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...