In modern communication systems, channel state information is of paramount
importance to achieve capacity. It is then crucial to accurately estimate the
channel. It is possible to perform SISO-OFDM channel estimation using sparse
recovery techniques. However, this approach relies on the use of a physical
wave propagation model to build a dictionary, which requires perfect knowledge
of the system's parameters. In this paper, an unfolded neural network is used
to lighten this constraint. Its architecture, based on a sparse recovery
algorithm, allows SISO-OFDM channel estimation even if the system's parameters
are not perfectly known. Indeed, its unsupervised online learning allows to
learn the system's imperfections in order to enhance the estimation
performance. The practicality of the proposed method is improved with respect
to the state of the art in two aspects: constrained dictionaries are introduced
in order to reduce sample complexity and hierarchical search within
dictionaries is proposed in order to reduce time complexity. Finally, the
performance of the proposed unfolded network is evaluated and compared to
several baselines using realistic channel data, showing the great potential of
the approach.

本研究使用了展开的神经网络，利用稀疏恢复算法实现了 SISO-OFDM 信道估计，即使在系统参数不完全知道的情况下，也可以学习系统的瑕疵以提高估计性能。