Date of Award
2020
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Nurgun, Erdol
Abstract
The success of deep learning has renewed interest in applying neural networks and other machine learning techniques to most fields of data and signal processing, including communications. Advances in architecture and training lead us to consider new modem architectures that allow flexibility in design, continued learning in the field, and improved waveform coding. This dissertation examines neural network architectures and training methods suitable for demodulation in power-limited communication systems, such as those found in wireless sensor networks. Such networks will provide greater connection to the world around us and are expected to contain orders of magnitude more devices than cellular networks. A number of standard and proprietary protocols span this space, with modulations such as frequency-shift-keying (FSK), Gaussian FSK (GFSK), minimum shift keying (MSK), on-off-keying (OOK), and M-ary orthogonal modulation (M-orth). These modulations enable low-cost radio hardware with efficient nonlinear amplification in the transmitter and noncoherent demodulation in the receiver.
Extent
184 p.
Recommended Citation
Gorday, Paul E., "MACHINE LEARNING DEMODULATOR ARCHITECTURES FOR POWER-LIMITED COMMUNICATIONS" (2020). Electronic Theses and Dissertations 2020 - Present. 553.
https://digitalcommons.fau.edu/etd_general/553
Identifier
FA00013511
Comments
Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2020.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.