References
- C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, 1995, pp. 273-297.
- 한국전자통신연구원, "5G Insight White Paper: 5G Vision & Enabling Technologies," 2015. 12.
- R.C. Daniels, C.M. Caramanis, and R.W. Heath, "Adaption in Convolutionally-Coded MIMO-OFDM Wireless Systems through Supervised Learning and SNR Ordering," IEEE Trans. Veh. Technol., vol. 59, no. 1, pp. 114-126, Jan. 2010. https://doi.org/10.1109/TVT.2009.2029693
- R.C. Daniels and R.W. Heath, "An Online Learing Framework for Link Adaptation in Wireless Networks," Proc. Inf. Theory Appl. Workshop, Feb. 2009, pp. 138-140.
- A. Rico-Alvarino and R.W. Heath, "Learning-Based Adaptive Transmission for Limited Feedback Multiuser MIMO-OFDM," Submitted to IEEE Transactions on Wireless Communications, 2013.
- S.S. Hong and S.R. Katti, "Dof: A Local Wireless Information Plane," ACM SIGCOMM, vol. 41, no. 4, Aug. 2011, pp. 230-241. https://doi.org/10.1145/2043164.2018463
- K. Joshi, S. Hong, and S. Katti, "PinPoint: Localizing Interfering Radios," Proc. USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2013.
- X. Wang, X. Li, and V. C.M. Leung, "Artificial Intelligence-Based Techniques for Emerging Heterogeneous Networks: State of the Arts, Opportunities, and Chanllenges," IEEE Access, vol. 3, Aug. 2015, pp. 1379-1391. https://doi.org/10.1109/ACCESS.2015.2467174
- M.A. Khan, H. Tembine, and A.V. Vasilakos, "Game Dynamics and Cost of Learning in Heterogeneous 4G Networks," IEEE J. Sel. Areas Commun., vol. 30, no. 1, Jan. 2012, pp. 198-213. https://doi.org/10.1109/JSAC.2012.120118
- M. Simsek, M. Bennis, and I. Guvenc, "Context-Aware Mobility Management in HetNets: A Reinforcement Learning Approach," in Proc. Netw. Internet Archit., May 2015.
- Z. Zhao, J. Chen, and N. Crespi, "A Policy-Based Framework for Autonomic Reconfiguration Management in Heterogeneous Networks," Proc. 7th Int. Conf. Mobile Ubiquitous Multimedia, 2008, pp. 71-78.
- P.T. Semov et al., "Use of Positioning Information for Performance Enhancement of Uncoordinated Heterogeneous Network Deployment," Proc. 3rd Int. Conf. VITAE, June 2013, p. 16.
- P.T. Semov et al. "Increasing Throughput and Fairness for Users in Heterogeneous Semi Coordinated Deployments," Proc. IEEE WCNC, Apr. 2014, pp. 40-45.
- Q. Li et al., "Dynamic Enhanced Inter-Cell Interference Coordination Using Reinforcement Learning Approach in Heterogeneous Network," Proc. IEEE ICCT, Nov. 2013, pp. 239-243.
- W. Guo et al., "Spectral- and Energy-Efficient Antenna Tilting in a HetNet Using Reinforcement Learning," Proc. IEEE WCNC, Apr. 2013, pp. 767-772.
- M. Simsek, M. Bennis, and A. Czylwik, "Coordinated Beam Selection in LTE-Advanced HetNets: A ReinForcement Learning Approach," Proc. IEEE Globecom Workshops, Dec. 2012, pp. 603-607.
- M. Simsek and A. Czylwik, "Improved Decentralized Fuzzy Q-Learning for Interference Reduction in Heterogeneous LTE-Networks," Proc. Int. OFDM Workshop, Aug. 2012, pp. 1-6.
- M. Dirani and Z. Altman, "Self-Organizing Networks in Next Generation Radio Access Networks: Application to Fractional Power Control," Computer Networks, vol. 55, no. 2, Feb. 2011, pp. 431-438. https://doi.org/10.1016/j.comnet.2010.08.012
- S. Fan, H. Tian, and C. Sengul, "Self-Optimization of Coverage and Capacity Based on a Fuzzy Neural Network with Cooperative Reinforcement Learning," EURASIP J. Wireless Commun. Netw., vol. 2014, no. 1, pp. 57:1-57:14, Apr. 2014.
- R. Chai et al., "Neural Network based Vertical Handoff Performance Enhancement in Heterogeneous Wireless Networks," Proc. WiCOM, Sept. 2011, pp. 1-4.
- Zander, S., Nguyen, T., & Armitage, G., "Automated Traffic Classification and Application Identification Using Machine Learning," IEEE Conf. Local Computer Networks 30th Anniversary, Nov. 2005, pp. 250-257.
- S. Zander and G. Armitage, "Practical Machine Learning based Multimedia Traffic Classification for Distributed QoS Management," Local Computer Networks(LCN) IEEE 36th Conference, Oct. 2011, pp. 399-406.
- T.T. Nguyen et al., "Timely and Continuous Machine-Learning-based Classification for Interactive IP Traffic," IEEE/ACM Transactions on Networking (TON), vo. 20, no. 6, 2012, pp. 1880-1894. https://doi.org/10.1109/TNET.2012.2187305
- A. McGregor, "Flow Clustering Using Machine Learning Techniques," International Workshop on Passive and Active Network Measurement Springer Berlin Heidelberg, Apr. 2004, pp. 205-214.
- T.T. Nguyen and G. Armitage, "A Survey of Techniques for Internet Traffic Classification using Machine Learning," IEEE Communications Surveys & Tutorials, vol. 10, no. 4, 2008. pp. 56-76. https://doi.org/10.1109/SURV.2008.080406
- http://arxiv.org/abs/1503.08855
- J. Cho et al., "Dynamic Learning Model Update of Hybrid-Classifiers for Intrusion Detection," Journal of Supercomputing, vol. 64, no. 2, 2013. pp. 522-526. https://doi.org/10.1007/s11227-011-0698-x
- C.F. Tsai and C.Y. Lin, "A Triangle Area based Nearest Neighbors Approach to Intrusion Detection," J. Pattern Recognition, vol. 43, no. 1, Jan. 2010. pp. 222-229. https://doi.org/10.1016/j.patcog.2009.05.017
- K.K. Patel and B.V. Buddhadev, "Machine Learning based Research for Network Intrusion Detection: A State-ofthe-Art," International Journal Information and Network Security (IJINS), vol. 3, no. 3. 2014.
- R. Sommer and V. Paxson, "Outside the Closed World: on Using Machine Learning for Network Intrusion Detection," IEEE Symposium on Security and Privacy, May. 2010, pp. 305-316.
- CogNet, "Initial Use Cases, Scenarios and Requirement," 2015.11.
- https://www.qualcomm.com/news/onq/2015/03/02/qualcommzeroth-advancing-deep-learning-devices-video
- https://www.qualcomm.com/news/releases/2016/05/02/qualcomm-helps-make-your-mobile-devices-smarternew-snapdragon-machine
- https://blog.networks.nokia.com/mobile-networks/2015/11/11/machine-learning-teach-networks-self-aware/
- M. Svensson, and J. Soderberg, "Machine-Learning Technologies in Telecommunications," Ericsson Review, 2008.