Improving satellite connectivity using artificial intelligence and inter-satellite links
Creators: name: Dakic, Kosta
Publisher: name: RMIT University
Year: 2024
Subjects: classificationCode: 400608
subject: Wireless communication systems and technologies (incl. microwave and millimetrewave)

description: The rapid expansion of the Internet of Things (IoT) through the last decade has allowed billions of devices and sensors to exchange vast amounts of data wirelessly, revolutionizing connectivity across smart appliances, automobiles, and urban infrastructure. This technological leap has necessitated advancements at the base stations where managing such extensive data exchange is intricate. Artificial intelligence emerges as a pivotal solution, with its proven prowess in handling complex, high-dimensional datasets in signal processing, computer vision, speech recognition, and more, by learning from data directly without the need for explicit programming.

As IoT technology extends its reach beyond terrestrial bounds into satellite communications, the importance of global connectivity comes into sharp focus. Satellites play a crucial role where terrestrial networks cannot suffice, but they introduce unique challenges, primarily energy constraints. Therefore, while traditional deep learning is instrumental in terrestrial IoT, it must be adapted to suit the energy-efficient requirements of satellite networks. This adaptation leads to the exploration of Spiking Neural Networks (SNNs), which offer a promising avenue for handling the energy efficiency needed in satellite IoT communications.

This dissertation delves into the role of artificial intelligence in addressing the complexities of next-generation wireless communications for IoT. It presents novel artificial intelligence frameworks tailored for signal detection in both terrestrial and satellite IoT networks, advocating for learning-based spectrum sensing and the development of energy-efficient inter-satellite-link (ISL) topologies within large satellite constellations. The study aims to demonstrate that by harnessing the power of artificial intelligence, it can effectively surmount the challenges presented by the IoT's vast scale and the energy limitations of satellite networks.

One of the key advantages of artificial intelligence techniques is their ability to automatically extract relevant features from the input data. For example, in the case of oscillator offset conditions, artificial intelligence techniques can be trained on large datasets of signals with varying degrees of frequency and time offset, enabling them to learn the underlying patterns and characteristics of signals affected by this offset. The techniques can then use this knowledge to accurately detect and classify signals even when a significant oscillator offset is present. As such, using deep learning networks for IoT signal detection on signals impaired with oscillator offset is one of the investigated topics in this thesis.

Another prominent issue in next-generation IoT networks is that high device density, which inherently leads to elevated levels of co-channel interference. This generally occurs when two or more signals are occupying the same frequency band at the same time and accordingly interfering with each other resulting in a degradation in the quality of the communication link. This can result in an incorrect signal demodulation/detection by the base station which then requires re-transmission by the user device. This is particularly a challenge for low-power devices as re-transmission results in higher energy consumption, a luxury that is not always available for IoT devices. To enhance the robustness of signal detection, this dissertation also explores deep learning networks for signal detection under co-channel interference scenarios. 

Furthermore, for resource-constrained base stations (receivers) such as low-Earth orbit (LEO) satellite terminals, SNNs have emerged to provide a promising solution for achieving energy-efficient inference. Unlike digital-based artificial neural networks, SNNs are designed to mimic the behavior of biological neurons, allowing them to operate more efficiently and with lower power consumption. The thesis investigates utilizing SNNs for signal detection with an elevated level of co-channel interference as well as for wideband Frequency spectrum analysis.

In addition, one area that can be used to increase energy efficiency in satellites is the design of an ISL topology for direct communication between satellites within a mega-satellite constellation. In this thesis, energy-efficient ISL topologies are explored, where a novel topology is proposed that constructs optimized ISLs. Once the topology is constructed, a classical algorithm is employed to find the shortest possible path between any two satellites within a constellation. 

In this thesis, mathematical analysis is utilized to investigate the merits of the proposed methods in addition to Monte Carlo analysis using computer simulations to test the hypotheses. Furthermore, the experimental results are compared with baseline schemes, from state-of-the-art literature, to showcase the benefits of the proposed solutions. The majority of the findings presented in this dissertation have been published, in part or, in well-respected peer-reviewed journals and conference proceedings, or are currently undergoing the peer-review process. These publications are duly highlighted and identified throughout this thesis.