ORCID Profile
0000-0002-7125-6738
Current Organisations
University of New South Wales
,
Deakin University
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Publisher: IEEE
Date: 05-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 17-06-2022
DOI: 10.36227/TECHRXIV.20073125.V1
Abstract: This is a preprint of the paper, "Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks".
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 16-10-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-04-2023
DOI: 10.36227/TECHRXIV.22358719.V1
Abstract: The obtained results demonstrate a significantly enhanced detection performance under heavy interference conditions when employing spiking-based and deep learning detection techniques, as opposed to traditional baseline matched-filter methods. Although spiking-based detection approaches exhibit error rates comparable to those of deep learning techniques, they consume considerably less power – several orders of magnitude lower, in fact. Owing to their power efficiency, spiking-based detection networks emerge as the optimal choice for signal detection in resource-limited systems, such as low-Earth orbit satellites.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 14-07-2023
DOI: 10.36227/TECHRXIV.23649927
Abstract: 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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-04-2023
DOI: 10.36227/TECHRXIV.22358719
Abstract: The obtained results demonstrate a significantly enhanced detection performance under heavy interference conditions when employing spiking-based and deep learning detection techniques, as opposed to traditional baseline matched-filter methods. Although spiking-based detection approaches exhibit error rates comparable to those of deep learning techniques, they consume considerably less power – several orders of magnitude lower, in fact. Owing to their power efficiency, spiking-based detection networks emerge as the optimal choice for signal detection in resource-limited systems, such as low-Earth orbit satellites.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-08-2023
DOI: 10.36227/TECHRXIV.20455860.V1
Abstract: 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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-08-2022
DOI: 10.36227/TECHRXIV.20445015
Abstract: This article is under review and upon acceptance: Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. (Copyright (c) 2015 IEEE.)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: MDPI AG
Date: 27-08-2022
DOI: 10.3390/RS14174232
Abstract: Low Earth orbit (LEO) satellite constellations are currently being explored to provide global and seamless coverage for IoT-over-Satellite applications. Random access techniques require low transmission overhead providing a compatible route for IoT-over-Satellite applications, however, coming at the expense of the offered quality-of-service. In this paper, we develop a realistic uplink performance framework that incorporates many practical parameters such as the satellite availability, packet collision and interference, Doppler shift, and impairments experienced in a typical Satellite-to-Ground channel. The framework is capable of assessing multiple key performance indicators of the overall IoT-over-Satellite random access system. The performance is presented in terms of the bit error rate, packet error rate, and the energy wasted per IoT device. To emulate a realistic IoT-over-Satellite network, LoRa modulated traffic is first generated and injected into the Satellite-to-Ground channel. The results indicate high resistance to Doppler shifts even without any Doppler correction and provide some resistance to highly congested environments.
Publisher: MDPI AG
Date: 31-10-2022
Abstract: Non-terrestrial networks (NTNs) have recently attracted elevated levels of interest in large-scale and ever-growing wireless communication networks through the utilization of flying objects, e.g., satellites and unmanned aerial vehicles/drones (UAVs). Interestingly, the applications of UAV-assisted networks are rapidly becoming an integral part of future communication services. This paper first overviews the key components of NTN while highlighting the significance of emerging UAV networks where for ex le, a group of UAVs can be used as nodes to exchange data packets and form a flying ad hoc network (FANET). In addition, both existing and emerging applications of the FANET are explored. Next, it provides key recent findings and the state-of-the-art of FANETs while examining various routing protocols based on cross-layer modeling. Moreover, a modeling perspective of FANETs is provided considering delay-tolerant networks (DTN) because of the intermittent nature of connectivity in low-density FANETs, where each node (or UAV) can perform store-carry-and-forward (SCF) operations. Indeed, we provide a case study of a UAV network as a DTN, referred to as DTN-assisted FANET. Furthermore, applications of machine learning (ML) in FANET are discussed. This paper ultimately foresees future research paths and problems for allowing FANET in forthcoming wireless communication networks.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 16-10-2022
DOI: 10.36227/TECHRXIV.20073125
Abstract: This is a preprint of the paper, "Artificial Intelligence Techniques for Next-Generation Mega Satellite Networks".
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-08-2022
DOI: 10.36227/TECHRXIV.20445015.V1
Abstract: This article is under review and upon acceptance: Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. (Copyright (c) 2015 IEEE.)
Publisher: IEEE
Date: 05-10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-08-2022
DOI: 10.36227/TECHRXIV.20455860
Abstract: 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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 14-07-2023
DOI: 10.36227/TECHRXIV.23649927.V1
Abstract: 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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-03-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 26-10-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: IEEE
Date: 12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 26-10-2023
No related grants have been discovered for Bassel Al Homssi.