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.
Akram A. Almohammedi, Mohammed Balfaqih, Sam Darshi et al.· 0 citations
The rapid growth of massive machine-type communications (mMTC), combined with advances in edge intelligence, is paving the way for low-latency, low-overhead connectivity. However, the sporadic nature of device activity in mMTC scenarios calls for efficient methods to determine which devices are active at any given time. This motivates collaborative learning within a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture, where the wide geographical distribution of access points (APs) and their joint coordination make distributed learning efficient and secure. Consequently, federated learning (FL) emerges as a promising solution. Indeed, FL enables participants to train a shared model without exchanging raw local data, thereby enhancing data privacy at the AP side and lowering the fronthaul load while leveraging heterogeneous, location-dependent data. The present study proposes a novel FL framework where the CF-mMIMO participants are APs. Due to differences in device behavior, mobility patterns, and environmental factors across the network, the data collected at each AP is often non-independent and non-identically distributed (non-IID). This heterogeneity slows down the convergence of standard FL training and increase variability among client updates, particularly under heterogeneous radio feature distributions. To address this, we propose a client selection strategy that prioritizes APs based on their average received signal power. Our approach shows competitive performance compared to baseline methods, while also addressing the scalability and privacy requirements of mMTC systems. Furthermore, our study analyzes the fairness achieved by APs across devices and presents a representative, percentage-scale analysis of power-consumption gains relative to detection performance when some APs are dropped (i.e., taken out of service), examining two AP-dropping strategies. These results bring valuable insights and set guidelines towards the implementation of FL-based activity detection in CF-mMIMO networks.
Ali Elkeshawy, W. Jaafar, Haifa Fares et al.· IEEE Transactions on Machine...· 0 citations
Integrated Terrestrial–Non-Terrestrial Networks (ITNTN), which combine terrestrial base stations (BSs), High-Altitude Platform Stations (HAPS), and Low-Earth Orbit (LEO) satellites, are key enablers of 6G communication and edge computing (EC) services. However, energy-limited BSs, particularly HAPS and satellites, pose significant sustainability challenges under continuous operation. To address this issue, we propose an on-demand EC server activation framework integrated with intelligent task offloading across ITNTN. A joint optimization problem is formulated to maximize task offloading success while satisfying energy and quality-of-service requirements. To solve it, we propose an online Q-learning policy that adaptively manages task offloading and EC server activation without prior knowledge of traffic dynamics. Simulation results show that the proposed method achieves superior task offloading success and energy efficiency compared to online heuristic and offline metaheuristic baselines. These findings highlight the importance of energyaware On-Off EC control for sustainable ITNTN systems.
Insaf Rzig, W. Jaafar, Safwan Alfattani· International Conference on...· 0 citations