Toward Agentic AI for the 6G Physical Layer: From Algorithm Discovery to Verified Baseband Silicon

Key points

• That silicon imposes hard memory and numeric limits, but also exposes cost structures a GPU lacks, and a design flow that never runs on the target sees neither.1
• We present an agentic workflow in which an LLM coding agent implements algorithms on a commercial baseband SoC, optimizes them down to agent-written vector assembly, and validates them on silicon.1
• We showcase the workflow on an AI-discovered orthogonal time frequency space (OTFS) detector portfolio on the open 6G4All platform.1
• What is not specific to OTFS is the workflow: an LLM agent exploring an implementation space under real hardware limits, with the cost of every choice measured rather than assumed.1

Sources (1)

• [1]Toward Agentic AI for the 6G Physical Layer: From Algorithm Discovery to Verified Baseband Silicon
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 07:56 AM

  6G will be designed faster than it can be built: AI-driven search already proposes physical-layer algorithms at machine speed, while carrying a single one onto embedded baseband silicon remains specialist work.

  That silicon imposes hard memory and numeric limits, but also exposes cost structures a GPU lacks, and a design flow that never runs on the target sees neither.

Extractive summary: sentences quoted from the sources.