arXiv research

Deep RL for uncoordinated cognitive radios finds near-optimal policies.

Deep reinforcement learning boosts throughput in RF-powered cognitive radio networks.

Survey on multiplayer bandits, highlighting theoretical gaps and future directions.

A novel approach of training data augmentation and domain adaptation is presented to support machine learning applications for cognitive radio. Machine learning provides effective tools to automate cognitive radio functionalities by reliably extracting and learning intrinsic spectrum dynamics. However, there are two im…

A strategy for spectrum sharing in CRNs with multiple PT power levels.

Owing to the ever-increasing demand in wireless spectrum, Cognitive Radio (CR) was introduced as a technique to attain high spectral efficiency. As the number of secondary users (SUs) connecting to the cognitive radio network is on the rise, there is an imminent need for centralized algorithms that provide high through…

This article reviews ML applications in wireless networks over 30 years.

New algorithm outperforms existing ones in multi-player bandit problems without sensing.

A new algorithm RESYNC for defenders against malicious attackers in multi-player bandits.

New algorithm robust to probabilistic unbounded adversarial attacks in bandit problems.

New approach to modulation recognition using unsupervised learning.

Paper proposes FMAB framework for federated learning with two models: approximate and exact.

Two key challenges in underlay dynamic spectrum access (DSA) are how to establish an interference limit from the primary network (PN) and how cognitive radios (CRs) in the secondary network (SN) become aware of the interference they create on the PN, especially when there is no exchange of information between the two n…

We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area. The channel chart captures the local spatial geometry of the area so that points that are close in space will also be close in the channel chart and vice versa. CC w…

New algorithm for multi-player bandits in changing environments.

Signal processing tasks as fundamental as sampling, reconstruction, minimum mean-square error interpolation and prediction can be viewed under the prism of reproducing kernel Hilbert spaces. Endowing this vantage point with contemporary advances in sparsity-aware modeling and processing, promotes the nonparametric basi…

Motivated by cognitive radio networks, we consider the stochastic multiplayer multi-armed bandit problem, where several players pull arms simultaneously and collisions occur if one of them is pulled by several players at the same stage. We present a decentralized algorithm that achieves the same performance as a centra…

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…

Classifies compact radio sources in the Galactic plane using machine learning.

We consider a variant of the stochastic multi-armed bandit problem, where multiple players simultaneously choose from the same set of arms and may collide, receiving no reward. This setting has been motivated by problems arising in cognitive radio networks, and is especially challenging under the realistic assumption t…

New algorithm reduces regret in asynchronous multiplayer bandits to constant or logarithmic levels.

Neural network detects spectrum signals under uncertain parameters.

Multi-player Multi-Armed Bandits (MAB) have been extensively studied in the literature, motivated by applications to Cognitive Radio systems. Driven by such applications as well, we motivate the introduction of several levels of feedback for multi-player MAB algorithms. Most existing work assume that sensing informatio…

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 …

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…

Quantum machine learning improves pulsar classification in radio astronomy.

Bayesian approach for adaptive radio tomography using SLFs.

Robust state-space radio interferometric imaging using Stochastic Approximation Expectation Maximization

GAN-based spoofing attacks improve wireless signal authentication.

Deep learning models, especially CNNs, can predict radio frequency power faster than traditional methods.

Study improves radio show segmentation using audio embeddings.

Research uses CPS to estimate uncertainty in ML radio metric models.

Paper tackles caching in fog radio access networks using RL.

Opportunistic spectrum access is one of the emerging techniques for maximizing throughput in congested bands and is enabled by predicting idle slots in spectrum. We propose a kernel-based reinforcement learning approach coupled with a novel budget-constrained sparsification technique that efficiently captures the envir…

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…

A distributed RL framework optimizes radio resource management for wireless networks.

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…

ASKAP observes a region of the Galactic plane, identifying 3963 radio sources.

Deep learning classifies mobile traffic from LTE PDCCH.

Paper predicts GNSS phase scintillations with machine learning.

New algorithm for multi-player bandits with collision-dependent rewards.

Model predicts cognitive health risks based on smartphone usage patterns.

The paper proposes a new framework to generate synthetic data with human-like imperfections to prevent model collapse.

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…

Develops deep learning for optimizing 5G radio resource allocation.

Graph neural networks optimize radio resource management policies for wireless networks.

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…