Quantum machine learning improves pulsar classification in radio astronomy.
problem Improving classification of pulsars in radio astronomy.
method Used a Born machine (quantum neural network) with a single-qubit architecture.
result Comparable accuracies to classical machine learning methods achieved.
We propose a scheme to employ backpropagation neural networks (BPNNs) for both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal strengths (FWIPS): radio map construction during the offline stage, and localization during the online stage. Given a training radio map (TRM), i.e., a set of coordinat…
Research uses CPS to estimate uncertainty in ML radio metric models.
problem Estimating uncertainty in machine learning models for radio metrics and path loss.
method Conformal Prediction (CP) in Conformal Predictive Systems (CPS) with diverse difficulty estimators.
result CPS models maintain high coverage and reliability across different cities.
ASKAP observes a region of the Galactic plane, identifying 3963 radio sources.
problem Characterizing radio sources in the Galactic plane using ASKAP observations.
method 912 MHz observations with 15 ASKAP antennas, source characterization using CAESAR.
result Differential source counts in agreement with previous surveys, spectral index estimation for sources.
New method uses SVBI to quickly generate radio maps for indoor positioning.
problem Time-consuming site surveys for creating radio maps in large indoor spaces.
method Combines fingerprinting positioning and SVBI for efficient radio map generation.
result Significantly reduces the cost and time of creating radio maps for indoor positioning.
Classifies compact radio sources in the Galactic plane using machine learning.
problem Challenges in processing large volumes of radio continuum survey data.
method Produced a curated dataset of ~20,000 images, trained two classifiers: gradient-boosted decision trees and convolutional neural networks.
result High classification accuracy (F1-score>90%) for separating Galactic objects from the extragalactic background.
Radio emitter recognition in dense multi-user environments is an important tool for optimizing spectrum utilization, identifying and minimizing interference, and enforcing spectrum policy. Radio data is readily available and easy to obtain from an antenna, but labeled and curated data is often scarce making supervised …
Study uses deep learning to quickly estimate tissue properties for personalized radio-frequency dosimetry.
problem Time-consuming tissue segmentation limits personalized radio-frequency dosimetry.
method Developed a learning-based approach using Convolutional Neural Networks (CNN) for magnetic resonance images.
result Smooth distribution of dielectric properties improves SAR distribution consistency.
CAESAR source finder improves automated source extraction for ASKAP surveys.
problem Automated source extraction challenges in ASKAP surveys.
method Extended CAESAR source finder for compact and extended sources.
result Improved algorithm performances and scalability for future ASKAP surveys.
Deep RL for uncoordinated cognitive radios finds near-optimal policies.
problem Resource allocation in uncoordinated cognitive radio networks.
method Distributed deep reinforcement learning algorithm.
result Algorithm converges to near-optimal policies in finite time.
In order to cope with the increased data volumes generated by modern radio interferometers such as LOFAR (Low Frequency Array) or SKA (Square Kilometre Array), fast and efficient calibration algorithms are essential. Traditional radio interferometric calibration is performed using nonlinear optimization techniques such…
The performance of a modulation classifier is highly sensitive to channel signal-to-noise ratio (SNR). In this paper, we focus on amplitude-phase modulations and propose a modulation classification framework based on centralized data fusion using multiple radios and the hybrid maximum likelihood (ML) approach. In order…
Bayesian approach for adaptive radio tomography using SLFs.
problem Accurately modeling spatial loss fields for interference management.
method Variational Bayes framework with hidden Markov random field model.
result Efficient field estimators at reduced complexity.
Deep reinforcement learning boosts throughput in RF-powered cognitive radio networks.
problem Maximizing throughput in large-scale, decentralized RF-powered cognitive radio networks.
method Proposes deep reinforcement learning to find optimal policies for network throughput maximization.
result Deep reinforcement learning outperforms existing techniques in large-scale RF-CRN environments.
DL-based radio signal classification is vulnerable to adversarial attacks.
problem Vulnerability of DL to adversarial attacks in radio signal classification.
method Crafted white-box and universal black-box adversarial attacks.
result Adversarial attacks can reduce classification performance with small perturbations.
