Inference-Driven Uplink for 6G: Architecture, Principles, and Challenges

Abstract

Next-generation wireless networks (6G) face a critical uplink challenge arising from stringent device-side resource constraints and the growing demand for intelligence services. This article introduces InferCom, an inference-driven communication architecture designed to enable robust 6G uplink transmission under low signal-to-noise (SNR) conditions. InferCom adopts a compute-asymmetric architecture, featuring a lightweight transmitter and an inference-capable receiver empowered by generative artificial intelligence (GenAI) models, together with a quality-of-experience (QoE)-aware retransmission mechanism. Grounded in the information bottleneck (IB) theory, InferCom redefines uplink communications through task-agnostic compression, inference-driven reconstruction, error-distribution channel coding, and QoE-aware feedback. The case study demonstrates that InferCom outperforms conventional 5G NR and Deep- JSCC in terms of transmitter-side computational complexity, required SNRs and retransmission efficiency. Finally, we outline key challenges and research directions toward making InferCom a practical enabler of human-centric, intelligent and sustainable wireless networks.