Cognitive Radio: From Spectrum Sharing to Adaptive Learning and Reconfiguration

This paper introduces important cognitive radio developments like spectrum sharing, learning and adaptation algorithms, and the software and hardware architecture to support these functions. A cognitive radio is defined here as a transceiver that is aware of its environment and can combine this awareness with knowledge of its user's priorities, needs, operational procedures, and governing regulatory rules. It adapts to its environment and configures itself in an appropriate fashion. The radio learns through experience and is capable of generating solutions for communications problems unforeseen by its designers. Our spectrum sharing cognitive radio is built upon GNU Radio and uses the Universal Software Radio Peripheral (USRP) device as our radio front end platform. We use cyclostationary feature analysis to detect low SNR modulated signals because of its ability to distinguish between modulated signals, interference, and noise in low signal to noise ratios. A parallel algorithm running on a Cell Broadband Engine (Cell BE) is used to attack the associated high computational complexity. A new spectrum sensing scheme, incorporating spectrum monitoring, data transmission, and dynamic channel switching, is designed to fully utilize the idle time of the primary user. Our work is based on the concept of a Cognitive Engine: an intelligent software package that "reads the meters" and "turns the knobs" of any attached software defined radio (SDR) platform. Using an eclectic combination of artificial intelligence techniques including case-based decision theory, multi-objective genetic algorithms, and neural networks, it implements a system of nested cognition loops.