Deep learning models for wireless signal classification with distributed low-cost spectrum sensors
This paper looks into the modulation classification problem for a distributed wireless
spectrum sensing network. First, a new data-driven model for automatic modulation …
spectrum sensing network. First, a new data-driven model for automatic modulation …
Machine learning for the detection and identification of Internet of Things devices: A survey
The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a
variety of emerging services and applications. However, the presence of rogue IoT devices …
variety of emerging services and applications. However, the presence of rogue IoT devices …
Automatic modulation classification based on deep learning for unmanned aerial vehicles
Deep learning has recently attracted much attention due to its excellent performance in
processing audio, image, and video data. However, few studies are devoted to the field of …
processing audio, image, and video data. However, few studies are devoted to the field of …
Specsense: Crowdsensing for efficient querying of spectrum occupancy
We describe an end-to-end platform called SpecSense to support large scale spectrum
monitoring. SpecSense crowdsources spectrum monitoring to low-cost, low-power …
monitoring. SpecSense crowdsources spectrum monitoring to low-cost, low-power …
Estimating the required training dataset size for transmitter classification using deep learning
Despite the recent surge in the application of deep learning to wireless communication
problems, very little is known about the required training dataset size to solve difficult …
problems, very little is known about the required training dataset size to solve difficult …
Simultaneous power-based localization of transmitters for crowdsourced spectrum monitoring
The current mechanisms for locating spectrum offenders are time consuming, human-
intensive, and expensive. In this paper, we propose a novel approach to locate spectrum …
intensive, and expensive. In this paper, we propose a novel approach to locate spectrum …
Wireless technology recognition based on RSSI distribution at sub-Nyquist sampling rate for constrained devices
Driven by the fast growth of wireless communication, the trend of sharing spectrum among
heterogeneous technologies becomes increasingly dominant. Identifying concurrent …
heterogeneous technologies becomes increasingly dominant. Identifying concurrent …
Spectrum awareness at the edge: Modulation classification using smartphones
N Soltani, K Sankhe, S Ioannidis… - 2019 IEEE …, 2019 - ieeexplore.ieee.org
As spectrum becomes crowded and spread over wide ranges, there is a growing need for
emcient spectrum management techniques that need minimal, or even better, no human …
emcient spectrum management techniques that need minimal, or even better, no human …
VIA: Establishing the link between spectrum sensor capabilities and data analytics performance
K Doke, B Okoro, A Zare… - IEEE INFOCOM 2024 …, 2024 - ieeexplore.ieee.org
Automated spectrum analytics inform critical decisions in dynamic spectrum access
networks such as (i) how to allocate network resources to clients,(ii) when to enforce …
networks such as (i) how to allocate network resources to clients,(ii) when to enforce …
Distributed deep learning models for wireless signal classification with low-cost spectrum sensors
This paper looks into the technology classification problem for a distributed wireless
spectrum sensing network. First, a new data-driven model for Automatic Modulation …
spectrum sensing network. First, a new data-driven model for Automatic Modulation …