Sequential decision making for automatic modulation classification
Name
1220877358-MIT.pdf
Size
2.28 MB
Format
Adobe PDF
Checksum (MD5)
bf607e625604a6d13ef456524c10d0ae
Author(s)
Waltman, Nicholas(Nicholas W.)
Advisor(s)
Tomas Palacios and Rebecca Russell.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
In this paper, an algorithm is introduced to use deep learning to perform automatic modulation classification in a sequential manner. At each time step, a decision is made whether to request more data or to return a classification decision. This allows for the data, and therefore time, needed to make a decision to be minimized while maintaining a high degree of accuracy. The performance of this algorithm is studied using multiple strategies and lists of modulations to be classified.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, June, 2019
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 41-42).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
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