Algoritmos de aprendizado de máquina para coordenação de interferência entre células
DOI:
https://doi.org/10.18046/syt.v16i46.3034Palavras-chave:
Aprendizado de máquina; redes auto-organizadas; ICIC; LTEResumo
As implantações atuais de LTE e LTE-A exigem maior esforço para o gerenciamento de recursos rádio devido ao aumento de usuários e à alta demanda por serviços, neste cenário a otimização automática é um ponto-chave para evitar problemas como a interferência entre células. O presente trabalho coleta propostas de algoritmos de aprendizado automáticos focados na resolução deste problema. A pesquisa busca que os sistemas celulares alcancem a sua auto-otimização, um conceito que faz parte das redes auto-organizadas (Self-Organizing Networks, SON), cujo objetivo é garantir que as redes respondam automaticamente às necessidades dos cenários dinâmicos do tráfego de rede.
Referências
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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
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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
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
Abusaid, O. & Salem, F. (2017). Self-organizing techniques to resource block scheduling in LTE-A communication systems. Engineering and Technology (ICET), 2017 International Conference on (pp. 1-6). Antalya: IEEE. doi:10.1109/ICEngTechnol.2017.8308183
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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2018-07-06
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