The general aim of my proposed research is to utilize deep-learning-based technologies to enable some key improvements in the physical layer of future wireless communication systems like 5G and 6G. The topic of my Ph.D. research is "Data & Model-driven(double driven) Massive MIMO Detection and Channel Estimation for 5&6G". It mainly focuses on the algorithmic side, which includes analyzing the performance & complexity of deep learning-based detection algorithm. Actually, the most important thing of communication algorithms is the trade-off between performance and complexity.
I am trying to implement the combination of a huge amount of wireless data (data-driven) and well-developed traditional communication knowledge in the DL-based detection system. My proposed double-driven detection algorithm aims to give a better trade-off between performance and complexity and make some contribution to the real implementation of DL-based technologies in 5G&6G. In my opinion, AI-based 5G&6G is one of the biggest chances for both research and industry(commerce) since AI and IoT are the key techs of the upcoming technological revolution.
Recent Publication(s):
- C. Liu and T. Arslan, "RecNet: Deep Learning-Based OFDM Receiver with Semi-Blind Channel Estimation," 2020 IEEE International Symposium on Circuits and Systems (ISCAS), 2020, pp. 1-4, doi: 10.1109/ISCAS45731.2020.9180878.
