Ultra-dense Low Data Rate (UDLD) Communication in the THz

TL;DR

This paper proposes a multi-agent reinforcement learning-based distributed D2D communication model in the THz band to support ultra-dense low data rate indoor applications, reducing infrastructure needs.

Contribution

It introduces a 2-layered distributed D2D model utilizing MARL to improve coverage and efficiency in dense indoor THz networks for low data rate devices.

Findings

MARL-based D2D improves coverage in dense THz networks

Densification enhances coverage without extra infrastructure

The model effectively manages interference and mobility

Abstract

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.