Abstract
The overall goal of evolving algorithms for femtocells is to create a continuous on-line evolution of the femtocell pilot power control algorithm to optimize their coverage. Two aspects of intelligence are used for increasing the complexity of the input and the behaviour, communication and learning. In this initial study we investigate how to evolve more complex behaviour in decentralized control algorithms by changing the representation of communication and learning. The communication is addressed by allowing the femtocell to identify its neighbours and take the values of its neighbours into account when making decisions regarding the increase or decrease of pilot power. Learning is considered in two variants: the use of input parameters and the implementation of a built-in reinforcement procedure. The reinforcement allows learning during the simulation in addition to the execution of fixed commands. The experiments compare the new representation in the form of different terminal symbols in a grammar. The results show that there are differences between the communication and learning combinations and that the best solution uses both communication and learning.
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Acknowledgements
This research is based upon works supported by the Science Foundation Ireland under Grant No. 08/IN.1/I1868.
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Hemberg, E., Ho, L., O’Neill, M., Claussen, H. (2013). Representing Communication and Learning in Femtocell Pilot Power Control Algorithms. In: Riolo, R., Vladislavleva, E., Ritchie, M., Moore, J. (eds) Genetic Programming Theory and Practice X. Genetic and Evolutionary Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6846-2_15
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DOI: https://doi.org/10.1007/978-1-4614-6846-2_15
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