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Ultra-dense Low Data Rate (UDLD) Communication in the THz
arXiv - CS - Machine Learning Pub Date : 2020-09-22 , DOI: arxiv-2009.10674
Rohit Singh and Doug Sicker

In the future, with the advent of Internet of Things (IoT), wireless sensors, and multiple 5G killer applications, an indoor room might be filled with $1000$s of devices demanding low data rates. Such high-level densification and mobility of these devices will overwhelm the system and result in higher interference, frequent outages, and lower coverage. The THz band has a massive amount of greenfield spectrum to cater to this dense-indoor deployment. However, a limited coverage range of the THz will require networks to have more infrastructure and depend on non-line-of-sight (NLOS) type communication. This form of communication might not be profitable for network operators and can even result in inefficient resource utilization for devices demanding low data rates. Using distributed device-to-device (D2D) communication in the THz, we can cater to these Ultra-dense Low Data Rate (UDLD) type applications. D2D in THz can be challenging, but with opportunistic allocation and smart learning algorithms, these challenges can be mitigated. We propose a 2-Layered distributed D2D model, where devices use coordinated multi-agent reinforcement learning (MARL) to maximize efficiency and user coverage for dense-indoor deployment. We show that densification and mobility in a network can be used to further the limited coverage range of THz devices, without the need for extra infrastructure or resources.

中文翻译:

太赫兹频率下的超密集低数据速率 (UDLD) 通信

未来,随着物联网 (IoT)、无线传感器和多个 5G 杀手级应用的出现,室内房间可能会塞满 1000 美元的设备,这些设备要求低数据速率。这些设备的这种高度密集化和移动性将使系统不堪重负,并导致更高的干扰、频繁的中断和更低的覆盖范围。太赫兹频段拥有大量的未开发频谱,以满足这种密集的室内部署。然而,太赫兹有限的覆盖范围将需要网络拥有更多的基础设施并依赖于非视距 (NLOS) 类型的通信。这种形式的通信对网络运营商来说可能无利可图,甚至可能导致要求低数据速率的设备资源利用效率低下。使用太赫兹分布式设备到设备 (D2D) 通信,我们可以满足这些超密集低数据速率 (UDLD) 类型的应用。THz 中的 D2D 可能具有挑战性,但通过机会分配和智能学习算法,这些挑战可以得到缓解。我们提出了一个 2 层分布式 D2D 模型,其中设备使用协调多代理强化学习 (MARL) 来最大化密集室内部署的效率和用户覆盖范围。我们表明,网络中的密集化和移动性可用于扩大太赫兹设备的有限覆盖范围,而无需额外的基础设施或资源。其中设备使用协调多智能体强化学习 (MARL) 来最大限度地提高密集室内部署的效率和用户覆盖范围。我们表明,网络中的密集化和移动性可用于扩大太赫兹设备的有限覆盖范围,而无需额外的基础设施或资源。其中设备使用协调多代理强化学习 (MARL) 来最大化密集室内部署的效率和用户覆盖范围。我们表明,网络中的密集化和移动性可用于扩大太赫兹设备的有限覆盖范围,而无需额外的基础设施或资源。
更新日期:2020-09-23
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