  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.
