`Efficiency' is a booming word in the current global scenario. The paper describes such an algorithm named `cell zooming' that aims at providing energy efficiency to the operating cellular network. Energy efficiency is achieved by operating some selected base stations in sleep mode. The energy consumed in sleep mode is much lesser when compared to the normal operation. Here, the performance analysis for cell zooming is done by comparing a number of channel models; taking into account the practical implications. Cell zooming algorithm helps in attaining a greener environment; besides satisfying all the user needs with appreciable quality of service.

With the development of mobile communication systems, increasing number of base stations causes a significant increase in power consumption. This increase brings necessity of energy-efficient systems to the forefront. The most important methods that provide energy efficiency is switch on / off (CSO) and cell zooming (CZ). In this paper, a new algorithm is proposed using real mobile operator data and based on the CSO/CZ methods. Simulation results are presented in the study, demonstrating the applicability of the method. Energy efficiency is provided by %2.96 in the simulated results.

While Sleep techniques show great promises in reducing the energy consumption of mobile networks, the necessity of providing “always-on” services with current cellular architectures significantly hinders the introduction of effective Sleep modes. As the process of network densification goes forward, it is paramount to lower to a minimum the additional energy consumption required by the deployment of more and more network sites. Fortunately, the deployment of Heterogeneous Networks opens new possibilities for the integration of simpler and highly effective Sleep technologies. In this paper, we evaluate and quantify the energy savings attainable by the latest Power Modulation and Sleep modes in a Heterogeneous LTE Network deployed to serve a traffic-intensive urban office area. Power consumption figures are based on those of real cells currently available on the market. Results indicate that the combination of Power Modulation and Sleep techniques is able to cut in half the energy required by the modeled Heterogeneous Network and that Sleep modes, in particular, can be the most beneficial.

Ultra-dense deployments and mobile cells significantly change cellular networking paradigm. Infrastructure and topology of cellular networks become dynamic as opposed to legacy systems where the infrastructure is assumed to be stationary. As topology morphs, base station or user density of networks also change impacting the performance in terms of resource utilization and quality of service. To increase network capacity, preserve coverage and conserve energy, network density should be considered in communication stacks to make the network density-aware and -adaptive. In this work, we analyze the impact of density on network outage in cellular networks. We propose a novel cell zooming technique at run-time considering network outage and density jointly with a three-dimensional base station density estimator.

This paper presents an energy efficient aware continuous cell zooming algorithm based on green random cellular heterogeneous networks (HetNet). Firstly, we derive a two-tier Poisson Voronoi Tessellation (PVT) random heterogeneous network model in which the macro base stations (MBS), small-cells base stations (SBS), macro users (MU) and small-cell users (SU) locations are drawn randomly from independent homogeneous Poisson point processes (HPPPs). Secondly, by updating the SUs’ locations in SBSs periodically, the radius of the cells can be adjusted adaptively to achieve cell zooming. The performance of this work is examined under different user densities and ratios compared with reference algorithm. Numerical evaluations show that our proposed algorithm can significantly decrease system energy consumption and improve energy efficient.

This paper presents an energy efficient and QoS aware cell zooming scheme assisted by low power Relay Base Stations (RBSs). Cell zooming is a promising technology for future cellular networks in order to achieve a higher energy efficiency, while maintain QoS of the mobile users. However, the benefit of cell zooming scheme is limited by the increased outage probability, pertaining to the fact that new coverage holes can be formed and inter-cell interference also increases as a result of increased transmission power. In this paper we propose a relay-assisted cell zooming algorithm that achieves energy efficiency in cellular networks while maintain QoS of the users. We analyze energy efficiency and resource consumption of the proposed scheme and compare the performance with that of the basic cell zooming scheme.

This paper investigate the problem regarding the minimization of the power of the base station by using the cell zooming technique. In this paper, we mainly focused on reducing the power consumed at the base station end, whereas most of the power minimization related work in the literature today is focused on the mobile end only. Cell zooming is a technology that promises to minimize the resource utilization. This technique is designed to save the energy consumed by the base station by maintaining the quality of services. To understand the working of the cell zooming techniques centralized algorithm is a very basic way. Our simulations shows that the proposed technique can achieve upto 25% reduction in the power consumption over the base station.

This paper evaluates and highlights the performance of three dynamic cell zooming algorithms applied in both omni-directional and sector-based networks. A possible framework compatible with dynamic cell zooming algorithms for user's location detection is presented. The performance of each cell zooming algorithm is simulated in terms of power saving and possible outage in a full-day operation. According to simulated results, there is no significant difference between the performance of each algorithm and others at low traffic hours, but their performances are different at high traffic hours. From an overall comparison, the continuous cell zooming algorithm illustrates the best performance in terms of power saving, followed by fuzzy algorithm and then discrete algorithm. However, in terms of possible outage, the continuous algorithm is very sensitive to user movement and it shows a very high possible outage ratio. Meanwhile, the outage is totally removed in the discrete and fuzzy algorithms according to their concept. The dynamic cell zooming algorithms show a larger power saving in sector-based network since it hosts a more detailed plan to perform cell zooming.

