🤖 AI Summary
This work addresses the fundamental trade-off in large-scale wireless network management between the high overhead of centralized algorithms and the suboptimal performance of distributed approaches. To overcome this challenge, the authors propose Tandem Apps, a novel architecture that enables tightly coupled cooperative optimization on the O-RAN dual-layer RAN Intelligent Controller (RIC). By strategically decomposing optimization tasks into upper- and lower-layer applications, Tandem Apps reconciles global network visibility with low computational complexity while maintaining full compatibility with O-RAN standards. The design facilitates efficient hierarchical decision-making without sacrificing system interoperability. Extensive experiments on real-world large-scale heterogeneous networks demonstrate that Tandem Apps significantly reduces both computational and communication overhead while achieving network performance close to the global optimum.
📝 Abstract
With growing mobile-network complexity, management and optimization have become increasingly difficult. Centralized algorithms face high control-data overhead and computational load, while distributed approaches often perform far from optimally. The O-RAN architecture introduces two tiers of RAN Intelligent Controllers (RICs), enabling hierarchical network-management schemes. This work proposes Tandem Apps: a pair of tightly coupled optimization mechanisms running on both controllers. We show how to design Tandem Apps through architectural and functional splitting to achieve an agile, low-complexity solution that still preserves a global network view. As an example, we implement Tandem Apps for cell on/off switching and evaluate them in a large heterogeneous network using real network data. Although the Tandem Apps concept is new, it remains fully compliant with the O-RAN standard, as validated using commercial network software.