Deep Learning-Enabled LoRa-JSCC for Efficient and Reliable Multivariate Sensor Data Transmission in IoT Environments

Integrating Joint Source–Channel Coding (JSCC) with the LoRa Chirp Spread Spectrum (CSS) physical layer (PHY) presents a significant challenge due to the complexity of joint optimization, which remains underexplored despite the known advantages of JSCC. Traditional LoRa systems rely on decoupled source and channel coding, resulting in redundant overhead and limited adaptability under dynamic Wireless Body Area Network (WBAN) conditions. To address these limitations, we propose a novel LoRa–JSCC framework: a fully learned, end-to-end differentiable architecture that jointly optimizes source compression and channel redundancy. The proposed system integrates a Denoising Autoencoder (DAE) for non-linear source compression with learned neural channel encoder and decoder modules, trained via backpropagation to minimize reconstruction distortion under noisy channel conditions. Rigorous Monte Carlo simulations conducted under unified and reproducible channel conditions demonstrate consistent performance improvements across LoRa configurations. The proposed approach achieves an average 25–30% improvement in goodput across moderate-to-high SNR regimes, with gains exceeding 100% under noise-limited conditions. It further reduces Time on Air (ToA) by approximately 30–35%, enhancing spectral efficiency and lowering effective energy cost per delivered bit. In the transitional Bit Error Rate (BER) region, the proposed LoRa–JSCC framework exhibits an effective SNR gain of approximately 18–20 dB relative to conventional LoRa, corresponding to multiple orders-of-magnitude reduction in BER. These results indicate substantial improvements in reliability, coverage robustness, and energy efficiency for WBAN and IoT deployments.