A Blind Super-Resolution Method for Near-Field Channel Estimation with Angle-Range Recovery

As massive MIMO and extremely large antenna array rapidly evolve, wireless communication is gradually shifting from the far-field to the near-field region. To accurately capture the characteristics of the near-field channel, the spherical-wave assumption has to be applied, making channel estimation a challenging task. In this paper, we model the near-field channel as a set of complex exponentials modulated by unknown waveforms and formulate the channel estimation problem as a blind super-resolution task. By representing the modulated waveforms within a low-dimensional subspace, this blind super-resolution problem can be efficiently solved using atomic norm minimization. Simulation results demonstrate that the proposed method not only enhances the performance of channel estimation but also provides accurate angle and range estimation in near-field communications.