Optimal Resource Allocation for Statistical QoS Provisioning in Supporting mURLLC Over FBC-Driven 6G Terahertz Wireless Nano-Networks

The new and important service class of massive Ultra-Reliable Low-Latency Communications (mURLLC) is defined in the 6G era to guarantee very stringent quality-of-service (QoS) requirements, such as ultra-high data rate, super-high reliability, tightly-bounded end-to-end latency, etc. Various 6G promising techniques, such as finite blocklength coding (FBC) and Terahertz (THz), have been proposed to significantly improve QoS performances of mURLLC. Furthermore, with the rapid developments in nano techniques, THz wireless nano-networks have drawn great research attention due to its ability to support ultra-high data-rate while addressing the spectrum scarcity and capacity limitations problems. However, how to efficiently integrate THz-band nano communications with FBC in supporting statistical delay/error-rate bounded QoS provisioning for mURLLC still remains as an open challenge over 6G THz wireless nano-networks. To overcome these problems, in this paper we propose the THz-band statistical delay/error-rate bounded QoS provisioning schemes in supporting mURLLC standards by optimizing both the transmit power and blocklength over 6G THz wireless nano-networks in the finite blocklength regime. Specifically, first, we develop the FBC-driven THz-band wireless channel models in nano-scale. Second, we build up the THz-band interference model and derive the channel capacity and channel dispersion functions using FBC. Third, we maximize the ϵ-effective capacity by developing the joint optimal resource allocation policies under statistical delay/error-rate bounded QoS constraints. Finally, we conduct the extensive simulations to validate and evaluate our proposed schemes at the THz band in the finite blocklength regime.