Multi-user Semantic Communications System with Spectrum Scarcity

Nowadays, the emerging paradigm of semantic com- munications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication systems. In particular, focusing on spectrum scarcity, expected to afflict the upcoming sixth-generation (6G) networks, this paper analyses the semantic communications behavior in the context of a cell-dense scenario, in which users belonging to different small base station areas may be allocated on a same channel giving rise to a non-negligible interference that severely affects the communications reliability. In such a context, artificial intelligence methodologies are of paramount importance in order to speed up the switch from traditional communication to the novel semantic communication paradigm. As a consequence, a deep-convolution neural networks based encoder-decoder architecture has been exploited here in the definition of the proposed semantic communications framework. Finally, extensive numerical simulations have been performed to test the advantages of the proposed framework in different interfering scenarios and in comparison with different traditional or semantic alternatives.