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Considering the dimensionality of a typical reconfigurable intelligentsurface (RIS), channel state information acquisition in RIS-assisted systemsrequires lengthy pilot transmissions. Moreover, the large aperture of the RISmay cause transmitters/receivers to fall in its near-field region, where bothdistance and angles affect the channel structure. This paper proposes aparametric maximum likelihood estimation (MLE) framework for jointly estimatingthe direct channel between the user and the base station (BS) and theline-of-sight channel between the user and the RIS, in both far-field andnear-field scenarios. The MLE framework is first developed for the case ofsingle-antenna BS and later extended to the scenario where the BS is equippedwith multiple antennas. A novel adaptive RIS configuration strategy is proposedto select the RIS configuration for the next pilot to actively refine theestimate. We design a minimal-sized codebook of orthogonal RIS configurationsto choose from during pilot transmission with a dimension much smaller than thenumber of RIS elements. To further reduce the required number of pilots, wepropose an initialization strategy with two wide beams. We demonstratenumerically that the proposed MLE method needs only a few pilots for achievingaccurate channel estimates and further show that the presented frameworkperforms well under Rician fading. We also showcase efficient user channeltracking in near-field and far-field scenarios.

Parametric Channel Estimation with Short Pilots in RIS-Assisted Near- and Far-Field Communications