Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Biologically inspired cognitive radio engine model utilizing distributed genetic algorithms for secure and robust wireless communications and networking
NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Fundamental design tradeoffs in cognitive radio systems
TAPAS '06 Proceedings of the first international workshop on Technology and policy for accessing spectrum
Wireless Communications & Mobile Computing - Cognitive Radio, Software Defined Radio And Adaptive Wireless Systems
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
Introduction to Space-Time Wireless Communications
Introduction to Space-Time Wireless Communications
Cognitive radio adaptation using particle swarm optimization
Wireless Communications & Mobile Computing
Development of a case-based reasoning cognitive engine for IEEE 802.22 WRAN applications
ACM SIGMOBILE Mobile Computing and Communications Review
An adaptive spectrum sensing architecture for dynamic spectrum access networks
IEEE Transactions on Wireless Communications
On balancing exploration vs. exploitation in a cognitive engine for multi-antenna systems
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Applications of Machine Learning to Cognitive Radio Networks
IEEE Wireless Communications
Achievable rates in cognitive radio channels
IEEE Transactions on Information Theory
Limits on communications in a cognitive radio channel
IEEE Communications Magazine
Cognitive networks: adaptation and learning to achieve end-to-end performance objectives
IEEE Communications Magazine
Wireless distributed computing in cognitive radio networks
Ad Hoc Networks
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In this paper, we present a Cognitive Engine (CE) design for link adaptation and apply it to a system which can adapt its use of multiple antennas in addition to modulation and coding. Our design moves forward the state of the art in several ways while having a simple structure. Specifically, the CE only needs to observe the number of successes and failures associated with each set of channel conditions and communication method. From these two numbers, the CE can derive all of its functionality. First, it can estimate confidence intervals of the packet success rate (PSR) using the Beta distribution. A low computational approximation to the CDF of the Beta distribution is also presented. Second, the designed CE balances the tradeoff between learning and short-term performance (exploration vs. exploitation) by applying the Gittins index. Third, the CE learns the radio abilities independently of the operation objectives. Thus, if an objective changes, information regarding the radio's abilities is not lost. Finally, prior knowledge such as capacity, BER curves, and basic communication principles are used to both initialize the CE's knowledge and maximize the learning rate across different channel conditions. The proposed CE is demonstrated to have the ability to learn in a dynamic scenario and quickly approach maximal performance.