Generative AI for Cross-Technology Communication in Consumer Electronics

Cross-Technology Communication (CTC) enables interoperability between wireless technologies such asWiFi, BLE, LoRa, and ZigBee in Consumer Electronics (CE). However, existing methods face limitations in scalability and adaptability, particularly under real-world impairments like Carrier Frequency Offset (CFO) and Adjacent-Channel Interference (ACI). To address these challenges, we propose GAI-CTC, a Generative AI-driven framework for CTC that optimizes waveform synthesis, spectrum adaptation, and semantic alignment in a unified, end-to-end neural architecture. GAI-CTC utilizes a multi-domain neural network that generates waveforms for multiple protocols while complying with spectral-mask, power, and latency constraints. The framework leverages a cyclic generative learning mechanism and semantic consistency loss to ensure efficient transmitter-receiver mapping. The model is trained through joint optimization of key loss components, includingLCTC,Lmask, andLsem. Simulation results show that GAI-CTC achieves up to 3 × higher goodput and 20-40% lower energy-per-bit compared to Baseline CTC. Additionally, GAI-CTC improves spectral mask compliance by 30-50 percentage points and enhances CFO tolerance by 20 ppm, demonstrating its potential to enable efficient and adaptive communication in multi-protocol wireless systems.