DeepFM: Outband-Semantic Enhancement for Analog Signal
Nan Che, Chuanyang Song, Hongyuan Gao, Xiaodan Chen, Zhijun Li · IEEE Communications Letters · 2025
Frequency modulation (FM) is widely used in broadcasting and IoT audio for its simplicity and low cost, yet remains susceptible to noise. We present DeepFM, a hybrid digital–analog system that supplements FM with a low-rate semantic side channel. A neural encoder compresses audio into quantized features sent alongside the FM signal. A channel-aware decoder fuses semantic bits, noisy analog input, and RSSI to restore high-fidelity audio. Evaluated on our SFR dataset at 0.5–12 kbps, DeepFM outperforms leading neural codecs in mainstream speech-quality metrics, particularly at low–medium bitrates (0.5–3 kbps). DeepFM demonstrates robust, real-time performance compatible with legacy FM infrastructure.