Abstract
This paper introduces a reinforcement learning solution to the problem of dynamic channel allocation for cellular telecommunication networks featuring either uniform or non-uniform offered traffic loads and call mobility. The performance of various dynamic channel allocation schemes are compared via extensive computer simulations, and it is shown that a reduced-state SARSA reinforcement learning algorithm can achieve superior new call and handoff blocking probabilities. A new reduced-state SARSA algorithm featuring an extended channel reassignment functionality and an initial table seeding is also demonstrated. The reduced-state SARSA incorporating the extended channel reassignment algorithm and table seeding is shown to produce superior new call and handoff blocking probabilities by way of computer simulations.
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© 2005 Springer-Verlag Berlin Heidelberg
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Lilith, N., Dogançay, K. (2005). Reduced-State SARSA Featuring Extended Channel Reassignment for Dynamic Channel Allocation in Mobile Cellular Networks. In: Lorenz, P., Dini, P. (eds) Networking - ICN 2005. ICN 2005. Lecture Notes in Computer Science, vol 3421. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31957-3_62
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DOI: https://doi.org/10.1007/978-3-540-31957-3_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25338-9
Online ISBN: 978-3-540-31957-3
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