Autonomous Discovery of Wireless Communications Algorithms

Researchers introduced The AI Telco Engineer (AITE), a large language model-driven evolutionary search framework that autonomously designs wireless communications algorithms, outperforming best-known solutions on two physical-layer problems. For an OTFS equalizer, AITE reduced computational latency by a factor of 3.6 versus the strongest baseline, and for an OFDM receiver it discovered the first explicit, explainable algorithms matching state-of-the-art neural receivers.

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.