HST-NNC: A Novel Hybrid Satellite-Terrestrial Communication With NOMA and Network Coding Systems

Karavolos, Michail H.; Νομικός, Νικόλαος; Vouyioukas, Demosthenes; Mathiopoulos, P. Takis

doi:10.1109/ojcoms.2021.3072110

Cited by 13 publications

(8 citation statements)

References 51 publications

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“…From now on, these two ways of communication will be referred to as UFBS NOMA transmission when the GMTs receive the data directly from the UFBS through the NOMA scheme and the D2D cooperative transmission when the GMTs cooperate to improve the overall communication quality. Concerning the UFBS NOMA transmission, all GMTs are served by the UFBS via the air-to-ground (A2G) link, utilizing the NOMA technique according to an optimal power allocation and user pairing strategy [ 27 , 28 ]. More specifically, the total available UFBS’ bandwidth

is divided into K slots, equally distributed to the GMT pairs, as depicted in Figure 1 .…”

Section: System Modelmentioning

confidence: 99%

Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

Tsipi

Karavolos

Bithas

et al. 2023

Sensors

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The cooperative aerial and device-to-device (D2D) networks employing non-orthogonal multiple access (NOMA) are expected to play an essential role in next-generation wireless networks. Moreover, machine learning (ML) techniques, such as artificial neural networks (ANN), can significantly enhance network performance and efficiency in fifth-generation (5G) wireless networks and beyond. This paper studies an ANN-based unmanned aerial vehicle (UAV) placement scheme to enhance an integrated UAV-D2D NOMA cooperative network.The proposed placement scheme selection (PSS) method for integrating the UAV into the cooperative network combines supervised and unsupervised ML techniques. Specifically, a supervised classification approach is employed utilizing a two-hidden layered ANN with 63 neurons evenly distributed among the layers. The output class of the ANN is utilized to determine the appropriate unsupervised learning method—either k-means or k-medoids—to be employed. This specific ANN layout has been observed to exhibit an accuracy of 94.12%, the highest accuracy among the ANN models evaluated, making it highly recommended for accurate PSS predictions in urban locations. Furthermore, the proposed cooperative scheme allows pairs of users to be simultaneously served through NOMA from the UAV, which acts as an aerial base station. At the same time, the D2D cooperative transmission for each NOMA pair is activated to improve the overall communication quality. Comparisons with conventional orthogonal multiple access (OMA) and alternative unsupervised machine-learning based-UAV-D2D NOMA cooperative networks show that significant sum rate and spectral efficiency gains can be harvested through the proposed method under varying D2D bandwidth allocations.

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is divided into K slots, equally distributed to the GMT pairs, as depicted in Figure 1 .…”

Section: System Modelmentioning

confidence: 99%

Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

Tsipi

Karavolos

Bithas

et al. 2023

Sensors

Self Cite

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“…The authors of [ 164 ] presented a novel method for successfully integrating network coding (NC) and NOMA operations in a multi-user HSTN context. In contrast to other HSTN systems where the satellite is used for NOMA transmission, requiring CSI, the proposed system uses the satellite just for NC, negating the need for CSI and greatly simplifying the network.…”

Section: Key Cooperative and Cognitive Technologiesmentioning

confidence: 99%

Hybrid Satellite–Terrestrial Networks toward 6G: Key Technologies and Open Issues

Tirmizi

Chen

Lakshminarayana

et al. 2022

Sensors

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Future wireless networks will be required to provide more wireless services at higher data rates and with global coverage. However, existing homogeneous wireless networks, such as cellular and satellite networks, may not be able to meet such requirements individually, especially in remote terrain, including seas and mountains. One possible solution is to use diversified wireless networks that can exploit the inter-connectivity between satellites, aerial base stations (BSs), and terrestrial BSs over inter-connected space, ground, and aerial networks. Hence, enabling wireless communication in one integrated network has attracted both the industry and the research fraternities. In this work, we provide a comprehensive survey of the most recent work on hybrid satellite–terrestrial networks (HSTNs), focusing on system architecture, performance analysis, design optimization, and secure communication schemes for different cooperative and cognitive HSTN network architectures. Different key technologies are compared. Based on this comparison, several open issues for future research are discussed.

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“…As reviewed in Section I-B, a low-coding coefficient density can be achieved with various strategies [14]. We focus on the systematic RLNC approach that intersperses only few coded packets with non-zero coding coefficients among the uncoded (so-called systematic) packets in a generation, i.e., so-called sparse systematic RLNC [15]- [19]. To date, sparse systematic RLNC has been mainly studied for end-to-end coding, i.e., sparse systematic RLNC encoding at the sending (source) node, and decoding at the ultimate destination node.…”

Section: A Motivationmentioning

confidence: 99%

SpaRec: Sparse Systematic RLNC Recoding in Multi-Hop Networks

et al. 2021

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Sparse Random Linear Network Coding (RLNC) reduces the computational complexity of the RLNC decoding through a low density of the non-zero coding coefficients, which can be achieved through sending uncoded (systematic) packets. However, conventional recoding of sparse RLNC coded packets at an intermediate node in a multi-hop network increases the density of non-zero coding coefficients. We develop and evaluate sparsity-preserving recoding (SpaRec) strategies that preserve the low density of non-zero coding coefficients of sparse RLNC with systematic packets. We develop SpaRec strategies with and without decoding at the intermediate nodes, with and without a specified coding rate, as well as with finite and infinite recoding window lengths. We evaluate the SpaRec strategies in multi-hop networks in terms of packet loss, packet delivery delay, as well as recoding and decoding (computation) throughput. We find that the SpaRec strategies substantially improve the RLNC performance compared to conventional recoding.

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HST-NNC: A Novel Hybrid Satellite-Terrestrial Communication With NOMA and Network Coding Systems

Cited by 13 publications

References 51 publications

Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

Machine Learning-Based Methods for Enhancement of UAV-NOMA and D2D Cooperative Networks

Hybrid Satellite–Terrestrial Networks toward 6G: Key Technologies and Open Issues

SpaRec: Sparse Systematic RLNC Recoding in Multi-Hop Networks

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