| Proj No. | A3196-251 |
| Title | Development of channel estimation for doubly selective fading channels using deep learning algorithms |
| Summary | In this project, the student is expected to study and develop a deep learning-based solution for accurate channel estimation in doubly selective fading channels, where both time and frequency variations significantly impact the channel response. Traditional channel estimation techniques struggle to accurately capture the varying channel characteristics, leading to performance degradation in communication systems. Leveraging deep learning, this project aims to train neural network models to effectively learn and estimate the complex mapping between transmitted and received signals in doubly selective fading channels. By exploiting the temporal and spectral correlations present in the channel data, the deep learning-based channel estimator will adaptively adjust to changing channel conditions, enhancing the robustness and reliability of communication systems operating in such environments. Through extensive simulation and experimentation, the project will evaluate the performance of the proposed channel estimation method in terms of estimation accuracy, computational efficiency, and robustness to varying channel dynamics. Matlab or Python programming will be used to generate numerical results for this project. |
| Supervisor | A/P Teh Kah Chan (Loc:S2 > S2 B2A > S2 B2A 03, Ext: +65 67905365) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Centre for Information Sciences & System (CISS) (Loc: S2-B4b-05) |
| Single/Group: | Single |
| Area: | Wireless and Communications Engineering |
| ISP/RI/SMP/SCP?: |