An Intelligent Cross-Layer Architecture for Multi-Connectivity in Heterogeneous Wireless Networks

The integration of next-generation immersive applications and ubiquitous computing calls for a fundamental shift in wireless networking to accommodate the massive, time-sensitive data transfers necessary for human–machine interactions. While emerging 6G technologies focus primarily on physical layer gains, existing siloed layered architectures remain a bottleneck by failing to leverage network diversity effectively. This paper introduces an augmented cross-layer architectural framework featuring a physical abstraction layer (PAL) and a cross-layer fabric (CLF) to decouple network logic from underlying hardware components. By using lightweight open interfaces, this model resolves cross-layer information asymmetry and enables interface-agnostic data distribution across heterogeneous wireless links. To evaluate operational feasibility, we formulate an open Jackson queuing model to quantify performance gains across aggregate throughput, packet latency, and server utilization. The analytical results demonstrate that while multi-interface aggregation significantly improves throughput and reduces latency compared to single-interface configurations, performance scaling is bounded by diminishing marginal returns due to physical layer scheduling overheads. This work establishes an analytical framework to quantify the performance bounds of wireless heterogeneity in future communication networks.