arXiv research

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

Interference among concurrent transmissions in a wireless network is a key factor limiting the system performance. One way to alleviate this problem is to manage the radio resources in order to maximize either the average or the worst-case performance. However, joint consideration of both metrics is often neglected as …

Optimizes data power control in cell-free networks for better spectral efficiency.

State-augmented algorithm optimizes wireless network resource management.

Meta-DRL improves resource allocation in O-RAN networks.

Optimizes wireless network resource management with state-augmented policies.

Supporting ultra-reliable low-latency communications (URLLC) is a major challenge of 5G wireless networks. Stringent delay and reliability requirements need to be satisfied for both scheduled and non-scheduled URLLC traffic to enable a diverse set of 5G applications. Although physical and media access control layer sol…

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

Develops deep learning for optimizing 5G radio resource allocation.

Solves challenges of drone communication in cellular networks.

We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through ex…

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicin…

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

This paper proposes a new D2D data sharing approach to improve distributed machine learning training speed.

New RL approach optimizes resource allocation in multiservice networks.

Radio tomographic imaging (RTI) is an emerging technology for localization of physical objects in a geographical area covered by wireless networks. With attenuation measurements collected at spatially distributed sensors, RTI capitalizes on spatial loss fields (SLFs) measuring the absorption of radio frequency waves at…

This article proposes and evaluates a technique to predict the level of interference in wireless networks. We design a recursive predictor that estimates future interference values by filtering measured interference at a given location. The predictor's parameterization is done offline by translating the autocorrelation…

Deep learning enhances water resources management through data analysis.

Study optimizes natural resource harvesting under model uncertainty using risk measures.

This paper optimizes revenue and resource balance in network revenue management.

Study on revenue management with limited switches, achieving strong performance and reduced switch counts.

HL algorithms improve resource allocation in cloud environments.

Mathematical approach assesses human resource competences accurately.

As most natural resources, fisheries are affected by random disturbances. The evolution of such resources may be modelled by a succession of deterministic process and random perturbations on biomass and/or growth rate at random times. We analyze the impact of the characteristics of the perturbations on the management o…

Paper tackles pandemic resource allocation challenges.

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

Enhances multi-project scheduling with multiple priority rules.

In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resourc…

Research develops a water quality prediction model using LSTM.

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 …

Paper assesses financial potential for enterprise development.

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…

EERO optimizes resource usage for efficient classification.

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…

ATA optimizes task allocation in distributed machine learning.

Quantum machine learning improves pulsar classification in radio astronomy.

Floating Content (FC) is a communication paradigm for the local dissemination of contextualized information through D2D connectivity, in a way which minimizes the use of resources while achieving some specified performance target. Existing approaches to FC dimensioning are based on unrealistic system assumptions that m…

A deep RL framework optimizes resource allocation in wireless networks.

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

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

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

Study improves radio show segmentation using audio embeddings.

In this paper, we introduce the problem of decision-oriented communications, that is, the goal of the source is to send the right amount of information in order for the intended destination to execute a task. More specifically, we restrict our attention to how the source should quantize information so that the destinat…

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…

Cost forecasting helps crowdsourcers manage growing task sets efficiently.

As deep neural network (NN) methods have matured, there has been increasing interest in deploying NN solutions to "edge computing" platforms such as mobile phones or embedded controllers. These platforms are often resource-constrained, especially in energy storage and power, but state-of-the-art NN architectures are de…

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