Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

DMD Prediction of MIMO Channel Using Tucker Decomposition

Irina Kopnina, Dmitry Artemasov, Sergey Matveev

Abstract

Accurate channel state information (CSI) prediction is crucial for next-generation multiple-input multiple-output (MIMO) communication systems. Classical prediction methods often become inefficient for high-dimensional and rapidly time-varying channels. To improve prediction efficiency, it is essential to exploit the inherent low-rank tensor structure of the MIMO channel. Motivated by this observation, we propose a dynamic mode decomposition (DMD)-based prediction framework operating on the low-dimensional core tensors obtained via a Tucker decomposition. The proposed method predicts reduced-order channel cores, significantly lowering computational complexity. Simulation results demonstrate that the proposed approach preserves the dominant channel dynamics and achieves high prediction accuracy.

DMD Prediction of MIMO Channel Using Tucker Decomposition

Abstract

Accurate channel state information (CSI) prediction is crucial for next-generation multiple-input multiple-output (MIMO) communication systems. Classical prediction methods often become inefficient for high-dimensional and rapidly time-varying channels. To improve prediction efficiency, it is essential to exploit the inherent low-rank tensor structure of the MIMO channel. Motivated by this observation, we propose a dynamic mode decomposition (DMD)-based prediction framework operating on the low-dimensional core tensors obtained via a Tucker decomposition. The proposed method predicts reduced-order channel cores, significantly lowering computational complexity. Simulation results demonstrate that the proposed approach preserves the dominant channel dynamics and achieves high prediction accuracy.
Paper Structure (15 sections, 22 equations, 3 figures, 1 table, 1 algorithm)

This paper contains 15 sections, 22 equations, 3 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Tucker Representation of the Channel
  4. DMD-Based Channel Prediction
  5. Prediction of Full Tensor
  6. Tucker Channel Predicition
  7. Tucker Channel Predicition Algorithm
  8. Similar eigenvalues for the Full DMD and Tucker DMD
  9. Snapshot matrices
  10. Relation between SVD of cores and full channel
  11. Reduced DMD operators coincide
  12. Results
  13. Simulation Scenario
  14. Experiments
  15. Conclusion

Figures (3)

  • Figure 1: Channel prediction performance for different prediction horizons with a fixed channel measurement period ${T_p=5\:\mathrm{ms}}$ and under SNR = 30 dB
  • Figure 2: Channel prediction performance for different SNR values at a fixed prediction horizon $\tau=5$ and a fixed channel measurement period $T_p=5\:\mathrm{ms}$
  • Figure 3: Channel prediction performance for different channel measurement periods under noiseless conditions at a fixed prediction horizon, $\tau=5$