2,406 research outputs found
Spectrum Sensing in Cognitive Radio Using CNN-RNN and Transfer Learning
Cognitive radio has been proposed to improve spectrum utilization in wireless communication. Spectrum sensing is an essential component of cognitive radio. The traditional methods of spectrum sensing are based on feature extraction of a received signal at a given point. The development in artificial intelligence and deep learning have given an opportunity to improve the accuracy of spectrum sensing by using cooperative spectrum sensing and analyzing the radio scene. This research proposed a hybrid model of convolution and recurrent neural network for spectrum sensing. The research further enhances the accuracy of sensing for low SNR signals through transfer learning. The results of modelling show improvement in spectrum sensing using CNN-RNN compared to other models studied in this field. The complexity of an algorithm is analyzed to show an improvement in the performance of the algorithm.publishedVersio
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
Spectral Attention-Driven Intelligent Target Signal Identification on a Wideband Spectrum
This paper presents a spectral attention-driven reinforcement learning based
intelligent method for effective and efficient detection of important signals
in a wideband spectrum. In the work presented in this paper, it is assumed that
the modulation technique used is available as a priori knowledge of the
targeted important signal. The proposed spectral attention-driven intelligent
method is consists of two main components, a spectral correlation function
(SCF) based spectral visualization scheme and a spectral attention-driven
reinforcement learning mechanism that adaptively selects the spectrum range and
implements the intelligent signal detection. Simulations illustrate that the
proposed method can achieve high accuracy of signal detection while observation
of spectrum is limited to few ranges via effectively selecting the spectrum
ranges to be observed. Furthermore, the proposed spectral attention-driven
machine learning method can lead to an efficient adaptive intelligent spectrum
sensor designs in cognitive radio (CR) receivers.Comment: 6 pages, 11 figure
Wide-band spectrum sensing with convolution neural network using spectral correlation function
Recognition of signals is a spectrum sensing challenge requiring simultaneous detection, temporal and spectral localization, and classification. In this approach, we present the convolution neural network (CNN) architecture, a powerful portrayal of the cyclo-stationarity trademark, for remote range detection and sign acknowledgment. Spectral correlation function is used along with CNN. In two scenarios, method-1 and method-2, the suggested approach is used to categorize wireless signals without any previous knowledge. Signals are detected and classified simultaneously in method-1. In method-2, the sensing and classification procedures take place sequentially. In contrast to conventional spectrum sensing techniques, the proposed CNN technique need not bother with a factual judgment process or past information on the signs’ separating qualities. The method beats both conventional sensing methods and signal-classifying deep learning networks when used to analyze real-world, over-the-air data in cellular bands. Despite the implementation’s emphasis on cellular signals, any signal having cyclo-stationary properties may be detected and classified using the provided approach. The proposed model has achieved more than 90% of testing accuracy at 15 dB
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