Publication Date
Spring 2025
Degree Type
Master's Project
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
First Advisor
Genya Ishigaki
Second Advisor
Navrati Saxena
Third Advisor
William Andreopoulos
Keywords
Integrated Access and Backhaul (IAB), Monte Carlo Tree Search (MCTS), Signal-to-Noise Ratio (SNR).
Abstract
Integrated Access and Backhaul (IAB) plays a central role in enabling scalability and high-throughput in wireless networks, especially in areas where wired backhaul is not feasible. Optimizing the topology of IAB networks is a complex task, involving trade-offs between path loss, node capacity, and link quality. To address this, this project investigates a Monte Carlo Tree Search (MCTS)-based approach to improve overall network performance by maximizing the capacity of network, considering Signal-to-Noise Ratio (SNR) on wireless links. MCTS provides a guided search mechanism to construct topologies efficiently under the constraints and is evaluated against baseline topologies. Experimental results show that our MCTS-based approach consistently produces topologies with higher minimum capacity and better SNR under a range of node constraints and network configurations.
Recommended Citation
Ummenthala, Srimanth Reddy, "Optimizing Integrated Access and Backhaul Topology using Monte Carlo Tree Search" (2025). Master's Projects. 1542.
DOI: https://doi.org/10.31979/etd.n5b4-zv5m
https://scholarworks.sjsu.edu/etd_projects/1542
