%0 Journal Article
%T Hybrid Experiential-Heuristic Cognitive Radio Engine Architecture and Implementation
%A Ashwin Amanna
%A Daniel Ali
%A David Gonzalez Fitch
%A Jeffrey H. Reed
%J Journal of Computer Networks and Communications
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/549106
%X 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. 1. Introduction Wireless communication devices and networks face outside influences that degrade performance and have potential to render links useless. New advances in the area of cognitive radio (CR), inspired by artificial intelligence integration with reconfigurable platforms, enable devices and networks to observe, make a decision and learn from past experience. Key problems faced by CR are how to effectively integrate both learning and decision onto software-defined radio (SDR) over-the-air platforms such that they can react to situations quickly and effectively. Specifically this paper address the realization and implementation of a cognitive engine (CE) on an SDR platform for the purpose of link adaptation. The problems addressed include incorporating mechanisms for system observation, triggering the engagement of a CE, architecting the CE such that it can both make decision when faced with new situations, and learn from past experience. Prior art has defined CE architectures based on heuristic decision making, such as GA, as
%U http://www.hindawi.com/journals/jcnc/2012/549106/