3 research outputs found
Spiking Neural Networks for Detecting Satellite-Based Internet-of-Things Signals
With the rapid growth of IoT networks, ubiquitous coverage is becoming
increasingly necessary. Low Earth Orbit (LEO) satellite constellations for IoT
have been proposed to provide coverage to regions where terrestrial systems
cannot. However, LEO constellations for uplink communications are severely
limited by the high density of user devices, which causes a high level of
co-channel interference. This research presents a novel framework that utilizes
spiking neural networks (SNNs) to detect IoT signals in the presence of uplink
interference. The key advantage of SNNs is the extremely low power consumption
relative to traditional deep learning (DL) networks. The performance of the
spiking-based neural network detectors is compared against state-of-the-art DL
networks and the conventional matched filter detector. Results indicate that
both DL and SNN-based receivers surpass the matched filter detector in
interference-heavy scenarios, owing to their capacity to effectively
distinguish target signals amidst co-channel interference. Moreover, our work
highlights the ultra-low power consumption of SNNs compared to other DL methods
for signal detection. The strong detection performance and low power
consumption of SNNs make them particularly suitable for onboard signal
detection in IoT LEO satellites, especially in high interference conditions
On Delay Performance in Mega Satellite Networks with Inter-Satellite Links
Utilizing Low Earth Orbit (LEO) satellite networks equipped with
Inter-Satellite Links (ISL) is envisioned to provide lower delay compared to
traditional optical networks. However, LEO satellites have constrained energy
resources as they rely on solar energy in their operations. Thus requiring
special consideration when designing network topologies that do not only have
low-delay link paths but also low-power consumption. In this paper, we study
different satellite constellation types and network typologies and propose a
novel power-efficient topology. As such, we compare three common satellite
architectures, namely; (i) the theoretical random constellation, the widely
deployed (ii) Walker-Delta, and (iii) Walker-Star constellations. The
comparison is performed based on both the power efficiency and end-to-end
delay. The results show that the proposed algorithm outperforms long-haul ISL
paths in terms of energy efficiency with only a slight hit to delay performance
relative to the conventional ISL topology
Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks
Space communications, particularly mega 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, mega
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 time
pass, 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 mega satellite network
communications. It details the unique features of mega satellite networks, and
the overarching challenges concomitant with their integration into the current
communication infrastructure. Moreover, the 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 link and satellite access network optimization, and network
security. Moreover, future paradigms and the mapping of these mechanisms onto
practical networks are outlined