So many beams, so little time: Revenue Management in the next generation of flexible communication satellites

Gunter, ipstar 1 (thaicom 4, measat 5, synertone 1), accessed: March 2019. URL https://space.skyrocket.de/docsdat/ipstar.1.htm. SES, O3b mpower, accessed: March 2019. URL https://www.ses.com/networks/networks.and.platforms/o3bmpower. Gunter, O3b mpower 1, ..., 11 (o3b 21, ..., 31), accessed: March 2019. URL https://space.skyrocket.de/docsdat/o3b.21.htm. Buttazzoni, 2017, Reconfigurable phased antenna array for extending cubesat operations to ka-band: design and feasibility, Acta Astronaut., 137, 114, 10.1016/j.actaastro.2017.04.012 Canabal, 2005, Multifunctional phased array antenna design for satellite tracking, Acta Astronaut., 57, 887, 10.1016/j.actaastro.2005.03.072 Aravanis, 2015, Power allocation in multibeam satellite systems: a two-stage multi-objective optimization, IEEE Trans. Wireless Commun., 14, 3171, 10.1109/TWC.2015.2402682 Paris, 2019, A genetic algorithm for joint power and bandwidth allocation in multibeam satellite systems, 1 He, 2017, A traffic-awareness dynamic resource allocation scheme based on multi-objective optimization in multi-beam mobile satellite communication systems, Int. J. Distributed Sens. Netw., 13 Pachler, 2020, Allocating power and bandwidth in multibeam satellite systems using particle swarm optimization Durand, 2017, Power allocation in multibeam satellites based on particle swarm optimization, AEU - International Journal of Electronics and Communications, 78, 124, 10.1016/j.aeue.2017.05.012 Wang, 2014, Optimization of power allocation for multiusers in multi-spot-beam satellite communication systems, Math. Probl Eng., 2014 Luis, 2019, Deep reinforcement learning architecture for continuous power allocation in high throughput satellites Cocco, 2018, Radio resource management optimization of flexible satellite payloads for dvb-s2 systems, IEEE Trans. Broadcast., 64, 266, 10.1109/TBC.2017.2755263 Hu, 2018, A deep reinforcement learning-based framework for dynamic resource allocation in multibeam satellite systems, IEEE Commun. Lett., 22, 1612, 10.1109/LCOMM.2018.2844243 Liu, 2018, Deep reinforcement learning based dynamic channel allocation algorithm in multibeam satellite systems, IEEE Access, 6, 15733, 10.1109/ACCESS.2018.2809581 Luis, 2021, Applicability and challenges of deep reinforcement learning for satellite frequency plan design Park, 2012, A dynamic bandwidth allocation scheme for a multi-spot-beam satellite system, ETRI J., 34, 613, 10.4218/etrij.12.0211.0437 Camino, 2016, Mixed-integer linear programming for multibeam satellite systems design: application to the beam layout optimization, 1 Kyrgiazos, 2013 Pachler, 2020, Static beam placement and frequency plan algorithms for leo constel- lations, Int. J. Satell. Commun. Netw. Liang, 2021, A precedence-rule-based heuristic for satellite onboard activity planning, Acta Astronaut., 178, 757, 10.1016/j.actaastro.2020.10.020 Gaudet, 2020, Terminal adaptive guidance via reinforcement meta-learning: applications to autonomous asteroid close- proximity operations, Acta Astronaut., 171, 1, 10.1016/j.actaastro.2020.02.036 Park, 2020, The economic impact analysis of satellite development and its application in korea, Acta Astronaut., 177, 9, 10.1016/j.actaastro.2020.06.031 del Monte, 2017, A socio-economic impact assessment of the european launcher sector, Acta Astronaut., 137, 482, 10.1016/j.actaastro.2017.01.005 Yi, 2013, Economic value analysis of the return from the Korean astronaut program and the science culture diffusion activity in korea, Acta Astronaut., 87, 1, 10.1016/j.actaastro.2012.12.010 2019 Stanley, 2017 Szalay, 2006, 2020 computing: science in an exponential world, Nature, 440, 413, 10.1038/440413a Kambatla, 2014, Trends in big data analytics, J. Parallel Distr. Comput., 74, 2561, 10.1016/j.jpdc.2014.01.003 Chen, 2014, Big data: a survey, Mobile Network. Appl., 19, 171, 10.1007/s11036-013-0489-0 Gantz, 2011, Extracting value from chaos, IDC iview, 1142 Team, 2017 2017 Kota, 2005, Broadband satellite networks: trends and challenges, vol. 3, 1472 Farserotu, 2000, A survey of future broadband multimedia satellite systems, issues and trends, IEEE Commun. Mag., 38, 128, 10.1109/35.846084 Hosseini, 2019, Uav command and control, navigation and surveillance: a review of potential 5g and satellite systems, 1 Talluri, 2004 Aldebert, 2004, Telecommunications demand and pricing structure: an econometric analysis, Telecommun. Syst., 25, 89, 10.1023/B:TELS.0000011198.50511.a4 Aldebert, 2004, Telecommunications demand and pricing structure: an econometric analysis, 89 Park, 1983, Price elasticities for local telephone calls, Econometrica, 51, 1699, 10.2307/1912113 Wolak, 1996, Can universal service survive in a competitive telecommunications environment? evidence from the United States consumer expenditure survey, Inf. Econ. Pol., 8, 163, 10.1016/0167-6245(96)00009-1 Gatto, 1988, Stochastic generalizations of demand systems with an application to telecommunications, Inf. Econ. Pol., 3, 283, 10.1016/0167-6245(88)90029-7 Gar′ın-Mun~oz, 1998, Econometric modelling of Spanish very long distance international calling, Inf. Econ. Pol., 10, 237, 10.1016/S0167-6245(97)00032-2 Ouwersloot, 2001, On the distance dependence of the price elasticity of telecommunications demand; review, analysis, and alternative theoretical backgrounds, Ann. Reg. Sci., 35, 577, 10.1007/s001680100060 Hackl, 1996, Demand for international telecommunication time-varying price elasticity, J. Econom., 70, 243, 10.1016/0304-4076(94)01691-7 Guerster, 2020, Revenue management for communication satellite operators – opportunities and challenges Cormen, 2009