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English(EN) Deep Learning-Based Multi-User Communication Design for Dense IoT Networks: Interference-Aware Finite-Blocklength Communication and Preliminary MIMO Extensions

深度学习提升物联网通信，减少干扰

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种基于深度学习的通信系统，专为密集物联网（IoT）网络设计。该系统旨在通过减轻多用户干扰来提高可靠性，尤其是在频谱有限且块长较短或中等的情况下。所提出的框架是SiameseNet收发器的一个扩展，在不进行复杂联合检测的情况下，在降低块错误率方面表现出强大的性能，并显示出适应多天线系统的潜力。 AI

影响 这项研究可能为物联网网络中不断增长的互联设备带来更强大、更高效的通信协议。

排序理由 学术论文，详细介绍了通信系统的创新深度学习方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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深度学习提升物联网通信，减少干扰

本文如何被排名

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2 / 100
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Tool
学术论文，详细介绍了通信系统的创新深度学习方法。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
paper, infra
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AI-industry relevance
High
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Story freshness
1 days old
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Arkadeep Sinha, Shubham Paul, R. Manivasakan ·

    基于深度学习的密集物联网网络多用户通信设计：面向干扰感知的有限块长通信及初步MIMO扩展

    arXiv:2608.22923v1 Announce Type: cross Abstract: Dense IoT networks require reliable communication despite limited spectrum and substantial multi-user interference while maintaining manageable receiver complexity. This work introduces a deep-learning-based end-to-end multi-user …

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