Network Sharing (NS) has gained increasing interest for Mobile Operators (MOs) because of the high investment costs of 5G combined with a period of low return of investment. The benefits that NS can offer include reduced capital and operational expenditures, because of fewer equipment, and lower energy consumption, possibly combined with higher network resiliency. While these aspects have been investigated in the literature, in those works more attention was paid to the overall benefits, disregarding asymmetries between the involved MOs. In this paper we address the issue of fairness in sharing network infrastructure among MOs and we introduce a Fair Cooperative Network Sharing (FCNS) framework that dynamically offloads traffic among co-located BSs owned by different MOs with two primary objectives: distributing active operational time more equitably between BSs in a pair, and significantly decreasing the failure rate of BS pairs. Simulation results based on empirical mobile traffic data demonstrate that the proposed FCNS framework effectively balances the BS active time across operators. In addition, FCNS achieves energy savings of up to 38% for each MO within a BS pair and reduces the failure rate by approximately 20%. These findings highlight the potential of cooperative network sharing as a feasible and sustainable solution for resilient 5G deployments.

The growing demand for mobile connectivity is pushing terrestrial radio access networks (RANs) toward congestion, especially during peak hours in dense urban areas. High Altitude Platform Stations (HAPSs) provide a flexible means to support overloaded base stations (BSs) through aerial traffic offloading. This paper presents a simulation-based framework that evaluates HAPS-assisted offloading under realistic traffic traces, considering baseline and upscaled demand scenarios. We assess packet-level delay, user denial, energy consumption, and infrastructure expansion. Results show that HAPS markedly reduce queuing delay and nearly eliminate user denial under moderate overload, while also lowering operational and capital expenditures by relieving congested BSs. These findings confirm the viability of HAPS as a complementary aerial layer for congestion mitigation, postponing the need for terrestrial densification in the path toward B5G networks.

Telecom base stations increasingly rely on solar power and battery storage to achieve sustainable, cost-effective energy solutions, but battery degradation poses a significant challenge to system reliability and longevity. This paper introduces an innovative optimization framework that accounts for lithium-ion battery aging, modeling both calendar and cycle degradation with a novel segment-based approach. Designed for seamless integration into cost-effective energy planning, the framework optimizes photovoltaic (PV) panel and battery sizing to minimize costs and extend system lifespan. Validated using real-world base station power consumption data, our approach outperforms traditional rainflow-based aging models, reducing battery cycle wear by up to 65.5% compared to aging-unaware methods and by an additional 10% over rainflow-based methods. By enabling real-time battery health tracking, it supports dynamic energy management, offering a practical solution for sustainable telecom networks.

Unmanned aerial base stations (UABSs) are a promising solution for improving coverage and capacity in vehicle-to-everything (V2X) communications, particularly in dense urban areas. However, their operation is constrained by onboard energy consumption, required for both flight and communication. Beamforming, while enhancing network performance, adds to this challenge due to its energy-intensive nature.This paper proposes a sequential and hierarchical decision-making framework for UABS operations, considering trajectory planning, dynamic beamforming, and radio resource assignment (RRA). While heuristic and optimal solutions are employed for trajectory planning and RRA respectively, the beamforming model is modeled as Markov decision process (MDP) to maximize served user demand, weighted by time-varying priorities, under strict energy constraints. Leveraging a dueling double deep q-network (3DQN) algorithm that penalizes energy budget violations, an agent policy for the beamformer is then trained. Simulation results demonstrate that the proposed approach outperforms static beamforming benchmarks and closely matches an ideal step-wise oracle, achieving a balance between energy efficiency and served user demand while adapting to dynamic V2X traffic conditions.

The transition towards the era of 5G and beyond is currently fostered by the extensive penetration of extremely demanding communication services, characterized by the need for exchanging increasingly larger traffic volumes with tight throughput and latency constraints. Nevertheless, the consequent massive densification of radio access networks (RANs) entails remarkable sustainability concerns, related to the staggering increase of energy demand and to the costly deployment of new infrastructure that, being dimensioned for future peak demands, may result underutilized for long periods of time. Furthermore, new potential vulnerabilities emerge that may impair the provisioning of resilient communication services. In this context, sharing network resources among different mobile operators (MOs) may play a key role to improve energy efficiency and to enhance resilience of future mobile networks. We hence investigate the potential benefits derived from the sharing of network infrastructure (primarily Base Stations with their portion of spectrum) among different MOs, comparing different areas, from a urban densely populated environment to a rural region. Based on real mobile traffic data, we design data-driven strategies to dynamically offload traffic among Base Stations owned by different MOs, allowing the switch off of unneeded resources. Our results shows that network sharing (NS) is effective in achieving huge energy saving and significant reduction of the electricity bill. Furthermore, proper configuration settings of the offloading strategies allow to trade off between sustainability goals and Quality of Service, hence enabling a feasible deployment of 5G scenarios and a sustainable evolution towards 6G.

