Radio Resource Management Techniques for Multibeam Satellite Systems

Next-generation of satellite communication (SatCom) networks are expected to support extremely high data rates for a seamless integration into future large satellite-terrestrial networks. In view of the coming spectral limitations, the main challenge is to reduce the cost per bit, which can only be achieved by enhancing the spectral efficiency. In addition, the capability to quickly and flexibly assign radio resources according to the traffic demand distribution has become a must for future multibeam broadband satellite systems. This article presents the radio resource management problems encountered in the design of future broadband SatComs and provides a comprehensive overview of the available techniques to address such challenges. Firstly, we focus on the demand-matching formulation of the power and bandwidth assignment. Secondly, we present the scheduling design in practical multibeam satellite systems. Finally, a number of future challenges and the respective open research topics are described.

[1]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[2]  Maria Angeles Vázquez-Castro,et al.  Joint Power and Carrier Allocation for the Multibeam Satellite Downlink with Individual SINR Constraints , 2010, 2010 IEEE International Conference on Communications.

[3]  Symeon Chatzinotas,et al.  Precoding in Multibeam Satellite Communications: Present and Future Challenges , 2015, IEEE Wireless Communications.

[4]  Symeon Chatzinotas,et al.  Satellite Communications in the 5G Era , 2018 .

[5]  Aleix Paris,et al.  A Genetic Algorithm for Joint Power and Bandwidth Allocation in Multibeam Satellite Systems , 2019, 2019 IEEE Aerospace Conference.

[6]  Symeon Chatzinotas,et al.  Carrier and Power Assignment for Flexible Broadband GEO Satellite Communications System , 2020, 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications.

[7]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[8]  Dae-Sub Oh,et al.  Flexible Bandwidth Allocation Scheme Based on Traffic Demands and Channel Conditions for Multi-Beam Satellite Systems , 2012, 2012 IEEE Vehicular Technology Conference (VTC Fall).

[9]  Symeon Chatzinotas,et al.  Precoding for Satellite Communications: Why, How and What next? , 2020 .

[10]  Björn E. Ottersten,et al.  The spectrum efficiency of a base station antenna array system for spatially selective transmission , 1994, Proceedings of IEEE Vehicular Technology Conference (VTC).

[11]  Shree Krishna Sharma,et al.  System Modeling and Design Aspects of Next Generation High Throughput Satellites , 2020, IEEE Communications Letters.

[12]  Symeon Chatzinotas,et al.  A Joint Solution for Scheduling and Precoding in Multiuser MISO Downlink Channels , 2019, IEEE Transactions on Wireless Communications.

[13]  Giuseppe Cocco,et al.  Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S 2 Systems , 2018 .

[14]  E. Lier,et al.  Multibeam active phased arrays for communications satellites , 2000 .

[15]  Symeon Chatzinotas,et al.  Deep Learning for Beam Hopping in Multibeam Satellite Systems , 2020, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).

[16]  Giuseppe Cocco,et al.  Radio Resource Management Optimization of Flexible Satellite Payloads for DVB-S2 Systems , 2018, IEEE Transactions on Broadcasting.

[17]  O. Kodheli,et al.  Satellite Communications in the New Space Era: A Survey and Future Challenges , 2020 .

[18]  Andrea J. Goldsmith,et al.  On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming , 2006, IEEE Journal on Selected Areas in Communications.

[19]  Björn E. Ottersten,et al.  On-board signal predistortion for digital transparent satellites , 2015, 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).