RESOURCE MANAGEMENT TECHNIQUES FOR FLEXIBLE BROADBAND SATELLITE COMMUNICATION SYSTEMS

Doctoral thesis (2022)

The application of Satellite Communications (SatCom) has recently evolved from providing simple Direct-To-Home television (DTHTV) to enable a range of broadband internet services. Typically, it offers ... [more ▼]

The application of Satellite Communications (SatCom) has recently evolved from providing simple Direct-To-Home television (DTHTV) to enable a range of broadband internet services. Typically, it offers services to the broadcast industry, the aircraft industry, the maritime sector, government agencies, and end-users. Furthermore, SatCom has a significant role in the era of 5G and beyond in terms of integrating satellite networks with terrestrial networks, offering backhaul services, and providing coverage for the Internet of Things (IoT) applications. Moreover, thanks to the satellite's wide coverage area, it can provide services to remote areas where terrestrial networks are inaccessible or expensive to connect. Due to the wide range of satellite applications outlined above, the demand for satellite service from user terminals is rapidly increasing. Conventionally, satellites use multi-beam technology with uniform resource allocation to provide service to users/beams. In this case, the satellite's resources, such as power and bandwidth, are evenly distributed among the beams. However, this resource allocation method is inefficient since it does not consider the heterogeneous demands of each beam, which may result in a beam with a low demand receiving too many resources while a beam with a high demand receiving few resources. Consequently, we may not satisfy some beam demands. Additionally, satellite resources are limited due to spectrum regulations and onboard batteries constraint, which require proper utilization. Therefore, the next generation of satellites must address the above main challenges of conventional satellites. For this, in this thesis, novel advanced resource management techniques are proposed to manage satellite resources efficiently while accommodating heterogeneous beam demands. In the above context, the second and third chapters of the thesis explore on-demand resource allocation methods with no precoding technique. These methods aim to closely match the beam traffic demand by using the minimum transmit power and utilized bandwidth while having tolerable interference among the beams. However, an advanced interference mitigation technique is required in a high interference scenario. Thus, in the fourth chapter of the thesis, we propose a combination of resource allocation and interference management strategies to mitigate interference and meet high-demand requirements with less power and bandwidth consumption. In this context, the performance of the resource management method for systems with full precoding, that is, all beams are precoded; without precoding, that is, no precoding is applied to any beams; and with partial precoding, that is, some beams are precoded, is investigated and compared. Thanks to emerging technologies, the next generation of satellite communication systems will deploy onboard digital payloads; thus, advanced resource management techniques can be implemented. In this case, the digital payload can be configured to change the bandwidth, carrier frequency, and transmit power of the system in response to heterogeneous traffic demands. Typically, onboard digital payloads consist of payload processors, each operating with specific power and bandwidth to process each beam signal. There are, however, only a limited number of processors, thus requiring proper management. Furthermore, the processors consume more energy to process the signals, resulting in high power consumption. Therefore, payload management will be crucial for future satellite generation. In this context, the fifth chapter of the thesis proposes a demand-aware onboard payload processor management method, which switches on the processors according to the beam demand. In this case, for low demand, fewer processors are in-use, while more processors become necessary as demand increases. Demand-aware resource allocation techniques may require optimization of large variables. Consequently, this may increase the computational time complexity of the system. Thus, the sixth chapter of the thesis explores the methods of combining demand-aware resource allocation and deep learning (DL) to reduce the computational complexity of the system. In this case, a demand-aware algorithm enables bandwidth and power allocation, while DL can speed up computation. Finally, the last chapter provides the main conclusions of the thesis, as well as the future research directions. [less ▲]

JOINT CARRIER ALLOCATION AND PRECODING OPTIMIZATION FOR INTERFERENCE-LIMITED GEO SATELLITE

Scientific Conference (2022)

The rise of flexible payloads on satellites opens a door for controlling satellite resources according to the user demand, user location, and satellite position. In addition to resource management ... [more ▼]

The rise of flexible payloads on satellites opens a door for controlling satellite resources according to the user demand, user location, and satellite position. In addition to resource management, applying precoding on flexible payloads is essential to obtain high spectral efficiency. However, these cannot be achieved using a conventional resource allocation algorithm that does not consider the user demand. In this paper, we propose a demand-aware algorithm based on multiobjective optimization to jointly design the carrier allocation and precoding for better spectral efficiency and demand matching with proper management of the satellite resources. The optimization problem is non-convex, and we solve it using convex relaxation and successive convex approximation. Then, we evaluate the performance of the proposed algorithm through numerical results. It is shown that the proposed method outperforms the benchmark schemes in terms of resource utilization and demand satisfaction. [less ▲]

A Deep Learning Based Acceleration of Complex Satellite Resource Management Problem

Poster (2022)

Radio Resource Management Techniques for Multibeam Satellite Systems

in IEEE Communications Letters (2021), 25(8), 2448-2452

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 ... [more ▼]

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 (satellite launch and operation) per bit, which can be achieved by enhancing the spectral efficiencies. 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. [less ▲]

Precoding-Aided Bandwidth Optimization for High Throughput Satellite Systems

Scientific Conference (2021)

Linear precoding boosts the spectral efficiency of the satellite system by mitigating the interference signal. Typically, all users are precoded and share the same bandwidth regardless of the user demand ... [more ▼]

Linear precoding boosts the spectral efficiency of the satellite system by mitigating the interference signal. Typically, all users are precoded and share the same bandwidth regardless of the user demand. This bandwidth utilization is not efficient since the user demand permanently varies. Hence, demand-aware bandwidth allocation with linear precoding is promising. In this paper, we exploited the synergy of linear precoding and flexible bandwidth allocation for geostationary (GEO) high throughput satellite systems. We formulate an optimization problem with the goal to satisfy the demand by taking into account that multiple precoded user groups can share the different bandwidth chunks. Hence, optimal beam groups are selected with minimum bandwidth requirement to match the per beam demand. The simulation results show that the proposed method of combining bandwidth allocation and linear precoding has better bandwidth efficiency and demand satisfaction than benchmark schemes. [less ▲]

Power and Bandwidth Minimization for Demand-Aware GEO Satellite Systems

Scientific Conference (2021)

Smart radio resource allocation combined with the recent advances of digital payloads will allow to control the transmit power and bandwidth of the satellites depending on the demand and the channel ... [more ▼]

Smart radio resource allocation combined with the recent advances of digital payloads will allow to control the transmit power and bandwidth of the satellites depending on the demand and the channel conditions of users. The system flexibility is important not only to handle divergent demand requirements but also to efficiently utilize the limited and expensive satellite resources. In this paper, we propose a demand-aware smart radio resource allocation technique, where the transmit power and the bandwidth of the GEO satellite are minimized while satisfying the user demand. The formulated optimization problem is non-convex mixed-integer nonlinear program which is difficult to solve. Hence, we apply a quadratic transform to solve the problem iteratively. The numerical results showed that the proposed scheme outperforms the benchmark schemes in terms of bandwidth utilization while accurately providing capacity-ondemand. [less ▲]