End-to-End Learning With EM-Aware Differentiable-Ready Discrete-Phase RIS for MIMO–OFDM via Ray Tracing
Most learning-based reconfigurable intelligent surface (RIS) designs assume continuous control or closed-box channel models, limiting physically grounded end-to-end (E2E) training and neglecting practical 1-bit hardware constraints. We propose a physics-informed framework for RIS-assisted MIMO–OFDM that embeds an optics-consistent differentiable ray tracer (RT) in the training loop while enforcing strictly discrete, frequency-flat 1-bit RIS control. Per-element phases are injected into the RT transition matrices and optimized via quantization-aware training (QAT) with hard binary forward passes and surrogate gradients. The same pipeline also acts as a digital twin, enabling controlled sweeps over geometry, materials, and LOS/NLOS conditions to generate labeled CIRs and OFDM channels. We study both staged optimization, where the RIS is trained using a pilot-energy proxy before neural receiver (NRX) training, and full E2E co-optimization by backpropagating NRX loss through the RT–RIS block. In the E2E setting, the RIS is optimized as part of the electromagnetic propagation environment using receiver-side bitwise loss, rather than through an intermediate channel-quality proxy alone. We compare QAT with straight-through estimator (STE), straight-through Gumbel-softmax (ST-Gumbel), and a non-differentiable dueling Double-DQN (DDQN) bit-flip baseline. In fully NLOS scenarios, QAT-optimized RIS with NRX consistently outperforms least-squares and unoptimized baselines, and narrows the gap to a perfect-CSI reference under both staged and E2E training.