  In next-generation Internet services, such as Metaverse, the mixed reality
(MR) technique plays a vital role. Yet the limited computing capacity of the
user-side MR headset-mounted device (HMD) prevents its further application,
especially in scenarios that require a lot of computation. One way out of this
dilemma is to design an efficient information sharing scheme among users to
replace the heavy and repetitive computation. In this paper, we propose a
free-space information sharing mechanism based on full-duplex device-to-device
(D2D) semantic communications. Specifically, the view images of MR users in the
same real-world scenario may be analogous. Therefore, when one user (i.e., a
device) completes some computation tasks, the user can send his own calculation
results and the semantic features extracted from the user's own view image to
nearby users (i.e., other devices). On this basis, other users can use the
received semantic features to obtain the spatial matching of the computational
results under their own view images without repeating the computation. Using
generalized small-scale fading models, we analyze the key performance
indicators of full-duplex D2D communications, including channel capacity and
bit error probability, which directly affect the transmission of semantic
information. Finally, the numerical analysis experiment proves the
effectiveness of our proposed methods.
