MAXIMAL-LENGTH SEQUENCE CODE CLASSIFICATION OPTIMIZATION PROCEDURE UTILIZING DEEP LEARNING NEURAL NETWORKS
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Authors
Reeder, Christina N.
Subjects
machine learning
signal detection
signal classification
maximal-length sequence
spread spectrum
deep learning
neural network
signal detection
signal classification
maximal-length sequence
spread spectrum
deep learning
neural network
Advisors
Romero, Ric
Date of Issue
2022-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Direct sequence spread spectrum techniques are often utilized in encoding communications signals because they can decrease signal spectrum lower than the thermal noise floor of a receiver, making them harder to detect. Accurate and timely classification of spreading codes for message decoding has become an area of interest. In this work, we evaluate the difference in classification performance between a traditional matched filter bank method and trained neural networks. We demonstrate that trained neural networks may out perform matched filters specifically in the medium SNR range. Additionally, we explore performance of a neural network trained to detect and classify direct sequence coded signals along with a null alternative by adding a “noise only” signal classification option. We find that there is a probability of false alarm (𝑃𝑓a) associated with a neural network trained to detect signals with a “noise only” classification option. We conclude that trained neural networks offer an increase in both percentage of classification (𝑃𝑐) and time-to-classify performance. However, we also conclude that more work is required to optimize the neural network for the decoding of preamble codes of different lengths and types. This work demonstrates the feasibility of using trained neural networks for use in decoding direct sequence coded signals.
Type
Thesis
Description
Series/Report No
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NPS Report Number
Sponsors
NAVAIR, Patuxent River, MD 20670
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Citation
Distribution Statement
Distribution Statement A. Approved for public release: Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.