Lightweight LLM-based End-to-End CSI Prediction for MIMO-OFDM Systems

Abstract

Accurate channel state information (CSI) is essential for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, yet fast time-varying channels pose significant prediction challenges. Traditional approaches fail under high mobility, while deep learning methods rely heavily on large labeled datasets, limiting generalization with scarce training data. Although large language models (LLMs) show promise, their massive parameter count hinders deployment on resource-constrained edge devices. This paper proposes a lightweight, end-to-end CSI prediction framework built upon a general LLM. A time-frequency dual-domain feature extraction module captures subcarrier correlations and temporal dynamics from historical CSI, overcoming single-domain limitations. The end-to-end design maps historical CSI directly to future states, avoiding error propagation inherent in explicit channel estimation. Parameter efficiency is achieved through low-rank adaptation (LoRA) combined with knowledge distillation from a pre-trained LLM, enabling effective few-shot learning at low computational cost. Simulations demonstrate that the proposed scheme delivers robust prediction accuracy across diverse mobility scenar-ios, maintains strong performance under limited training data, and exhibits zero-shot cross-scenario generalization, significantly outperforming both conventional and deep learning baselines in TDD and FDD modes. Keywords: Channel prediction, multiple-input multiple-output (MIMO), orthogonal frequency division multiplexing (OFDM), large language model (LLM), knowledge distillation, low-rank adaptation (LoRA)