Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/11
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Article Citation - WoS: 5Citation - Scopus: 8Coverage Analysis of Physical Layer Network Coding in Massive Mimo Systems(Institute of Electrical and Electronics Engineers Inc., 2021) İlgüy, Mert; Özbek, Berna; Mumtaz, Rao; Busari, Sherif A.; Gonzalez, JonathanWireless networks are prone to interference due to their broadcast nature. In the design of most of the traditional networks, this broadcast nature is perceived as a performance-degrading factor. However, Physical Layer Network Coding (PNC) exploits this broadcast nature by enabling simultaneous transmissions from different sources and thereby enhances the performance of the wireless networks with respect to improvement in spectral efficiency, coverage, latency and security of the system. For fifth generation (5G) networks and beyond, massive multiple input multiple output (MIMO) is considered as a key physical layer technology. Thus, its combination with PNC can significantly enhance the performance of the network, facilitating capacity-coverage improvement, among other benefits. While the bit error rate performance of multiuser massive MIMO-PNC systems through linear detection has been investigated extensively, their coverage probability for a given target signal-to-noise ratio has not been explored yet. In this paper, we derive a closed form expression for coverage probability in PNC based multiuser massive MIMO systems employing zero-forcing equalization. Both theoretical and simulation results are provided for different number of users and antennas in the multiuser massive MIMO-PNC communications systems.Article Citation - WoS: 4Citation - Scopus: 5Hybrid Beamforming Strategies for Secure Multicell Multiuser Mmwave Mimo Communications(Elsevier, 2021) Özbek, Berna; Erdoğan, Oğulcan; Busari, Sherif A.; Gonzalez, JonathanOver 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.Conference Object Citation - Scopus: 7Compressive Sensing Based Low Complexity User Selection for Massive MIMO Systems(IEEE, 2020) Yllmaz, Saadet Simay; Ozbck, BcmaMassive Multiple-input Multiple-output (MIMO) is widely considered as a key enabler of the next-generation networks. In these systems, user selection strategies are important to achieve spatial diversity and maximize spectral efficiency. In this paper, a user selection algorithm is proposed with the reconstruction of the sparse Massive MIMO channel using Compressive Sensing (CS) algorithm. The proposed algorithm eliminates the users based on the channel correlation by employing the CS algorithm which reduces the feedback overhead in the system. The simulation results show that the proposed algorithm outperforms the traditional user selection algorithms in terms of sum data rate and computational complexity. Moreover, the effects of the sparsity level and feedback measurement on the performance are examined.