Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği

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

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  • Conference Object
    Citation - WoS: 2
    Citation - Scopus: 2
    Limited Feedback Design for Massive Full Dimension Mimo Systems
    (IEEE, 2022) Özbek, Berna; Arslan, Caner; Demirtaş, Mahmut; Şahan, Hüsne; Kadı, Furkan Kerim; Elçi, Erdem
    Massive Multiple-input Multiple-output (MIMO) systems serve simultaneously multiple users to increase spectral efficiency in wireless communication systems. Using two dimension antenna design for massive MIMO systems namely massive FD-MIMO, the overall system performance is further improved. For the massive FD-MIMO systems, the availability of channel state information (CSI) at the base station is essential to achieve overall performance gain. In this paper, we design limited feedback link for massive FD-MIMO by designing two separate codebooks for horizontal and elevation domains to reduce the feedback load. The simulation results are provided for the proposed scheme by considering 3-dimension wireless channel models.
  • Conference Object
    Citation - Scopus: 7
    Compressive Sensing Based Low Complexity User Selection for Massive MIMO Systems
    (IEEE, 2020) Yllmaz, Saadet Simay; Ozbck, Bcma
    Massive 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.