Transcription of Convolutional Radio Modulation Recognition Networks
{{id}} {{{paragraph}}}
Example: quiz answers
Convolutional Radio Modulation Recognition Networks
Convolutional Radio Modulation RecognitionNetworksTimothy J. O Shea1, Johnathan Corgan2, and T. Charles Clancy11 Bradley Department of Electrical and Computer Engineering, Virginia Tech, NGlebe Road, Arlington, VA Labs, Meridian Ave., Suite - , San Jose, CA study the adaptation of Convolutional neural networksto the complex-valued temporal Radio signal domain. We compare theefficacy of Radio Modulation classification using naively learned featuresagainst using expert feature based methods which are widely used todayand e show significant performance improvements. We show that blindtemporal learning on large and densely encoded time series using deepconvolutional neural Networks is viable and a strong candidate approachfor this task especially at low signal to noise :machine learning, Radio , software Radio , Convolutional Networks ,deep learning, Modulation Recognition , cognitive Radio , dynamic spectrum access IntroductionRadio communications present a unique signal processing domain with a numberof interesting challenges and opportunities for the machine learning this field expert features and decision criterion have been extensively devel-oped, and analyzed for optimali
a continuous signal or a series of discrete bits modulated onto a sinusoid with either varying frequency, phase, amplitude, trajectory, or some permutation of multiple thereof. c is …
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: