Channel estimation and data detection are challenging in high speed train, mm-Wave and LEO satellite communication channels because of dual signal distortion caused by high Doppler spread and severe multipath fading, known as doubly selective fading.  We have developed channel-tracking receivers capable of recovering 5G uplink and downlink signals corrupted by doubly selective fading.  The design philosophy is to apply data-aided channel estimator and PSP (per-survivor processing) equalizer to perform joint channel estimation and data equalization with interference cancellation.  At 500km/h vehicular speed in 20GHz frequency band, the receiver performance is only about 1 dB from the ideal performance line.

Reference:

• X. Liu, K. Anand, Y. L. Guan, L. Deng, P. Fan and Z. Zhou, "BEM-PSP for Single-Carrier and SC-FDMA Communication Over a Doubly Selective Fading Channel," in IEEE Transactions on Wireless Communications, vol. 19, no. 6, pp. 3924-3937, June 2020.
• LIU Xiaobei, Kushai ANAND, GUAN Yongliang, FAN Pingzhi, “BEM Based Channel-Tracking Equalizer For Fast-Fading Wireless Multipath Channels”, Singapore patent number 10201909843V, filing date 22-Oct-2019.

For more information, you may approach our professor Guan Yong Liang.

Intelligent reflecting surfaces (IRSs) are envisioned to provide reconfigurable wireless environments for beyond-5G networks. Furthermore, non-orthogonal multiple access (NOMA) has been proposed as a candidate technology for future networks. As NOMA prefers to organize users with distinctive channel conditions and IRSs enhance the network coverage, we investigate the combination of these two techniques to further improve system performance beyond 5G. We explore the performance of IRS-aided NOMA networks for downlink and uplink networks. The derived results will contribute to the deployment of IRSs in the future.

Reference:

• Y. Cheng, K. H. Li, Y. Liu, K. C. Teh, and H. V. Poor, "Downlink and uplink intelligent reflecting surface aided networks: NOMA and OMA," IEEE Trans. Wireless Commun., minor revision.

For more information, you may approach our professor Li Kwok Hung.

Non-orthogonal multiple access (NOMA) technique has drawn much attention in recent years. It has also been a promising technique for the fifth-generation (5G) wireless communication system and beyond. In this project, we develop a novel deep learning (DL) aided receiver for NOMA joint signal detection. The DL-based receiver serves as an end-to-end mode, which simultaneously fulfills the function of channel estimation, equalization, and demodulation. Compared with the traditional signal detection method for the NOMA scheme, the proposed deep learning method shows feasible improvement in performance and robustness with the tapped delay line (TDL) channel model, which is adopted for the 5G communication environment. 

For more information, you may approach our professor Teh Kah Chan.