Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/11

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Author: search.filters.author.Özbek, BernaPublisher: search.filters.publisher.Elsevier

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  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Deep Learning Based Adaptive Bit Allocation for Heterogeneous Interference Channels
    (Elsevier, 2021) Aycan, Esra; Özbek, Berna; Le Ruyet, Didier
    This paper proposes an adaptive bit allocation scheme by using a fully connected (FC) deep neural network (DNN) considering imperfect channel state information (CSI) for heterogeneous networks. Achieving an accurate CSI has a crucial role on the system performance of the heterogeneous networks. Different quantization techniques have been employed to reduce the feedback overhead. However, the system performance cannot increase linearly with the number of bits increasing exponentially. Since optimizing the total number of bits is too complex for the entire network, an initial step is performed to distribute the bits to each cell in the conventional method. Then, the distributed bits are further allocated to each channel optimally. In order to enable direct allocation for the entire network, a FC-DNN based method is presented in this study. The optimized number of bits can be directly obtained for a different number of bits and scenarios by the proposed approach. The simulations are performed by using various scenarios with different allocation schemes. The performance results show that the DNN based method achieves a closer performance to the conventional approach. (C) 2021 Elsevier B.V. All rights reserved.
  • Article
    Citation - WoS: 4
    Citation - Scopus: 5
    Hybrid Beamforming Strategies for Secure Multicell Multiuser Mmwave Mimo Communications
    (Elsevier, 2021) Özbek, Berna; Erdoğan, Oğulcan; Busari, Sherif A.; Gonzalez, Jonathan
    Over the last decade, many advancements have been made in the field of wireless communications. Among the major technology enablers being explored for the beyond fifth-generation (B5G) networks at the physical layer (PHY), a great deal of attention has been focused on millimeter-wave (mmWave) communications, massive multiple-input multiple-output (MIMO) antenna systems and beamforming techniques. These enablers bring to the forefront great opportunities for enhancing the performance of B5G networks, concerning spectral efficiency, energy efficiency, latency, and reliability. The wireless communication is prone to information leakage to the unintended nodes due to its open nature. Hence, the secure communication is becoming more critical in the wireless networks. To address this challenge, the concept of Physical Layer Security (PLS) is explored in the literature. In this paper, we examine the mmWave transmission through linear beamforming techniques for PLS based systems. We propose the secure multiuser (MU) MIMO mmWave communications by employing hybrid beamforming at the base stations (BSs), legitimate users and eavesdroppers. Using three Dimensional (3D) mmWave channel model for each node, we utilize the artificial noise (AN) beamforming to jam the transmission of eavesdropper and to enhance the secrecy rate. The secrecy performance on multicell mmWave MU-MIMO downlink communications is demonstrated to reveal the key points directly related to the system security for B5G wireless systems. (C) 2021 Elsevier B.V. All rights reserved.