arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2407.08424eess.SP
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面向多用户数字干扰网络的语义特征分址多址接入

Semantic Feature Division Multiple Access for Multi-user Digital Interference Networks

Shuai Ma, Chuanhui Zhang, Bin Shen, Youlong Wu, Hang Li, Shiyin Li, Guangming Shi, Naofal Al-Dhahir

AI总结:

针对5G网络大规模接入挑战,提出新型SFDMA范式,通过深度学习提取离散语义表示实现同资源多用户传输,构建两类任务网络并推导ABG公式与功率控制方法,仿真验证其有效性与优越性。

摘要:

随着用户密度和服务质量(QoS)需求的不断增长,频谱资源有限的5G网络正面临大规模接入挑战。为应对这些挑战,本文提出了一种面向多用户数字干扰网络的新型离散语义特征分址多址接入(SFDMA)范式。具体而言,SFDMA利用深度学习技术将多用户语义信息提取为可区分语义子空间中的离散表示,使多个用户能够在相同时频资源上同时传输。此外,基于鲁棒信息瓶颈,我们设计了一种用于推理任务的基于SFDMA的多用户数字语义干扰网络,可实现近似正交传输。我们还提出了一种用于图像重建任务的基于SFDMA的多用户数字语义干扰网络,其中用户语义编码器的离散输出近似正交,显著降低了多用户干扰。进一步地,我们提出了语义通信的Alpha-Beta-Gamma(ABG)公式,这是推理精度与发射功率之间的首个理论关系。随后,我们推导了用于推理任务的具有闭式表达式的自适应功率控制方法。大量仿真验证了所提SFDMA的有效性和优越性。

英文摘要:

With the ever-increasing user density and quality of service (QoS) demand,5G networks with limited spectrum resources are facing massive access challenges. To address these challenges, in this paper, we propose a novel discrete semantic feature division multiple access (SFDMA) paradigm for multi-user digital interference networks. Specifically, by utilizing deep learning technology, SFDMA extracts multi-user semantic information into discrete representations in distinguishable semantic subspaces, which enables multiple users to transmit simultaneously over the same time-frequency resources. Furthermore, based on a robust information bottleneck, we design a SFDMA based multi-user digital semantic interference network for inference tasks, which can achieve approximate orthogonal transmission. Moreover, we propose a SFDMA based multi-user digital semantic interference network for image reconstruction tasks, where the discrete outputs of the semantic encoders of the users are approximately orthogonal, which significantly reduces multi-user interference. Furthermore, we propose an Alpha-Beta-Gamma (ABG) formula for semantic communications, which is the first theoretical relationship between inference accuracy and transmission power. Then, we derive adaptive power control methods with closed-form expressions for inference tasks. Extensive simulations verify the effectiveness and superiority of the proposed SFDMA.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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