Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Author: search.filters.author.Yilmaz, Saadet SimayWoS Q: search.filters.wosQuartile.Q2

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  • Article
    HAPS Assisted Cooperative Offloading for Space–Air–Ground Integrated Networks
    (Elsevier GmbH, 2025) Yilmaz, Saadet Simay; Ozbek, Berna; Erdogan, Eylem
    Mobile edge computing (MEC) has significantly enhanced computational capabilities at the network edge, enabling computation-intensive applications. However, traditional MEC implementations face significant challenges in areas without reliable terrestrial network infrastructure, such as rural regions or disaster-affected zones. To address this, we present a novel MEC-enabled space-air-ground integrated network (SAGIN) framework that combines high-altitude platform station (HAPS) and low Earth orbit (LEO) satellite to ensure comprehensive coverage and reduce execution delays for ground users (GUs) in areas lacking terrestrial infrastructure. By leveraging the complementary capabilities of HAPS, which provide wide-area coverage and reliable connectivity, and LEO satellites, which offer high-throughput communication, the proposed SAGIN framework enhances computation offloading. We propose a cooperative approach between GUs and the LEO satellite via the HAPS to maximize offloaded data while satisfying stringent delay constraints under a partial offloading mode. A nonlinear optimization problem is formulated to minimize execution delay while increasing offloaded data by jointly optimizing task offloading decisions and resource allocation between the HAPS and LEO satellite. Simulation results show that the proposed cooperative offloading scheme significantly outperforms random and non-cooperative schemes considering execution delay. These results highlight that the proposed cooperative, HAPS-assisted SAGIN framework effectively enables low-delay edge computing in infrastructure-limited regions.
  • Article
    Citation - WoS: 15
    Citation - Scopus: 17
    Massive Mimo-Noma Based Mec in Task Offloading for Delay Minimization
    (IEEE, 2023) Yilmaz, Saadet Simay; Özbek, Berna
    Mobile edge computing (MEC) has been considered a promising technology to reduce task offloading and computing delay by enabling mobile devices to offload their computation-intensive tasks. Non-orthogonal multiple access (NOMA) is regarded as a promising method of increasing spectrum efficiency, while Massive multiple-input multiple-output (MIMO) can support a larger number of users for simultaneous offloading. These two technologies can effectively facilitate offloading and further improve the performance of MEC systems. In this work, we propose a NOMA and Massive MIMO assisted MEC system for delay-sensitive applications. Our objective is to minimize the overall computing and transmission delay under users' transmit power and MEC computing capability. Through the pairing scheme for Massive MIMO-NOMA, the users with the higher channel gain can offload all their data, while the users with the lower channel gain can offload a portion of their data to the MEC. Performance results are provided regarding to the sum data rate and overall system delay compared with the orthogonal multiple access (OMA)-MIMO based and Massive MIMO (M-MIMO) based MEC systems.