Abstract
This chapter focuses on how to realize context-aware network selection. The context information provides a characterization of user-, traffic-, and network-related properties, which is able to enable fine-grained optimization for network selection. Following a similar way with Chap. 4, we can formulate context-aware network selection as an MDP model by generalizing the state to context information. However, the high resolution of context information may lead to large state space, which could result in low learning efficiency. To handle this issue, we employ a transfer learning idea. Specifically, the time–location- dependent periodic changing rule of load statistical distributions is used to realize efficient online network selection via knowledge transfer. Simulation results show that the proposed transfer RL algorithm could achieve better convergence performance by reusing learning experience.
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References
Sim GH, Klos S et al (2018) An online context-aware machine learning algorithm for 5G mmWave vehicular communications. IEEE ACM T Network 26(6):2487–2500
Pantisano F, Bennis M, Saad W et al (2013) Matching with externalities for context-aware user-cell association in small cell networks. In: IEEE global telecommunications conference (GLOBECOM)
Yang H, Du P et al (2019) Reinforcement learning-based intelligent resource allocation for integrated VLCP systems. IEEE Wirel Commun Lett 8(4):1204–1207
Monteiro A, Souto E et al (2019) Context-aware network selection in heterogeneous wireless networks. Comput Commun 135:1–15
Du Z, Wang C, Sun Y, Wu G (2018) Context-aware indoor VLC/RF heterogeneous network selection: reinforcement learning with knowledge transfer. IEEE Access 6:33275–33284
Du Z, Wu Q, Yang P et al (2015) Exploiting user demand diversity in heterogeneous wireless networks. IEEE Trans Wirel Commun 14(8):4142–4155
Bianchi G (2000) Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J Sel Areas Commun 8(3):535–547
Basnayaka DA Haas H (2015) Hybrid RF and VLC systems: improving user data rate performance of VLC systems. In: IEEE Vehicular Technology Conference (VTC)
Kavehrad M (2010) Sustainable energy-efficient wireless applications using light. IEEE Commun Mag 48(12):66–73
GigaIR, Infrared Data Association Standards, http://irda.org
González MC, Hidalgo CA, Barabási AL (2008) Understanding individual human mobility patterns. Nature 453:779–782
Lee D, Zhou S, Zhong X et al (2014) Spatial modeling of the traffic density in cellular networks. IEEE Wirel Commun 21(1):80–88
Talvitie E, Singh S (2007) An experts algorithm for transfer learning. In: international joint conference on artificial intelligence (IJCAI)
Ibrahim M, Khawam K, Tohme S (2010) Congestion games for distributed radio access selection in broadband networks. In: IEEE global telecommunications conference (GLOBECOM)
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Du, Z., Jiang, B., Wu, Q., Xu, Y., Xu, K. (2020). Learning the Optimal Network with Context Awareness: Transfer RL Based Network Selection. In: Towards User-Centric Intelligent Network Selection in 5G Heterogeneous Wireless Networks. Springer, Singapore. https://doi.org/10.1007/978-981-15-1120-2_5
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DOI: https://doi.org/10.1007/978-981-15-1120-2_5
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