Machine learning algorithms for inter-cell interference coordination
DOI:
https://doi.org/10.18046/syt.v16i46.3034Keywords:
Machine learning; self-organization; ICIC; LTE.Abstract
The current LTE and LTE-A deployments require larger efforts to achieve the radio resource management. This, due to the increase of users and the constantly growing demand of services. For this reason, the automatic optimization is a key point to avoid issues such as the inter-cell interference. This paper presents several proposals of machine-learning algorithms focused on this automatic optimization problem. The research works seek that the cellular systems achieve their self-optimization, a key concept within the self-organized networks, where the main objective is to achieve that the networks to be capable to automatically respond to the particular needs in the dynamic network traffic scenarios.
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Miramá, V., & Quintero, V. (2016). Control de potencia basado en teoría de juegos en sistemas de comunicaciones inalámbricas. Gerencia Tecnológica Informática, 15(42), 69-78.
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Sierra, C. A. & Marca, J. R. (2015). Load balancing in self-organized heterogeneous LTE networks: A statistical learning approach. 2015 7th IEEE Latin-American Conference on Communications (LATINCOM). Arequipa. IEEE. doi:10.1109/LATINCOM.2015.7430131
Supratim D. & Monogioudis, P. (2015). Learning-based uplink interference management in 4G LTE cellular systems. IEEE-ACM Transactions on Networking, 23(2), 398 - 411.
Thilina, K. M., Choi, K. W., Saquib, N., & Hossain, E. (2013). Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 31(11), 2209-2221
Trabelsi, N., Chen, C. S., Azouzi, R. E., Roullet, L., & Altman, E. (Junio de 2017). User association and resource allocation optimization in LTE cellular networks. IEEE Transactions on Network and Service Management, 14(2), 429-440.
Van den Berg, J. L., Litjens, R., Eisenblätter, A., Amirijoo, M., Linnell, O., Blondia, C., ... & Schmelz, L. C. (2008). Self-organisation in future mobile communication networks. In Proceedings of ICT-Mobile Summit 2008, Stockholm, Sweden, 2008. Retrieved from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.302.3075&rep=rep1&type=pdf
Xie, Z. (2009). Enhanced fractional frequency reuse to increase capacity of OFDMA Systems. Proceedings of the 3rd international conference on New technologies, mobility and security, NTMS. Cairo, Egypt: IEEE. doi:10.1109/NTMS.2009.5384757
5GAmericas. (2013). Self-optimizing networks: The benefits of SON in LTE. Retrieved from: http://www.5gamericas.org/files/7714/0759/1264/Self-Optimizing_Networks-Benefits_of_SON_in_LTE_10.7.13.pdf
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Barranco, R. (2012, June 18). IBM. Retrieved from: https://www.ibm.com/developerworks/ssa/local/im/que-es-big-data/que-es-big-data-pdf.pdf
Behjati, M. (2014). Self-organizing interference coordination for future LTE-advanced network QoS improvements. In Broadband Multimedia Systems and Broadcasting (BMSB), 2014 IEEE International Symposium on. Dublin. doi:10.1109/BMSB.2014.6873493
Bojović, B., Meshkova, E., Baldo, N., Riihijärvi, J., & Petrova, M. (2016). Machine learning-based dynamic frequency and bandwidth allocation in self-organized LTE dense small cell deployments. Eurasip Journal on Wireless Communication Network, 143. doi: 10.1186/s13638-016-0679-0
Borkar, S., & Pande, H. (2016). Application of 5G next generation network to Internet of Things. Internet of Things and Applications (IOTA), International Conference on. Pune. IEEE. doi:10.1109/IOTA.2016.7562769
Budihal, V., Sandhya, R., Siddamal, S., & Banakar, R. M. (2016). Framework for intercell interference avoidance in MIMO OFDMA system. Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on (pp. 1626-1630). Chennai: IEEE.
Cordina, M., & Debono, C. J. (2017). A support vector machine based sub-band CQI feedback compression scheme for 3GPP LTE systems. Wireless Communication Systems (ISWCS), 2017 International Symposium on (pp. 325-330). Bologna. IEEE.
Daeinabi, A., & Sandrasegaran, K. (2015). A Fuzzy Q-Learning Approach for Enhanced Intercell Interference Coordination in LTE-Advanced Heterogeneous Networks. Communications (APCC), 2014 Asia-Pacific Conference on (pp. 139-144). Pattaya. IEEE.
Daeinabi, A., &Sandrasegaran, K. (2014). Networks: A fuzzy Q-learning approach for enhanced intercell interference coordination in LTE-Advanced heterogeneous. Communications (APCC), 2014 Asia-Pacific Conference on (pp. 139-144). Pattaya. IEEE.
Dai, K., & Hiroyuky, S. (2012). Intercell interference coordination (ICIC) Technology. Fujitsu Sci. Tech., 48(1), 89-94.
