Hybrid Experiential-Heuristic Cognitive Radio Engine Architecture and Implementation

Abstract

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.