The rapidly evolving communication landscape, with the advent of 6G technology, brings new challenges to the design and operation of wireless networks. One of the key concerns is the energy efficiency of the Radio Access Network (RAN), as the exponential growth in wireless traffic demands increasingly higher energy consumption. In this paper, we assess the potential of integrating a High Altitude Platform Station (HAPS) to improve the energy efficiency of a RAN, and quantify the potential energy conservation through meticulously designed simulations. We propose a quantitative framework based on real traffic patterns to estimate the energy consumption of the HAPS-integrated RAN and compare it with the conventional terrestrial RAN. Our simulation results elucidate that HAPS can significantly reduce energy consumption by up to almost 30% by exploiting the unique advantages of HAPS, such as its self-sustainability, high altitude, and wide coverage. We further analyze the impact of different system parameters on performance, and provide insights for the design and optimization of future 6G networks. Our work sheds light on the potential of HAPS-integrated RAN to mitigate the energy challenges in the 6G era, and contributes to the sustainable development of wireless communications.

While designed for being energy efficient, the deployment of 5G networks will further increase Radio Access Networks (RANs) energy consumption with the twofold effect to raise sustainability issues and increase operational costs for Mobile Network Operators (MNOs). However, the energy waste occurring during low traffic periods can be mitigated through Advanced Sleep Modes (ASMs) that make the BSs enter into progressively deeper and less consuming sleep modes. Deep sleep modes, unfortunately, have longer reactivation times, and may jeopardize service quality. In this paper, focusing on 5G latency requirements in low traffic periods, we propose a framework to dynamically adapt the ASM configuration settings to the actual traffic load so as to meet a desired constraint on the average BS reactivation delay.

The extensive densification of mobile networks is increasing the network energy consumption and leading to remarkable economical and sustainability concerns. At the same time, regulatory and physical constraints, especially in urban environments, may limit the network expansion and the free installation of Base Stations (BSs). In this context, High Altitude Platform Stations (HAPSs) are emerging as a promising solution to host aerial BSs that can provide additional capacity over a wide geographical area, to offload the on-ground mobile network and support a sustainable transition towards the 6G era. This paper investigates the potential of HAPS offloading to reduce the energy demand from the grid and the operational cost of mobile networks. Our results highlight the effectiveness of HAPS offloading in reducing the size of the RE supply that is required to achieve grid energy reduction on the terrestrial network, thus enhancing the feasibility of a sustainable evolution towards 6G networks. Different allocation strategies are designed and analyzed under several configuration settings, to dynamically adapt the HAPS capacity to the traffic variability in space and over time. A fine tuning of the strategy settings is proved effective in trading off physical constraints, operational cost, sustainability goals, and Quality of Service.

Due to the evolution in communications technologies and antennas, as well as advances in solar panel efficiency, High Altitude Platforms (HAPS) have been recently considered as a promising aerial network component, to support Radio Access Networks (RANs). Through their directional antenna they can activate beams and provide coverage to up to 1.5 km radius ground area. In this work, we consider a HAPS equipped with a Multi Access Edge Computing (MEC) server, which provides caching capabilities. The HAPS is used to off-load content requests. We analyse an urban environment scenario, as well as the effects of the simultaneous activation of beams in different areas. Results demonstrate that the HAPS is a suitable solution to bring additional capacity to the RAN and highlight that the provided performance strictly depends on the traffic demand profile of the covered portion of RAN.

The staggering growth of mobile traffic fostered by the extensive spreading of 5G technology and massive Internet of Things (IoT) applications is leading to network densification, entailing a boost in network power consumption, with consequent higher operational cost for Mobile Network Operators (MNOs) and raising sustainability issues. To reduce energy consumption when the traffic is low, new BSs feature Advanced Sleep Modes (ASM) that allow to reduce the network energy consumption by gradually deactivating the BSs into progressively deeper sleep modes with lower power consumption. However, the deep sleep modes cause high reactivation delays that may jeopardize the Quality of Service.In this paper, focusing on the periods in which traffic is very low, we extensively investigate the potentiality of ASMs based operation in terms of the trade-off between energy saving and delay under different 5G scenarios and traffic loads. By observing that optimal configuration settings depend on the scenario and on the load, we design a framework based on a stochastic model to perform dynamic tuning of the configuration settings that adapts in real time the parameters to the actual traffic load and scenario.

Power supply of big infrastructures is today a tremendous operational cost for providers and the expected growth of Internet traffic and services will lead to a further expansion of the computing and networking infrastructures and this, in its turn, raises also concerns in terms of sustainability. In this context, renewable energy generators can help to both reduce costs and alleviate the concerns of sustainability of big infrastructures. In this paper, we consider the case of Data Centers (DCs) composed of a few sites located in different geographical positions and powered with solar energy. Due to the intermittent nature of solar energy, different time zones and price of electricity in different locations, load management strategies are fundamental. We consider predictions of the solar energy production performed through Artificial Neural Networks and we assess the impact of predictions on load management decisions and, ultimately, on the DC performance.

