An advanced neuromorphic accelerator on FPGA for next-G spectrum sensing

In modern communication systems, it’s important to detect and use available radio frequencies effectively. However, current methods face challenges with complexity and noise interference. We’ve developed a new approach using advanced artificial intelligence (AI) based computing techniques to improve efficiency and accuracy in this process. Our method shows promising results, requiring only minimal additional resources in exchange of improved performance compared to older techniques.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Azmine, Muhammad Farhan

Chair dc:contributor.committeechair

• Yi, Yang

Committee members dc:contributor.committeemember

• Ha, Dong
• Jones, Creed F. III

• Field Programmable Gate Array
• Hardware accelerator
• On-Chip learning
• Artificial intelligence
• Recurrent Neural Network
• Spike-Time-Dependent-Plasticity
• Cognitive-Radio-Network
• Spectrum Sensing

Statement dc:rights

• Creative Commons Attribution-NoDerivatives 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nd/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10919/119039

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119039