Hybrid Experiential-Heuristic Cognitive Radio Engine Architecture and Implementation

• Main Authors: Ashwin Amanna, Daniel Ali, David Gonzalez Fitch, Jeffrey H. Reed
• Format: Article
• Language: English
• Published: Hindawi Limited 2012-01-01
• Series: Journal of Computer Networks and Communications
• Online Access: http://dx.doi.org/10.1155/2012/549106
• ISSN: 2090-7141; 2090-715X

The concept of cognitive radio (CR) focuses on devices that can sense their environment, adapt configuration parameters, and learn from past behaviors. Architectures tend towards simplified decision-making algorithms inspired by human cognition. Initial works defined cognitive engines (CEs) founded on heuristics, such as genetic algorithms (GAs), and case-based reasoning (CBR) experiential learning algorithms. This hybrid architecture enables both long-term learning, faster decisions based on past experience, and capability to still adapt to new environments. This paper details an autonomous implementation of a hybrid CBR-GA CE architecture on a universal serial radio peripheral (USRP) software-defined radio focused on link adaptation. Details include overall process flow, case base structure/retrieval method, estimation approach within the GA, and hardware-software lessons learned. Unique solutions to realizing the concept include mechanisms for combining vector distance and past fitness into an aggregate quantification of similarity. Over-the-air performance under several interference conditions is measured using signal-to-noise ratio, packet error rate, spectral efficiency, and throughput as observable metrics. Results indicate that the CE is successfully able to autonomously change transmit power, modulation/coding, and packet size to maintain the link while a non-cognitive approach loses connectivity. Solutions to existing shortcomings are proposed for improving case-base searching and performance estimation methods.