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 cancelation (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.

中文翻译：

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在中等干扰信道上进行提示辅助语义干扰消除


当干扰功率与信号功率相当时，传统干扰管理策略的性能会降低。我们考虑了使用语义通信进行干扰管理的新视角。具体来说，在中等干扰信道 （IC） 上考虑了多用户语义通信系统，为此提出了一种新的基于深度学习的提示辅助语义干扰消除 （DeepPASIC） 框架。每个传输的信号都分为公共部分和私人部分。不同用户的公共部分在一个共享介质中同时传输，从而产生叠加。另一方面，private 部分作为提示，帮助消除 common 部分在语义层面遭受的干扰。仿真结果表明，在中等干扰条件下，所提出的 DeepPASIC 优于传统的干扰管理策略。