Autonomous Discovery of Wireless Communications Algorithms
2026-07-20 • Information Theory
Information TheoryArtificial IntelligenceMultiagent Systems
AI summaryⓘ
The authors present AITE, a framework that uses large language models (LLMs) combined with evolutionary search to automatically create algorithms for wireless communication problems. They tested AITE on two tough tasks: improving signal equalization for OTFS systems and building a pilot-free receiver for OFDM systems with special signal patterns. Their system found algorithms that worked better and faster than existing ones for the first task, and created clear, understandable algorithms that matched advanced neural networks' performance for the second. This shows that AI-driven search can help design new and efficient wireless communication methods.
Large language model (LLM)Evolutionary searchWireless communicationsEqualizerOrthogonal time-frequency space (OTFS)Orthogonal frequency-division multiplexing (OFDM)Pilot signalsAlgorithm designNeural receiversPerformance-complexity tradeoff
Authors
Fayçal Aït Aoudia, Jakob Hoydis, Sebastian Cammerer, Gian Marti, Merlin Nimier-David, Nicolas Roussel, Alexander Keller
Abstract
Large language model (LLM)-driven evolutionary search is an emerging algorithm-discovery paradigm that has already produced novel results in several scientific fields. Yet its application to wireless communications remains largely unexplored. To bridge this gap, we introduce The AI Telco Engineer (AITE), a framework to autonomously design algorithms for complex communication problems, while navigating performance-complexity tradeoffs. We showcase AITE on two challenging physical-layer problems: designing an equalizer for an orthogonal time-frequency space (OTFS) system, and constructing a receiver algorithm for an orthogonal frequency-division multiplexing (OFDM) system using a custom constellation and operating without pilots. For the first task, AITE develops algorithms that outperform the best-known solutions while reducing computational latency by a factor of 3.6 compared to the strongest baseline. For the second task, it discovers the first explicit, explainable algorithms that achieve performance parity with state-of-the-art neural receivers. These results demonstrate the strong potential of LLM-driven evolutionary search for the autonomous discovery of next-generation wireless communications algorithms.