Advancing End-to-End Communication Systems With Physics-Aware Learning
The advent of neural network (NN)-based autoencoders has enabled joint transceiver optimization via end-to-end (E2E) learning. However, most existing approaches rely on differentiable surrogate channel models, leading to performance mismatch when deployed over real-world channels. Emerging model-free variants alleviate this issue but still lack principled means to obtain reliable and informative gradients from physical channels. In this work, we propose a novel E2E optimization framework, termed physics-aware learning (PAL), that eliminates the need for channel modeling by executing forward propagation directly over real channels. To support gradient-based training under such model-free settings, a fused gradient proxy is designed that combines gradient jumps and local Jacobian estimations to derive transmitter-side gradients. We instantiate this framework through the development of a physics-aware learning autoencoder (PALAE), which jointly integrates probabilistic shaping, geometric shaping, and neural equalization within a carrier-less amplitude and phase (CAP) modulation system. While retaining compatibility with conventional DSP-based transceivers, PALAE enables online, interpretable, and modular E2E optimization across heterogeneous components. Simulation and experimental evaluations demonstrate the effectiveness of PALAE, achieving a net bit rate exceeding 400 Gbps in a practical 0.5-km intensity modulation-direct detection (IMDD) fiber system, representing the state-of-the-art rate achieved through CAP modulation with single wavelength.