Model-based learning for joint channel estimationand hybrid MIMO precoding
This work addresses the challenge of optimizing hybrid precoders for cost-effective massive MIMO transceivers, which is critical for improving wireless communication efficiency, though it appears incremental by building on existing unfolded methods.
The paper tackles the joint channel estimation and hybrid precoding problem in massive MIMO systems with hardware impairments, proposing a model-based neural network that uses unfolded algorithms to achieve lightweight and interpretable results, as demonstrated on realistic synthetic channels.
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 pre-coders. 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.