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Transforming Time-Varying to Static Channels: The Power of Fluid Antenna Mobility

Weidong Li, Haifan Yin, Fanpo Fu, Yandi Cao, Merouane Debbah

TL;DR

This work tackles mobility-induced channel variation by leveraging a fluid antenna (FA) to slide ports and transform a time-varying channel into a quasi-static one. It introduces the matrix pencil–based moving port prediction (MPMP) that first estimates path parameters via a 2-D matrix pencil method and then optimizes FA port selection to keep the channel effectively static, thereby reducing CSI delay and Doppler effects. Theoretical results show that for LoS channels, the MPMP prediction MSE can converge to zero as the BS antenna count $N_t$ and port density grow, with explicit bounds extended to multi-path scenarios; simulations at 60–120 km/h confirm MPMP outperforms traditional channel prediction methods. The approach holds potential for practical high-mobility deployments by exploiting FA reconfigurability to maintain stable DL precoding and enhanced spectral efficiency.

Abstract

This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction method, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. Theoretical analysis shows that for the line-ofsight (LoS) channel, the prediction error of our proposed MPMP method may converge to zero, as the number of BS antennas and the port density of the FA are large enough. For a multi-path channel, we also derive the upper and lower bounds of the prediction error when the number of paths is large enough. When the UEs move at a speed of 60 or 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction method.

Transforming Time-Varying to Static Channels: The Power of Fluid Antenna Mobility

TL;DR

This work tackles mobility-induced channel variation by leveraging a fluid antenna (FA) to slide ports and transform a time-varying channel into a quasi-static one. It introduces the matrix pencil–based moving port prediction (MPMP) that first estimates path parameters via a 2-D matrix pencil method and then optimizes FA port selection to keep the channel effectively static, thereby reducing CSI delay and Doppler effects. Theoretical results show that for LoS channels, the MPMP prediction MSE can converge to zero as the BS antenna count and port density grow, with explicit bounds extended to multi-path scenarios; simulations at 60–120 km/h confirm MPMP outperforms traditional channel prediction methods. The approach holds potential for practical high-mobility deployments by exploiting FA reconfigurability to maintain stable DL precoding and enhanced spectral efficiency.

Abstract

This paper addresses the mobility problem with the assistance of fluid antenna (FA) on the user equipment (UE) side. We propose a matrix pencil-based moving port (MPMP) prediction method, which may transform the time-varying channel to a static channel by timely sliding the liquid. Different from the existing channel prediction method, we design a moving port selection method, which is the first attempt to transform the channel prediction to the port prediction by exploiting the movability of FA. Theoretical analysis shows that for the line-ofsight (LoS) channel, the prediction error of our proposed MPMP method may converge to zero, as the number of BS antennas and the port density of the FA are large enough. For a multi-path channel, we also derive the upper and lower bounds of the prediction error when the number of paths is large enough. When the UEs move at a speed of 60 or 120 km/h, simulation results show that, with the assistance of FA, our proposed MPMP method performs better than the existing channel prediction method.
Paper Structure (15 sections, 7 theorems, 112 equations, 6 figures, 1 table, 1 algorithm)

This paper contains 15 sections, 7 theorems, 112 equations, 6 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Channel Model
  3. The moving port prediction method
  4. The channel parameters estimation
  5. The optimization problem
  6. The port selection method for a LoS channel
  7. The port selection method for a multi-path channel
  8. Performance Analysis
  9. Numerical Results
  10. Conclusion
  11. Proof of Lemma \ref{['Lemma1']}
  12. Proof of Theorem \ref{['Theorem1']}
  13. Proof of Lemma \ref{['Lemma2']}
  14. Proof of Lemma \ref{['Lemma3']}
  15. Proof of Theorem \ref{['Theorem2']}

Key Result

Proposition 1

Under Assumption Assumption-Mild, if three samples are available, the asymptotic performance of the estimated parameters yields where $p = 1, \cdots, {P+1}$.

Figures (6)

  • Figure 1: The DL wireless communication system
  • Figure 2: The SE versus SNR, the BS has 16 antennas, the CSI delay is 4 $\rm{ms}$, $W=20$, $M=300$.
  • Figure 3: The SE versus SNR, the BS has 16 antennas, CSI delay is 4 $\rm{ms}$, $W=20$, $M=300$, multiple velocity levels of UEs, i.e., two at 30 $\rm{km/h}$, two at 60 $\rm{km/h}$, two at 90 $\rm{km/h}$, and two at 120 $\rm{km/h}$.
  • Figure 4: (a) The SE versus SNR and (b) the prediction error versus $\rho$, the BS has 16 antennas, the CSI delay is 4 $\rm{ms}$, the UEs move at 120 $\rm{km/h}$.
  • Figure 5: (a) The SE versus SNR and (b) the prediction error versus $M$, the BS has 16 antennas, the CSI delay is 4 $\rm{ms}$, the UEs move at 120 $\rm{km/h}$.
  • ...and 1 more figures

Theorems & Definitions (7)

  • Proposition 1
  • Lemma 1
  • Theorem 1
  • Lemma 2
  • Lemma 3
  • Lemma 4
  • Theorem 2