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Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Aging

Nipuni Ginige, Arthur Sousa de Sena, Nurul Huda Mahmood, Nandana Rajatheva, Matti Latva-aho

TL;DR

This work tackles channel estimation under aging in RIS-assisted MIMO systems by proposing a CNN-AR framework that first identifies aging patterns from CSI and then uses an AR predictor to forecast CSI for future coherence intervals. The method reduces pilot overhead relative to conventional TDD schemes and improves prediction accuracy, leading to higher spectral efficiency when computing RIS-assisted beamforming. Key contributions include a generalized CNN-AR model for the cascaded RIS channels, an AR coefficient extraction via CNN, and SDR-based optimization for RIS phase shifts, with results showing NMSE improvements and SE gains approaching perfect CSI in simulations. The approach enhances the practicality of RIS in mobile scenarios by mitigating aging effects and lowering training costs.

Abstract

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology to enhance the performance of sixth-generation (6G) and beyond communication systems. The passive nature of RISs and their large number of reflecting elements pose challenges to the channel estimation process. The associated complexity further escalates when the channel coefficients are fast-varying as in scenarios with user mobility. In this paper, we propose an extended channel estimation framework for RIS-assisted multiple-input multiple-output (MIMO) systems based on a convolutional neural network (CNN) integrated with an autoregressive (AR) predictor. The implemented framework is designed for identifying the aging pattern and predicting enhanced estimates of the wireless channels in correlated fast-fading environments. Insightful simulation results demonstrate that our proposed CNN-AR approach is robust to channel aging, exhibiting a high-precision estimation accuracy. The results also show that our approach can achieve high spectral efficiency and low pilot overhead compared to traditional methods.

Machine Learning-Based Channel Prediction for RIS-assisted MIMO Systems With Channel Aging

TL;DR

This work tackles channel estimation under aging in RIS-assisted MIMO systems by proposing a CNN-AR framework that first identifies aging patterns from CSI and then uses an AR predictor to forecast CSI for future coherence intervals. The method reduces pilot overhead relative to conventional TDD schemes and improves prediction accuracy, leading to higher spectral efficiency when computing RIS-assisted beamforming. Key contributions include a generalized CNN-AR model for the cascaded RIS channels, an AR coefficient extraction via CNN, and SDR-based optimization for RIS phase shifts, with results showing NMSE improvements and SE gains approaching perfect CSI in simulations. The approach enhances the practicality of RIS in mobile scenarios by mitigating aging effects and lowering training costs.

Abstract

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology to enhance the performance of sixth-generation (6G) and beyond communication systems. The passive nature of RISs and their large number of reflecting elements pose challenges to the channel estimation process. The associated complexity further escalates when the channel coefficients are fast-varying as in scenarios with user mobility. In this paper, we propose an extended channel estimation framework for RIS-assisted multiple-input multiple-output (MIMO) systems based on a convolutional neural network (CNN) integrated with an autoregressive (AR) predictor. The implemented framework is designed for identifying the aging pattern and predicting enhanced estimates of the wireless channels in correlated fast-fading environments. Insightful simulation results demonstrate that our proposed CNN-AR approach is robust to channel aging, exhibiting a high-precision estimation accuracy. The results also show that our approach can achieve high spectral efficiency and low pilot overhead compared to traditional methods.
Paper Structure (12 sections, 26 equations, 7 figures, 1 table)

This paper contains 12 sections, 26 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Channel Estimation Scheme
  4. Channel Aging Model
  5. ML-Based Channel Prediction
  6. CNN-AR Model
  7. Pilot overhead
  8. Performance Metrics
  9. Prediction Accuracy
  10. Spectral Efficiency
  11. Numerical Results
  12. Conclusion

Figures (7)

  • Figure 1: System model. An RIS is deployed to assist transmissions between a multi-antenna BS and multiple users.
  • Figure 2: Simplified transmission block: Conventional TDD versus ML-based TDD. In the prediction phase, uplink pilot training is removed due to the introduction of ML-based CSI prediction.
  • Figure 3: Architecture of the proposed CNN-AR predictor.
  • Figure 4: Simulation setup. Scenario with two users.
  • Figure 5: Comparison of prediction NMSE among the AR predictors and CNN-AR for the first $P$ intervals.
  • ...and 2 more figures