| dc.description.abstract | Low Earth Orbit (LEO) satellite communication plays a crucial role in the development of
6G technology, enabling wide-area coverage and ubiquitous wireless services. LEO satellites
operate within a height range of 500 to 2000 kilometers above the Earth′s surface. Recently,
millimeter-wave communication has gained significant attention in LEO satellite
communication systems, offering higher data transmission rate potential. However, the use of
mmWave in LEO satellite communication faces the challenge of increased channel path loss
due to higher operating frequencies and longer transmission distances, with losses reaching up
to 180 dB.
To address this challenge, an efficient solution is to adopt hybrid beamforming using
Multiple-Input Multiple-Output (MIMO) technology, which utilizes a large number of small
antenna elements with fewer Radio Frequency (RF) chains. The successful implementation of
MIMO beamforming relies on accurate Channel State Information (CSI). In LEO satellite
communication systems, the fast movement of satellites leads to rapid channel variations,
making accurate channel estimation and CSI tracking crucial. However, with an increasing
number of antennas, obtaining complete CSI becomes computationally intensive.
Unlike ground communication, LEO satellite communication primarily involves Line-of-
Sight (LOS) channels between satellites and ground stations, resulting in fewer variations in iv
the angles of arrival (AOA) and departure (AOD). Therefore, when employing a large number
of antennas for MIMO technology, the LEO satellite communication channel exhibits sparse
characteristics. In this thesis, our goal is to utilize this inherent sparse channel property and
design low-complexity yet efficient transmitter precoding and receiver compressive sensing
techniques to address the channel estimation problem. The main design challenges can be
summarized as follows:
(1). Massive MIMO is a key technology in LEO satellite communication for overcoming
long-distance and large channel path losses. However, as the number of antenna
elements increases, the computational complexity of channel estimation also
increases. Therefore, it is crucial to develop efficient transmitter and receiver designs
that reduce estimation complexity while maintaining estimation performance.
(2). LEO satellite communication is typically deployed in wide-open areas ranging from
500 to 2000 kilometers above the ground. Consequently, the number of multipath
propagation paths significantly decreases, and the channel becomes dominated by
LOS or only a few propagation paths. This results in highly sparse characteristics of
the channel along the entire satellite-ground communication link. Hence, it is
important to investigate efficient channel estimation methods that leverage this sparse
property to reduce computational complexity.
(3). In the studied MIMO system architecture, due to the sparsity of the channel, only a
few wireless channel components need to be estimated for specific AOA and AOD in
the satellite-to-ground communication channel. This poses the challenge of designing default waveforms at the transmitter and signal processing techniques such as
compressive sensing at the receiver to effectively estimate these channels.
(4). For sparse channels that contain only a few non-zero channel coefficients, sparse
channel reconstruction algorithms are needed to improve accuracy and stability. With
the proposed pilot waveforms and compressive sensing framework, designing
v
computationally affordable sparse channel reconstruction algorithms becomes crucial,
which can extract sparse channel components from the compressed received signal at
the receiver. | en_US |