Efficient Near-Field Channel Feature Estimation: Criteria and Algorithms

When the radio signal propagates in the near-field (NF), the distance can be estimated by the curvature of the wavefront. This brings new opportunities for sensing and communication, but a computational burden as well. This paper first establishes a theoretical foundation for computationally efficient NF feature (i.e., angle-of-arrival (AOA) and distance) estimation. We reveal that the computation of feature estimates can be reduced without sacrificing the estimation accuracy, if the distance between the receiver and transmitter (or scatterer) exceeds approximately one quarter of the Rayleigh distance. Based on this criterion, we introduce a zone-wise search-based algorithm that reduces computation by restricting the high-dimensional (HD) search to the angle-distance search space where the criterion is not met. In the other search space, sequential low-dimensional (LD) searches are carried out. Along with the algorithm, the estimation-theoretic analysis for NF feature estimation is provided to present the theoretical performance limit. The simulation results demonstrate that even under severe NF effect, the accuracy of the proposed algorithm reaches that of the HD search-based algorithm, which represents a realistically achievable upper bound. Nevertheless, the computation complexity of the proposed algorithm is significantly lower than the HD search-based algorithm.