In modern Open RAN architectures, the traditional gNB protocol stack is disaggregated into virtualized components, Centralized Unit (CU), Distributed Unit (DU), and Radio Unit (RU), which are flexibly deployed across the infrastructure to meet diverse QoS requirements. This paper proposes an energyaware model that jointly optimizes user association and virtual network function (VNF) placement to minimize total system energy consumption. By dynamically consolidating workloads and selectively activating radio and compute resources, the model reduces energy usage without compromising service constraints. We formulate the problem as an integer linear problem (ILP) to obtain optimal solutions and introduce a Graph Neural Network (GNN)-based heuristic that closely approximates optimal placements in real time. Simulation results demonstrate up to 75% energy savings at low loads and show the GNN reduces execution time by over $99 \%$ while maintaining near-optimal performance.

Ensuring energy efficiency is an important requirement for mobile networks and sleep mode is an essential enabler for it. With the rise of open-RAN, the disaggregation of network functions at different locations has enabled to switch off baseband processing functions whenever possible. In this work, we aim to reduce the energy consumption of the radio access network by leveraging advanced sleep modes, namely micro, light, and deep, with different energy profiles. The main difficulty when implementing these sleep modes on small time scales originates from the non-negligible transition times. We model the problem of the processing units activation/deactivation that host the DUs as an MILP (Mixed Integer Linear Program). We offer an LPMIP (Linear Program - Mixed Integer Program) version, with binary and continuous variables. We propose exact solutions for the two formulated problems and an additional heuristic with lower complexity.

In the present paper a closed-form framework is derived for the analysis and optimization of the coverage probability (Pcov) and of the area spectral efficiency (ASE) in long-range (LoRa) networks. The proposed framework exploits stochastic geometry tools to associate the Pcov and the ASE to the end device (ED) transmit power and to the ED density. The analysis reveals the trends of the Pcov and of the ASE curves, with respect to both of the two parameters, while the robustness of the framework holds even at the asymptotic cases. Building upon the derived framework, the analysis demonstrates that no joint global optimum exists that jointly maximizes the Pcov over both parameters, suggesting that the optimization of the Pcov must be performed separately, for the two key network parameters considered. As opposed to that, the analysis demonstrates that a set of global optima exists that jointly maximize the ASE over both parameters, and these global maxima are subsequently derived in closed form. Thus, the derived framework fully characterizes the performance of LoRa networks, while defining in closed form the optimal points of operation that can be proven of significant value, for the transceiver and network design, of practical LoRa networks.

Ultra dense networks (UDNs) allow for efficient spatial reuse of the spectrum, giving rise to substantial capacity and power gains. However, recent findings have demonstrated that the reduction of inter-site distances after a certain threshold, has a detrimental effect on the network performance. In such cases, interference mitigation strategies to counteract the effect of primarily the line of sight (LOS) interference are imperative. In this course, the present paper investigates a minimum coordination strategy among immediate neighbors. The adopted scheme realizes the interference mitigation objective, enhancing system capacity, while being implementable in practice. Building upon this scheme, the present paper provides tractable closed form expressions for the downlink (DL) ergodic capacity of a reference user under the examined coordination scheme. The analysis is performed for a scenario consisting of base stations (BSs) whose positions follow a Poisson point process of a given spatial density. The tractability and accuracy of the derived expressions renders them ideal for quantifying the performance of additional coordination strategies and for revealing trends in complex optimization problems. Thus, constituting a valuable tool for network operators, toward assessing different interference mitigation techniques and coordination schemes in UDNs.

The increasing demand for highly sophisticated cloud services and the consequent worldwide increase in the number of data centers (DC), make the need for energy efficient operation of DCs more actual than ever. Additionally, in the context of smart city and smart grid integration, flexible DCs can play a key role toward acting as grid-stabilization factors. In this paper, a review of the available techniques toward energy-efficient DC operation is performed. The available techniques give rise to four axes of flexibility within a federated DC network. Next, the applicability of the flexible, energy-efficient DC operation is highlighted, by proposing a simple scheduling algorithm, applicable to legacy DC equipment, for effective host and re-arrangement of the DC computing load. The proposed algorithm can provide substantial energy savings during periods of low computing load and improved Quality of Service during periods of high computing load. Concluding, the paper focuses on the grid-stabilization aspects of a federated DC network and the interoperability with the energy network via an Electronic Energy Marketplace in the context of a Smart City.

Multibeam satellite systems offer flexibility that aims at efficiently reusing the available spectrum. To fully exploit the flexibility advantages, the payload resources—transmit power and bandwidth—must be efficiently allocated among multiple beams. This paper investigates the resource optimization problem in multibeam satellites. The NP-hardness and inapproximability of the problem are demonstrated motivating the use of metaheuristics. A systematic approach accomplishing the best traffic match is carried out. The additional requirement of minimizing the total power consumption is then considered, giving rise to a multi-objective optimization approach. The solutions to thea prioriaccomplished traffic matching optimization are used to enhance the efficiency of the multi-objective metaheuristic method proposed and, consequently, of the multibeam satellite system. The optimized performance is represented by the Pareto front, which provides trade-off points between total power consumption and rate achieved. This allows the decomposition of the problem into independent color-based sub-problems rendering the proposed two-stage optimization framework suitable for dimensioning the next generation multispot satellite systems.