Results show that online twin-fidelity monitoring enables robust digital-twin-assisted xApp conflict resolution while preserving utility-aware throughput-power optimization, and achieves the closest throughput-power trade-off to the optimum across operator energy priorities.
Abstract
Open Radio Access Network (O-RAN) allows independently developed xApps to control RAN functions through the Near-Real-Time RAN Intelligent Controller (Near-RT RIC). When xApps with conflicting objectives operate concurrently, they may issue incompatible actions that degrade network performance. This paper addresses a direct conflict in which an energy-saving (ES) xApp and a coverage/throughput-oriented (CTO) xApp request different downlink transmit-power settings for the same cell. We formulate conflict resolution as online selection of a continuous blend of the two proposals, maximizing an energy-aware utility that jointly considers throughput and power consumption. A network digital twin (NDT) predicts this utility for candidate actions before live deployment, but selecting the highest twin-predicted utility becomes ineffective when the twin drifts. We therefore propose a twin-fidelity-aware hard-switching arbiter that monitors the error between predicted and observed utilities using an exponentially weighted moving average. While the error remains below a threshold, the arbiter follows the NDT-selected action; otherwise, it switches to the best previously observed action learned online. The arbiter is lightweight, training-free, and requires no oracle knowledge of the optimal policy. System-level 5G evaluations show that it achieves the closest throughput-power trade-off to the optimum across operator energy priorities, yielding normalized utility regret of $0.017 \pm 0.006$, versus $0.159 \pm 0.052$ for a COMIX-style twin-based selector. Under severe NDT drift (10 dB), it reduces utility regret from $11.19 \pm 3.58$ to $0.55 \pm 0.25$. These results show that online twin-fidelity monitoring enables robust digital-twin-assisted xApp conflict resolution while preserving utility-aware throughput-power optimization.
This paper proposes CORMO-RAN, a data-driven orchestrator that dynamically activates compute nodes based on xApp load to save energy, and performs lossless migration of xApps from nodes to be turned off to active ones while ensuring xApp availability during migration.
Antonio Calagna, Stefano Maxenti, Leonardo Bonati et al.· IEEE Transactions on Mobile...· 2 citations
Heterogeneous 6G radio access networks (RANs) must allocate resources reliably under interference, latency limits, imperfect channel state information (CSI), and architectural diversity. We propose a degeneracy-aware resource allocation (DG-RA) framework that casts multi-architecture orchestration as a probabilistic game and, unlike single-solution optimization, deliberately favors allocations realizable by many structurally distinct yet performance-equivalent strategy profiles. Resilience is quantified across three layers through Degeneracy-Weighted Path Robustness (DWPR), Functional Substitution Score (FSS), and an Algorithmic Resilience Quotient (ARQ). Across centralized (C-RAN), open (O-RAN), virtualized (V-RAN), and hybrid RAN architectures, and benchmarked against a fractional-programming optimizer, DG-RA matches the state-of-the-art throughput and outage at the static operating point, then exploits its equivalence set to recover $\sim$$99\%$ of throughput from a resource-unit failure with a single switch, where a single-solution optimizer needs tens of iterations to re-converge. The results recast degeneracy not as a rate booster but as a precomputed resilience reserve for disruption-tolerant 6G orchestration.
A Proportional Fairness (PF)-driven framework for allocating Co-SR transmit power on a per-Transmission Opportunity (TXOP) basis is introduced, and it is proved that any Pareto-optimal power pair keeps at least one AP at its maximum power.
Multipath QUIC (MPQUIC) enables simultaneous uplink transmission over heterogeneous access networks such as Wi-Fi and LTE, improving reliability and performance. However, aggressive LTE utilization increases operational cost, creating an inherent trade-off between upload delay and cellular usage. Existing MPQUIC schedulers typically optimize a single performance objective and operate at fixed points within this trade-off space, without explicitly supporting cost-aware operation. This paper formulates uplink MPQUIC scheduling as a multi-objective optimization problem that jointly considers maximum upload completion time and total LTE usage. We propose a Bayesian Optimization-based framework that treats the MPQUIC system as a black box and systematically explores probabilistic path selection configurations to uncover Pareto-efficient operating points. Rather than committing to a predefined scheduling policy, the framework exposes a spectrum of delay--cost trade-offs without modifying protocol internals. Experiments conducted using the Mininet-WiFi emulator show that the proposed approach characterizes a wide delay--cost region and identifies configurations that achieve substantial LTE savings (up to 80%) with controlled increases in upload time. The results further indicate that, under higher contention levels, systematic multi-objective exploration provides increased flexibility compared to fixed-policy schedulers in cost-aware heterogeneous uplink deployments.
T. Nguyen, T. Lê, Phi Le Nguyen et al.· arXiv.org· 0 citations
This paper investigates the energy-aware joint placement and migration of cloud-native functions (CNFs) in an O-RAN edge cloud with a Mixed-Integer Linear Program 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.
Nguyen Phuc Tran, B. Jaumard, Oscar Delgado· 0 citations
