Prompt-Assisted Semantic Interference Cancellation on Moderate Interference Channels
This addresses interference management in multi-user semantic communication systems, but it appears incremental as it builds on existing semantic communication concepts with a novel prompt-based approach.
The paper tackles performance degradation in interference management when interference power is comparable to signal power by proposing DeepPASIC, a deep learning-based prompt-assisted semantic interference cancellation framework for moderate interference channels, which outperforms conventional strategies in simulations.
The performance of conventional interference management strategies degrades when interference power is comparable to signal power. We consider a new perspective on interference management using semantic communication. Specifically, a multi-user semantic communication system is considered on moderate interference channels (ICs), for which a novel framework of deep learning-based prompt-assisted semantic interference cancellation (DeepPASIC) is proposed. Each transmitted signal is partitioned into common and private parts. The common parts of different users are transmitted simultaneously in a shared medium, resulting in superposition. The private part, on the other hand, serves as a prompt to assist in canceling the interference suffered by the common part at the semantic level. Simulation results demonstrate that the proposed DeepPASIC outperforms conventional interference management strategies under moderate interference conditions.