Energy efficiency (EE) constitutes a key target in the deployment of 5G networks, especially due to the increased densification and heterogeneity. In this paper, a Deep Q-Network (DQN) based power control scheme is proposed for improving the system-level EE of two-tier 5G heterogeneous and multi-channel cells. The algorithm aims to maximize the EE of the system by regulating the transmission power of the downlink channels and reconfiguring the user association scheme. To efficiently solve the EE problem, a DQN-based method is established, properly modified to ensure adequate QoS of each user (via defining a demand-driven rewarding system) and near-optimal power adjustment in each transmission link. To directly compare different DQN-based approaches, a centralized (C-DQN), a multi-agent (MA-DQN) and a transfer learning-based (T-DQN) method are deployed to address whether their applicability is beneficial in the 5G HetNets. Results confirmed that DQN-assisted actions could offer enhanced network-wide EE performance, as they balance the trade-off between the power consumption and achieved throughput (in Mbps/Watt). Excessive performance was observed for the MA-DQN approach (>5 Mbps/Watt), since the decentralized learning supports low-dimensional agents to be coordinated with each other through global rewards. In further comparing the T-DQN against MA-DQN solutions, T-DQN presents beneficial usage for very low or very high inter-cell distances, whereas the usage of MA-DQN is preferred (by a factor of ~1.3) for intermediate inter-cell distances (100-600m), where the power savings are feasible towards achieving increased EE. Furthermore, T-DQN scheme guarantees good EE solutions (above 2 Mbps/Watt), even for densely-deployed macro-cells, with effortless training and memory requirements. On the contrary, MA-DQN offers the best EE solutions at the expense of massive training resources and required training time.

Future data-intensive intelligent applications are required to traverse across the cloud-to-edge-to-IoT continuum, where cloud and edge resources elegantly coordinate, alongside sensor networks and data. However, current technical solutions can only partially handle the data outburst associated with the IoT proliferation experienced in recent years, mainly due to their hierarchical architectures. In this context, this paper presents a reference architecture of a meta-operating system (RAMOS), targeted to enable a dynamic, distributed and trusted continuum which will be capable of facilitating the next-generation smart applications at the edge. RAMOS is domain-agnostic, capable of supporting heterogeneous devices in various network environments. Furthermore, the proposed architecture possesses the ability to place the data at the origin in a secure and trusted manner. Based on a layered structure, the building blocks of RAMOS are thoroughly described, and the interconnection and coordination between them is fully presented. Furthermore, illustration of how the proposed reference architecture and its characteristics could fit in potential key industrial and societal applications, which in the future will require more power at the edge, is provided in five practical scenarios, focusing on the distributed intelligence and privacy preservation principles promoted by RAMOS, as well as the concept of environmental footprint minimization. Finally, the business potential of an open edge ecosystem and the societal impacts of climate net neutrality are also illustrated.

In the present work, a deep learning-based neural network for magnetic dipole modeling (MDMnet) is introduced in the framework of DC magnetic cleanliness for space missions. The developed method targets at modeling the static magnetic signature of a spacecraft unit that is obtained during the unit level characterization stage of the extensive pre-launch electromagnetic compatibility test campaign. By employing synthetic magnetic field data generated by virtual dipole sources, the MDMnet can be trained to accurately estimate the magnetic parameters of real equipment based on its near field measurements. The target of the proposed deep learning algorithm is, on the one hand, to effectively minimize the prediction errors (loss function) throughout the training process and, on the other hand, to enable the generalization of the model predictions, i.e. exhibit accurate model estimations with unseen magnetic field data. Extensive simulations towards the stabilization of the MDMnet hyperparameters are outlined, and indicative model inferences employing artificial magnetic field data are carried out. Finally, the MDMnet can achieve a predictive accuracy of 0.8 mm with respect to the dipole localization and 1% with respect to the magnetic field magnitude, verifying the potency of the developed method.

Open Radio Access Network (O-RAN) is transforming the telecommunications landscape by enabling flexible, intelligent, and multi-vendor networks. Central to its architecture are xApps hosted on the Near-Real-Time RAN Intelligent Controller (Near-RT RIC), which optimize network functions in real time. However, the concurrent operation of multiple xApps with conflicting objectives can lead to suboptimal performance. This paper introduces a generalized Conflict Management scheme for Multi-Channel Power Control in O-RAN xApps (COMIX), designed to detect and resolve conflicts between xApps. To demonstrate COMIX, we focus on two Deep Reinforcement Learning (DRL)-based xApps for power control: one maximizes the data rare across UEs, and the other optimizes system-level energy efficiency. COMIX employs a standardized Conflict Mitigation Framework (CMF) for conflict detection and resolution and leverages the Network Digital Twin (NDT) to evaluate the impact of conflicting actions before applying them to the live network. We validate the framework using a realistic multi-channel power control scenario under various conflict resolution policies, demonstrating its effectiveness in balancing antagonistic objectives. Evaluation results show that COMIX achieves up to 60% energy savings across different Service-Level Agreement (SLA) policies compared to a baseline conflict-unaware system, with negligible impact (around 3%) on system throughput. While this study considers power control xApps, the COMIX framework is generalizable and can be applied to any xApp conflict scenario involving resource contention or KPI interdependence.

Maritime and shipping are unambiguously the cornerstones of the global economy and transportation. To improve efficiency, maritime sector activities are focused on the realization of Smart Shipping (SMS), leveraging 6G Communications, Energy Efficiency (EE) and Machine Learning (ML). However, conventional Centralized Machine Learning (CML) cannot be easily applied in the maritime, mainly due to the drawbacks: (i) prohibitive data communication overhead and bandwidth limitations, since CML requires centralization of massive data through transmissions from heterogeneous sources, (ii) excessive energy consumption associated with massive data transfers, (iii) remarkable transmission errors due to harsh propagation conditions, and (iv) data privacy violation, since the data carries sensitive and commercial information. This article proposes a two-fold Federated Learning (FL) scheme (FedShip) to improve the privacy, EE and communication-efficiency of future 6G maritime networks. FedShip uses the Over-the-Air computation (AirComp) principles to exploit the signal superposition property and ensure that local models are accurately and efficiently combined. Using real data regarding the fuel consumption of multiple cargo ships, we compared the FL performance, building multiple timeseries forecasting models, with collaborative ML baselines. AirComp performance was also assessed using simulation data about channel measurements. After optimizing the hyperparameters of the local models, extensive results revealed that: (i) FL shows enhanced fuel prediction accuracy (95.5% relative to the CML), while ensuring data privacy and (ii) AirComp can be adopted to combine the local models with low computation error, offering significant EE and spectrum efficiency improvements, especially when dense 6G scenarios are considered.