GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing
2026-05-26 • Networking and Internet Architecture
Networking and Internet ArchitectureArtificial Intelligence
AI summaryⓘ
The authors explain that cellular research is slow because it involves many complex steps that take a long time to do manually. They show that existing AI tools have problems when applied to cellular networks, like making up wrong information or relying too much on unrealistic simulations. To fix this, the authors created GENESIS, an AI system that turns ideas or problems into tested solutions using real wireless experiments, storing all results so it can learn and improve over time. This system uses building blocks called agents, skills, and hooks, supported by a knowledge base named SYNAPSE.
Radio Access Network (RAN)Large Language Models (LLMs)Application Programming Interfaces (APIs)Over-the-air experimentsCellular network optimizationAgentic AIKnowledge baseWaveform prototyping
Authors
Tamerlan Aghayev, Maxime Elkael, Michele Polese, Minh Dat Nguyen, Gabriele Gemmi, Andrea Lacava, Ali Saeizadeh, Reshma Prasad, Paolo Testolina, Angelo Feraudo, Soumendra Nanda, Pedram Johari, Salvatore D'Oro, Tommaso Melodia
Abstract
Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i) synthesizing new features from standards or research papers into production code; (ii) conformance and interoperability testing; (iii) hardening against field anomalies and diverse deployment environments; (iv) data-driven optimization of network functionalities; (v) discovering and prototyping novel waveforms, functionalities, and capabilities for future standards; and (vi) securing the stack against vulnerabilities. Although Large Language Models (LLMs) have compressed comparable R&D work in general software engineering from days to minutes, their known pitfalls worsen on Radio Access Network (RAN) use cases: they hallucinate Application Programming Interfaces (APIs) and mis-read specifications, which kills interoperability of RAN components at the first mistake, and they heavily rely on simulations for designing algorithms, which is notorious for breaking when transferred to real hardware. To address these challenges, we present GENESIS, an agentic Artificial Intelligence (AI) framework that converts intents (e.g., a specification clause, a telemetry anomaly, or a research hypothesis) into solutions validated with over-the-air experiments, fed back into a persistent knowledge base. GENESIS is built on three composable primitives (agents, skills, hooks) and a knowledge layer (SYNAPSE) that doubles as the source of ground truth and the recipient of every artifact the framework produces, making capabilities compound across runs.