Publication Date

Spring 2025

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Navrati Saxena

Second Advisor

Fabio Di Troia

Third Advisor

Abhishek Roy

Keywords

Dynamic Spectrum Allocation(DSA), Reinforce- ment Learning, Deep Q-Network, Wireless Network Manage- ment, Spectrum Efficiency, Bandwidth Optimization, Machine Learning

Abstract

The growth of wireless communication has introduced challenges in the dynamic and resource contrived space which is the efficient utilization of bandwidth and spectrum. This research presents a model for dynamic spectrum allocation with the help of Convolutional Neural Network (CNN) for feature extraction and the Deep Q-Network (DQN) model’s reinforcement learning architecture. The CNN captures both spatial and temporal features of the network states and gives them to the DQN for optimal allocation decision making. This CNN-DQN architecture effectively implements spectrum resource allocation in wireless networks and adapts to resource allocation changes within performance bounds. The system’s performance is evaluated in spectrum resource allocation accuracy within a loss factor, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) showed where the allocation accuracy could still be improved. This will open the door to developing more sophisticated solutions for efficient spectrum resource management with the aid of machine learning approaches.

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Available for download on Monday, May 25, 2026

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