َََََReview of Terrestrial and Satellite Networks based on Machine Learning Techniques
It has been broadly admitted that the upcoming networks will require to provide meaningfully more capacity than the existing ones to be able to deal with the growing traffic requirements of the users. Specifically, in the areas that optical fibers are improbable to be spread out because of the economical limitations; so, this would be a very crucial challenge. To address the above-mentioned issue, the combination of Terrestrial and Satellite Networks (TSNs) together would be an option. Satellite networks can cover enormous regions and current improvements have significantly raised the existing capacity while diminishing the cost. However, the characteristics of the geostationary satellite links are potentially various than the frequent terrestrial ones, essentially because the propagation time of the signal is high. The current study reviews the cutting-edge issues with respects to TSNs with machine learning methods.
Ahmadi, N. (2019a). Intelligent Approaches towards Fuzzy Segmentation and Fuzzy Edge Detection. Journal of Soft Computing and Decision Support Systems, 6(6), 9-13.
Ahmadi, N. (2019b). Morphological-Edge Detection Approach for the Human Iris Segmentation. Journal of Soft Computing and Decision Support Systems, 6(4), 15-19.
Ahmadi, N. (2020). A Hybrid Intelligent Approach for Image Segmentation and Feature Extraction Using Fuzzy Clustering, Lattice Boltzmann and GLDM Techniques. Journal of Soft Computing and Decision Support Systems, 7(3), 1-5.
Ahmadi, N., & Akbarizadeh, G. (2018). Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier. Neural Computing and Applications, 1-15.
Ahmadi, N., Nilashi, M., Samad, S., Rashid, T. A., & Ahmadi, H. (2019). An intelligent method for iris recognition using supervised machine learning techniques. Optics & Laser Technology, 120, 105701.
Al-Turjman, F. (2019). 5G-enabled devices and smart-spaces in social-IoT: an overview. Future Generation Computer Systems, 92, 732-744.
Ali, Z., Baldo, N., Mangues-Bafalluy, J., & Giupponi, L. (2016). Machine learning based handover management for improved QoE in LTE. Paper presented at the NOMS 2016-2016 IEEE/IFIP Network Operations and Management Symposium.
Alias, M., Saxena, N., & Roy, A. (2016). Efficient cell outage detection in 5G HetNets using hidden Markov model. IEEE Communications Letters, 20(3), 562-565.
An, J., Yang, K., Wu, J., . . . Z. (2017). Achieving sustainable ultra-dense heterogeneous networks for 5G. IEEE Communications Magazine, 55(12), 84-90.
An, K., Lin, M., Ouyang, J., . . . W. (2016). Secure transmission in cognitive satellite terrestrial networks. IEEE Journal on Selected Areas in Communications, 34(11), 3025-3037.
Anand, A., De Veciana, G., & Shakkottai, S. (2020). Joint scheduling of URLLC and eMBB traffic in 5G wireless networks. IEEE/ACM Transactions on Networking.
Bennis, M., Perlaza, S. M., Blasco, P., Han, Z., & Poor, H. V. (2013). Self-organization in small cell networks: A reinforcement learning approach. IEEE transactions on wireless communications, 12(7), 3202-3212.
Bergner, E. (2012). Unsupervised learning of traffic patterns in self-optimizing 4th generation mobile networks. Master of Science Thesis, KTH Computer Sicence and Communications, Stockolm, Sweden.
Blanco, B., Fajardo, J. O., Giannoulakis, I., Kafetzakis, E., Peng, S., Pérez-Romero, J., . . . Paolino, M. (2017). Technology pillars in the architecture of future 5G mobile networks: NFV, MEC and SDN. Computer Standards & Interfaces, 54, 216-228.
Boccardi, F., Heath, R. W., Lozano, A., Marzetta, T. L., & Popovski, P. (2014). Five disruptive technology directions for 5G. IEEE Communications Magazine, 52(2), 74-80.
Burian, R., Gontijo, M., & Alvarez, H. (2019). Robustness and Reliability in Smart Grid Solutions. Paper presented at the 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE).
