Abstract
This paper addresses the problem of effective quality-of-service (QoS) provisioning in two-tier cognitive radio femtocell networks. The incorporation of primary user activity in the design of radio resource allocation technique is provided for this. Considering the spectrum environment as time-varying and that each femtocell network is able to use an adaptive strategy, the QoS provisioning provisioning guarantees are identified by finding the effective capacity of the femtocell and the femtouser. An integrated method based on fuzzy reinforcement learning algorithm is proposed to solve the formulated problem. It is confirmed by the simulation study that the effective solution of QoS provisioning in these networks is achieved.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Akin, S., Gursoy, M.C.: Effective Capacity Analysis of Cognitive Radio Channels for Quality of Service Provisioning. IEEE Wireless Communications 9(11), 3354–3364 (2010)
Balakrishnan, R., Canberle, B.: Traffic-Aware Provisioning and Admission Control in OFDMA Hybrid Small Cells. IEEE Trans. on Vehicular Technology 63(2), 802–810 (2014)
Beon, H.R., Chen, H.S.: A Sensor-based Navigation for a Mobile Robot Using Fuzzy-Logic and Reinforcement Learning. IEEE Trans. SMC 25(3), 467–477 (1995)
Berenji, H.R., Vengerov, D.: Advantages of Cooperation Between Reinforcement Learning Agent in Difficult Stochastic Problems. In: Proc. The Ninth IEEE International Conference on Fuzzy Systems, San Antonio, TX, pp. 871–876 (2000)
Chang, C.-S.: Stability, Queue Length, and Delay of Deterministic and Stochastic Queueing Networks. IEEE Trans. on Automat. Control 39(5), 913–931 (1994)
Courcoubetis, C., Weber, R.: Effective Bandwidth for Stationary Souces. Probability in Engineering and Information Science 9(2), 285–294 (1995)
Gür, G., Bayhan, S., Alagoz, F.: Cognitive Femtocell Networks: an Overlay Architecture for Localized Dynamic Spectrum Access [Dynamic Spectrum Management]. IEEE Wireless Communications 17(4), 62–70 (2010)
Liang, Y.-S., Chung, W.-H., Ni, G.-K., Chen, I.-Y., Zhang, H., Kuo, S.-Y.: Resource Allocation with Interference Avoidance in OFDMA Femtocell Networks. IEEE Trans. on Vehicular Technology 61(5), 2243–2255 (2012)
Watkins, C.J.C.H., Dayan, P.: Technical Note: Q-learning. Machine Learning 8, 279–292 (1992)
Wu, D., Negi, R.: Effective Capacity: A Wireless Link Model for Support Quality of Service. IEEE Trans. on Wireless Comm. 2(4), 630–643 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Martyna, J. (2015). Fuzzy Q-Learning Approach to QoS Provisioning in Two-Tier Cognitive Femtocell Networks . In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-19066-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19065-5
Online ISBN: 978-3-319-19066-2
eBook Packages: Computer ScienceComputer Science (R0)