This paper addresses the power saving problem in mobile networks. Base station (BS) power and network traffic volume (NTV) models are first established. The BS power is modeled based on in-house equipment measurement by sampling different BS load configurations. The NTV model is built based on traffic data in the literature. Then, a threshold-based adaptive power saving method is discussed, serving as the benchmark. Next, a BS power control framework is created using Q-learning. The action-state function of the Q-learning is approximated via a deep convolutional neural network (DCNN). The DCNN-Q agent is designed to control the loads of cells in order to adapt to NTV variations and reduce power consumption. The DCNN-Q power saving framework is trained and simulated in a heterogeneous network including macrocells and microcells. It can be concluded that with the proposed DCNN-Q method, the power saving outperforms the threshold-based method.

The remarkable growth of the marine economy has made the maritime wideband communication a research focus. This article presents a multiple backhauls based maritime wideband communication (MBC) architecture to provide the wideband access to the ocean vessels. In MBC architecture, the ships equipped with shipborne LTE base stations (BSs) could autonomously choose the maritime satellites or the onshore high-tower BSs as the backhaul nodes. The other ships carrying the shipborne customer premise equipment (CPE) can access the Internet through the shipborne BSs. Furthermore, an interference-and-voyage based cell zooming strategy is proposed to coordinate the inter-cell interference in the MBC architecture. The proposed strategy adjusts the BS cell size based on the BS inter-ference level and ship voyage. Simulation results using the real ship data show that under the MBC architecture, the proposed cell zooming strategy can effectively coordinate the inter-cell interference and increase the throughput of the maritime wideband network.

The rapid escalation of user traffic and service innovation has made the deployment of small cell base stations essential for eventually decreasing energy consumption in future generation wireless network. An energy efficient small cell zooming strategy is proposed using weighted majority cooperative game for two-tier fifth generation (5G) mobile network. The proposed strategy is referred as ‘5G-ZOOM-Game’. Small cells ‘zoom in’ and ‘zoom out’ dynamically according to the proposed ‘5G-ZOOM-Game’ algorithm. Different frequency sets are assigned to small cells based on adjacency for reducing interference. In the proposed approach femtocells are used as small cells. The proposed algorithm is applied between two adjacent femtocells. Out of two adjacent femtocells, higher majority femtocell is selected based on weighted majority game; this femtocell zooms its coverage area. The utility function of the proposed approach is defined to connect maximum possible number of mobile devices by increasing the higher majority femtocell’s coverage area. Higher majority femtocell is chosen based on the load and minimum distance between mobile device and femtocell base station. Proposed 5G-ZOOM-Game network reduces ~ 35% of power consumption and increases signal-to-interference-plus-noise-ratio (SINR) and spectral efficiency by ~ 30% and ~ 60% respectively than the existing approaches.

The growing environmental awareness coupled with the rising costs of energy have sparked a keen interest in the deployment of energy-efficient communication technologies over the infrastructure of cellular networks. Base stations (BSs) are responsible for the largest portion of power consumption and energy usage in cellular networks. Thus, sleep/wake-up scheduling strategies for BSs can significantly improve the energy-efficiency (EE) of cellular networks. In this paper, we propose a Fuzzy Q-Learning based energy-efficient sleep/wake-up mechanism for BSs in a heterogeneous network. The goal is to save energy, without compromising the offered Quality of Service (QoS), by switching off the redundant BSs according to the local traffic profile and depending on the required area coverage and cell EE. The energy savings achieved under our proposed sleep scheduling strategy may lead to inevitable coverage holes as fewer BSs are active. To this end, we propose to enhance the sleep/wake-up mechanism with device-to-device communications to compensate for the coverage loss in areas where BSs are switched off. Simulation results validate that the proposed framework provides significant improvements in power consumption and the EE while satisfying the minimum coverage and QoS requirements.

The explosive growth of mobile users has brought great challenges to traditional cellular networks. The deployment of various Small-cell Base Stations (SBSs) provides a flexible way to address the problem of covering blind spots in Macro-cell Base Station (MBS) and reduce the traffic loads from MBS. In this paper, we investigate energy-saving based on various densities of SBSs including picocells and femtocells in multi-tier heterogeneous networks. We propose an elastic cell-zooming algorithm (ECZA) in order to solve the problem of power consumption and traffic loads in the three-tier heterogeneous cellular networks. Besides, to solve the SBSs zooming problem, which is a NP hard problem, we devise the greedy algorithm to find the suboptimal solution. Simulation results show that the proposed scheme can effectively improve the energy efficiency under the constraint of the outage probability of mobile users in the multi-tier HetNets.

