Deep Transfer Learning for Model-Driven Signal Detection in Downlink MIMO-NOMA Systems
Dongcai Zhang, Xiaoming Wang, Yuanxue Xin, Ting Liu, Youyun Xu · 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) · 2022
In this paper, a model-driven signal detection method with deep transfer learning (DTL) is proposed for downlink multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Specifically, we first introduce some learnable parameters to an unfolded iterative algorithm for MIMO detection and improve it through a preconditioned process to speed up its convergence. Then we combine this modified algorithm with the successive interference cancellation (SIC) structure in NOMA detection to propose our learned preconditioned conjugate gradient descent network with SIC (LPCG-SIC). Furthermore, to improve the reusability of the trained network, a DTL-based detection algorithm and three model-driven transfer strategies are proposed for our LPCG-SIC detector. Simulation results show that the proposed detection network outperforms conventional detectors, and the transfer strategies can obtain significant performance gain compared to no-transfer methods.