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Multi-dimensional Parameter Estimation in RIS-aided MU-MIMO-OFDM Channels

Linlin Mo, Yi Song, Fabio Saggese, Xinhua Lu, Zhongyong Wang, Petar Popovski

TL;DR

The paper tackles channel estimation in RIS-aided MU-MIMO-OFDM systems where cascaded channel parameters are high-dimensional and structured across multiple dimensions. It introduces DS-MDT, a dual-structure and multi-dimensional transformation algorithm that exploits common and offset features across users and separates AoD, delay, AoA, and gain parameters via MDT, followed by MUSIC-based high-resolution estimation. Key contributions include revealing the dual-structure of cascaded channels, proposing MDT to isolate parameter dimensions, employing a reference UE with offset-based estimation for robustness, and achieving up to ~10 dB NMSE improvement with lower complexity than state-of-the-art methods. The approach demonstrates strong performance under unknown path counts and short pilot overhead, highlighting practical viability for RIS-enabled mmWave CE, with future work on stochastic offsets and perturbation-aware adaptations.

Abstract

We address the channel estimation (CE) problem in reconfigurable intelligent surface (RIS) aided orthogonal frequency-division multiplexing (OFDM) systems by proposing a dual-structure and multi-dimensional transformations (DS-MDT) algorithm.The proposed approach leverages the dual-structure features of the channel parameters to assist users experiencing weaker channel conditions, thereby enhancing CE performance. Moreover, given that the channel parameters are distributed across multiple dimensions of the received tensor, the proposed algorithm employs multi-dimensional transformations to isolate and extract distinct parameters. The numerical results demonstrate the proposed algorithm reduces the normalized mean square error (NMSE) by up to 10 dB while maintaining lower complexity compared to state-of-the-art methods.

Multi-dimensional Parameter Estimation in RIS-aided MU-MIMO-OFDM Channels

TL;DR

The paper tackles channel estimation in RIS-aided MU-MIMO-OFDM systems where cascaded channel parameters are high-dimensional and structured across multiple dimensions. It introduces DS-MDT, a dual-structure and multi-dimensional transformation algorithm that exploits common and offset features across users and separates AoD, delay, AoA, and gain parameters via MDT, followed by MUSIC-based high-resolution estimation. Key contributions include revealing the dual-structure of cascaded channels, proposing MDT to isolate parameter dimensions, employing a reference UE with offset-based estimation for robustness, and achieving up to ~10 dB NMSE improvement with lower complexity than state-of-the-art methods. The approach demonstrates strong performance under unknown path counts and short pilot overhead, highlighting practical viability for RIS-enabled mmWave CE, with future work on stochastic offsets and perturbation-aware adaptations.

Abstract

We address the channel estimation (CE) problem in reconfigurable intelligent surface (RIS) aided orthogonal frequency-division multiplexing (OFDM) systems by proposing a dual-structure and multi-dimensional transformations (DS-MDT) algorithm.The proposed approach leverages the dual-structure features of the channel parameters to assist users experiencing weaker channel conditions, thereby enhancing CE performance. Moreover, given that the channel parameters are distributed across multiple dimensions of the received tensor, the proposed algorithm employs multi-dimensional transformations to isolate and extract distinct parameters. The numerical results demonstrate the proposed algorithm reduces the normalized mean square error (NMSE) by up to 10 dB while maintaining lower complexity compared to state-of-the-art methods.
Paper Structure (11 sections, 27 equations, 6 figures, 1 algorithm)

This paper contains 11 sections, 27 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Model
  4. Tensor Based System Model
  5. Proposed DS-MDT algorithm
  6. Jointly estimate the cascaded AoD parameter $\bm \phi$
  7. Estimate cascaded delay parameter $\bm \tau^k$
  8. Estimate cascaded AoA parameters $\{\bm{\omega}^{k}, \bm{\psi}^{k}\}$
  9. Estimate $\bm \beta^k$
  10. Simulation Results
  11. Conclusions & Future works

Figures (6)

  • Figure 1: Offset feature of matrix $\bm x^k, x \in \{ \tau,\omega,\psi\}$.
  • Figure 2: Tensor representation of the channel ${\cal H}^k$.
  • Figure 3: Path estimation accuracy and reference UE mis-selection robustness.
  • Figure 4: NMSE performance as a function of $P$, $\mathrm{SNR}$ and $Q$.
  • Figure 5: NMSE vs M. Two angles located at 5° and -5°. SNR=10
  • ...and 1 more figures