Ten Years of Deep Learning for Wireless Communications: From Learned Blocks to Deployable Wireless Intelligence
TL;DR
It is argued that the next era of wireless artificial intelligence (AI) depends on more than scaling model capacity, and 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.
Abstract
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.