Related papers: Deep Learning for RF Signal Classification in Unkn…

The application of machine learning in wireless communications has been extensively explored, with deep unfolding emerging as a powerful model-based technique. Deep unfolding enhances interpretability by transforming complex iterative…

In this paper, dynamic non-cooperative coexistence between a cognitive pulsed radar and a nearby communications system is addressed by applying nonlinear value function approximation via deep reinforcement learning (Deep RL) to develop a…

In this paper, we optimize a faster region-based convolutional neural network (FRCNN) for 1-dimensional (1D) signal processing and electromagnetic spectrum sensing. We target a cluttered radio frequency (RF) environment, where multiple RF…

In this paper, we propose two novel and practical deep-learning-based algorithms to solve the wireless channel type (WCT) recognition problem. Specifically, the WCT recognition problem is recast as a classification problem in deep learning…

Localizing more sources than sensors with a sparse linear array (SLA) has long relied on minimizing a distance between two covariance matrices and recent algorithms often utilize semidefinite programming (SDP). Although deep neural network…

Unmanned aerial vehicles (UAVs) enable efficient in-situ radiation characterization of large-aperture antennas directly in their deployment environments. In such measurements, a continuous-wave (CW) probe tone is commonly transmitted to…

This paper addresses a critical preliminary step in radar signal processing: detecting the presence of a radar signal and robustly estimating its bandwidth. Existing methods which are largely statistical feature-based approaches face…

Automatic Modulation Classification (AMC) is a signal processing technique widely used at the physical layer of wireless systems to enhance spectrum utilization efficiency. In this work, we propose a fast and accurate AMC system, termed…

With the development and widespread use of wireless devices in recent years (mobile phones, Internet of Things, Wi-Fi), the electromagnetic spectrum has become extremely crowded. In order to counter security threats posed by rogue or…

In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain to construct a smooth radio frequency map (RFMap) and then perform…

The increasingly crowded radio frequency (RF) spectrum forces communication signals to coexist, creating heterogeneous interferers whose structure often departs from Gaussian models. Recovering the interference-contaminated signal of…

Deep learning models are widely used for time series classification (TSC) due to their scalability and efficiency. However, their performance degrades under challenging data conditions such as class similarity, multimodal distributions, and…

Deep neural networks (DNNs) have been introduced for designing wireless policies by approximating the mappings from environmental parameters to solutions of optimization problems. Considering that labeled training samples are hard to…

Deep reinforcement learning (DRL) has long been a promising solution for sequential resource management in wireless networks. However, conventional DRL methods are fundamentally limited by their reliance on unimodal policy distributions,…

Spectrum sharing among users is a fundamental problem in the management of any wireless network. In this paper, we discuss the problem of distributed spectrum collaboration without central management under general unknown channels. Since…

With increasing density and heterogeneity in unlicensed wireless networks, traditional MAC protocols, such as carrier-sense multiple access with collision avoidance (CSMA/CA) in Wi-Fi networks, are experiencing performance degradation. This…

This paper aims to handle the joint transmitter and noncoherent receiver design for multiuser multiple-input multiple-output (MU-MIMO) systems through deep learning. Given the deep neural network (DNN) based noncoherent receiver, the…

Radar sensors are crucial for environment perception of driver assistance systems as well as autonomous cars. Key performance factors are a fine range resolution and the possibility to directly measure velocity. With a rising number of…

Deep learning provides powerful means to learn from spectrum data and solve complex tasks in 5G and beyond such as beam selection for initial access (IA) in mmWave communications. To establish the IA between the base station (e.g., gNodeB)…

Radio signal classification has a very wide range of applications in cognitive radio networks and electromagnetic spectrum monitoring. In this article, we consider scenarios where multiple nodes in the network participate in cooperative…