Learning from experts in cognitive radio networks: The docitive paradigm

In this paper we introduce the novel paradigm of docition for cognitive radio (CR) networks. We consider that the CRs are intelligent radios implementing a learning process through which they interact with the surrounding environment to make self-adaptive decisions. However, in distributed settings the learning may be complex and slow, due to interactive decision making processes, which results in a non-stationary environment. The docitive paradigm proposes a timely solution based on knowledge sharing, which allows CRs to develop new capacities for selecting actions. We demonstrate that this improves the CRs´ learning ability and accuracy, and gives them strategies for action selection in unvisited states. We evaluate the docitive paradigm in the context of a secondary system modeled as a multi-agent system, where the agents are IEEE 802.22 CR base stations, implementing a real-time multi-agent reinforcement learning technique known as decentralized Q-learning. Our goal is to solve the aggregated interference problem generated by multiple CR systems at the receivers of a primary system. We propose three different docitive algorithms and we show their superiority to the well know paradigm of independent learning in terms of speed of convergence and precision.