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.
Highlights
Wireless communication devices and networks face outside influences that degrade performance and have potential to render links useless
The goal of the experimentation was to compare the performance of the cognitive engines (CEs) against a noncognitive radio that is incapable of changing its initial configuration parameters
A cognitive system that employed dynamic spectrum access may identify a new channel based on past experiences on which ones were most vacant
Summary
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. Today’s CE architectures maintain this vision with designs oriented around observing system performance metrics, known as meters, in order to make decisions about how to set configuration parameters of the SDR to support a defined goal. Simplified decision algorithms identify new radio configuration parameters that ideally will improve performance such that the observable meters fall within a defined threshold. These new radio parameters are passed to the SDR for implementation and return to the start of the loop. Note that this process flow is reactionary in nature and sometimes considered flawed due to its inability to be proactive
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