Kavli Affiliate: Ke Wang
| First 5 Authors: Bassel Al Homssi, Kosta Dakic, Ke Wang, Tansu Alpcan, Ben Allen
| Summary:
Space communications, particularly massive satellite networks, re-emerged as
an appealing candidate for next generation networks due to major advances in
space launching, electronics, processing power, and miniaturization. However,
massive satellite networks rely on numerous underlying and intertwined
processes that cannot be truly captured using conventionally used models, due
to their dynamic and unique features such as orbital speed, inter-satellite
links, short pass time, and satellite footprint, among others. Hence, new
approaches are needed to enable the network to proactively adjust to the
rapidly varying conditions associated within the link. Artificial intelligence
(AI) provides a pathway to capture these processes, analyze their behavior, and
model their effect on the network. This article introduces the application of
AI techniques for integrated terrestrial satellite networks, particularly
massive satellite network communications. It details the unique features of
massive satellite networks, and the overarching challenges concomitant with
their integration into the current communication infrastructure. Moreover, this
article provides insights into state-of-the-art AI techniques across various
layers of the communication link. This entails applying AI for forecasting the
highly dynamic radio channel, spectrum sensing and classification, signal
detection and demodulation, inter-satellite and satellite access network
optimization, and network security. Moreover, future paradigms and the mapping
of these mechanisms onto practical networks are outlined.
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