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Low-complexity eigenvector prediction-based precoding matrix prediction in massive MIMO with mobility

Ziao Qin, Haifan Yin, Weidong Li

TL;DR

This work tackles the challenge of timely and efficient precoding in massive MIMO under mobility by introducing eigenvector prediction (EGVP), which factorizes the precoder into channel weights and CSI. By proving that channel weights follow a complex exponential model tied to Doppler, and deploying a fast Matrix Pencil-based predictor for CSI, EGVP achieves asymptotically error-free precoder prediction with polynomial complexity. The extension EGVP-FMPP addresses CSI delays, further reducing computational load while delivering sizeable SE gains in realistic channels. Theoretical analysis and CDL-A simulations demonstrate substantial complexity reductions (up to ~80%) and SE improvements (often >20%) compared with traditional full-time SVD and other benchmarks, making mobility-aware precoding practical for next-generation systems.

Abstract

In practical massive multiple-input multiple-output (MIMO) systems, the precoding matrix is often obtained from the eigenvectors of channel matrices and is challenging to update in time due to finite computation resources at the base station, especially in mobile scenarios. In order to reduce the precoding complexity while enhancing the spectral efficiency (SE), a novel precoding matrix prediction method based on the eigenvector prediction (EGVP) is proposed. The basic idea is to decompose the periodic uplink channel eigenvector samples into a linear combination of the channel state information (CSI) and channel weights. We further prove that the channel weights can be interpolated by an exponential model corresponding to the Doppler characteristics of the CSI. A fast matrix pencil prediction (FMPP) method is also devised to predict the CSI. We also prove that our scheme achieves asymptotically error-free precoder prediction with a distinct complexity advantage. Simulation results show that under the perfect non-delayed CSI, the proposed EGVP method reduces floating point operations by 80\% without losing SE performance compared to the traditional full-time precoding scheme. In more realistic cases with CSI delays, the proposed EGVP-FMPP scheme has clear SE performance gains compared to the precoding scheme widely used in current communication systems.

Low-complexity eigenvector prediction-based precoding matrix prediction in massive MIMO with mobility

TL;DR

This work tackles the challenge of timely and efficient precoding in massive MIMO under mobility by introducing eigenvector prediction (EGVP), which factorizes the precoder into channel weights and CSI. By proving that channel weights follow a complex exponential model tied to Doppler, and deploying a fast Matrix Pencil-based predictor for CSI, EGVP achieves asymptotically error-free precoder prediction with polynomial complexity. The extension EGVP-FMPP addresses CSI delays, further reducing computational load while delivering sizeable SE gains in realistic channels. Theoretical analysis and CDL-A simulations demonstrate substantial complexity reductions (up to ~80%) and SE improvements (often >20%) compared with traditional full-time SVD and other benchmarks, making mobility-aware precoding practical for next-generation systems.

Abstract

In practical massive multiple-input multiple-output (MIMO) systems, the precoding matrix is often obtained from the eigenvectors of channel matrices and is challenging to update in time due to finite computation resources at the base station, especially in mobile scenarios. In order to reduce the precoding complexity while enhancing the spectral efficiency (SE), a novel precoding matrix prediction method based on the eigenvector prediction (EGVP) is proposed. The basic idea is to decompose the periodic uplink channel eigenvector samples into a linear combination of the channel state information (CSI) and channel weights. We further prove that the channel weights can be interpolated by an exponential model corresponding to the Doppler characteristics of the CSI. A fast matrix pencil prediction (FMPP) method is also devised to predict the CSI. We also prove that our scheme achieves asymptotically error-free precoder prediction with a distinct complexity advantage. Simulation results show that under the perfect non-delayed CSI, the proposed EGVP method reduces floating point operations by 80\% without losing SE performance compared to the traditional full-time precoding scheme. In more realistic cases with CSI delays, the proposed EGVP-FMPP scheme has clear SE performance gains compared to the precoding scheme widely used in current communication systems.
Paper Structure (19 sections, 6 theorems, 77 equations, 8 figures, 1 table, 1 algorithm)

This paper contains 19 sections, 6 theorems, 77 equations, 8 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Formulation of CSI and eigenvector
  3. Channel model
  4. Periodic eigenvector acquisition
  5. EGVP with perfect CSI
  6. Linear eigenvector decomposition
  7. Channel weight estimation model
  8. Precoder prediction
  9. EGVP-FMPP with delayed CSI
  10. Performance analysis
  11. Prediction error performance
  12. Complexity analysis
  13. Simulation results
  14. Conclusion
  15. Proof of Proposition \ref{['Proposition eigen decomposition']}
  16. ...and 4 more sections

Key Result

Proposition 1

The eigenvectors $\overline {\bf{u}}_m \left( t \right)$ can be decomposed into a linear combination of the channels ${\bf{h}}_j\left( t \right)$ and channel weights $a_{m,j}\left(t\right)$

Figures (8)

  • Figure 1: Illustration of the precoding matrix prediction in the EGVP scheme within a period of $T_{\rm{egsp}}$ subframes.
  • Figure 2: Complexity comparison between EGVP and the other schemes in FLOPs under different configurations of the SVD cycle length and the number of eigenvector samples.
  • Figure 3: SE performances with noise-free channel samples, $v=30$ km/h, $L_{\rm{ce}}=3$.
  • Figure 4: SE performances with noise-free channel samples, $v=60$ km/h.
  • Figure 5: PE performances vs the number of antennas with noise-free channel samples, $L_{\rm{ce}}=3$.
  • ...and 3 more figures

Theorems & Definitions (11)

  • Proposition 1
  • proof
  • Lemma 1
  • proof
  • Theorem 1
  • proof
  • Corollary 1
  • Theorem 2
  • proof
  • Theorem 3
  • ...and 1 more