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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 andmobility of these devices will overwhelm the system and result in higherinterference, frequent outages, and lower coverage. The THz band has a massiveamount of greenfield spectrum to cater to this dense-indoor deployment.However, a limited coverage range of the THz will require networks to have moreinfrastructure and depend on non-line-of-sight (NLOS) type communication. Thisform of communication might not be profitable for network operators and caneven result in inefficient resource utilization for devices demanding low datarates. Using distributed device-to-device (D2D) communication in the THz, wecan cater to these Ultra-dense Low Data Rate (UDLD) type applications. D2D inTHz can be challenging, but with opportunistic allocation and smart learningalgorithms, these challenges can be mitigated. We propose a 2-Layereddistributed D2D model, where devices use coordinated multi-agent reinforcementlearning (MARL) to maximize efficiency and user coverage for dense-indoordeployment. We show that densification and mobility in a network can be used tofurther the limited coverage range of THz devices, without the need for extrainfrastructure or resources.

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