Deb, S. (2014). Algorithms for enhanced inter-cell interference coordination (eICIC) in LTE HetNets. IEEE/ACM Transactions on Networking, 22(1), 137-150.
Eberle, A. (2015, Mach 15). Markov processes. Retrieved from: http://wt.iam.uni-bonn.de/fileadmin/WT/Inhalt/people/Andreas_Eberle/MarkovProcesses/MPSkript1415.pdf
ETSI TS 136 423 V11.2.0. (2018, April). Retrieved from: http://www.etsi.org/deliver/etsi_ts/136400_136499/136423/11.02.00_60/ts_136423v110200p.pdf
Feng, S. (2008). Self-organizing networks (SON) in 3GPP long term evolution. Munich, Germany: Nomor Research.
Fernández, B., González, L., & Hernández, C. (2014). Impacto del reuso de frecuencia fraccional en la reducción de interferencia intercelda en LTE. Revista de Investigaciones, 25(1), 28-39.
Gadam, M., Ahmed, M. A., & Kyun, C. (2016). Wireless personal communications: A review of adaptive cell selection techniques in LTE-advanced HetNets. Journal of Computer Networks and Communications, 2016, Art.3. doi:10.1155/2016/7394136
Galindo-Serrano, A., & Giupponi, L. (2013). Self-organized femto-to-macro interference coordination with partial information. Personal, Indoor and Mobile Radio Communications (PIMRC Workshops), 2013 IEEE 24th International Symposium on (pp. 111-116). London. IEEE.
Gao, C., Chen, X., & Li, L. (2014). An I-FFR algorithm for interference coordination in twin-layer femtocell networks. In Game Theory for Networks (GAMENETS), 2014 5th International Conference on. Beijing: IEEE. doi:10.1109/GAMENETS.2014.7043724
Glenn, O., Imran, A., & Evans, M. I. (2013). A Survey of Self Organization in Future Cellular Networks. IEEE Communications Surveys & Tutorials, 15(1), 336-361.
Guio, I. & Hernández, M. Á. (2013). Gestión de recursos radio en redes móviles celulares basadas en tecnología OFDMA para la provisión de QoS y control de la interferencia [tesis]. Universidad de Zaragoza: España.
Hajjar, M., Aldabbagh, G., & Dimitriou, N. (2015). Using clustering techniques to improve capacity of LTE networks. Communications (APCC), 2015 21st Asia-Pacific Conference on (pp. 68-73). Kyoto. IEEE.
Hamza, A., Khalifa, S., & Hamza, H. (2013). A survey on inter-cell interference coordination techniques in OFDMA-based cellular networks. IEEE Communications Surveys & Tutorials, 15(4), 1642-1670.
Hinton, G., Srivastava, N., & Swersky, K. (2014, April). Neural networks for machine learning - Lecture 1A [slides]. Retrieved from: https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec1.pdf
Hu, T., & Pang, J. (2012). LTE-Advanced heterogeneous networks: Release 10 and beyond. In Communications (ICC), 2012 IEEE International Conference on. Ottawa. IEEE. doi:10.1109/ICC.2012.6364785.
Jain, S. (July 31). Introduction to genetic algorithm & their application in data science. Retrieved from: https://www.analyticsvidhya.com/blog/2017/07/introduction-to-genetic-algorithm/
Jiang, C., Zhang, H., Ren, Y., Han, Z., Chen, K.-C., & Hanzo, L. (2017). Machine learning paradigms for next-generation wireless networks. IEEE Wireless Communications, 24(2), 98 - 105.
Kashaf, A., Tiwana, M. I., & Usman, I. (2017). Self-organizing inter-cell interference coordination in 4G and beyond networks using genetic algorithms. Automatika, 58(1), 48-54.
Keysight Technologies (2018, April). LTE physical layer overview. Retrieved from: http://rfmw.em.keysight.com/wireless/helpfiles/89600b/webhelp/subsystems/lte/content/lte_overview.htm
Kibria, M. G., Villardi, G. P., & Nguyen, K. (2016). Heterogeneous Networks in Shared Spectrum Access Communications. IEEE Journal on Selected Areas in Communications, 35(1), 45-58.
Klaine, P., Imra, M., Onireti, O., & Souza, R. (2017). A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Communications Surveys & Tutorials, 19(4), 2392-2431.
Kumar, D., Kanagaraj, N. N., & Srilakshmi, R. (2013, April). Harmonized Q-learning for radio resource management in LTE based networks. In ITU Kaleidoscope: Building Sustainable Communities (K-2013), 2013 Proceedings of (pp. 1-8). IEEE.
