Machine Learning-Based Multi-Layer Multi-Hop Transmission Scheme for Dense Networks

El-Banna, Ahmad A. Aziz; ElHalawany, Basem M.; Zaky, Ahmed B.; Huang, Joshua Zhexue; Wu, Kaishun

doi:10.1109/lcomm.2019.2941932

Cited by 13 publications

(6 citation statements)

References 17 publications

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“…Moreover, it is interesting to consider the case where more than two satellites in the LEO constellation are coordinating their transmissions. In addition to exploiting machinelearning (ML) techniques to improve the performance of coordinated LEO satellite transmissions [36] including traditional ML and reinforcement learning (RL) techniques which are widely used in many communication networks [37]- [42].…”

Section: Resultsmentioning

confidence: 99%

Outage Analysis of Coordinated NOMA Transmission for LEO Satellite Constellations

ElHalawany

Gamal

Elsayed

et al. 2022

IEEE Open J. Commun. Soc.

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This work investigates the performance of a downlink non-orthogonal multiple access (NOMA) based coordinated low-earth orbit (LEO) satellite system. Two LEO satellites are assumed to coordinate their transmissions to serve three users simultaneously using NOMA protocol, where only one user lies in the intersection of the footprints of the two satellites. We investigate the reliability of the proposed architecture, which is expressed in terms of the outage probability (OP). We derive closedform expressions of the users' OPs taking into considerations different realistic losses effects on the link budget including receiver antenna gain, satellite antenna gain, antennas pointing errors, shadowing, smallscale fading, and large-scale free space path loss. The channels between satellites and the three users are assumed to follow shadowed-Rician (SR) fading. The mathematical analysis is verified by the extensive representative Monto-Carlo simulations. Finally, we demonstrate the impact of the system parameters on the considered system as well as the superiority of the NOMA scheme over conventional OMA system.

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Section: Resultsmentioning

confidence: 99%

Outage Analysis of Coordinated NOMA Transmission for LEO Satellite Constellations

ElHalawany

Gamal

Elsayed

et al. 2022

IEEE Open J. Commun. Soc.

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“…[71] considered multi-hop covert communication over a moderate size network where the relays can transmit covertly by using either a single key for all relays or different independent keys at the relays. [72] proposed a machine-learning-based selection approach that adaptively chooses the best forwarding scheme in hybrid multihop dense networks. [73] developed a discrete-componentbased backscatter tag-to-tag transceiver and a communication protocol suite.…”

Section: A Physical Network (Pn) Tiermentioning

confidence: 99%

Networking Systems of AI: On the Convergence of Computing and Communications

Song

Zhang

et al. 2022

IEEE Internet Things J.

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with the focus on six areas: transparency and accountability; ethics and fairness; the preservation of privacy and security; human rights, diversity and inclusivity. It maximizes the benefits of AI while managing the disruption of its implementation. T-mobileFrom AI to VR and Beyond: T-Mobile Accelerator Named Class of 2020 Startups [29], is driving the development in AI, drones, robotics, autonomous vehicles and more on the carrier's nationwide 5G/4G networks. China Mobile China Mobile, Haier and Huawei jointly [30] launched the world's first AI+5G interconnected factory at the 2019 World Industrial Internet Conference (WIIC) in Qingdao. This improvement refines future smart manufacturing by innovating and transforming enterprise organizations, business models, and ICT technologies, and integrating key technologies of AI and 5G.

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“…Based on the trellis, we next propose a dynamic programming based efficient path selection algorithm. The main focus on this paper is to find a low-complexity optimal relay selection solution and as commonly assumed in literature, we assume that full channel state information (CSI) is available at a central control node and it is in charge of RS decision [1], [2], [11]. At this control node, branch weights g[i, j, l] are computed based on global CSI and given as input to the proposed algorithm along with all possible relay combinations Z and the number of hops L.…”

Section: Trellis Based Optimal Relay Selectionmentioning

confidence: 99%

“…Taking a different approach, in [8], the authors express the outage probability in a recursive manner and propose a dynamic programming based algorithm for path selection. More recent work in this area focuses on machine learning techniques to select relay nodes in singleuser, multi-hop relay networks [9]- [11]. While these approaches provide interesting solutions in single-user networks, work on multi-user relay networks is still limited.…”

Section: Introductionmentioning

confidence: 99%

Optimal Routing for Multi-user Multi-hop Relay Networks via Dynamic Programming

Dayarathna¹,

Senanayake²,

Evans³

2022

Preprint

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In this paper, we study the relay selection problem in multi-user, multi-hop relay networks with the objective of minimizing the maximum outage probability across all users. When only one user is present, it is well known that the optimal relay selection problem can be solved efficiently via dynamic programming. This solution breaks down in the multi-user scenario due to dependence between users.We resolve this challenge using a novel relay aggregation approach. On the expanded trellis, dynamic programming can be used to solve the optimal relay selection problem with computational complexity linear in the number of hops. Numerical examples illustrate the efficient use of this algorithm for relay networks.

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Machine Learning-Based Multi-Layer Multi-Hop Transmission Scheme for Dense Networks

Cited by 13 publications

References 17 publications

Outage Analysis of Coordinated NOMA Transmission for LEO Satellite Constellations

Outage Analysis of Coordinated NOMA Transmission for LEO Satellite Constellations

Networking Systems of AI: On the Convergence of Computing and Communications

Optimal Routing for Multi-user Multi-hop Relay Networks via Dynamic Programming

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