Monte Carlo Evaluation of Dynamic Spectrum Allocation Techniques for Bandwidth Optimization in Wireless Communication Systems
Session Number
Project ID: CMPS 21
Advisor(s)
Dr. Randall Berry; Northwestern University
Dr. Igor Kadota; Northwestern University
Discipline
Computer Science
Start Date
17-4-2024 8:15 AM
End Date
17-4-2024 8:30 AM
Abstract
The heightening demand for spectrum from wireless communication services has necessitated the development of more effective frequency allocation methods. Leveraging Dynamic Spectrum Allocation (DSA) techniques, we generated and modeled data from several allocation algorithms to understand trends in optimal performances. In wireless networks, transmitters with overlapping coverage must be assigned different frequencies to avoid harmful interference, posing complex frequency allocation problems and tradeoffs as the density of transmitters increases. For example, a tradeoff between prioritizing many smaller transmitters or a few prominent transmitters is studied. Transmitter characteristics such as signal propagation range and bandwidth requirement were considered for priority-based allocation.
Simulations based on Monte Carlo methods were developed to run allocation algorithms tackling complex allocation methods. Parameters such as the available bandwidth, geographical area, transmitter coverage, and the number of transmitters were changed individually to gauge their effect on the performance of various scenarios. Performance was evaluated using metrics including feasibility (ability to allocate all transmitters), coverage, bandwidth usage, and transmitter capacity (number of transmitters successfully allocated). Applying the more efficient Left-to-Right Spectrum Allocation (LRSA) algorithm, the highest-degree sorting algorithms performed best in terms of feasibility, coverage, and bandwidth usage while lower-degree methods had higher transmitter capacities.
Monte Carlo Evaluation of Dynamic Spectrum Allocation Techniques for Bandwidth Optimization in Wireless Communication Systems
The heightening demand for spectrum from wireless communication services has necessitated the development of more effective frequency allocation methods. Leveraging Dynamic Spectrum Allocation (DSA) techniques, we generated and modeled data from several allocation algorithms to understand trends in optimal performances. In wireless networks, transmitters with overlapping coverage must be assigned different frequencies to avoid harmful interference, posing complex frequency allocation problems and tradeoffs as the density of transmitters increases. For example, a tradeoff between prioritizing many smaller transmitters or a few prominent transmitters is studied. Transmitter characteristics such as signal propagation range and bandwidth requirement were considered for priority-based allocation.
Simulations based on Monte Carlo methods were developed to run allocation algorithms tackling complex allocation methods. Parameters such as the available bandwidth, geographical area, transmitter coverage, and the number of transmitters were changed individually to gauge their effect on the performance of various scenarios. Performance was evaluated using metrics including feasibility (ability to allocate all transmitters), coverage, bandwidth usage, and transmitter capacity (number of transmitters successfully allocated). Applying the more efficient Left-to-Right Spectrum Allocation (LRSA) algorithm, the highest-degree sorting algorithms performed best in terms of feasibility, coverage, and bandwidth usage while lower-degree methods had higher transmitter capacities.