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 optimality under specific criteria for many years.
Convolutional Radio Modulation Recognition Networks TimothyJ.O’Shea 1,JohnathanCorgan2,andT.CharlesClancy ... forming modulation recognition and for forming analytically derived decision trees to sort modulations into different classes. In …
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: