Near-Field Multiuser Communications based on Sparse Arrays

TL;DR

This paper explores near-field multiuser communication using sparse arrays, analyzing beam gains, optimizing antenna positions, and developing super-resolution channel estimation algorithms to improve system performance.

Contribution

It introduces a novel optimization algorithm for antenna placement and super-resolution channel estimation tailored for near-field sparse array systems.

Findings

Increasing antenna spacing enhances spatial resolution and sum rate.

Optimized nonuniform sparse arrays reduce multiuser interference.

Proposed algorithms outperform existing methods in simulations.

Abstract

This paper considers near-field multiuser communications based on sparse arrays (SAs). First, for the uniform SAs (USAs), we analyze the beam gains of channel steering vectors, which shows that increasing the antenna spacings can effectively improve the spatial resolution of the antenna arrays to enhance the sum rate of multiuser communications. Then, we investigate nonuniform SAs (NSAs) to mitigate the high multiuser interference from the grating lobes of the USAs. To maximize the sum rate of near-field multiuser communications, we optimize the antenna positions of the NSAs, where a successive convex approximation-based antenna position optimization algorithm is proposed. Moreover, we find that the channels of both the USAs and the NSAs show uniform sparsity in the defined surrogate distance-angle (SD-A) domain. Based on the channel sparsity, an on-grid SD-A-domain orthogonal matching pursuit (SDA-OMP) algorithm is developed to estimate multiuser channels. To further improve the resolution of the SDA-OMP, we also design an off-grid SD-A-domain iterative super-resolution channel estimation algorithm. Simulation results demonstrate the superior performance of the proposed methods.