用上下文学习提升MIMO系统的软输入软输出均衡性能。
Turbo-ICL: In-Context Learning-Based Turbo Equalization
- 基于Transformer和状态空间模型，从导频与解码反馈中推断符号后验分布。
- 低精度量化下优于传统方法，即使有完美信道信息也更优。
- 适合资源受限场景，且在训练数据少时仍保持稳定表现。
本文提出一种受大语言模型启发的上下文学习（ICL）框架，用于编码多输入多输出（MIMO）系统中的软输入软输出信道均衡。该方法直接从导频信号和解码器反馈构成的提示中学习符号后验分布。关键创新在于通过提示增强，将解码器输出的外在信息作为额外上下文，使ICL模型能在迭代解码过程中逐步优化符号估计。开发了基于Transformer和状态空间架构的两种模型变体并进行评估。大量仿真表明，当传统线性假设失效（如低分辨率量化存在时），ICL均衡器始终优于经典基于模型的基准方法，即使后者拥有完美的信道状态信息。结果还显示，Transformer模型在训练多样性有限时更具优势，而状态空间模型在资源受限场景中效率更高。
原文摘要 · Abstract (English)
This paper introduces a novel in-context learning (ICL) framework, inspired by large language models (LLMs), for soft-input soft-output channel equalization in coded multiple-input multiple-output (MIMO) systems. The proposed approach learns to infer posterior symbol distributions directly from a prompt of pilot signals and decoder feedback. A key innovation is the use of prompt augmentation to incorporate extrinsic information from the decoder output as additional context, enabling the ICL model to refine its symbol estimates iteratively across turbo decoding iterations. Two model variants, based on Transformer and state-space architectures, are developed and evaluated. Extensive simulations demonstrate that, when traditional linear assumptions break down, e.g., in the presence of low-resolution quantization, ICL equalizers consistently outperform conventional model-based baselines, even when the latter are provided with perfect channel state information. Results also highlight the advantage of Transformer-based models under limited training diversity, as well as the efficiency of state-space models in resource-constrained scenarios.
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