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Multi-Modal Variable-Rate CSI Reconstruction for FDD Massive MIMO Systems

Yunseo Nam, Jiwook Choi

TL;DR

The paper tackles CSI reconstruction under rate-distortion limits in FDD massive MIMO by introducing a multi-modal, variable-rate autoencoder that can generate CSI bitstreams of arbitrary length. It couples wireless CSI with auxiliary sensor data (RGB images or uplink CSI) via a transfer learning–driven multi-modal fusion network, enabling site-dependent super-resolution and improved beamforming gains. A differentiable, low-parameter quantization scheme and a two-stage training procedure on synthetic ray-traced datasets enable robust performance across LOS/NLOS scenarios and various feedback rates. Results show near-optimal beamforming gains in 5G NR-like settings, with notable gains from RGB imagery and uplink CSI especially at low feedback rates or low SNR, demonstrating practical benefits for reducing feedback overhead while enhancing reliability.

Abstract

In frequency division duplex (FDD) systems, acquiring channel state information (CSI) at the base station (BS) traditionally relies on limited feedback from mobile terminals (MTs). However, the accuracy of channel reconstruction from feedback CSI is inherently constrained by the rate-distortion trade-off. To overcome this limitation, we propose a multi-modal channel reconstruction framework that leverages auxiliary data, such as RGB images or uplink CSI, collected at the BS. By integrating contextual information from these modalities, the framework mitigates CSI distortions caused by noise, compression, and quantization. At its core, the framework utilizes an autoencoder network capable of generating variable-length CSI, tailored for rate-adaptive multi-modal channel reconstruction. By augmenting the foundational autoencoder network using a transfer learning-based multi-modal fusion strategy, we enable accurate channel reconstruction in both single-modal and multi-modal scenarios. To train and evaluate the network under diverse and realistic wireless conditions, we construct a synthetic dataset that pairs wireless channel data with sensor data through 3D modeling and ray tracing. Simulation results demonstrate that the proposed framework achieves near-optimal beamforming gains in 5G New Radio (5G NR)-compliant scenarios, highlighting the potential of sensor data integration to improve CSI reconstruction accuracy.

Multi-Modal Variable-Rate CSI Reconstruction for FDD Massive MIMO Systems

TL;DR

The paper tackles CSI reconstruction under rate-distortion limits in FDD massive MIMO by introducing a multi-modal, variable-rate autoencoder that can generate CSI bitstreams of arbitrary length. It couples wireless CSI with auxiliary sensor data (RGB images or uplink CSI) via a transfer learning–driven multi-modal fusion network, enabling site-dependent super-resolution and improved beamforming gains. A differentiable, low-parameter quantization scheme and a two-stage training procedure on synthetic ray-traced datasets enable robust performance across LOS/NLOS scenarios and various feedback rates. Results show near-optimal beamforming gains in 5G NR-like settings, with notable gains from RGB imagery and uplink CSI especially at low feedback rates or low SNR, demonstrating practical benefits for reducing feedback overhead while enhancing reliability.

Abstract

In frequency division duplex (FDD) systems, acquiring channel state information (CSI) at the base station (BS) traditionally relies on limited feedback from mobile terminals (MTs). However, the accuracy of channel reconstruction from feedback CSI is inherently constrained by the rate-distortion trade-off. To overcome this limitation, we propose a multi-modal channel reconstruction framework that leverages auxiliary data, such as RGB images or uplink CSI, collected at the BS. By integrating contextual information from these modalities, the framework mitigates CSI distortions caused by noise, compression, and quantization. At its core, the framework utilizes an autoencoder network capable of generating variable-length CSI, tailored for rate-adaptive multi-modal channel reconstruction. By augmenting the foundational autoencoder network using a transfer learning-based multi-modal fusion strategy, we enable accurate channel reconstruction in both single-modal and multi-modal scenarios. To train and evaluate the network under diverse and realistic wireless conditions, we construct a synthetic dataset that pairs wireless channel data with sensor data through 3D modeling and ray tracing. Simulation results demonstrate that the proposed framework achieves near-optimal beamforming gains in 5G New Radio (5G NR)-compliant scenarios, highlighting the potential of sensor data integration to improve CSI reconstruction accuracy.
Paper Structure (16 sections, 26 equations, 15 figures, 4 tables)

This paper contains 16 sections, 26 equations, 15 figures, 4 tables.

Table of Contents

  1. Introduction
  2. System Model
  3. Massive MIMO-OFDM Downlink
  4. DL-Assisted CSI Feedback Process
  5. Network Architecture
  6. Basic Autoencoder Architecture
  7. Variable-Rate Quantization
  8. Multi-Modal Fusion
  9. Dataset Generation and Network Training
  10. Sensor Data-Wireless Channel Paired Dataset
  11. Two-Stage Training using Heterogeneous Datasets
  12. Simulation Results
  13. Wireless-Only Channel Reconstruction
  14. Multi-Modal Channel Reconstruction Using RGB Images
  15. Multi-Modal Channel Reconstruction Using Uplink CSI
  16. ...and 1 more sections

Figures (15)

  • Figure 1: Multi-modal channel reconstruction using image data and wireless data.
  • Figure 2: Illustration of multi-modal channel reconstruction processes at the BS and the MT.
  • Figure 3: Hierarchical self-attention computation using local windows/shifted-windows.
  • Figure 4: Network layers of the autoencoder.
  • Figure 5: Architecture of the channel-relevant feature extraction network.
  • ...and 10 more figures