Adilya Bakambekova
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Adilya Bakambekova has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Alex Pappachen James
Digital University Kerala
1 shared publication - Nour Kouzayha
King Abdullah University of Science and Technology (KAUST)
1 shared publication - Olga Krestinskaya
King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
1 shared publication - Tareq Y. Al-Naffouri
King Abdullah University of Science and Technology
1 shared publication
Latest Publications
- Open Access
- 16 July 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Open Journal of the Communications Society
- Vol. 5, 4613-4673
On the Interplay of Artificial Intelligence and Space-Air-Ground Integrated Networks: A Survey
Space-Air-Ground Integrated Networks (SAGINs), which incorporate space and aerial networks with terrestrial wireless systems, are vital enablers of the emerging sixth-generation (6G) wireless networks. They offer significant benefits, including extending high-speed broadband coverage to remote and hard-to-reach areas. However, due to constraints like limited power and storage resources, SAGINs must be intelligently configured and managed to meet their envisioned requirements. Meanwhile, Artificial Intelligence (AI) is another critical enabler of 6G. Due to the recent advancements in AI techniques and hardware capabilities, AI has been leveraged to address the pressing challenges of current and future wireless networks. By adding AI and facilitating the decision-making and prediction procedures, SAGINs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics. In this work, we aim to investigate the interplay of AI and SAGINs by providing a holistic overview of state-of-the-art research in AI-enabled SAGINs. Specifically, we present a comprehensive overview of some potential applications of AI in SAGINs. We also cover open issues in employing AI and detail the contributions of SAGINs in developing AI. Finally, we highlight some limitations of the existing research works and outline potential future research directions.
- 16 July 2024
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Open Journal of the Communications Society
- Vol. 5, 4613-4673
Open AccessOn the Interplay of Artificial Intelligence and Space-Air-Ground Integrated Networks: A Survey
Space-Air-Ground Integrated Networks (SAGINs), which incorporate space and aerial networks with terrestrial wireless systems, are vital enablers of the emerging sixth-generation (6G) wireless networks. They offer significant benefits, including extending high-speed broadband coverage to remote and hard-to-reach areas. However, due to constraints like limited power and storage resources, SAGINs must be intelligently configured and managed to meet their envisioned requirements. Meanwhile, Artificial Intelligence (AI) is another critical enabler of 6G. Due to the recent advancements in AI techniques and hardware capabilities, AI has been leveraged to address the pressing challenges of current and future wireless networks. By adding AI and facilitating the decision-making and prediction procedures, SAGINs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics. In this work, we aim to investigate the interplay of AI and SAGINs by providing a holistic overview of state-of-the-art research in AI-enabled SAGINs. Specifically, we present a comprehensive overview of some potential applications of AI in SAGINs. We also cover open issues in employing AI and detail the contributions of SAGINs in developing AI. Finally, we highlight some limitations of the existing research works and outline potential future research directions.
- 28 July 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 112-115
Generate while Sensing - Intelligent Imaging with Memristive Pixel-CNN
Gated Pixel Convolution Neural Network (Pix-eICNN) is a computationally intensive network that is useful for generating visual data. The prediction and generating pixels is a challenging but useful task for many fields such as forensics, machine vision and robotics. However, implementing PixeICNN in edge devices is a challenging task due to learning complexity and computational limits. In this paper, we present the design of neuro-memristive circuits for computing PixelCNN cells in analog domain as a coprocessor unit in edge devices. The architecture was designed using 180nm CMOS technology and carbon-chalcogenide memristive devices. On-chip area of the proposed architecture unit is 24.756mm 2 , while power depends on the size of the input image and the configuration of the overall network. The power required to generate the images sequentially is 154.336mW.
- 28 July 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 112-115
Generate while Sensing - Intelligent Imaging with Memristive Pixel-CNN
Gated Pixel Convolution Neural Network (Pix-eICNN) is a computationally intensive network that is useful for generating visual data. The prediction and generating pixels is a challenging but useful task for many fields such as forensics, machine vision and robotics. However, implementing PixeICNN in edge devices is a challenging task due to learning complexity and computational limits. In this paper, we present the design of neuro-memristive circuits for computing PixelCNN cells in analog domain as a coprocessor unit in edge devices. The architecture was designed using 180nm CMOS technology and carbon-chalcogenide memristive devices. On-chip area of the proposed architecture unit is 24.756mm 2 , while power depends on the size of the input image and the configuration of the overall network. The power required to generate the images sequentially is 154.336mW.