Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks

Homssi, Bassel Al; Dakic, Kosta; Wang, Ke; Alpcan, Tansu; Allen, Ben; Kandeepan, Sithamparanathan; Al‐Hourani, Akram; Saad, Walid

doi:10.36227/techrxiv.20073125.v1

Cited by 8 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The utilization of ML techniques has been shown to provide low-complexity solutions in wireless networks [135]- [137]. Due to its technology agnostic nature and adaptability, ML is quite flexible and can be used across the different layers of wireless networks [138]. Most notably, AI and ML have led to significant improvements in (i) the physical layer by enhancing digital signal detection [139], (ii) the medium access layer through cognitive radio and spectrum access [140], and (iii) the network layer using network management and optimization [141].…”

Section: Applications Of Machine Learning In Fanetmentioning

confidence: 99%

Non-Terrestrial Networks with UAVs:A Projection on Flying Ad-Hoc Networks

Homssi¹,

Krishnan²,

Park³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Applications Of Machine Learning In Fanetmentioning

confidence: 99%

Non-Terrestrial Networks with UAVs:A Projection on Flying Ad-Hoc Networks

Homssi¹,

Krishnan²,

Park³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…This method is proven to be optimal under additive white Gaussian noise (AWGN) conditions [9]. However, the rapid evolution of neural networks (NN), particularly deep learning (DL) methods, has shown great potential for signal detection [10]- [15]. The main appeal of DL signal detection is its strength against non-linearities [16].…”

Section: Introductionmentioning

confidence: 99%

HybNet: A Hybrid Deep Learning-Matched Filter Approach for IoT Signal Detection

Dakic

Homssi

Lech

et al. 2023

Trans. Mach. Learn. Comm. Netw.

View full text Add to dashboard Cite

Random access schemes are widely used in IoT wireless access networks. They enable a reduced complexity and overcome power consumption constraints. Nevertheless, random access results in high packet losses which are caused by overlapping transmissions. Signal detection methods for digital modulation techniques are typically based on the well-established matched filter, which is proven as the optimal filter under additive white Gaussian noise for minimizing error probability. However, with the colored interference arising from the overlapping IoT transmissions, deep learning approaches are being considered a suitable alternative. In this paper, we present a hybrid framework, dubbed as HybNet, that alternates between deep learning and match filter pathways based on the perceived interference level. This helps the detector to work in a broader range of conditions, optimally leveraging both the matched filter and deep learning advantages. We compare the performance of several possible data modalities and detection architectures concerning the interference-to-noise ratio, demonstrating that leveraging domain knowledge by pre-processing the input data in conjunction with the proposed HybNet surpasses the complex conjugate matched filter performance under interference-limited scenarios.

show abstract

“…As such, the elevation angle is continuously varying resulting in signal envelope variations due to interaction with both the clutter and the atmospheric effects. While the satellite passes are predictable in the short-term and thus are approximated using methods such as the two-line element, the channel variations are challenging to predict and require more complex techniques [8].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels

Homssi¹,

Chan²,

Wang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

<p>This paper presents a practical approach for Q/V-band modeling for low Earth orbit satellite channels based on tools from machine learning and statistical modeling. The developed Q/V-band LEO satellite channel model is presented in two folds; (i) a real-time forecasting method using model-based deep learning, intended for real-time operation of satellite terminals, and (ii) a statistical channel simulator that generates the path loss as a time-series random process, intended for system design and research. The provided approach capitalizes on real satellite measurements that are obtained from AlphaSat's Q/V-band transmitter at different geographic latitudes, to model the radio channel. The results show that model-based deep learning forecasting can outperform conventional statistically derived prediction methods for varying rain and elevation angle profiles. Moreover, it can also provide more accuracy in long-term prediction in comparison to current state-of-the-art machine learning approaches for radio channel prediction. Results for the statistical channel simulator is shown to produce synthetic radio excess path loss values for varying satellite passes by capitalizing on empirical statistical models obtained from real measurements.</p>

show abstract

Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks

Abstract: <p>This is a preprint of the paper, "Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks".</p>

Cited by 8 publications

References 1 publication

Non-Terrestrial Networks with UAVs:A Projection on Flying Ad-Hoc Networks

Non-Terrestrial Networks with UAVs:A Projection on Flying Ad-Hoc Networks

HybNet: A Hybrid Deep Learning-Matched Filter Approach for IoT Signal Detection

Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels

Contact Info

Product

Resources

About