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CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels

Ye Zeng, Li Qiao, Zhen Gao, Tong Qin, Zhonghuai Wu, Emad Khalaf, Sheng Chen, Mohsen Guizani

TL;DR

By integrating the generative pre-trained Transformer with federated-tuning, this work proposes a CSI-GPT approach to realize efficient downlink CSI acquisition and proposes a variational auto-encoder-based channel sample generator (VAE-CSG), which can generate sufficient CSI samples based on a limited number of high-quality CSI data obtained from the current cell.

Abstract

In massive multiple-input multiple-output (MIMO) systems, how to reliably acquire downlink channel state information (CSI) with low overhead is challenging. In this work, by integrating the generative pre-trained Transformer (GPT) with federated-tuning, we propose a CSI-GPT approach to realize efficient downlink CSI acquisition. Specifically, we first propose a Swin Transformer-based channel acquisition network (SWTCAN) to acquire downlink CSI, where pilot signals, downlink channel estimation, and uplink CSI feedback are jointly designed. Furthermore, to solve the problem of insufficient training data, we propose a variational auto-encoder-based channel sample generator (VAE-CSG), which can generate sufficient CSI samples based on a limited number of high-quality CSI data obtained from the current cell. The CSI dataset generated from VAE-CSG will be used for pre-training SWTCAN. To fine-tune the pre-trained SWTCAN for improved performance, we propose an online federated-tuning method, where only a small amount of SWTCAN parameters are unfrozen and updated using over-the-air computation, avoiding the high communication overhead caused by aggregating the complete CSI samples from user equipment (UEs) to the BS for centralized fine-tuning. Simulation results verify the advantages of the proposed SWTCAN and the communication efficiency of the proposed federated-tuning method. Our code is publicly available at https://github.com/BIT-ZY/CSI-GPT

CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels

TL;DR

By integrating the generative pre-trained Transformer with federated-tuning, this work proposes a CSI-GPT approach to realize efficient downlink CSI acquisition and proposes a variational auto-encoder-based channel sample generator (VAE-CSG), which can generate sufficient CSI samples based on a limited number of high-quality CSI data obtained from the current cell.

Abstract

In massive multiple-input multiple-output (MIMO) systems, how to reliably acquire downlink channel state information (CSI) with low overhead is challenging. In this work, by integrating the generative pre-trained Transformer (GPT) with federated-tuning, we propose a CSI-GPT approach to realize efficient downlink CSI acquisition. Specifically, we first propose a Swin Transformer-based channel acquisition network (SWTCAN) to acquire downlink CSI, where pilot signals, downlink channel estimation, and uplink CSI feedback are jointly designed. Furthermore, to solve the problem of insufficient training data, we propose a variational auto-encoder-based channel sample generator (VAE-CSG), which can generate sufficient CSI samples based on a limited number of high-quality CSI data obtained from the current cell. The CSI dataset generated from VAE-CSG will be used for pre-training SWTCAN. To fine-tune the pre-trained SWTCAN for improved performance, we propose an online federated-tuning method, where only a small amount of SWTCAN parameters are unfrozen and updated using over-the-air computation, avoiding the high communication overhead caused by aggregating the complete CSI samples from user equipment (UEs) to the BS for centralized fine-tuning. Simulation results verify the advantages of the proposed SWTCAN and the communication efficiency of the proposed federated-tuning method. Our code is publicly available at https://github.com/BIT-ZY/CSI-GPT
Paper Structure (15 sections, 11 equations, 4 figures, 2 tables, 1 algorithm)

This paper contains 15 sections, 11 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Proposed Swin Transformer-Based Downlink CSI Acquisition Scheme
  3. System Model
  4. Pilot Design and CSI Acquisition
  5. Generative Pre-Training and Federated-Tuning for the Proposed SWTCAN
  6. Generative AI-Based Pre-Training
  7. Federated Learning-Based Online Fine-Tuning
  8. Communication-Efficient Federated-Tuning
  9. AirComp for Efficient Federated-Tuning
  10. Simulation Results
  11. Performance of Proposed SWTCAN
  12. Performance of Proposed Generative Pre-Training
  13. Performance of Proposed Federated-Tuning Method
  14. Conclusions
  15. Introduction

Figures (4)

  • Figure 1: Structure of the proposed SWTCAN.
  • Figure 2: NMSE performance of different schemes versus the feedback overhead $B$ for CDL-B.
  • Figure 3: NMSE performance of different schemes versus the feedback signaling overhead for CDL-A/B/C.
  • Figure 4: Performance comparison of federated-tuning and CL schemes versus the uplink communication overhead.