Model-based learning for joint channel estimationand hybrid MIMO precoding
Journal:
arXiv
Published Date:
May 7, 2025
Abstract
Hybrid precoding is a key ingredient of cost-effective massive multiple-input
multiple-output transceivers. However, setting jointly digital and analog
precoders to optimally serve multiple users is a difficult optimization
problem. Moreover, it relies heavily on precise knowledge of the channels,
which is difficult to obtain, especially when considering realistic systems
comprising hardware impairments. In this paper, a joint channel estimation and
hybrid precoding method is proposed, which consists in an end-to-end
architecture taking received pilots as inputs and outputting precoders. The
resulting neural network is fully model-based, making it lightweight and
interpretable with very few learnable parameters. The channel estimation step
is performed using the unfolded matching pursuit algorithm, accounting for
imperfect knowledge of the antenna system, while the precoding step is done via
unfolded projected gradient ascent. The great potential of the proposed method
is empirically demonstrated on realistic synthetic channels.