The field of networking, like many others, is experiencing a peak of interest in the use of Machine Learning (ML) algorithms. In this paper, we focus on the application of ML tools to resource management in a portion of a Radio Access Network (RAN) and, in particular, to Base Station (BS) activation and deactivation, aiming at reducing energy consumption while providing enough capacity to satisfy the variable traffic demand generated by end users. In order to properly decide on BS (de)activation, traffic predictions are needed, and Artificial Neural Networks (ANN) are used for this purpose. Since critical BS (de)activation decisions are not taken in proximity of minima and maxima of the traffic patterns, high accuracy in the traffic estimation is not required at those times, but only close to the times when a decision is taken. This calls for careful processing of the ANN traffic predictions to increase the probability of correct decision. Numerical performance results in terms of energy saving and traffic lost due to incorrect BS deactivations are obtained by simulating algorithms for traffic predictions processing, using real traffic as input. Results suggest that good performance trade-offs can be achieved even in presence of non-negligible traffic prediction errors, if these forecasts are properly processed.

The use of base station (BS) sleep modes is one of the most studied approaches for the reduction of the energy consumption of radio access networks (RANs). Many papers have shown that the potential energy saving of sleep modes is huge, provided the future behavior of the RAN traffic load is known. This paper investigates the effectiveness of sleep modes combined with machine learning (ML) approaches for traffic forecast. A portion of an RAN is considered, comprising one macro BS and a few small cell BSs. Each BS is powered by a photovoltaic (PV) panel, equipped with energy storage units, and a connection to the power grid. The PV panel and battery provide green energy, while the power grid provides brown energy. This paper examines the impacts of different prediction models on the consumed energy mix and on QoS. Numerical results show that the considered ML algorithms succeed in achieving effective trade-offs between energy consumption and QoS. Results also show that energy savings strongly depend on traffic patterns that are typical of the considered area. This implies that a widespread implementation of these energy saving strategies without the support of ML would require a careful tuning that cannot be performed autonomously and that needs continuous updates to follow traffic pattern variations. On the contrary, ML approaches provide a versatile framework for the implementation of the desired trade-off that naturally adapts the network operation to the traffic characteristics typical of each area and to its evolution.

This paper studies the performance gains resulting from the sharing of energy and network resources in the case of co-located base stations of different mobile network operators, powered by photovoltaic panels, and equipped with energy storage. Three configurations are considered for base station cooperation. The first one assumes two non-cooperating base stations, each one exploiting its own power system and serving its own customers, hence with no sharing. The second considers a shared power system, but no cooperation in customer service. The third looks at cooperation in both energy production and service provisioning, since only one base station handles all customers when traffic is low. Using an analytical modeling framework, we compute performance metrics for the three cases, and we show that significant gains are possible in the case of energy and network sharing.

In the last years, Green Mobile Networks that are powered with renewable energy sources have been designed and deployed with the twofold objective of reducing operational costs and providing service in scenarios in which the power grid is not reliable. At the same time, the introduction of Smart Grids is deeply changing the energy market, by effect of the grid actively interacting with its customers. In this paper, we consider a scenario in which a green mobile network is integrated in a smart grid. The mobile network interacts with the smart grid, responding to its requests by adapting its load. Load adaptation is obtained by resource on demand strategies that operate on Base Stations, and by taking decisions about the use of the renewable energy that is locally produced and that can be used for powering the green mobile network, it can be stored or even returned to the grid. The results, derived through a Markovian model, show that the use of resource on demand strategies in the green mobile network improves the interaction between the network and the smart grid: significant cost gain can be achieved, the responsiveness to the smart grid requests increases, low storage probability decreases.

In this study, the potential of applying different strategies to reduce the energy consumption of a wireless access network, powered by a photovoltaic panel system, during energy shortages is investigated. The goal is to reduce the amount of energy that should be bought from the traditional energy grid during a renewable energy shortage. Three different strategies are compared and the results are looking very promising. Depending on the strategy, up to 72% less energy should be bought compared to the fully operational network for a worst case scenario and a time period of 1 week. However, applying such a strategy has also its influence on the network performance. The influence on the user coverage is limited, with a reduction of 3% at maximum, but the capacity offered by the network decreases significantly with 51% up to even 71%.

Being cheap and easy to deploy, dense WLANs are becoming the most popular solution to providing Internet access in locations where the population of users is large, such as on campuses, large enterprises, etc. The large density of access points (APs) comes from the need to have enough capacity to carry the traffic generated at peak hours although, in these scenarios, traffic varies a lot on a daily, weekly, or seasonal basis. During low or no traffic periods, APs are underutilized, even if they are consuming energy almost in the same amount as if they were fully loaded. Promising solutions to reducing this form of energy waste consist of activating only the number of APs that is strictly needed to carry the actual traffic; in other words, to make capacity dynamically adaptive through resource-on-demand (RoD) strategies. In this paper, we investigate the case of a portion of the dense WLAN on our campus. Through real trace analysis, we investigate users' behavior in accessing the WLAN and formulate a stochastic characterization of it. We propose a simple model that describes RoD strategies and use it to study the system performance that is evaluated in terms of AP activity and inactivity periods, AP switching frequency, and energy saving. Finally, we present some results obtained by experimenting with RoD strategies in a portion of the WLAN. Our results show that RoD strategies for dense WLANs are feasible and effective in trading-off the opposite needs to save some energy and to guarantee a smooth network operation and high quality of service.