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<p>We are heading toward an “everything, everywhere, and always connected” future, and the IoT will become a key part of our future lives. To achieve this goal, high-efficiency communication networks capable of handling high-rate data transmissions are required. In this paper, the network performance of the MIMO-OFDM-NOMA system was evaluated based on user location estimation by a deep CNN and channel estimation using the V-BLAST ZF technique. The results showed that the proposed method improved communication performance by approximately 9.59%, energy efficiency by 23.53%, and throughput by about 53.71% compared to the MMSE technique. Additionally, to examine the impact of using NOMA, BER and delay metrics were used. The results showed that by using the V-BLAST ZF technique, the BER for the MIMO-OFDM-NOMA network would be approximately 70.9% better than the MIMO-OFDM. Furthermore, the delay would be reduced by 21.29%.</p>

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Improving the performance of the MIMO-OFDM-NOMA System using a V-BLAST ZF approach based on deep CNN in IoT

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月4日

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  • Mehdi Izadi,
  • Gholam-Reza Mohammad-Khani,
  • Gholamreza Farahani

摘要

We are heading toward an “everything, everywhere, and always connected” future, and the IoT will become a key part of our future lives. To achieve this goal, high-efficiency communication networks capable of handling high-rate data transmissions are required. In this paper, the network performance of the MIMO-OFDM-NOMA system was evaluated based on user location estimation by a deep CNN and channel estimation using the V-BLAST ZF technique. The results showed that the proposed method improved communication performance by approximately 9.59%, energy efficiency by 23.53%, and throughput by about 53.71% compared to the MMSE technique. Additionally, to examine the impact of using NOMA, BER and delay metrics were used. The results showed that by using the V-BLAST ZF technique, the BER for the MIMO-OFDM-NOMA network would be approximately 70.9% better than the MIMO-OFDM. Furthermore, the delay would be reduced by 21.29%.