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Channel Estimation for FAS-assisted Multiuser mmWave Systems

Hao Xu, Gui Zhou, Kai-Kit Wong, Wee Kiat New, Chao Wang, Chan-Byoung Chae, Ross Murch, Shi Jin, Yangyang Zhang

TL;DR

This paper tackles CSI estimation for a multiuser mmWave system where mobile users employ fluid antenna systems (FAS). It introduces L3SCR, a low-sample-size sparse channel reconstruction method that first collects reduced-dimension LS estimates from a few estimating locations (ELs) and then exploits sparsity to recover the full channel via AoA/AoD and gain estimation, followed by channel reconstruction under the planar-wave model. Simulation results show that L3SCR achieves accurate CSI with substantially reduced hardware switching and pilot overhead, with the system sum-rate approaching the upper bound achievable with perfect CSI. The work provides a practical pathway to harness FAS gains in multiuser mmWave deployments by leveraging sparse-channel structure and efficient ANG/Gain estimation. Overall, L3SCR offers a favorable trade-off between estimation accuracy, overhead, and computational complexity for FAS-enabled networks.

Abstract

This letter investigates the challenge of channel estimation in a multiuser millimeter-wave (mmWave) time-division duplexing (TDD) system. In this system, the base station (BS) employs a multi-antenna uniform linear array (ULA), while each mobile user is equipped with a fluid antenna system (FAS). Accurate channel state information (CSI) plays a crucial role in the precise placement of antennas in FAS. Traditional channel estimation methods designed for fixed-antenna systems are inadequate due to the high dimensionality of FAS. To address this issue, we propose a low-sample-size sparse channel reconstruction (L3SCR) method, capitalizing on the sparse propagation paths characteristic of mmWave channels. In this approach, each fluid antenna only needs to switch and measure the channel at a few specific locations. By observing this reduced-dimensional data, we can effectively extract angular and gain information related to the sparse channel, enabling us to reconstruct the full CSI. Simulation results demonstrate that our proposed method allows us to obtain precise CSI with minimal hardware switching and pilot overhead. As a result, the system sum-rate approaches the upper bound achievable with perfect CSI.

Channel Estimation for FAS-assisted Multiuser mmWave Systems

TL;DR

This paper tackles CSI estimation for a multiuser mmWave system where mobile users employ fluid antenna systems (FAS). It introduces L3SCR, a low-sample-size sparse channel reconstruction method that first collects reduced-dimension LS estimates from a few estimating locations (ELs) and then exploits sparsity to recover the full channel via AoA/AoD and gain estimation, followed by channel reconstruction under the planar-wave model. Simulation results show that L3SCR achieves accurate CSI with substantially reduced hardware switching and pilot overhead, with the system sum-rate approaching the upper bound achievable with perfect CSI. The work provides a practical pathway to harness FAS gains in multiuser mmWave deployments by leveraging sparse-channel structure and efficient ANG/Gain estimation. Overall, L3SCR offers a favorable trade-off between estimation accuracy, overhead, and computational complexity for FAS-enabled networks.

Abstract

This letter investigates the challenge of channel estimation in a multiuser millimeter-wave (mmWave) time-division duplexing (TDD) system. In this system, the base station (BS) employs a multi-antenna uniform linear array (ULA), while each mobile user is equipped with a fluid antenna system (FAS). Accurate channel state information (CSI) plays a crucial role in the precise placement of antennas in FAS. Traditional channel estimation methods designed for fixed-antenna systems are inadequate due to the high dimensionality of FAS. To address this issue, we propose a low-sample-size sparse channel reconstruction (L3SCR) method, capitalizing on the sparse propagation paths characteristic of mmWave channels. In this approach, each fluid antenna only needs to switch and measure the channel at a few specific locations. By observing this reduced-dimensional data, we can effectively extract angular and gain information related to the sparse channel, enabling us to reconstruct the full CSI. Simulation results demonstrate that our proposed method allows us to obtain precise CSI with minimal hardware switching and pilot overhead. As a result, the system sum-rate approaches the upper bound achievable with perfect CSI.
Paper Structure (11 sections, 1 theorem, 37 equations, 4 figures)

This paper contains 11 sections, 1 theorem, 37 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. LS Estimation
  4. L3SCR
  5. Estimation of Number of Paths and AoAs
  6. Estimation of AoDs and Channel Gains
  7. Channel Reconstruction
  8. Analysis of Pilot Overhead and Computational Complexity
  9. Simulation Results
  10. Conclusions
  11. Proof of Lemma \ref{['lemma1']}

Key Result

Lemma 1

If $M \rightarrow + \infty$, $\bm \varOmega^H \bm A_{u,{\text{R}}}$ is a row sparse matrix with a full column rank. Only one element in each column of $\bm \varOmega^H \bm A_{u,{\text{R}}}$ is $1$ while all the others are $0$.

Figures (4)

  • Figure 1: Illustration of a FAS-assisted uplink system.
  • Figure 2: NMSE versus $K$ with $\rho = 10$ dB and $T = 1$.
  • Figure 3: Average sum rate versus $K$ with $\rho = 10$ dB and $T = 1$.
  • Figure 4: NMSE and computational time with $M = 64$ and $K = 10$.

Theorems & Definitions (4)

  • Lemma 1
  • proof
  • Remark 1
  • Remark 2