  In this paper, we propose a method to design the training data that can
support robust generalization of trained neural networks to unseen channels.
The proposed design that improves the generalization is described and analysed.
It avoids the requirement of online training for previously unseen channels, as
this is a memory and processing intensive solution, especially for battery
powered mobile terminals. To prove the validity of the proposed method, we use
the channels modelled by different standards and fading modelling for
simulation. We also use an attention-based structure and a convolutional neural
network to evaluate the generalization results achieved. Simulation results
show that the trained neural networks maintain almost identical performance on
the unseen channels.
