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Deep Learning for Joint Pilot Design and Channel Estimation in MIMO-OFDM Systems
| Content Provider | MDPI |
|---|---|
| Author | Kang, Xiao-Fei Liu, Zi-Hui Yao, Meng |
| Copyright Year | 2022 |
| Description | In MIMO-OFDM systems, pilot design and estimation algorithm jointly determine the reliability and effectiveness of pilot-based channel estimation methods. In order to improve the channel estimation accuracy with less pilot overhead, a deep learning scheme for joint pilot design and channel estimation is proposed. This new hybrid network structure is named CAGAN, which is composed of a concrete autoencoder (concrete AE) and a conditional generative adversarial network (cGAN). We first use concrete AE to find and select the most informative position in the time-frequency grid to achieve pilot optimization design and then input the optimized pilots to cGAN to complete channel estimation. Simulation experiments show that the CAGAN scheme outperforms the traditional LS and MMSE estimation methods with fewer pilots, and has good robustness to environmental noise. |
| Starting Page | 4188 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22114188 |
| Journal | Sensors |
| Issue Number | 11 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-31 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Autoencoder Channel Estimation Conditional Generative Adversarial Network Mimo-ofdm Pilot Design |
| Content Type | Text |
| Resource Type | Article |
