IEEE Standard for Information Technology - Portable Operating System Interface (POSIX): System Application Program Interface (API), Amendment 1: Realtime Extension (C Language), IEEE Std 1003.1b-1993
Machine Learning
WICON '06 Proceedings of the 2nd annual international workshop on Wireless internet
Heuristic selection of actions in multiagent reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Implications of power control in wireless networks: a quantitative study
PAM'07 Proceedings of the 8th international conference on Passive and active network measurement
Performance analysis of the IEEE 802.11 distributed coordination function
IEEE Journal on Selected Areas in Communications
Transmission power control in wireless ad hoc networks: challenges, solutions and open issues
IEEE Network: The Magazine of Global Internetworking
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The success of dynamic spectrum access through simple listen-before-talk etiquettes has paved the way for opening up the spectrum. However, many problems still remain in these networks. Due to the complex nature of IEEE 802.11 networks, for instance, optimizing these networks regarding power, rate and carrier sense threshold remains a very tough challenge. In this paper, we introduce Spatial Learning. This new optimization algorithm for IEEE 802.11 networks employs learning to find an optimal combination of power, rate and carrier sense threshold. It is assumed that nodes behave selfishly and are only interested in optimizing their own throughput. Extensive network simulations show that Spatial Learning performs better than the state-of-the-art solution, Spatial Backoff, on all axes of interest: network-wide throughput, fairness and power consumption.