Channel Estimation Optimization Model in Internet of Things Based on MIMO/OFDM with Deep Extended Kalman Filter
Channel estimation plays a vital role in the performance of wireless communication systems. However, apart from the usual OFDM modes, there are also orthogonal conditions for modulationbased multi-channel systems, which make channel estimation on networks such as the Internet of Things (IoT) more co...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Bilijipub publisher
2022-07-01
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| Series: | Advances in Engineering and Intelligence Systems |
| Subjects: | |
| Online Access: | https://aeis.bilijipub.com/article_153085_c966ff870d12cec40e12aae3cbed2ad0.pdf |
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| Summary: | Channel estimation plays a vital role in the performance of wireless communication systems. However, apart from the usual OFDM modes, there are also orthogonal conditions for modulationbased multi-channel systems, which make channel estimation on networks such as the Internet of Things (IoT) more complex. To estimate the IoT channel, its type is considered a narrow or wide band. The purpose of this research is narrowband IoT based on OFDM. There are various classical methods for channel estimation such as Least Squares (LS) and Linear Minimum Mean Square Error (LMMSE). However, due to high computational complexity as well as inaccurate channel estimation and remaining weaknesses such as latency and other quality of service criteria, especially Bit Error Rate (BER), Signal Noise Ratio (SNR), and Maximum to Average Power Ratio (PAPR), this research improves these two methods based on the Deep Extended Kalman filter. |
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| ISSN: | 2821-0263 |