Robust state-space radio interferometric imaging using Stochastic Approximation Expectation Maximization
problem Improving state-space radio interferometric imaging in the presence of heavy-tailed noise
method Stochastic Approximation Expectation Maximization
result Significant improvement in reconstruction fidelity and robustness to radio-frequency interference
Deep learning models, especially CNNs, can predict radio frequency power faster than traditional methods.
problem Accurate radio frequency power prediction for optimal transmitter location.
method Empirical analysis of deep learning models including CNNs and UNET variations for power prediction.
result Deep learning models, particularly CNNs, are effective and generalize well to new regions for power prediction.
WiFi helps align and calibrate foot-mounted IMU trajectories.
problem Inertial drift and unknown initial states in FMIP.
method Graph-based SLAM with RSS measurements for WiFi APs.
result Aligns and calibrates trajectories accurately.
Study improves radio show segmentation using audio embeddings.
problem Automated segmentation of radio shows.
method Created audio embeddings from multi-class classification tasks on different datasets, evaluated performance against text-only baseline.
result Audio embeddings from non-speech sound event classification significantly outperformed text-only baseline by 32.3% in F1-measure.
Paper tackles caching in fog radio access networks using RL.
problem Distributed edge caching in fog radio access networks with fluctuating traffic demands.
method Q-learning framework for optimal caching policy, value function approximation.
result Proposed method outperforms traditional methods in simulations.
We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of userdefined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to da…
Recently, the need of deploying new wireless networks for smart gas metering has raised the problem of radio planning in the169 MHz band. Unluckily, software tools commonly adopted for radio planning in cellular communication systems cannot be employed to solve this problem because of the substantially lower transmissi…
Survey on multiplayer bandits, highlighting theoretical gaps and future directions.
problem Theoretical advancements in multiplayer bandits lack practical implementation in real-world scenarios.
method Organizes and contextualizes existing literature on multiplayer bandits.
result Clear directions for future research in adapting theoretical algorithms to real-world situations.
A distributed RL framework optimizes radio resource management for wireless networks.
problem Interference in wireless networks limits performance; maximizing average and worst-case throughput is challenging.
method Multi-agent deep reinforcement learning (RL) for distributed link scheduling.
result The framework achieves superior average and 5th percentile user throughput compared to decentralized methods.
DeepSource uses deep learning to detect sources in radio interferometry images.
problem Challenging point source detection at low signal-to-noise in radio interferometry images.
method Convolutional neural networks to enhance SNR, dynamic blob detection.
result DeepSource achieves essentially perfect purity and completeness down to SNR = 4, outperforming PyBDSF.
Automatic modulation classification (AMC) is an important task for modern communication systems; however, it is a challenging problem when signal features and precise models for generating each modulation may be unknown. We present a new biologically-inspired AMC method without the need for models or manually specified…
Kernel-based RL learns efficient spectrum access with budget constraints.
problem Efficient spectrum access in congested bands.
method Kernel-based reinforcement learning with budget-constrained sparsification.
result Performance gains over carrier-sense systems.
CC charts radio geometry for user localization.
problem Locating users in radio environments.
method Unsupervised learning from passive CSI.
result Extracts channel features for spatial comparison.
Deep learning classifies mobile traffic from LTE PDCCH.
problem Automatic classification of mobile applications and services.
method Convolutional Neural Network (CNN) trained on DCI messages.
result 99% accuracy in classifying traffic from LTE PDCCH.
Paper predicts GNSS phase scintillations with machine learning.
problem Predicting phase scintillations due to ionosphere disturbances.
method Proposes a novel machine learning architecture and loss function.
result Achieves state-of-the-art prediction of phase scintillations 1 hour in advance.
Develops deep learning for optimizing 5G radio resource allocation.
problem Optimizing 5G base station radio resources for diverse QoS requirements.
method Cascaded neural network structure with deep transfer learning for non-stationary conditions.
result Cascaded neural networks outperform fully connected neural networks in QoS guarantee.
Graph neural networks optimize radio resource management policies for wireless networks.
problem Optimizing user selection and power control in wireless networks with fairness constraints.
method Formulated as a Lagrangian dual problem, RRM policies are parameterized by a GNN architecture trained on channel conditions.
result The method achieves superior tradeoff between average and 5th percentile rates, demonstrating fairness.
