Yazar "Shaik, Parvez" seçeneğine göre listele

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    Hybrid RF-VLC Communication System: Performance Analysis and Deep Learning Detection in the Presence of Blockages
    (Institute of Electrical and Electronics Engineers Inc., 2024) Shaik, Parvez; Keçeci, Cihat; Garg, Kamal K.; Boyacı, Ali; Ismail, Muhammad; Serpedin, Erchin
    This paper analyzes the performance of a dual-hop hybrid radio frequency - visible light communications (VLCs) system. VLC exploits high frequencies for signal propagation for short-range and is highly susceptible to blockages. Hence, in this work, the sensitivity of the VLC toward the blockages is explored. This study examines outdoor-indoor communication for an urban scenario featuring an office environment. The VLC system is designed by considering multiple VLC access points serving the end users in the presence of human blockages. Selection combining is employed to select the access point which offers the maximum instantaneous signal-to-noise ratio at the user. To capture the outdoor scenario, path loss modeling is conducted to account for signal attenuation from outdoor to indoor spaces. The outdoor scenario is modeled using Nakagami-m fading channels while VLC is employed for the indoor scenario. System performance is assessed in closed-form expression for outage probability and the symbol error rate for binary phase shift keying and quadrature amplitude modulation. A deep learning-based approach detects the symbols by tracking dynamic changes in the channel. Simulation results corroborate the correctness of derived analytical expressions and reveal that blockages significantly impact both single and multiple LED based communication channels.
  • Küçük Resim
    Öğe
    Performance of Deep Learning Assisted Visible Light Communications Impaired by Blockages
    (IEEE, 2023) Shaik, Parvez; Keçeci, Cihat; Garg, Kamal K.; Boyacı, Ali; Ismail, Muhammad; Serpedin, Erchin
    This study investigates the performance of visible light communications (VLCs) in the presence of blockages. An indoor office scenario with a single VLC access point serving the user nodes in the presence of human blockages is examined. System performance is assessed through closed-form expressions for outage probability and symbol error rate for binary phase shift keying and quadrature amplitude modulation. A deep neural network for symbol detection is deployed at the receiver. Performance metrics illustrate that the blockages cause significant impact on signal detection. Computer simulations corroborate the correctness of the obtained analytical expressions.