1,720,968 research outputs found
Dimensioning Renewable Energy Systems to Power Mobile Networks
Full text linkTo face the huge increase in the mobile traffic demand, denser cellular access networks are extensively deployed by mobile operators, entailing high cost for energy supply. Hence, renewable energy (RE) sources are often adopted to power base stations (BSs), in order to make them more self-sufficient and reduce the energy bill. Nevertheless, sizing an RE generation system is a critical task, and the dimensioning methods available in the literature are based on simulation or optimization approaches, hence resulting time consuming or computationally complex. This paper proposes and validates a simple still effective analytical method that, based on the location dependent mean value and variance of RE production, allows to find feasible combinations of photovoltaic (PV) panel and battery sizes, suitable to power a BS and decrease the storage depletion probability below a target threshold. Furthermore, the application of this method highlights the role of RE production variance. Higher values of the variance require larger PV panels, almost doubled with respect to locations with low variance. However, only locations with higher variance benefit from increasing the battery size and relaxing the constraint on energy self-sufficiency, with the scope of reducing the required PV panel capacity and the capital expenditures
Integrating Aerial Base Stations for sustainable urban mobile networks
Full text linkThe 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
Advanced Sleep Modes to comply with delay constraints in energy efficient 5G networks
Full text linkThe 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
Household users cooperation to reduce cost in green mobile networks
Full text linkThe staggering mobile traffic growth is leading to a huge increase of operational costs for Mobile Operators (MOs) due to power supply. In a Smart Grid (SG) scenario, where Demand Response (DR) strategies are widely adopted to better balance the Demand-Supply mismatch, new opportunities arise for MOs, that can receive some monetary rewards for accomplishing the SG requests of periodically increasing or decreasing their energy consumption. This study considers a mobile network that exploits Renewable Energy (RE) to power the BSs and Resource on Demand (RoD) strategies to dynamically adapt the number of active radio resources to the varying traffic demand, in order to better react to the SG requests. On top of this, the purpose of this work is investigating the effects of the cooperation between Household Customers (HCs) engaged in the DR program and the mobile network. Based on a predefined agreement, HCs cooperate with the MO in order to increase its capability to accomplish the SG requests, receiving in return some benefits when stipulating the Internet provisioning contract with the MO. HCs can contribute to achieving the MO goals by means of two techniques. On the one hand, a fraction of the electric loads that are postponed by the HCs when the SG asks for a reduction of the energy consumption can be shifted on behalf of the mobile network, that will receive the corresponding monetary rewards (HC Trade - HCT). On the other hand, HCs can accept to handle some additional mobile traffic, that is moved to their own WiFi Access Points from the BSs, in order to reduce the energy load of the mobile network (WiFi Offloading - WO).Our results show that, although HCT alone provides limited saving in the energy bill due to the poor attitude of HCs to postpone their electric loads, up to 18% of cost saving can be achieved under full HCs cooperation when HCT is combined with WO. The effects of HCs cooperation can be further enhanced by installing larger sized RE generators, allowing to significantly reduce the energy bill up to more than 90%
Load Management with Predictions of Solar Energy Production for Cloud Data Centers
Full text linkPower 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
Reducing the operational cost of cloud data centers through renewable energy
Full text linkThe success of cloud computing services has led to big computing infrastructures that are complex to manage and very costly to operate. In particular, power supply dominates the operational costs of big infrastructures, and several solutions have to be put in place to alleviate these operational costs and make the whole infrastructure more sustainable. In this paper, we investigate the case of a complex infrastructure composed of data centers (DCs) located in different geographical areas in which renewable energy generators are installed, co-located with the data centers, to reduce the amount of energy that must be purchased by the power grid. Since renewable energy generators are intermittent, the load management strategies of the infrastructure have to be adapted to the intermittent nature of the sources. In particular, we consider EcoMultiCloud, a load management strategy already proposed in the literature for multi-objective load management strategies, and we adapt it to the presence of renewable energy sources. Hence, cost reduction is achieved in the load allocation process, when virtual machines (VMs) are assigned to a data center of the considered infrastructure, by considering both energy cost variations and the presence of renewable energy production. Performance is analyzed for a specific infrastructure composed of four data centers. Results show that, despite being intermittent and highly variable, renewable energy can be effectively exploited in geographical data centers when a smart load allocation strategy is implemented. In addition, the results confirm that EcoMultiCloud is very flexible and is suited to the considered scenario
A Novel Energy Model for Renewable Energy-Enabled Cellular Networks Providing Ancillary Services to the Smart Grid
Full text linkIn this paper, we consider cellular networks powered by the smart grid (SG) and by local renewable energy (RE) sources. While this configuration promises energy savings, usage of cleaner energy, and cost reduction, it has some intrinsic complexity due to the interaction between the network operators and the SG. Motivated by the significant advancement in the SG, we consider the case where cellular networks provide the SG with ancillary services by replying to the grid's explicit requests to increase or decrease their grid consumption. We propose a new approach for configuring and operating base stations (BSs) to provide ancillary services. Based on real data, we model the energy state of a BS as a Markov chain taking into account the proposed energy management policy, randomness of SG requests, and RE generation. We use the model to evaluate the performance of the system, and to decide proper settings of its parameters in order to minimize the energy operational cost. The performance of our proposal is then compared against those of other approaches. Results show that important cost savings, with negligible degradation in quality of service, are possible when RE generation, SG patterns, and storage sizes are properly taken into account
Adaptive HAPS Offloading: A Strategy for Supporting RAN During High Traffic Load
Full text linkHigh altitude platform stations (HAPS) have been
proposed to support terrestrial mobile networks, offering a
sustainable alternative to network densification. With their wide
coverage areas and green energy consumption model, HAPS
super macro base stations (SMBSs) are well suited to handle the
massive and dynamic mobile data traffic demand. This research
introduces an adaptive traffic offloading strategy that leverages
the capabilities of HAPS to support radio access network (RAN),
particularly during periods of high network demand. To enable
HAPS to effectively assist the RAN, it is crucial to accurately
predict which base stations (BSs) will experience high loads.
Precise forecasting of these demands is hence essential to ensure
timely and targeted offloading of traffic to the HAPS when and
where it is most needed. The proposed approach predicts and
manages loads by considering temporal and geographical factors.
At the core of this approach is the Q-learning update rule,
which is continuously used to refine offloading decisions and
flexibly adapt to changing conditions. Our simulation results
demonstrate that the proposed HAPS offloading approach is
effective in maintaining balanced loads in the terrestrial RAN
during peak periods, by dynamically adapting to the typical
traffic characteristics of different areas and to their evolution
over time
- …
