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Wavefront Transformation-based Near-field Channel Prediction for Extremely Large Antenna Array with Mobility

Weidong Li, Haifan Yin, Ziao Qin, Merouane Debbah

TL;DR

The paper tackles mobility-induced performance loss in extremely large antenna arrays (ELAA) by introducing a wavefront transformation-based channel prediction (WTMP) that converts near-field spherical wavefronts into a plane-wave-like form using a transform matrix ${\cal B}_n$, augmented with a time-frequency projection ${\cal D}_d$ to capture time-varying path delays. It estimates path parameters via OMP, constructs the wavefront-transformation matrix through a null-space design of ${\bf A}_{\theta,\phi}$, and uses the matrix-pencil method to estimate Doppler for forecasting future channels in the angular-time-frequency domain ${\bf S}$. Theoretical results show that as the number of BS antennas grows, ${\cal B}_n$ becomes determined by geometry $(\theta,\phi,r)$ and that, under finite samples, the asymptotic prediction error can vanish; simulations at high mobility demonstrate substantial gains over existing near-field predictors, approaching stationary performance. Overall, WTMP provides a practical framework to predict ELAA channels under mobility by mitigating near-field effects and tracking time-varying delays, enabling reliable downlink performance in future 6G-like systems.

Abstract

This paper addresses the mobility problem in extremely large antenna array (ELAA) communication systems. In order to account for the performance loss caused by the spherical wavefront of ELAA in the mobility scenario, we propose a wavefront transformation-based matrix pencil (WTMP) channel prediction method. In particular, we design a matrix to transform the spherical wavefront into a new wavefront, which is closer to the plane wave. We also design a time-frequency projection matrix to capture the time-varying path delay. Furthermore, we adopt the matrix pencil (MP) method to estimate channel parameters. Our proposed WTMP method can mitigate the effect of near-field radiation when predicting future channels. Theoretical analysis shows that the designed matrix is asymptotically determined by the angles and distance between the base station (BS) antenna array and the scatterers or the user when the number of BS antennas is large enough. For an ELAA communication system in the mobility scenario, we prove that the prediction error converges to zero with the increasing number of BS antennas. Simulation results demonstrate that our designed transform matrix efficiently mitigates the near-field effect, and that our proposed WTMP method can overcome the ELAA mobility challenge and approach the performance in stationary setting.

Wavefront Transformation-based Near-field Channel Prediction for Extremely Large Antenna Array with Mobility

TL;DR

The paper tackles mobility-induced performance loss in extremely large antenna arrays (ELAA) by introducing a wavefront transformation-based channel prediction (WTMP) that converts near-field spherical wavefronts into a plane-wave-like form using a transform matrix , augmented with a time-frequency projection to capture time-varying path delays. It estimates path parameters via OMP, constructs the wavefront-transformation matrix through a null-space design of , and uses the matrix-pencil method to estimate Doppler for forecasting future channels in the angular-time-frequency domain . Theoretical results show that as the number of BS antennas grows, becomes determined by geometry and that, under finite samples, the asymptotic prediction error can vanish; simulations at high mobility demonstrate substantial gains over existing near-field predictors, approaching stationary performance. Overall, WTMP provides a practical framework to predict ELAA channels under mobility by mitigating near-field effects and tracking time-varying delays, enabling reliable downlink performance in future 6G-like systems.

Abstract

This paper addresses the mobility problem in extremely large antenna array (ELAA) communication systems. In order to account for the performance loss caused by the spherical wavefront of ELAA in the mobility scenario, we propose a wavefront transformation-based matrix pencil (WTMP) channel prediction method. In particular, we design a matrix to transform the spherical wavefront into a new wavefront, which is closer to the plane wave. We also design a time-frequency projection matrix to capture the time-varying path delay. Furthermore, we adopt the matrix pencil (MP) method to estimate channel parameters. Our proposed WTMP method can mitigate the effect of near-field radiation when predicting future channels. Theoretical analysis shows that the designed matrix is asymptotically determined by the angles and distance between the base station (BS) antenna array and the scatterers or the user when the number of BS antennas is large enough. For an ELAA communication system in the mobility scenario, we prove that the prediction error converges to zero with the increasing number of BS antennas. Simulation results demonstrate that our designed transform matrix efficiently mitigates the near-field effect, and that our proposed WTMP method can overcome the ELAA mobility challenge and approach the performance in stationary setting.
Paper Structure (11 sections, 3 theorems, 107 equations, 7 figures, 1 table, 3 algorithms)

This paper contains 11 sections, 3 theorems, 107 equations, 7 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. Channel Model
  3. The Proposed WTMP Channel Prediction Method
  4. The Parameters Estimation
  5. Wavefront Transformation
  6. Time-frequency-domain Projection
  7. Doppler Estimation
  8. Performance Analysis of The WTMP Prediction Method
  9. Numerical results
  10. Conclusion
  11. Proof of Theorem 1

Key Result

Proposition 1

If the number of the BS antennas is large enough, the designed wavefront-transformation matrix ${{\cal {\bm B}}_n}$ is determined by the angles and distance between the BS antenna array and the scatterer or the UE.

Figures (7)

  • Figure 1: The typical UL near-field channel of ELAA communication system.
  • Figure 2: The SE versus SNR, the BS has 512 antennas.
  • Figure 3: The prediction error versus the number of BS antennas, the UEs move at 120 km/h.
  • Figure 4: The SE versus SNR, the BS is equipped with 512 antennas, multiple velocity levels of UEs, i.e., four at 30 km/h, four at 60 km/h, four at 90 km/h and four at 120 km/h.
  • Figure 5: The SE versus SNR, the UEs move at 120 km/h.
  • ...and 2 more figures

Theorems & Definitions (3)

  • Proposition 1
  • Proposition 2
  • Theorem 1