Energy-Latency Optimization for Dynamic 5G Mobile Radio Access Networks

TL;DR

This work addresses the energy–latency trade-off in dynamic 5G RANs by formulating a MILP that jointly selects functional splits, places VNFs, and routes X-haul traffic across disaggregated RU/DU/CU architectures for eMBB, URLLC, and mMTC. It introduces a TSN-informed latency model for deterministic FH/MH performance and provides a scalable heuristic to enable larger scenarios. The study compares energy-focused, latency-focused, and bi-objective configurations across hierarchical and mesh topologies, revealing clear trade-offs in VC selection, computing utilization, and FH/MH delays. The results demonstrate that dynamic RAN reconfiguration is essential to balance service quality with energy efficiency, and that the proposed bi-objective approach yields favorable energy savings while controlling latency increases in realistic deployments.

Abstract

In 5G networks, base station (BS) disaggregation and new services present challenges in radio access network (RAN) configuration, particularly in meeting their bandwidth and latency constraints. The BS disaggregation is enabled by functional splitting (FS), which distributes the RAN functions in processing nodes and alleviates latency and bandwidth requirements in the fronthaul (FH). Besides network performance, energy consumption is a critical concern for mobile network operators (MNO), since RAN operation constitutes a major portion of their operational expenses (OPEX). RAN configuration optimization is essential to balance service performance with cost-effective energy consumption. In this paper, we propose a mixed-integer linear programming (MILP) model formulated with three objective functions: (i) minimizing fronthaul (FH) latency, (ii) minimizing energy consumption, and (iii) a bi-objective optimization that jointly balances both latency and energy consumption. The model determines the optimal FS option, RAN function placement, and routing for eMBB, URLLC, and mMTC slices. Although prior studies have addressed RAN configuration either from an energy minimization or latency reduction perspective, few have considered both aspects in realistic scenarios. Our evaluation spans different topologies, accounts for variations in aggregated gNB demand, explores diverse FS combinations, and incorporates Time Sensitive Networking (TSN) modeling for latency analysis, as it is also crucial in RAN performance. Given that MILP's execution time can be significant, we propose a heuristic algorithm that adheres to RAN constraints. Our results reveal a trade-off between latency and energy consumption, highlighting the need for dynamic RAN reconfiguration. These insights provide a foundation to optimize existing and future RAN deployments.

Figures (10)

• Figure 1: Employed RAN architecture
• Figure 2: Virtual configuration options
• Figure 3: Topologies
• Figure 4: VC Selection for Hierarchical Topology
• Figure 5: VC Selection for Mesh Topology