sensor networks.
method Zoning-based localization using statistical learning.
result Efficient sensor zone determination in indoor environments.
A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security. Such a fingerprint should be able to distinguish between devices sending the same message, and should be robust against standard spoofing techniques. Since the information in wirele…
Proposes efficient calibration for indoor localization models.
problem Calibration data scarcity in wireless indoor localization.
method Uses synthetic labels and prediction sets to fine-tune a predictor and estimate bias.
result Yields rigorous coverage guarantees for prediction sets.
We discuss recently emerging applications of the state-of-art deep learning methods on optical microscopy and microscopic image reconstruction, which enable new transformations among different modes and modalities of microscopic imaging, driven entirely by image data. We believe that deep learning will fundamentally ch…
Despite the advantages of all-weather and all-day high-resolution imaging, SAR remote sensing images are much less viewed and used by general people because human vision is not adapted to microwave scattering phenomenon. However, expert interpreters can be trained by compare side-by-side SAR and optical images to learn…
The works of William Rowan Hamilton in Geometrical Optics are presented, with emphasis on the Malus-Dupin theorem. According to that theorem, a family of light rays depending on two parameters can be focused to a single point by an optical instrument made of reflecting or refracting surfaces if and only if, before ente…
Improved DL models robust against adversarial attacks for wireless signal classification.
problem Adversarial attacks on deep learning-based wireless signal classifiers.
method Knowledge distillation and network pruning followed by adversarial training.
result Proposed models achieve better robustness and higher accuracy than standard models.
This chapter deals with decentralized learning algorithms for in-network processing of graph-valued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the alternating-direction method of multipliers (ADMM) so as to gain the desired degree of paralleliza…
Lin et al. (Reports, 7 September 2018, p. 1004) reported a remarkable proposal that employs a passive, strictly linear optical setup to perform pattern classifications. But interpreting the multilayer diffractive setup as a deep neural network and advocating it as an all-optical deep learning framework are not well jus…
The paper constructs a generalized metrical multi-time Lagrange space, which allows a natural development of relativistic geometrical optics theories, in a general setting.
Optimizes convergence time of federated learning over wireless networks.
problem Limited resource blocks in wireless networks affect federated learning convergence time and performance.
method Formulates an optimization problem to minimize convergence time while optimizing performance, proposes a probabilistic user selection scheme and uses ANNs for estimation.
result Improves convergence time and performance of federated learning over wireless networks.
A-FADMM improves FL scalability and privacy via wireless channel perturbations and interference.
problem Challenges in model training due to wireless channel randomness and interference.
method Formulated a novel constrained optimization problem and proposed A-FADMM framework.
result Proves convergence and privacy guarantees for A-FADMM under time-varying channels.