Learning in Cross-layer Wireless Network Optimization
نویسندگان
چکیده
We study the use of learning for cross-layer optimization of wireless networks. In particular, we incorporate learning in the form of graphical models into our cognitive engine performing network utility maximization task using simulated annealing. Our results show that this learning approach can significantly accelerate the convergence rate of the optimizer, and help in adjusting to changes in network conditions. However, we also observed significant differences in the behavior and performance between the various types of graphical models studied. We discuss these results at length, and identify some of the key challenges faced when incorporating learning into cross-layer optimization design.
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