论文标题
THZ中超密集的低数据率（UDLD）通信
Ultra-dense Low Data Rate (UDLD) Communication in the THz
论文作者
论文摘要
将来，随着物联网（IoT），无线传感器和多个5G杀手级应用的出现，室内房间可能会填充$ 1000 $的设备，要求较低的数据速率。这些设备的高级致密化和迁移率将压倒系统，并导致更高的干扰，频繁停机和较低的覆盖范围。 THZ乐队有大量的绿地频谱，可满足这种密集的室内部署。但是，THZ的覆盖范围有限，将要求网络具有更多的基础架构，并依赖于非线（NLOS）类型通信。这种形式的通信可能对网络运营商而言可能不会有利可图，甚至可能导致需要低数据速率的设备的资源利用率效率低下。使用THZ中的分布式设备对设备（D2D）通信，我们可以迎合这些超密集的低数据速率（UDLD）类型的应用程序。 THZ中的D2D可能具有挑战性，但是通过机会主义分配和智能学习算法，可以缓解这些挑战。我们提出了一个2层的分布式D2D模型，其中设备使用协调的多代理增强学习（MARL）来最大程度地提高效率和密度室内部署的用户覆盖范围。我们表明，网络中的致密化和移动性可用于进一步进一步的THZ设备覆盖范围，而无需额外的基础架构或资源。
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.
