The transition toward Open Radio Access Networks (O-RANs) is reshaping how cellular infrastructure is deployed, managed, and optimized. This paper investigates the energy-aware joint placement and migration of cloud-native functions (CNFs) in an O-RAN edge cloud. We consider both a Single-CU-UP association model and a slice-aware Multi-CU-UP relaxation, in which distinct slice-flow groups of the same distributed unit (DU) may be assigned to different Centralized Unit User Plane (CU-UP) processing targets under one Centralized Unit Control Plane (CU-CP). For brevity, these scenarios are referred to as Single-CU and Multi-CU, respectively; Multi-CU never denotes multiple CU-CP associations. We formulate the problem as a Mixed-Integer Linear Program (MILP) that minimizes server, transmission, wake-up, and migration energy while satisfying server-resource capacities and one-way delay requirements over the F1 user-plane interface (F1-U) between each DU and its selected CU-UP in a fat-tree edge data center. To improve computational scalability, we also develop a deterministic k-means-based heuristic that approximates the MILP decisions without requiring repeated exact optimization. Over the evaluated 24-hour workload, the theoretical Multi-CU relaxation reduces modeled energy consumption by 5.7% relative to the Single-CU baseline. For the Multi-CU case, the proposed heuristic remains within approximately 9.7% of the proposed MILP, demonstrating a favorable trade-off between energy efficiency and computational tractability.
Nguyen Phuc Tran, B. Jaumard, Oscar Delgado· 0 citations
Fixed Wireless Access (FWA) has recently emerged as a cost-effective alternative to optical fiber in rural areas, particularly where fiber deployment is economically infeasible. To extend coverage and increase capacity, FWA networks have begun to integrate Integrated Access and Backhaul (IAB) with mid- and high-band spectrum. However, the energy consumption of multi-hop IAB networks scales significantly with the number of hops, a challenge that prior research has not adequately addressed. This paper proposes an energy-efficient framework that minimizes network energy consumption by maximizing Resource Block (RB) utilization while avoiding both over- and under-allocation in multi-hop IAB-based FWA deployments. The proposed method jointly allocates RBs and selects modulation and coding schemes across a mixed set of 5G numerologies to satisfy data rate requirements while minimizing energy consumption. The inherent dynamic interactions among IAB stations render the problem highly complex and non-convex; therefore, we design a disciplined multi-convex programming supported by dynamic programming algorithms to obtain tractable solutions. Furthermore, we introduce a transformer-based prediction to forecast RB distribution, thereby mitigating the need for frequent short-timescale coordination among IAB stations. Our simulation results demonstrate that the proposed approach achieves the required data rates while reducing energy consumption by 14%.
Anselme Ndikumana, K. Nguyen, Oscar Delgado et al.· IEEE Transactions on Network...· 0 citations
