Abstract
The continuous increase in the number of mobile and Internet of Things (IoT) devices, as well as in the wireless data traffic they generate, represents an essential challenge in terms of spectral coexistence. As a result, these devices are now expected to make efficient and dynamic use of the spectrum by employing Cognitive Radio (CR) techniques. In this work, we focus on the Automatic Modulation Classification (AMC). AMC is essential to carry out multiple CR techniques, such as dynamic spectrum access, link adaptation and interference detection, aimed at improving communications throughput and reliability and, in turn, spectral efficiency. In recent years, multiple Deep Learning (DL) techniques have been proposed to address the AMC problem. These DL techniques have demonstrated better generalization, scalability and robustness capabilities compared to previous solutions. However, most of these techniques require high processing and storage capabilities that limit their applicability to energy- and computation-constrained end-devices. In this work, we propose a new gated recurrent unit neural network solution for AMC that has been specifically designed for resource-constrained IoT devices. We trained and tested our solution with over-the-air measurements of real radio signals. Our results show that the proposed solution has a memory footprint of 73.5 kBytes, 51.74% less than the reference model, and achieves a classification accuracy of 92.4%.
Highlights
The number of mobile and Internet of Things (IoT) devices, as well as the wireless data traffic they generate, continues to grow at an unprecedent rate [1]
The field that has emerged from the application of Cognitive Radio (CR) techniques to the IoT is known as Cognitive IoT (CIoT) [6], [7]
In this work, we study these proposals in depth, and we evaluate the parameters that affect the memory footprint and the classification accuracy to propose an Automatic Modulation Classification (AMC) model optimized for resource-constrained devices
Summary
The number of mobile and Internet of Things (IoT) devices, as well as the wireless data traffic they generate, continues to grow at an unprecedent rate [1]. These devices often operate in the same frequency bands, substantially increasing spectrum occupancy and posing new and essential challenges to overcome One of these challenges is to achieve highly efficient and reliable communications in increasingly complex, heterogeneous, and dynamic scenarios, where many different radio systems coexist. Performing these CR tasks in a centralized manner would generate latency problems and massive network traffic due to the data distribution between the devices, complicating the growing spectrum scarcity problem For this reason, and aided by the improved capabilities of end-devices, non-cooperative and edge computing approaches are gaining importance in areas like the IoT [3], [4]. The field that has emerged from the application of CR techniques to the IoT is known as Cognitive IoT (CIoT) [6], [7]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
