Title: BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization Show Chinese title

Title: BERT4beam：大型人工智能模型支持的广义波束成形优化

Abstract: Artificial intelligence (AI) is anticipated to emerge as a pivotal enabler for the forthcoming sixth-generation (6G) wireless communication systems. However, current research efforts regarding large AI models for wireless communications primarily focus on fine-tuning pre-trained large language models (LLMs) for specific tasks. This paper investigates the large-scale AI model designed for beamforming optimization to adapt and generalize to diverse tasks defined by system utilities and scales. We propose a novel framework based on bidirectional encoder representations from transformers (BERT), termed BERT4beam. We aim to formulate the beamforming optimization problem as a token-level sequence learning task, perform tokenization of the channel state information, construct the BERT model, and conduct task-specific pre-training and fine-tuning strategies. Based on the framework, we propose two BERT-based approaches for single-task and multi-task beamforming optimization, respectively. Both approaches are generalizable for varying user scales. Moreover, the former can adapt to varying system utilities and antenna configurations by re-configuring the input and output module of the BERT model, while the latter, termed UBERT, can directly generalize to diverse tasks, due to a finer-grained tokenization strategy. Extensive simulation results demonstrate that the two proposed approaches can achieve near-optimal performance and outperform existing AI models across various beamforming optimization tasks, showcasing strong adaptability and generalizability. Show Chinese abstract

Abstract: 人工智能（AI）预计将成为下一代第六代（6G）无线通信系统的关键推动者。 然而，目前针对无线通信中大型AI模型的研究主要集中在微调预训练的大型语言模型（LLMs）以完成特定任务。 本文研究了专为波束成形优化设计的大型AI模型，以适应和泛化由系统效用和规模定义的各种任务。 我们提出了一种基于双向编码器表示的变压器（BERT）的新框架，称为BERT4beam。 我们的目标是将波束成形优化问题表述为一个逐标记的序列学习任务，对信道状态信息进行分词，构建BERT模型，并进行任务特定的预训练和微调策略。 基于该框架，我们分别提出了两种基于BERT的单任务和多任务波束成形优化方法。 这两种方法均可泛化到不同用户规模。 此外，前者可以通过重新配置BERT模型的输入和输出模块来适应不同的系统效用和天线配置，而后者，称为UBERT，由于更细粒度的分词策略，可以直接泛化到各种任务。 大量仿真结果表明，所提出的两种方法可以实现接近最优的性能，并在各种波束成形优化任务中优于现有的AI模型，展示了强大的适应性和泛化能力。