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

Performance Analysis of Free-Space Information Sharing in Full-Duplex Semantic Communications