Baseband-Free End-to-End Communication System Based on Diffractive Deep Neural Network

Diffractive deep neural network (D2NN), also known as reconfigurable intelligent metasurface based deep neural networks (Rb-DNNs) or stacked intelligent metasurfaces (SIMs) in wireless communications, has emerged as a promising signal processing paradigm that enables computing-by-propagation. However, existing architectures are limited to implementing specific functions such as precoding and combining, while still relying on digital baseband modules for other essential tasks like modulation and detection. In this work, we propose a baseband-free end-to-end (BBF-E2E) wireless communication system where modulation, beamforming, and detection are jointly realized through the propagation of electromagnetic (EM) waves. The BBF-E2E system employs D2NNs at both the transmitter and the receiver, forming an autoencoder architecture optimized within a complex-valued neural network (CVNN) framework. The transmission coefficients of each metasurface layer are trained using the mini-batch stochastic gradient descent (SGD) to minimize the cross-entropy loss. To reduce computational complexity during diffraction calculation, the angular spectrum method (ASM) is adopted over the Rayleigh–Sommerfeld formula. Extensive simulations demonstrate that BBF-E2E achieves robust symbol transmission under various channel conditions with significantly reduced hardware requirements. In particular, the proposed system matches the performance of a conventional multi-antenna system with 81 RF chains while requiring only a single RF chain and 1024 metasurface elements. These results highlight the potential of this wave-domain neural computing paradigm to replace digital baseband modules in future wireless transceivers.