Calarco, G., Casoni, M., Paganelli, A., Vassiliadis, D., & Wódczak, M. (2010). A satellite based system for managing crisis scenarios: The E-SPONDER perspective. Paper presented at the 2010 5th Advanced Satellite Multimedia Systems Conference and the 11th Signal Processing for Space Communications Workshop.
Celandroni, N., Ferro, E., Gotta, A., Oligeri, G., Roseti, C., Luglio, M., . . . Panagopoulos, A. D. (2013). A survey of architectures and scenarios in satellite‐based wireless sensor networks: system design aspects. International Journal of Satellite Communications and Networking, 31(1), 1-38.
Chang, Z., Lei, L., Zhou, Z., Mao, S., & Ristaniemi, T. (2018). Learn to cache: Machine learning for network edge caching in the big data era. IEEE Wireless Communications, 25(3), 28-35.
Chernogorov, F., Turkka, J., Ristaniemi, T., & Averbuch, A. (2011). Detection of sleeping cells in LTE networks using diffusion maps. Paper presented at the 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).
Chitre, P., & Yegenoglu, F. (1999). Next-generation satellite networks: architectures and implementations. IEEE Communications Magazine, 37(3), 30-36.
Daróczy, B., Vaderna, P., & Benczúr, A. (2015). Machine learning based session drop prediction in LTE networks and its SON aspects. Paper presented at the 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).
Deng, B., Jiang, C., Yan, J., Ge, N., Guo, S., & Zhao, S. (2019). Joint Multigroup Precoding and Resource Allocation in Integrated Terrestrial-Satellite Networks. IEEE Transactions on Vehicular Technology, 68(8), 8075-8090.
Di, B., Song, L., Li, Y., & Poor, H. V. (2019). Ultra-Dense LEO: Integration of satellite access networks into 5G and beyond. IEEE Wireless Communications, 26(2), 62-69.
Dirani, M., & Altman, Z. (2010). A cooperative reinforcement learning approach for inter-cell interference coordination in OFDMA cellular networks. Paper presented at the 8th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks.
Dudnikova, A., Dini, P., Giupponi, L., & Panno, D. (2015). Fuzzy multiple criteria switch off method for dense heterogeneous networks. Paper presented at the 2015 IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD).
Farooq, H., & Imran, A. (2016). Spatiotemporal mobility prediction in proactive self-organizing cellular networks. IEEE Communications Letters, 21(2), 370-373.
Feng, B., Zhou, H., Zhang, H., Li, G., Li, H., Yu, S., & Chao, H.-C. (2017). HetNet: A flexible architecture for heterogeneous satellite-terrestrial networks. IEEE network, 31(6), 86-92.
Franco, C. A. S., & de Marca, J. R. B. (2015). Load balancing in self-organized heterogeneous LTE networks: A statistical learning approach. Paper presented at the 2015 7th IEEE Latin-American Conference on Communications (LATINCOM).
Galindo-Serrano, A., Giupponi, L., & Auer, G. (2011). Distributed learning in multiuser OFDMA femtocell networks. Paper presented at the 2011 IEEE 73rd Vehicular Technology Conference (VTC Spring).
Huang, X., Zhang, J. A., Liu, R. P., Guo, Y. J., & Hanzo, L. (2019). Integrating space and terrestrial networks with passenger airplanes for 6th generation wireless-will it work? IEEE Vehicular Technology Magazine.
Iacoboaiea, O., Sayrac, B., Jemaa, S. B., & Bianchi, P. (2014). SON Coordination for parameter conflict resolution: A reinforcement learning framework. Paper presented at the 2014 IEEE Wireless Communications and Networking Conference Workshops (WCNCW).
Jiao, J., Hu, Y., Zhang, Q., & Wu, S. (2018). Performance modeling of LTP-HARQ schemes over OSTBC-MIMO channels for hybrid satellite terrestrial networks. IEEE Access, 6, 5256-5268.
Jiuling, X., Chaojie, Z., Chunhui, W., & Xiaojun, J. (2018). Approach to inter-satellite time synchronization for micro-satellite cluster. Journal of Systems Engineering and Electronics, 29(4), 805-815.
