Sparse Channel Representation and Estimation in Near Field Communications

The employment of extremely large antenna arrays and high-frequency signaling makes future communication systems likely to operate in the near-field region, where the conventional planar wave assumption is invalid. Instead, a spherical wave assumption which provides both the user angle and distance information is more accurate. Spherical wave-based channel representation and estimation is still under investigation. In this work, we propose a distance-parameterized angular-domain sparse model to represent the near-field channel, followed by a joint dictionary learning and sparse recovery based channel estimation algorithm. The proposed sparse representation model overcomes challenges such as the storage burden and high dictionary coherence that arise in the existing polar-domain method. Simulations in multi-user communication scenarios support the superiority of the proposed near-field channel sparse representation and estimation over the polar-domain method in channel estimation error and pave the way to efficient and accurate modeling in the near-field regime.