Stacked Intelligent Metasurfaces for Task-Oriented Semantic Communications

Semantic communication leveraging advanced deep learning (DL) technologies enhances the efficiency, reliability, and security of information transmission. Emerging stacked intelligent metasurface (SIM) having a diffractive neural network (DNN) architecture allows performing complex calculations at the speed of light. In this letter, we introduce an innovative SIM-aided semantic communication system for image recognition tasks. In the considered model, a SIM is positioned in front of the transmitting antenna. In contrast to conventional communication systems transmitting the modulated signals carrying the image information or compressed semantic information, the carrier electromagnetic (EM) wave is directly transmitted from the source in the proposed system. The input layer of the SIM is utilized for source encoding, while the remaining multi-layer architecture constitutes a DNN for semantic encoding. Specifically, the semantic encoder aims to transform the signals passing through the input layer of the SIM into a unique beam towards a receiving antenna corresponding to the image class. Remarkably, both the source and semantic encoding occur naturally as the EM waves propagate through the SIM. At the receiver, the image is recognized by probing the received signal magnitude across the receiving array. To this end, we develop an efficient algorithm to train the transmission coefficients of SIM's meta-atoms to learn the semantic representation of the image. Extensive numerical results verify the effectiveness of utilizing the SIM-based DNN for image recognition task-oriented semantic communications, achieving more than 90% recognition accuracy.