Yazar "Ibrahim, Shakir Mahmood" seçeneğine göre listele

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    Performance analyses of channel estimation and precoding for massive MIMO downlink in the TDD system
    (Institute of Electrical and Electronics Engineers Inc., 2020) Abdulateef, Alaa Amjed; Ibrahim, Shakir Mahmood; Mohammed, Alaa Hamid; Abdulateef, Ihsan Amjad
    The fundamental limitation of massive MIMO technology is pilot contamination effect. This effect occurs during uplink training when terminals use the same orthogonal signals. In this paper, a pilot reuse factor with large scale fading precoding is proposed to mitigate the pilot contamination effect. The pilot reuse factor is designed to assign unique orthogonal signals to the adjacent cells. These unique orthogonal signals are reused only within the cell and hence, intra-pilot contamination is the only concern. Large scale fading precoding is then used to mitigate the intra-pilot contamination effect. The average achievable sum rate is computed for different pilot reuse factors. Experimental results through MATLAB simulation show that a higher pilot reuse factor gives better average achievable sum rates. © 2020 IEEE.
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    Öğe
    Suggestion new monitoring system by depending on the human activity recognition videos
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ibrahim Al-Siraj, Marwah Nabeel; Çevik, Mesut; Ibrahim, Shakir Mahmood
    Modern home monitoring system techniques, such as motion detection technology and home camera system intrusion warning, are said to be insufficient, especially to meet the needs of whole automation with flaws such as needing human interaction. We suggest a substitute system, a human activity recognition (HAR) method based on the video, and a combination of long short-term memory (LSTM) and convolution neural networks (CNN) algorithm, to address the flaws that have been found. Our suggestion doesn't need to be changed. is simply deployed utilizing just low-cost modifications to the current home security protocols, and commercially available hardware The conventional security camera may be used with ease for computer vision applications. Utilizing information on actual activity gathered by video-based sensors, we assess our strategy. By drawing Loss and Accuracy curves, we demonstrate how successful it is. Show Results demonstrate that the video-approved human activity recognition method can deliver complete home automation. The monitoring system has higher accuracy as compared to traditional camera motion detectors. The precision of the system may be improved further, and we can attain for best results. (Long-term Recurrent Cnvlution Network) implementation yields result better.