多用户多输入单输出系统中用于直接导频到波束成形器设计的自进化上下文学习
Self-Evolving In-Context Learning for Direct Pilot-to-Beamformer Design in MU-MISO Systems
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中文总结 AI 辅助
研究多用户多输入单输出系统中导频到波束成形器设计，提出将ICL-Transformer与导频及波束成形器EDN集成的框架，通过课程学习、自进化机制和失配感知扩展提高性能，优于现有波束成形方案。
中文摘要 AI 辅助
我们开发了一种增强的上下文学习（ICL）框架，以提高多用户多输入单输出（MU-MISO）系统中基于导频的波束成形性能。该方案将ICL-Transformer主干与导频编码器-解码器网络（EDN）和波束成形器EDN集成。我们的ICL网络的一个关键特性是，通过构建特定模型的上下文数据集，它可以在无需重新训练的情况下处理多个信道模型。为了提高收敛性和鲁棒性，我们引入了三项关键创新：（a）一种课程学习（CL）策略，从有监督的LMMSE标签模仿平滑过渡到无监督的和速率最大化；（b）一种自进化机制，在基于CL的训练期间动态扩展和细化所有信道模型的上下文数据集；（c）一种失配感知扩展，将几种失配纳入通用ICL框架并绕过显式信道校准。消融研究验证了上下文架构和增强训练策略的有效性。在不同通信环境下的仿真结果表明，该方案能够在无需基于梯度的参数更新的情况下快速适应已见和未见的信道模型，并通过智能上下文构建减轻失配问题。此外，我们的方案在基于导频的设置下始终优于现有波束成形方案，包括WMMSE基准和最近基于Transformer的方法。
英文摘要
We develop an enhanced in-context learning (ICL) framework to improve the performance of pilot-based beamforming in multi-user multiple-input single-output (MU-MISO) systems. The proposed scheme integrates the ICL-Transformer backbone with the pilot encoder-decoder network (EDN) and the beamformer EDN. A crucial feature of our ICL network is that it can handle multiple channel models without retraining, enabled by the construction of model-specific context datasets. To improve convergence and robustness, we introduce three key innovations: (a) a curriculum learning (CL) strategy that smoothly transitions from supervised LMMSE-labeled imitation to unsupervised sum-rate maximization, (b) a self-evolving mechanism that dynamically expands and refines the context datasets for all channel models during CL-based training, and (c) a mismatch-aware extension that incorporates several mismatches into the general ICL framework and bypasses explicit channel calibrations. Ablation studies validate the effectiveness of the in-context architecture and enhanced training strategies. Simulation results over diverse communication environments show that the proposed scheme is able to rapidly adapt to both seen and unseen channel models without gradient-based parameter updates, and can mitigate the mismatch issues via intelligent context constructions. Furthermore, our scheme consistently outperforms the existing beamforming schemes under pilot-based settings, including the WMMSE benchmark and the recent Transformer-based methods.
发表机构
- Columbia University（哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
