| Proj No. | A3197-251 |
| Title | Development of deep learning-based spectrum sensing technique for cognitive radio networks |
| Summary | When a signal is transmitted over a wireless communication system, the received signals always face the problems of fading channels and strong background noise. Traditional method such as energy detection, may have limitations on its detection performance and robustness to the uncertainty of noise. In this project, the objective is to first study existing traditional spectrum sensing methods. Following that, deep learning framework such as convolutional neural network (CNN), long short-term memory (LSTM) network, will be applied to detecting the weak signal under low signal-to-noise ratio (SNR) level. The numerical results will be compared with that of existing traditional methods. 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?: |