Date of Award
5-1-2026
Document Type
Thesis
Degree Name
M.S. in Engineering Science
First Advisor
Lei Cao
Second Advisor
Hamid Bahrami
Third Advisor
Ramanarayanan Viswanathan
School
University of Mississippi
Relational Format
dissertation/thesis
Abstract
Binary hypothesis testing is a fundamental problem in statistical signal processing and decision theory, with applications in communications, radar systems, and distributed sensor networks. Classical approaches, particularly the likelihood ratio test (LRT), provide optimal detection performance when the underlying probability distributions are known. However, in many practical scenarios, the resulting decision rules can become analytically complex or computationally intensive, especially in multi-sensor and dependent data settings.
This thesis investigates the use of neural networks for binary hypothesis testing under diverse statistical conditions. A data-driven framework is developed to learn decision rules directly from observed data, enabling the approximation of likelihood ratio–based detectors without explicit evaluation of probability density functions. The approach is evaluated across multiple statistical models, including Gaussian, exponential, bivariate Gaussian, copula-based dependent distributions, and mixed Gaussian models.
Simulation results show that neural network-based detectors closely approximate the optimal LRT for Gaussian distributions while maintaining consistent performance in more complex settings where analytical solutions are difficult. Performance is evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics across different sensor configurations. Overall, this work bridges classical detection theory and data-driven learning, providing a flexible framework for multi-sensor detection problems.
Additionally, an AI-based encoder–detector framework is proposed, where a neural network is used to learn optimal signal transformations under power constraints. The performance of the learned encoder is compared with the identity encoder, demonstrating improvements in detection performance by using the learned encoder.
Recommended Citation
Geddam, Sadhana, "Study of Binary Hypothesis testing using Deep Learning" (2026). Electronic Theses and Dissertations. 8819.
https://egrove.olemiss.edu/etd/8819