A Systematic Review of Channel Estimation Methods and Proposing an Optimal Approach in MIMO-OFDM and NOMA-based Networks

  • سال انتشار: 1402
  • محل انتشار: دهمین کنفرانس بین المللی مهندسی برق، الکترونیک و شبکه های هوشمند
  • کد COI اختصاصی: EESCONF10_065
  • زبان مقاله: انگلیسی
  • تعداد مشاهده: 166
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نویسندگان

Mehdi Izadi

Electrical Engineering and Information Technology Department, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran

Gholamreza Farahani

Electrical Engineering and Information Technology Department, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran

Gholamreza Mohammadkhani

Electrical Engineering and Information Technology Department Iranian Research Organization for Science and Technology (IROST), Tehran, Iran

چکیده

One of the critical issues in broadband wireless access is the channel estimation problem. Efficient channel estimation leads to spectrally efficient wireless communications. The main parts of channel estimation are the features of the fast time change, the presence of noise, and the identification of network structure based on MIMO-OFDM or NOMA. Optimizing Quality of Service (QoS) criteria, including throughput, Bit Error Rate (BER), delay and NimaX, and Mean Square Error (MSE) during routing, as well as improving energy consumption, reducing interference and overlapping along with reducing noise and congestion are listed as the main targets of channel estimation. In this article, an attempt has been made to review and evaluate the latest methods and techniques of channel estimation in all types of wireless networks, including Wireless Sensor Networks (WSNs), wireless mesh networks, Internet of Things (IoT) networks, etc. based on MIMO-OFDM or NOMA. Finally, an optimal channel estimation for the TV broadcasting system is proposed. The results show that the proposed method has effectively improved the QoS criteria such as MSE, Signal to Noise Ratio (SNR), and Peak Signal to Noise Ratio (PSNR).

کلیدواژه ها

Channel Estimation, MIMO-OFDM, NOMA, Wireless Networks, IoT, Adaptive Neuro-Fuzzy Inference System (ANFIS), SNR

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