Li, H., Liang, Z., & Ascheid, G. (2016). Artificial neural network aided dynamic scheduling for eICIC in LTE HetNets. In Signal Processing Advances in Wireless Communications (SPAWC), 2016 IEEE 17th International Workshop on (pp. 1-5). Edinburgh: IEEE. doi:10.1109/SPAWC.2016.7536877
Li, L., Peng, M., Yan, Z., Zhao, Z., & Li, Y. (2016). Success coverage probability for dynamic resource allocation in small cell networks. In IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-5). Doha. IEEE. doi:10.1109/WCNC.2016.7565150
Madariaga, D. F., Rodríguez, J. L., Lozano, M. R., & Vallejo, E. C. (2013). Inferencia estadística: módulo de regresión lineal simple. Documentos de Investigación, 147. Retrieved from: http://www.urosario.edu.co/Administracion/documentos/Documentos-de-Investigacion/BI_147-Web.pdf
Ming, C., Ye, H., & Xinyu, G. (2017). A self-organizing resource allocation strategy based on Q-learning approach in ultra-dense networks. In Network Infrastructure and Digital Content (IC-NIDC), 2016 IEEE International Conference on (pp. 155-160). Beijing. IEEE.
Miramá, V., & Quintero, V. (2016). Control de potencia basado en teoría de juegos en sistemas de comunicaciones inalámbricas. Gerencia Tecnológica Informática, 15(42), 69-78.
Mohamed, A., & Abd-Elnaby, M. (2016). Self-organised dynamic resource allocation scheme using enhanced fractional frequency reuse in long term evolution-advanced relay-based networks. IET Communications, 10(10), 1163-1174.
Morel, M.-L. A., & Randriamasy, S. (2017). Quality of experience-aware enhanced inter-cell interference coordination for self organized HetNet. In Wireless and Mobile Networking Conference (WMNC), 2017 10th IFIP (pp. 1-8). Valencia. IEEE. doi:10.1109/WMNC.2017.8248841
Morozs, N., Clarke, T., & Grace, D. (2015). Distributed heuristically accelerated q-learning for robust cognitive spectrum management in LTE cellular systems. IEEE Transactions on Mobile Computing, 15(4), 817-825.
Moysen, J., Giupponi, L., Carl, A., & Gauss, F. (2014). A reinforcement learning based solution for self-healing in LTE networks. In 2014 IEEE Vehicular Technology Conference (VTC Fall). Vancouver. IEEE. doi:10.1109/VTCFall.2014.6965842
ns-3 (2011). ns-3 A discrete-event network simulator. Retrieved from: https://www.nsnam.org/docs/models/html/lte-design.html
Qi, W., Zhang, B., Chen, B., & Zhang, J. (2018). A user-based K-means clustering offloading algorithm for heterogeneous network. In 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV (pp. 307-312). IEEE.
Rayón, A. (2017, April). Guía para comenzar con algoritmos de machine learning. Retrieved from: https://blogs.deusto.es/bigdata/guia-para-comenzar-con-algoritmos-de-machine-learning/
Razavi, R., Klein, S., & Claussen, H. (2010). Self-optimization of capacity and coverage in LTE networks using a fuzzy reinforcement learning approach. In Personal Indoor and Mobile Radio Communications (PIMRC), 2010 IEEE 21st International Symposium on. Instanbul. IEEE. doi:10.1109/PIMRC.2010.5671622
Shanthamallu, U., Spanias, A., Tepedelenlioglu, C., & Stanley, M. (2017). A brief survey of machine learning methods and their sensor and IoT applications. In Information, Intelligence, Systems & Applications (IISA), 2017 8th International Conference on (pp. 1-8). Larnaca, Cyprus. IEEE. doi:10.1109/IISA.2017.8316459
Sierra, C. A. & Marca, J. R. (2015). Load balancing in self-organized heterogeneous LTE networks: A statistical learning approach. 2015 7th IEEE Latin-American Conference on Communications (LATINCOM). Arequipa. IEEE. doi:10.1109/LATINCOM.2015.7430131
Supratim D. & Monogioudis, P. (2015). Learning-based uplink interference management in 4G LTE cellular systems. IEEE-ACM Transactions on Networking, 23(2), 398 - 411.
Thilina, K. M., Choi, K. W., Saquib, N., & Hossain, E. (2013). Machine learning techniques for cooperative spectrum sensing in cognitive radio networks. IEEE Journal on Selected Areas in Communications, 31(11), 2209-2221
Trabelsi, N., Chen, C. S., Azouzi, R. E., Roullet, L., & Altman, E. (Junio de 2017). User association and resource allocation optimization in LTE cellular networks. IEEE Transactions on Network and Service Management, 14(2), 429-440.
Van den Berg, J. L., Litjens, R., Eisenblätter, A., Amirijoo, M., Linnell, O., Blondia, C., ... & Schmelz, L. C. (2008). Self-organisation in future mobile communication networks. In Proceedings of ICT-Mobile Summit 2008, Stockholm, Sweden, 2008. Retrieved from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.302.3075&rep=rep1&type=pdf
Xie, Z. (2009). Enhanced fractional frequency reuse to increase capacity of OFDMA Systems. Proceedings of the 3rd international conference on New technologies, mobility and security, NTMS. Cairo, Egypt: IEEE. doi:10.1109/NTMS.2009.5384757
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