Tiny Neural Receiver: Enabling On-Device Learning for Scalable and Adaptive 6G Devices

The evolution toward 6G communications requires integrating Tiny Machine Learning (TinyML) principles to enable intelligent, energy-efficient, and adaptable signal processing at the network edge. However, current receiver architectures face a fundamental trade-off: classical model-driven designs, while naturally efficient due to their basis in communication theory, lack the flexibility to adapt to varying channel conditions. Meanwhile, fully data-driven deep-learning-based approaches break the stringent resource constraints of TinyML. This paper introduces the tiny neural receiver (TNR), a pioneering architecture that bridges these paradigms by integrating model-based signal processing with lightweight neural optimization to overcome this challenge. The TNR’s primary contribution is its unique hybrid design, which combines the efficiency and interpretability of traditional theory-based receivers with the ability to adapt to different contexts using trainable neural components. This integration occurs within resource budgets that align with TinyML specifications. Experimental results show that the TNR achieves a 5 dB SNR reduction at a target block error rate of 10−4. The reported 5 dB SNR gain is a direct result of our resource-aware design framework, which selectively applies lightweight neural optimization to only the most impactful receiver blocks (channel estimation and decoding) to maximize gain without exceeding TinyML complexity limits. This achievement is further supported by an end-to-end training protocol that uses 15,000 iterations of over-the-air data to fine-tune these parameters for the specific static 3.5 GHz propagation channel and OFDM configuration evaluated. Furthermore, the TNR’s modular design enables flexible deployment across a range of 6G scenarios, from mobile broadband to mission-critical IoT. This establishes the TNR as a promising framework for AI-native 6G receivers.