Proposes a multi-stage algorithm for efficient spectrum access in CR networks.
problem High demand for wireless spectrum and need for high throughput and energy efficiency in SUs.
method Centralized multi-stage algorithm with non-parametric learning and adaptive collision avoidance.
result Ensures minimum interference to licensed users while providing high throughput and energy efficiency.
We propose a method for downlink coordinated multipoint (DL CoMP) in heterogeneous fifth generation New Radio (NR) networks. The primary contribution of our paper is an algorithm to enhance the trigger of DL CoMP using online machine learning. We use support vector machine (SVM) classifiers to enhance the user downlink…
Optimizes power allocation for WDM in RoFSO systems.
problem Maximizing total capacity with power and eye safety constraints.
method Model-based Stochastic Dual Gradient algorithm and model-free Primal-Dual Deep Learning algorithm.
result Deep Learning algorithm outperforms average equal power allocation.
Deep RL improves cellular network fault management and performance.
problem Fault management and radio performance improvement in outdoor cellular networks.
method Deep Q-Learning for self-organizing networks fault management.
result The proposed algorithm learns to clear alarms and improve radio performance better than existing methods.
Paper tackles ML for non-scheduled URLLC traffic in 5G networks.
problem Coexistence of scheduled and non-scheduled URLLC traffic with stringent reliability and latency requirements.
method Distributed risk-aware ML solution for resource management (RRM).
result 75% increase in data rate for scheduled traffic with 99.99% reliability for both types.
RadioUNet uses deep learning to estimate pathloss in urban environments.
problem Accurate pathloss estimation for urban environments.
method Deep neural networks trained on physical simulation data.
result RadioUNet generates pathloss estimations faster and more accurately than existing methods.
Landmark2Vec maps unknown landmarks without GPS.
problem Estimate positions of unknown landmarks without GPS.
method Unsupervised neural network trained on landmark signals.
result Maps landmarks up to scale, rotation, and shift.
New deep learning model estimates scattering timescale of FRBs efficiently.
problem Estimating scattering timescale of fast radio bursts (FRBs) is a bottleneck.
method Multimodal Transformer Based Generic Mixture Density Network (MT-GMDN) that ingests dynamic spectrum and timeseries profile.
result Achieves 94% R2 on expected value of τ for measurable scattering. This article reviews ML applications in wireless networks over 30 years.
problem Achieving high-rate, low-latency, and reliable wireless services in complex networks.
method Exploration of various ML algorithms including supervised, unsupervised, reinforcement, and deep learning.
result Machine learning algorithms have been successfully applied to wireless networks.
Generative adversarial network improves spectrum sensing accuracy.
problem Lack of sufficient and adaptable training data for spectrum sensing.
method Generative adversarial network (GAN) for data augmentation and adaptation.
result Training data augmentation significantly increases classifier accuracy.
New method uses SBI to infer magnetorotational properties of isolated pulsars.
problem Constrain magnetorotational properties of isolated Galactic radio pulsars.
method Combines population synthesis with SBI to model neutron star birth and evolution.
result Inferred μlogB=13.10−0.10+0.08, σlogB=0.45−0.05+0.05 for lognormal distributions. A new ML framework for RF fingerprinting across various applications.
problem Extracting unique RF fingerprints for specific emitter identification.
method Generic machine learning framework for automatic RF fingerprinting.
result Framework achieves superior performance compared to traditional methods.
Paper presents a simple method for accurate user localization in urban areas.
problem Poor GPS performance in dense urban environments.
method Localization based on pathloss using base station signal strengths and RadioUNet approximations.
result Accurate user location extraction in urban environments.
Results of the application of pattern recognition techniques to the problem of identifying Giant Radio Sources (GRS) from the data in the NVSS catalog are presented and issues affecting the process are explored. Decision-tree pattern recognition software was applied to training set source pairs developed from known NVS…
Optimizes data power control in cell-free networks for better spectral efficiency.
problem Maximizing overall spectral efficiency in cell-free networks with multi-objective optimisation.
method Applied scalable multi-objective Bayesian optimisation to solve convergence-time limitations.
result Improved radio resource management in cell-free networks.
Developing a visual platform for faster astronomical source cataloging.
problem Speeding up cataloging of large area surveys in radio astronomy.
method Integration of advanced source finding and classification tools into a visual analytic platform.
result Improvement and acceleration of cataloging process in astronomical surveys.