Symbol detection is a fundamental and challenging problem in modern
communication systems, e.g., multiuser multiple-input multiple-output (MIMO)
setting. Iterative Soft Interference Cancellation (SIC) is a state-of-the-art
method for this task and recently motivated data-driven neural network models,
e.g. DeepSIC, that can deal with unknown non-linear channels. However, these
neural network models require thorough timeconsuming training of the networks
before applying, and is thus not readily suitable for highly dynamic channels
in practice. We introduce an online training framework that can swiftly adapt
to any changes in the channel. Our proposed framework unifies the recent deep
unfolding approaches with the emerging generative adversarial networks (GANs)
to capture any changes in the channel and quickly adjust the networks to
maintain the top performance of the model. We demonstrate that our framework
significantly outperforms recent neural network models on highly dynamic
channels and even surpasses those on the static channel in our experiments.

本研究提出了一种在线训练框架，用于快速适应于任何通道变化，以保持模型的最佳性能，该框架将最新的深度展开方法与对抗生成网络相结合，以捕获通道的任何变化并迅速调整网络，在高动态通道上显著优于最近的神经网络模型，并在我们的实验中甚至超过静态通道的性能。