Improving the performance of the MIMO-OFDM-NOMA System using a V-BLAST ZF approach based on deep CNN in IoT
Abstract 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 perf...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
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| Online Access: | https://doi.org/10.1186/s13638-025-02481-w |
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| _version_ | 1849238797198295040 |
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| author | Mehdi Izadi Gholam-Reza Mohammad-Khani Gholamreza Farahani |
| author_facet | Mehdi Izadi Gholam-Reza Mohammad-Khani Gholamreza Farahani |
| author_sort | Mehdi Izadi |
| collection | DOAJ |
| description | Abstract 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%. |
| format | Article |
| id | doaj-art-cde24faa07f14296aebed77c183f7f71 |
| institution | Kabale University |
| issn | 1687-1499 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj-art-cde24faa07f14296aebed77c183f7f712025-08-20T04:01:24ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992025-07-012025112310.1186/s13638-025-02481-wImproving the performance of the MIMO-OFDM-NOMA System using a V-BLAST ZF approach based on deep CNN in IoTMehdi Izadi0Gholam-Reza Mohammad-Khani1Gholamreza Farahani2Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST)Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST)Department of Electrical Engineering and Information Technology, Iranian Research Organization for Science and Technology (IROST)Abstract 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%.https://doi.org/10.1186/s13638-025-02481-wMIMO-OFDM-NOMA systemV-BLAST ZFDeep learningChannel estimationInternet of things (IoT) |
| spellingShingle | Mehdi Izadi Gholam-Reza Mohammad-Khani Gholamreza Farahani Improving the performance of the MIMO-OFDM-NOMA System using a V-BLAST ZF approach based on deep CNN in IoT EURASIP Journal on Wireless Communications and Networking MIMO-OFDM-NOMA system V-BLAST ZF Deep learning Channel estimation Internet of things (IoT) |
| title | Improving the performance of the MIMO-OFDM-NOMA System using a V-BLAST ZF approach based on deep CNN in IoT |
| title_full | Improving the performance of the MIMO-OFDM-NOMA System using a V-BLAST ZF approach based on deep CNN in IoT |
| title_fullStr | Improving the performance of the MIMO-OFDM-NOMA System using a V-BLAST ZF approach based on deep CNN in IoT |
| title_full_unstemmed | Improving the performance of the MIMO-OFDM-NOMA System using a V-BLAST ZF approach based on deep CNN in IoT |
| title_short | Improving the performance of the MIMO-OFDM-NOMA System using a V-BLAST ZF approach based on deep CNN in IoT |
| title_sort | improving the performance of the mimo ofdm noma system using a v blast zf approach based on deep cnn in iot |
| topic | MIMO-OFDM-NOMA system V-BLAST ZF Deep learning Channel estimation Internet of things (IoT) |
| url | https://doi.org/10.1186/s13638-025-02481-w |
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