Johansson, N. A., Wang, Y.-P. E., Eriksson, E., & Hessler, M. (2015). Radio access for ultra-reliable and low-latency 5G communications. Paper presented at the 2015 IEEE International Conference on Communication Workshop (ICCW).
Kamel, M., Hamouda, W., & Youssef, A. (2016). Ultra-dense networks: A survey. IEEE Communications Surveys & Tutorials, 18(4), 2522-2545.
Khambari, N., & Ghita, B. (2019). QoE Enhancements for Video Traffic in Wireless Networks through Selective Packet Drops. In Computational Science and Technology (pp. 295-304): Springer.
Khan, A., Kellerer, W., Kozu, K., & Yabusaki, M. (2011). Network sharing in the next mobile network: TCO reduction, management flexibility, and operational independence. IEEE Communications Magazine, 49(10), 134-142.
Khanafer, R. M., Solana, B., Triola, J., Barco, R., Moltsen, L., Altman, Z., & Lazaro, P. (2008). Automated diagnosis for UMTS networks using Bayesian network approach. IEEE Transactions on vehicular technology, 57(4), 2451-2461.
Khawaja, W., Guvenc, I., Matolak, D. W., Fiebig, U.-C., & Schneckenburger, N. (2019). A survey of air-to-ground propagation channel modeling for unmanned aerial vehicles. IEEE Communications Surveys & Tutorials, 21(3), 2361-2391.
Lagunas, E., Sharma, S. K., Maleki, S., Chatzinotas, S., & Ottersten, B. (2015). Resource allocation for cognitive satellite communications with incumbent terrestrial networks. IEEE Transactions on Cognitive Communications and Networking, 1(3), 305-317.
Laliberte, A. S., & Rango, A. (2009). Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery. IEEE Transactions on Geoscience and Remote Sensing, 47(3), 761-770.
Li, J., Zeng, J., Su, X., Luo, W., & Wang, J. (2012). Self-optimization of coverage and capacity in LTE networks based on central control and decentralized fuzzy Q-learning. International Journal of Distributed Sensor Networks, 8(8), 878595.
Lin, K., Wang, D., Hu, L., Hossain, M. S., & Muhammad, G. (2019). Virtualized QoS-Driven Spectrum Allocation in Space-Terrestrial Integrated Networks. IEEE network, 33(1), 58-63.
Liu, Y., Tang, A., & Wang, X. (2019). Joint Incentive and Resource Allocation Design for User Provided Network under 5G Integrated Access and Backhaul Networks. IEEE Transactions on Network Science and Engineering.
Meloni, A., & Atzori, L. (2017). The role of satellite communications in the smart grid. IEEE Wireless Communications, 24(2), 50-56.
Miozzo, M., Giupponi, L., Rossi, M., & Dini, P. (2015). Distributed Q-learning for energy harvesting heterogeneous networks. Paper presented at the 2015 IEEE International Conference on Communication Workshop (ICCW).
Moysen, J., Garcia-Lozano, M., Giupponi, L., & Ruiz, S. (2018). Conflict resolution in mobile networks: a self-coordination framework based on non-dominated solutions and machine learning for data analytics [Application notes]. IEEE Computational Intelligence Magazine, 13(2), 52-64.
Moysen, J., & Giupponi, L. (2014). A reinforcement learning based solution for self-healing in LTE networks. Paper presented at the 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall).
Moysen, J., & Giupponi, L. (2015). Self-coordination of parameter conflicts in D-SON architectures: a Markov decision process framework. EURASIP Journal on Wireless Communications and Networking, 2015(1), 82.
Moysen, J., & Giupponi, L. (2018). From 4G to 5G: Self-organized network management meets machine learning. Computer Communications, 129, 248-268.
Moysen, J., Giupponi, L., Baldo, N., & Mangues-Bafalluy, J. (2015). Predicting QoS in LTE HetNets based on location-independent UE measurements. Paper presented at the 2015 IEEE 20th International Workshop on Computer Aided Modelling and Design of Communication Links and Networks (CAMAD).
Moysen, J., Giupponi, L., & Mangues-Bafalluy, J. (2016). On the potential of ensemble regression techniques for future mobile network planning. Paper presented at the 2016 IEEE Symposium on Computers and Communication (ISCC).
