发表机构
Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London; School of Mathematics and Statistics, Beijing Jiaotong University（电气与电子工程系，工程学院，伦敦帝国理工学院; 数学与统计学学院，北京交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对下一代无线网络中人工智能技术泛化瓶颈问题，提出分层无线基础模型（WFM），通过几何感知交叉注意力耦合上下游模块，采用混合训练策略，实现多任务优化，学习高保真信道表示，降低延迟，有强大泛化能力。
AI 中文摘要
下一代无线网络日益复杂，推动了人工智能融入无线通信。但现有多数研究专注单场景特定任务深度学习技术，限制了跨任务、信道条件和系统配置的泛化能力。为此提出分层无线基础模型（WFM）用于多任务优化，通过几何感知交叉注意力将上游基础信道编码器（FCE）与下游基础优化解码器（FOD）耦合。FCE经自监督掩码重建提取与任务无关的信道表示，FOD通过可微输出头生成多任务优化决策。采用混合监督到无监督训练策略克服纯监督学习性能上限，其模块化架构能高效适应未见通信任务且参数开销小。仿真结果表明，该模型能学习高保真信道表示，实现有竞争力的多任务优化性能，大幅降低优化推理延迟，对未见传播环境、变化约束参数和异构系统配置有强大泛化能力。
英文摘要
The increasing complexity of next-generation wireless networks has driven the integration of artificial intelligence (AI) into wireless communications. However, most existing studies focus on developing task-specific deep learning techniques for single scenarios, which limits their ability to generalize across diverse tasks, channel conditions, and system configurations. To address this generalization bottleneck, we propose a hierarchical wireless foundation model (WFM) for multi-task optimization. The proposed WFM couples an upstream foundation channel encoder (FCE) with a downstream foundation optimization decoder (FOD) via geometry-aware cross-attention. Specifically, the FCE extracts task-agnostic channel representations via self-supervised masked reconstruction while the FOD generates multi-task optimization decisions through differentiable output heads. Moreover, a hybrid supervised-to-unsupervised training strategy is employed to overcome the performance ceiling of purely supervised learning, and the modular architecture of the WFM enables efficient adaptation to unseen communication tasks with minimal parameter overhead. Simulation results show that the proposed WFM learns high-fidelity channel representations and achieves competitive multi-task optimization performance while substantially reducing optimization inference latency relative to numerical baselines. Furthermore, it exhibits robust generalization to unseen propagation environments, varying constraint parameters, and heterogeneous system configurations.
Comments15 pages, 10 figures, 3 tables
