Social Intelligence for Cognitive Radios

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Virginia Tech


This dissertation introduces the concept of an artificial society based on the use of an action based social language combined with the behavior-based approach to the construction of multi-agent systems to address the problem of developing decentralized, self-organizing networks that dynamically fit into their environment. In the course of accomplishing this, social language is defined as an efficient method for communicating coordination information among cognitive radios inspired by natural societies. This communication method connects the radios within a network in a way that allows the network to learn in a distributed holistic manner. The behavior-based approach to developing multi-agent systems from the field of robotics provides the framework for developing these learning networks. In this approach several behaviors are used to address the multiple objectives of a cognitive radio society and then combined to achieve emergent properties and behaviors. This work presents a prototype cognitive radio society.

This society is implemented, using low complexity hardware, and evaluated. The work does not focus on the development of optimized techniques, but rather the complementary design of techniques and agents to create dynamic, decentralized self-organizing networks



Cognitive radio networks, Cognitive Engine, Social Language, Social Learning, Behavior Based Approach, LTE Organization