Moysen, J., Giupponi, L., & Mangues-Bafalluy, J. (2017). A mobile network planning tool based on data analytics. Mobile Information Systems, 2017.
Mukherjee, A. (2018). Energy efficiency and delay in 5G ultra-reliable low-latency communications system architectures. IEEE Network, 32(2), 55-61.
Muñoz, P., Barco, R., & de la Bandera, I. (2013). Optimization of load balancing using fuzzy Q-learning for next generation wireless networks. Expert Systems with Applications, 40(4), 984-994.
Muñoz, P., Barco, R., & de la Bandera, I. (2015). Load balancing and handover joint optimization in LTE networks using fuzzy logic and reinforcement learning. Computer Networks, 76, 112-125.
Mwanje, S. S., & Mitschele-Thiel, A. (2013). Minimizing handover performance degradation due to LTE self organized mobility load balancing. Paper presented at the 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).
Mwanje, S. S., & Mitschele-Thiel, A. (2014). Distributed cooperative Q-learning for mobility-sensitive handover optimization in LTE SON. Paper presented at the 2014 IEEE Symposium on Computers and Communications (ISCC).
Niephaus, C., Kretschmer, M., & Ghinea, G. (2016). QoS provisioning in converged satellite and terrestrial networks: A survey of the state-of-the-art. IEEE Communications Surveys & Tutorials, 18(4), 2415-2441.
Niephaus, C., Mödeker, J., & Ghinea, G. (2018). Toward Traffic Offload in Converged Satellite and Terrestrial Networks. IEEE Transactions on Broadcasting, 65(2), 340-346.
Nilashi, M., Samad, S., Ahmadi, N., . . . E. (2020). Neuromarketing: A Review of Research and Implications for Marketing. Journal of Soft Computing and Decision Support Systems, 7(2), 23-31.
Nilashi, M., Ahmadi, N., Samad, S., Shahmoradi, L., Ahmadi, H., Ibrahim, O., . . . Yadegaridehkordi, E. (2020). Disease Diagnosis Using Machine Learning Techniques: A Review and Classification. Journal of Soft Computing and Decision Support Systems, 7(1), 19-30.
Nomikos, N., Michailidis, E. T., Trakadas, P., Vouyioukas, D., Zahariadis, T., & Krikidis, I. (2019). Flex-NOMA: exploiting buffer-aided relay selection for massive connectivity in the 5G uplink. IEEE Access, 7, 88743-88755.
Onireti, O., Zoha, A., Moysen, J., Imran, A., Giupponi, L., Imran, M. A., & Abu-Dayya, A. (2015). A cell outage management framework for dense heterogeneous networks. IEEE Transactions on Vehicular Technology, 65(4), 2097-2113.
Parvez, I., Rahmati, A., Guvenc, I., Sarwat, A. I., & Dai, H. (2018). A survey on low latency towards 5G: RAN, core network and caching solutions. IEEE Communications Surveys & Tutorials, 20(4), 3098-3130.
Peng, M., Liang, D., Wei, Y., Li, J., & Chen, H.-H. (2013). Self-configuration and self-optimization in LTE-advanced heterogeneous networks. IEEE Communications Magazine, 51(5), 36-45.
Popovski, P., Stefanović, Č., Nielsen, J. J., De Carvalho, E., Angjelichinoski, M., Trillingsgaard, K. F., & Bana, A.-S. (2019). Wireless access in ultra-reliable low-latency communication (URLLC). IEEE Transactions on Communications, 67(8), 5783-5801.
Qin, W., Teng, Y., Song, M., Zhang, Y., & Wang, X. (2013). AQ-learning approach for mobility robustness optimization in Lte-Son. Paper presented at the 2013 15th IEEE International Conference on Communication Technology.
Qiu, C., Yao, H., Yu, F. R., Xu, F., & Zhao, C. (2019). Deep q-learning aided networking, caching, and computing resources allocation in software-defined satellite-terrestrial networks. IEEE Transactions on Vehicular Technology, 68(6), 5871-5883.
