5G Indoor/Outdoor Field Trial of Deep Joint Source-Channel Coding Method

This paper presents the first outdoor field trials of deep joint source-channel coding (DeepJSCC) for image transmission over a 5G system. DeepJSCC is a deep learning-based end-to-end method that unifies source and channel coding to enable robust and low-latency image transmission, particularly in low signal-to-noise power ratio (SNR) environments. Unlike conventional methods, DeepJSCC inherently avoids the cliff effect and maintains stable image quality under harsh channel conditions. To evaluate its feasibility of practical environments, we modified a commercial 5G base station (gNB) and user equipment (UE) to support DeepJSCC signal transmission and reception. Extensive experiments were conducted under indoor and outdoor settings, including line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. A key contribution of this study is the empirical verification that even a DeepJSCC model trained solely on an additive white Gaussian noise (AWGN) channel can maintain stable reconstruction performance in real 5G environments, demonstrating its generalization capability. Compared to baseline systems using JPEG2000 and LDPC, DeepJSCC achieved higher PSNR stability and was able to restore image content even when conventional schemes completely failed. These findings suggest that DeepJSCC is a promising candidate for next-generation visual communication over 5G infrastructure.