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Near-Field Multiuser Communications based on Sparse Arrays

Kangjian Chen, Chenhao Qi, Geoffrey Ye Li, Octavia A. Dobre

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.

Near-Field Multiuser Communications based on Sparse Arrays

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.
Paper Structure (16 sections, 62 equations, 10 figures, 3 algorithms)

This paper contains 16 sections, 62 equations, 10 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Analysis of Uniform Sparse Arrays
  4. Antenna Position Optimization of Nonuniform Sparse Arrays
  5. Overcoming the First Challenge
  6. Overcoming the Second Challenge
  7. Overcoming the Third Challenge
  8. Channel Estimation and Precoding
  9. Sparsity Exploration for SAs
  10. Sparsity Exploration for USAs
  11. Sparsity Exploration for NSAs
  12. SD-A-Domain Orthogonal Matching Pursuit Algorithm
  13. SD-A-Domain Iterative Super-Resolution Channel Estimation Algorithm
  14. Multiuser Beamforming
  15. Simulation Results
  16. ...and 1 more sections

Figures (10)

  • Figure 1: Illustration of the system model.
  • Figure 2: Illustration of $|G(\boldsymbol{u},b,\Theta)|$: (a) The calculated beam gain of $\boldsymbol{u}$ in the SD-A domain; (b) Comparisons of calculated and approximated beam gains.
  • Figure 3: Illustration of $|G(\boldsymbol{u},b,\Omega)|$.
  • Figure 4: Illustration of $|G(\widehat{\boldsymbol{u}},b,\Theta)|$.
  • Figure 5: Comparisons of different methods in terms of the NMSE for different SNRs.
  • ...and 5 more figures