3,302 research outputs found
Convolutional Radio Modulation Recognition Networks
We study the adaptation of convolutional neural networks to the complex
temporal radio signal domain. We compare the efficacy of radio modulation
classification using naively learned features against using expert features
which are widely used in the field today and we show significant performance
improvements. We show that blind temporal learning on large and densely encoded
time series using deep convolutional neural networks is viable and a strong
candidate approach for this task especially at low signal to noise ratio
Deep Neural Network Architectures for Modulation Classification
In this work, we investigate the value of employing deep learning for the
task of wireless signal modulation recognition. Recently in [1], a framework
has been introduced by generating a dataset using GNU radio that mimics the
imperfections in a real wireless channel, and uses 10 different modulation
types. Further, a convolutional neural network (CNN) architecture was developed
and shown to deliver performance that exceeds that of expert-based approaches.
Here, we follow the framework of [1] and find deep neural network architectures
that deliver higher accuracy than the state of the art. We tested the
architecture of [1] and found it to achieve an accuracy of approximately 75% of
correctly recognizing the modulation type. We first tune the CNN architecture
of [1] and find a design with four convolutional layers and two dense layers
that gives an accuracy of approximately 83.8% at high SNR. We then develop
architectures based on the recently introduced ideas of Residual Networks
(ResNet [2]) and Densely Connected Networks (DenseNet [3]) to achieve high SNR
accuracies of approximately 83.5% and 86.6%, respectively. Finally, we
introduce a Convolutional Long Short-term Deep Neural Network (CLDNN [4]) to
achieve an accuracy of approximately 88.5% at high SNR.Comment: 5 pages, 10 figures, In proc. Asilomar Conference on Signals,
Systems, and Computers, Nov. 201
Semi-Supervised Radio Signal Identification
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 learning strategies difficult and time consuming in
practice. We demonstrate that semi-supervised learning techniques can be used
to scale learning beyond supervised datasets, allowing for discerning and
recalling new radio signals by using sparse signal representations based on
both unsupervised and supervised methods for nonlinear feature learning and
clustering methods
Wireless Interference Identification with Convolutional Neural Networks
The steadily growing use of license-free frequency bands requires reliable
coexistence management for deterministic medium utilization. For interference
mitigation, proper wireless interference identification (WII) is essential. In
this work we propose the first WII approach based upon deep convolutional
neural networks (CNNs). The CNN naively learns its features through
self-optimization during an extensive data-driven GPU-based training process.
We propose a CNN example which is based upon sensing snapshots with a limited
duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs
between 15 classes. They represent packet transmissions of IEEE 802.11 b/g,
IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the
2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII
approaches and has a classification accuracy greater than 95% for
signal-to-noise ratio of at least -5 dB
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