Technical Note: \cal Q-Learning
Machine Learning
Least-squares policy iteration
The Journal of Machine Learning Research
Software-defined radio: basics and evolution to cognitive radio
EURASIP Journal on Wireless Communications and Networking
Adaptive Routing for Sensor Networks using Reinforcement Learning
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Self-Organization Based Network Architecture for New Generation Networks
EMERGING '09 Proceedings of the 2009 First International Conference on Emerging Network Intelligence
Multi-agent Q-learning of channel selection in multi-user cognitive radio systems: a two by two case
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Cognitive network management with reinforcement learning for wireless mesh networks
IPOM'07 Proceedings of the 7th IEEE international conference on IP operations and management
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
Reinforcement Learning and Dynamic Programming Using Function Approximators
Reinforcement Learning and Dynamic Programming Using Function Approximators
Wireless Personal Communications: An International Journal
Value-difference based exploration: adaptive control between epsilon-greedy and softmax
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Cognitive networks: adaptation and learning to achieve end-to-end performance objectives
IEEE Communications Magazine
Network planning in wireless ad hoc networks: a cross-Layer approach
IEEE Journal on Selected Areas in Communications
Cognitive radio: brain-empowered wireless communications
IEEE Journal on Selected Areas in Communications
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Due to a drastic increase in the number of wireless communication devices, these devices are forced to interfere or interact with each other. This raises the issue of possible effects this coexistence might have on the performance of these networks. Negative effects are a consequence of contention for network resources (such as free wireless communication frequencies) between different devices, which can be avoided if co-located networks cooperate with each other and share the available resources. This paper presents a self-learning, cognitive cooperation approach for heterogeneous co-located networks. Cooperation is performed by activating or deactivating services such as interference avoidance, packet sharing, various MAC protocols, etc. Activation of a cooperative service might have both positive and negative effects on a network's performance, regarding its high level goals. Such a cooperation approach has to incorporate a reasoning mechanism, centralized or distributed, capable of determining the influence of each symbiotic service on the performance of all the participating sub-networks, taking into consideration their requirements. In this paper, a cooperation method incorporating a machine learning technique, known as the Least Squares Policy Iteration (LSPI), is proposed and discussed as a novel network cooperation paradigm.