| Proj No. | A3045-251 |
| Title | Channel Estimation and Signal Detection in OFDM Systems using Deep Learning |
| Summary | Orthogonal frequency division multiplexing (OFDM) is used in wireless communication such as 5G to combat frequency selective fading in wireless channels. The method is to divide a broadband data into several narrowband data transmitted with respective subcarriers. In this project, we will study a deep learning-based signal recognition and channel estimation for orthogonal frequency-division multiplexing (OFDM) systems. Here, deep learning is used to fully regulate wireless OFDM channels. Instead of estimating the channel state information (CSI) explicitly before identifying or recovering the broadcast symbols using the estimated CSI, as is the case with the standard or traditional OFDM receivers, the deep learning-based method directly recovers the transmitted symbols while implicitly estimating the CSI. Student who has taken subjects in machine learning or artificial intelligent and good in mathematics is preferred. |
| Supervisor | A/P Erry Gunawan (Loc:S1 > S1 B1C > S1 B1C 80, Ext: +65 67905392) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Communication Research II (Loc: S2-B3c-26) |
| Single/Group: | Single |
| Area: | Wireless and Communications Engineering |
| ISP/RI/SMP/SCP?: |