Rao, S. K. (2015). Advanced antenna technologies for satellite communications payloads. IEEE Transactions on Antennas and Propagation, 63(4), 1205-1217.
Romano, S. P., Luglio, M., Roseti, C., & Zito, M. (2019). The SHINE testbed for secure in-network caching in hybrid satellite-terrestrial networks. Paper presented at the 2019 European Conference on Networks and Communications (EuCNC).
Rost, P., Bernardos, C. J., De Domenico, A., Di Girolamo, M., Lalam, M., Maeder, A., . . . Wübben, D. (2014). Cloud technologies for flexible 5G radio access networks. IEEE Communications Magazine, 52(5), 68-76.
Sánchez, A. H., Soares, T., & Wolahan, A. (2017). Reliability aspects of mega-constellation satellites and their impact on the space debris environment. Paper presented at the 2017 Annual Reliability and Maintainability Symposium (RAMS).
Shariatmadari, H., Ratasuk, R., Iraji, S., Laya, A., Taleb, T., Jäntti, R., & Ghosh, A. (2015). Machine-type communications: current status and future perspectives toward 5G systems. IEEE Communications Magazine, 53(9), 10-17.
Sinclair, N., Harle, D., Glover, I. A., Irvine, J., & Atkinson, R. C. (2013). An advanced SOM algorithm applied to handover management within LTE. IEEE Transactions on vehicular technology, 62(5), 1883-1894.
Soldani, D., & Manzalini, A. (2015). Horizon 2020 and beyond: On the 5G operating system for a true digital society. IEEE Vehicular Technology Magazine, 10(1), 32-42.
Takahashi, M., Kawamoto, Y., Kato, N., Miura, A., & Toyoshima, M. (2019). Adaptive Power Resource Allocation with Multi-Beam Directivity Control in High-Throughput Satellite Communication Systems. IEEE Wireless Communications Letters.
Ternon, E., Agyapong, P., Hu, L., & Dekorsy, A. (2014). Energy savings in heterogeneous networks with clustered small cell deployments. Paper presented at the 2014 11th international symposium on wireless communications systems (ISWCS).
Wang, P., Zhang, J., Zhang, X., Yan, Z., Evans, B. G., & Wang, W. (2019). Convergence of Satellite and Terrestrial Networks: A Comprehensive Survey. IEEE Access.
Wu, H., Zou, Y., Cao, W., Chen, Z., Tsiftsis, T. A., Bhatnagar, M. R., & De Lamare, R. C. (2019). Impact of Hardware Impairments on Outage Performance of Hybrid Satellite-Terrestrial Relay Systems. IEEE Access, 7, 35103-35112.
Xiong, J., Ma, D., Zhao, H., . . . F. (2019). Secure multicast communications in cognitive satellite-terrestrial networks. IEEE Communications Letters, 23(4), 632-635.
Yan, X., Xiao, H., An, K., Zheng, G., & Chatzinotas, S. (2019). Ergodic capacity of NOMA-based uplink satellite networks with randomly deployed users. IEEE Systems Journal.
Yang, P., Cao, X., Yin, C., Xiao, Z., Xi, X., & Wu, D. (2017). Proactive drone-cell deployment: Overload relief for a cellular network under flash crowd traffic. IEEE Transactions on Intelligent Transportation Systems, 18(10), 2877-2892.
Zhang, Y., Wang, & Y. (2016). SDN based ICN architecture for the future integration network. Paper presented at the 2016 16th International Symposium on Communications and Information Technologies (ISCIT).
Zhang, Z., Zhang, W., Tseng, & F. (2019). Satellite mobile edge computing: Improving QoS of high-speed satellite-terrestrial networks using edge computing techniques. IEEE network, 33(1), 70-76.
Zhao, S., Li, S., & Yao, Y. (2019). Blockchain enabled industrial Internet of Things technology. IEEE Transactions on Computational Social Systems, 6(6), 1442-1453.
Zhu, X., Jiang, C., Kuang, L., Ge, N., Guo, S., & Lu, J. (2019). Cooperative transmission in integrated terrestrial-satellite networks. IEEE network, 33(3), 204-210.
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