十年深度学习在无线通信中的应用：从学习模块到可部署的无线智能
Ten Years of Deep Learning for Wireless Communications: From Learned Blocks to Deployable Wireless Intelligence
AI总结：
本文梳理了十年间深度学习在无线通信领域从替代孤立模块到开发无线智能的三次转变，指出未来无线AI发展需结合物理基础、智能体能力与标准化机制。
AI中文摘要：
在过去十年中，深度学习已从替代孤立无线模块的工具，发展为开发无线智能的更广泛方法论。本文梳理了其发展轨迹中的三次转变：学习无线功能模块、重新设计与归一化通信目标，以及在实际物理约束下实现泛化。这些转变共同推进了“通过任何合适手段实现随时随地通信”的更广泛目标。早期研究表明，神经网络可近似复杂的物理层推理与网络优化映射；后续研究则嵌入领域特定结构、转向面向任务的语义，并解决边缘高效适配的需求。展望未来，我们认为无线人工智能（AI）的下一个时代不仅依赖模型容量的扩展，有前景的方向包括基于物理的无线世界模型、智能体推理与执行，以及标准化机制——该机制可让学习组件以清晰边界、物理一致性和系统级互操作性运行。
英文摘要：
Over the past decade, deep learning has evolved from a tool for replacing isolated wireless blocks into a broader methodology for developing wireless intelligence. This article traces that trajectory through three shifts: learning wireless functional modules, redesigning and re-normalizing communication goals, and enabling generalization under practical physical constraints. Together, these shifts advance the broader pursuit of communication anytime and anywhere, through any appropriate means. Early studies showed that neural networks could approximate difficult physical-layer inference and network-optimization mappings, while subsequent research embedded domain-specific structure, shifted toward task-oriented semantics, and addressed the need for edge-efficient adaptation. Looking ahead, we argue that the next era of wireless artificial intelligence (AI) depends on more than scaling model capacity. Promising directions include physically grounded wireless world models, agentic reasoning and fulfillment, and standardization mechanisms that allow learned components to operate with clear boundaries, physical consistency, and system-level interoperability.
