Semantics-Division Duplexing: A Novel Full-Duplex Paradigm

Kai Niu, Zijian Liang, Chao Dong, Jincheng Dai, Zhongwei Si, Ping Zhang
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Abstract

In-band full-duplex (IBFD) is a theoretically effective solution to increase the overall throughput for the future wireless communications system by enabling transmission and reception over the same time-frequency resources. However, reliable source reconstruction remains a great challenge in the practical IBFD systems due to the non-ideal elimination of the self-interference and the inherent limitations of the separate source and channel coding methods. On the other hand, artificial intelligence-enabled semantic communication can provide a viable direction for the optimization of the IBFD system. This article introduces a novel IBFD paradigm with the guidance of semantic communication called semantics-division duplexing (SDD). It utilizes semantic domain processing to further suppress self-interference, distinguish the expected semantic information, and recover the desired sources. Further integration of the digital and semantic domain processing can be implemented so as to achieve intelligent and concise communications. We present the advantages of the SDD paradigm with theoretical explanations and provide some visualized results to verify its effectiveness.
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语义分割双工:新颖的全双工范例
理论上,带内全双工(IBFD)是提高未来无线通信系统总体吞吐量的有效解决方案,它可以在相同的时频资源上进行传输和接收。然而,由于无法理想地消除自干扰以及独立信源和信道编码方法的固有局限性,可靠的信源重构在实际的 IBFD 系统中仍然是一个巨大的挑战。另一方面,人工智能支持的语义通信可以为 IBFD 系统的优化提供一个可行的方向。本文介绍了一种以语义通信为指导的新型 IBFD 范式--语义分割双工(SDD),它利用语义域处理进一步抑制自干扰、区分预期的语义信息并恢复所需的信源。我们通过理论解释介绍了 SDD 范式的优势,并提供了一些可视化结果来验证其有效性。
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