Performance Boundary Analyses for Statistical Multi-QoS Framework Over 6G SAGINs

To enable cost-effective universal access and the enhancement of current communication services, the space-air-ground integrated networks (SAGINs) have recently been developed due to their exceptional 3D coverage and the ability to guarantee rigorous and multidimensional demands for quality-of-service (QoS) provisioning, including delay and reliability across vast distances. The integration of spatial, aerial, and terrestrial dimensions is thus regarded as a critical facilitator for accommodating massive Ultra-Reliable Low-Latency Communications (mURLLC) applications. In response to the complex, heterogeneous, and dynamic serving scenarios and stringent performance expectations for 6G SAGINs, it is crucial to undertake modeling, assurance, and analysis of the key technologies, aligned with the diverse demands for QoS provisioning in the non-asymptotic regime, i.e., when implementing finite blocklength coding (FBC) as a new dimension for error-rate bounded QoS metric. However, how to design new statistical QoS-driven performance modeling approaches that accurately delineate the complex and dynamic behaviors of networks, particularly in terms of constraining both delay and error rate, persists as a significant challenge for implementing mURLLC within 6G SAGINs in the finite blocklength regime. To overcome these difficulties, in this paper we propose to develop a set of analytical modeling frameworks for 6G SAGIN in supporting statistical delay and error-rate bounded QoS in the finite blocklength regime. First, we establish the SAGIN system architecture model. Second, the aggregate interference and decoding error probability functions are modeled and examined by using Laplace transform. Third, we introduce modeling techniques aimed at defining the $\epsilon $ -effective capacity function as a crucial metric for facilitating statistical QoS standards with respect to delay and error-rate. To validate the effectiveness of the developed performance modeling schemes, we have executed a series of simulations over SAGINs.