The non-terrestrial network (NTN) systems are expected to play a pivotal role in the expansion of coverage and high mobility demands of the upcoming sixth generation (6G) telecommunication systems. These systems are set to transform the dynamics of NTNs, bringing forth a wide array of prospects and challenges.
The focus of this dissertation is the enhancement of 5G-NR physical layer components, crucial for facilitating high mobility in NTNs - a key aspect of 6G services. Utilizing 5G-NR numerologies and assessing the effects of diverse modulation and coding schemes (MCS), MIMO/beamforming strategies, and 3GPP NR-NTN channel models, this research provides an in-depth analysis of link-level performance. This includes scenarios with pilot-aided perfect and DM-RS based practical channel estimations. Particularly, the use of larger 5G-NR numerologies, like 120 kHz, is highlighted for its effectiveness in supporting high User Equipment (UE) mobility, enhancing throughput and spectral efficiency, especially in the TDL-E (LOS) channel model.
This dissertation proposes an adaptive solution based on 5G-NR, tailored for NTN's unique demands, especially the high mobility of UE. Introducing a new DM-RS symbol pattern with front-loading and additional orthogonal symbols has demonstrated potential in accommodating UE mobility up to 500 km/h. Our advanced 5G-NR link-level simulator (LLS) evaluates the performance of the physical downlink shared channel (PDSCH) with the new DM-RS symbol pattern and beamforming/MIMO schemes based on precoding. Results indicate a significant improvement in link reliability and spectral efficiency with the front-loaded DM-RS-based channel estimation, marking a significant advancement for NTN in 6G networks.
The dissertation introduces a cutting-edge deep learning approach for channel estimation in NTN. This approach integrates neural networks, Doppler pre-compensation, and compensation techniques, presenting an innovative framework for 6G-NTN systems. The CNN model developed for DM-RS channel estimation excels in Mean Squared Error (MSE) performance at high speeds and altitudes. The application of Doppler techniques effectively mitigates high mobility challenges, ensuring consistent link reliability and spectral efficiency. These developments are set to significantly boost the performance of 6G-NTN systems, enabling dependable communication in the most demanding conditions.
Furthermore, the research conducts an extensive link budget analysis for 6G NTN, specifically designed for high-altitude platforms. These platforms, situated in the stratosphere and LEO, act as intermediaries between terrestrial and non-terrestrial networks, requiring special attention. The research thoroughly examines the downlink link budget challenges of 6G NTNs at these heights, including atmospheric disturbances and platform dynamics. Through comprehensive modeling and simulations, the study sheds light on the performance capabilities of high-altitude NTNs, providing insights into their optimal operation in these unique communication environments.
This dissertation offers a complete perspective on the role of 5G-NR components in advancing NTN for 6G services. Covering aspects from support for high mobility to innovative deep learning-based channel estimation and adaptations for high-altitude conditions, the findings set the stage for future developments in seamless and efficient communication for the forthcoming era of 6G telecommunication systems.