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Deep Learning assisted Port-Cycling based Channel Sounding for Precoder Estimation in Massive MIMO Arrays

Advaith Arun, Shiv Shankar, Dhivagar Baskaran, Klutto Milleth, Bhaskar Ramamurthi

TL;DR

The paper tackles the high instantaneous CSI sounding overhead in massive MIMO by introducing a port-cycling scheme that activates only a subset of ports at a time. It pairs this with CSIAdaNet, a deep-learning architecture that fuses spatial and temporal information from temporally aggregated, sparse CSI measurements to reconstruct the full-port Type-II codebook precoder. The approach demonstrates near-baseline beam alignment and beamforming performance across moderate-to-high SNRs while significantly reducing instantaneous RF/port overhead; complexity scales favorably with the number of sub-panels, supporting scalability to large arrays. This work offers a practical pathway for scalable, energy-efficient CSI acquisition in future 6G systems by leveraging spatial-temporal correlations in port-cycled sounding data.

Abstract

Future wireless systems are expected to employ a substantially larger number of transmit ports for channel state information (CSI) estimation compared to current specifications. Although scaling ports improves spectral efficiency, it also increases the resource overhead to transmit reference signals across the time-frequency grid, ultimately reducing achievable data throughput. In this work, we propose an deep learning (DL)-based CSI reconstruction framework that serves as an enabler for reliable CSI acquisition in future 6G systems. The proposed solution involves designing a port-cycling mechanism that sequentially sounds different portions of CSI ports across time, thereby lowering the overhead while preserving channel observability. The proposed CSI Adaptive Network (CsiAdaNet) model exploits the resulting sparse measurements and captures both spatial and temporal correlations to accurately reconstruct the full-port CSI. The simulation results show that our method achieves overhead reduction while maintaining high CSI reconstruction accuracy.

Deep Learning assisted Port-Cycling based Channel Sounding for Precoder Estimation in Massive MIMO Arrays

TL;DR

The paper tackles the high instantaneous CSI sounding overhead in massive MIMO by introducing a port-cycling scheme that activates only a subset of ports at a time. It pairs this with CSIAdaNet, a deep-learning architecture that fuses spatial and temporal information from temporally aggregated, sparse CSI measurements to reconstruct the full-port Type-II codebook precoder. The approach demonstrates near-baseline beam alignment and beamforming performance across moderate-to-high SNRs while significantly reducing instantaneous RF/port overhead; complexity scales favorably with the number of sub-panels, supporting scalability to large arrays. This work offers a practical pathway for scalable, energy-efficient CSI acquisition in future 6G systems by leveraging spatial-temporal correlations in port-cycled sounding data.

Abstract

Future wireless systems are expected to employ a substantially larger number of transmit ports for channel state information (CSI) estimation compared to current specifications. Although scaling ports improves spectral efficiency, it also increases the resource overhead to transmit reference signals across the time-frequency grid, ultimately reducing achievable data throughput. In this work, we propose an deep learning (DL)-based CSI reconstruction framework that serves as an enabler for reliable CSI acquisition in future 6G systems. The proposed solution involves designing a port-cycling mechanism that sequentially sounds different portions of CSI ports across time, thereby lowering the overhead while preserving channel observability. The proposed CSI Adaptive Network (CsiAdaNet) model exploits the resulting sparse measurements and captures both spatial and temporal correlations to accurately reconstruct the full-port CSI. The simulation results show that our method achieves overhead reduction while maintaining high CSI reconstruction accuracy.
Paper Structure (14 sections, 11 equations, 10 figures, 2 tables, 3 algorithms)

This paper contains 14 sections, 11 equations, 10 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Type II codebook in 5G NR
  4. Sub-array partitioning
  5. Port Cycling Scheme
  6. Simulation and results
  7. Link-Level Simulation Assumptions
  8. Model
  9. Metrics
  10. Performance Evaluation and Analysis
  11. Precoder Alignment and Beamforming Performance
  12. Analysis of CSI feedback quantities
  13. Complexity Analysis
  14. Conclusion

Figures (10)

  • Figure 1: Illustration of sub-panel based partitioning
  • Figure 2: Plot of variation scores
  • Figure 3: Model Architecture of CSIAdaNet
  • Figure : (a) Cosine Similarity
  • Figure : (a) Cosine Similarity
  • ...and 5 more figures