Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking
Cognitive engine implementation for wireless multicarrier transceivers
Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Application of artificial intelligence to wireless communications
Application of artificial intelligence to wireless communications
Cognitive radio adaptation using particle swarm optimization
Wireless Communications & Mobile Computing
Applications of Machine Learning to Cognitive Radio Networks
IEEE Wireless Communications
From theory to practice: an overview of MIMO space-time coded wireless systems
IEEE Journal on Selected Areas in Communications
Cognitive engine design for link adaptation: an application to multi-antenna systems
IEEE Transactions on Wireless Communications
Wireless distributed computing in cognitive radio networks
Ad Hoc Networks
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In this paper, we define the problem of balancing exploration vs. exploitation in a cognitive engine controlled multi-antenna communication system in terms of the classical multiarmed bandit framework. We then employ the ε-greedy strategy and Gittins' indices methods for addressing the problem in a system with no prior information. Results show that the Gittins' indices assuming a normal reward process had the best overall performance compared to the Gittins' indices with a Bernoulli reward process and the ε-greedy strategy. The latter was found to be more consistent albeit inefficient for most of the cases except in the case of both a low number of trials and a low SNR in which it was found to have better performance than the other methods. Nevertheless, the Gittins' indices method should be generally preferred as it is more consistent than the ε-greedy strategy across different scenarios.