Dataset for GLOBECOM paper "Spectrum Monitoring for Radar Bands using Deep Convolutional Neural Networks"

This dataset refers to all the images+labels used for training a Convolutional Neural Network (CNN) for RF signal classification/monitoring. All samples in this dataset were collected over-the-air using a USRP N210 RF front-end. The raw IQ samples were converted into two different representations: (i) spectrograms, and (ii) amplitude+phase shift matrices. Both representations were then stored in JPEG format. The label files are present in txt format, with each line defining the image path and its class (0 or 1). Class 1 means that radar emissions (the incumbent) are present in the image, while class 0 means that they are not. Several types of radar signal waveforms were stored (e.g. LFM, barker, and, pulsed carrier). Class 0 means that no radar emissions are present. In both class 0 and class 1 images, WiFi and LTE signals may be present. For more information regarding the context/scenario where the CNN was used, we encourage reading the paper https://arxiv.org/pdf/1705.00462.pd

Exploring radio environment map architectures for spectrum sharing in the radar bands

Abstract The need for extra spectrum to support future 5G networks and the fact that a large amount of spectrum below 6 GHz is allocated for different radar systems have motivated regulatory bodies and wireless researchers to investigate the feasibility of Dynamic Spectrum Access (DSA) in radar bands. Moreover, the next generation of infrastructures and devices will need to be more flexible to support a much wider range of application services, operate over much wider bandwidths, and exploit multiple radio access technologies. Radio Environment Maps (REMs) can be utilized to enhance the awareness of network entities of their operational radio environment. In the context of potential spectrum sharing in radar bands, REMs can be used to determine spectrum usage and propagation patterns of different incumbent radar systems operating in a given area. This information in turn can be utilized to establish different radar systems protection requirements in a particular band, and can also help to mitigate any interference between incumbent radar systems and Secondary Users (SUs). In this paper, based on spectrum measurement campaigns in Finland and Ireland, we explore the functional architecture of REMs for potential spectrum sharing in radar bands. We argue that the unique operating principle and signal characteristics of this type of incumbent needs to be considered in implementation of the sensor network, REM communication protocol, and REM architecture. We also discuss the potential of REM to facilitate the use of more advanced interference cancellation or avoidance technologies such as temporal sharing that can significantly reduce exclusion zones

DySPAN Spectrum Challenge: situational awareness and opportunistic spectrum access benchmarked

In this paper, we describe the original problem statement and the two winning solutions to the IEEE DySPAN Challenge, organized in Baltimore in 2017. The idea of the challenge was to invite teams to propose as diverse as possible solutions to a well defined problem, and evaluate the performance of the proposed solutions in a realistic environment. The challenge is defined to enable benchmarking and comparison of multiple teams, possibly working on different parts of the system, in a real environment. The winning solutions represented a complete and working system, working robustly and adapting to both anticipated scenario changes, as well as random effects caused by the conference setting. The code for running the challenge along with the winning solutions is publicly available, so that interested teams can start from the code when designing or benchmarking solutions, as well as when setting up own challenges and competitions. As a result, the challenge can serve as a milestone towards the creation of a benchmarking series. This paper contains all the necessary details about the software repositories so that it becomes possible to rerun the challenge and start building novel solutions based on the winners in IEEE DySPAN 2017.status: publishe