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[en] 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.