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Power-Minimizing Resource Allocation for Coexisting Semantic and Bit Users in Hybrid-NOMA

A wireless resource-allocation framework that lets deep-learning semantic-communication users share a NOMA network with conventional bit-based users while minimizing total transmit power. Students learn how a data-driven regression model links wireless resources to semantic-transceiver performance, and how beamforming, bandwidth, and a semantic symbol factor are jointly optimized via a closed-form beamformer plus block coordinate descent in a hybrid-NOMA cluster structure.

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Power-Efﬁcient Optimization for Coexisting Semantic and Bit-Based Users in NOMA Networks Ximing

Semantic communication transmits the meaning of data rather than exact bits, while non-orthogonal multiple access (NOMA) lets several users share the same spectrum, separated by successive interferen…