The ever-increasing demands of current wireless devices for high data throughput and frequent mobility forces network operators to provide seamless connectivity with guaranteed QoS to all wireless users. This situation creates a big concern in design and development of cost-effective and energy efficient solution for 5G cellular networks. small cells (Femtocells) deployment provides low cost and energy efficient solution if equipped with smart and efficient power saving techniques. In this paper, we have proposed a data traffic demands aware approach for optimum throughput and maximum energy utilization while maintaining the desired QoS required by users. The results and analysis confirm that the proposed approach will not only provide better energy utilization and optimum data throughput but will also contribute towards energy efficient aspect of 5G cellular networks.

The dense deployment of heterogeneous networks (HetNets) has shown to be a promising direction to cope with the capacity demands in the future 5G wireless networks. The large number of small cell base stations (SBSs) in HetNets intended to help in achieving the capacity requirement of 5G networks can also result in a significant increase in energy consumption. This is due to the fact that there might be few associated users in certain SBSs, intelligently switching them to low energy consumption modes, or turning them off without seriously degrading system capacity is desirable in order to improve the energy savings in the HetNets. Also, the unnecessary handovers caused due to this dynamic power level switching in the SBS should not be neglected. In this paper, fuzzy logic-based game-theoretic framework is utilized to address these issues and examine the energy efficiency improvements in HetNets. We design fuzzy inference rules for handover decisions, and target base station selection is performed through a fuzzy ranking technique, while simultaneously considering both energy/spectral efficiency and signaling overhead. The results show that energy consumption can be improved considerably especially for high user velocities, while also managing ping-pong handovers.

The current base station centric cellular network architecture hinders the implementation of effective sleep techniques, often resulting in energy-inefficient mobile networks. The efforts towards 5G and network densification, however, open new possibilities and may, at last, allow the integration of sleep modes without any QoS degradation. In this paper, we consider heterogeneous networks in which data and control planes are split and independent, referred to as SDHN. We present an energy consumption metric that can be used to evaluate the radio access power consumption and the associated energy efficiency of these networks. Concerning other metrics in literature, the proposal accounts for both the coverage area as well as the traffic load, and it is relatively simple to use. The proposed metric is applied to evaluate the power consumption performance of an LTE SDHN in an urban indoor scenario. Results confirm that sleep modes in such architectures can effectively cut power consumption and improve energy efficiency while preserving QoS.

The concept of sleep mode is widely applicable in a cellular network in which the base station can be turned off if certain traffic and power conditions are met. So this technique is most often used for power saving. On the other hand, the concept of cell zooming is emerging as a very promising technology to reduce power consumption. In cell zooming, the original size of the cell can be changed according to the incoming or outgoing traffic load of that particular cell both for homogenous and heterogeneous networks. So, in this process the cell size is increased or decreased based on the traffic intensity of the cell. In this paper an algorithm is proposed which merges the concept of cell zooming and sleep mode together for significant power reduction in a cellular network. The algorithm is examined for three different traffic conditions to calculate the power saving. Keywords—Sleep Mode, Cell Zooming, Heterogenous network, HomogeneousNetwork, PowerEfficiency, SwithingFrequency

Telecommunication industry is predicted to have an important role in total energy consumption of industry area. Thus, the increasing energy cost in operational costs demands effective tools to identify energy efficiency indicators (EEIs) and predict the evolution of energy efficiency performance (EEP). The energy efficiency (EE) improvement of any segment in the communication networks (CNs) can help lower energy cost and protect environment of network operators. The spread of users in overall areas results in the increasing number of base stations (BSs), which are main components of CN. As a supporting function of management tools, predicting EEP is a requested function of energy management system (EMS) by network operators. In this context, this paper presents the demand of EMS for integrating prediction of EEP deterioration. In addition, the energy efficiency function block to modelling the BSs with numerous of indicators is proposed. By prognostic approaches (PA) and suitable aggregation methods, network operators can handle their situations. An EEP degradation demonstration has been conducted by using PA and EE model of BS. An additional benefit can also be seen as increasing reliability of energy backup units in various scenarios of power source disturbance. Finally, the requirement of sensor networks in acquiring technical data of BS deterioration states is mentioned.

Summary In this paper, we address the issue of energy saving in wireless communication networks; a special interest is given to heterogeneous networks (HetNets) which are supposed to be the key feature of the future communication networks, namely, the 5G cellular networks [1]. In a HetNet, different communication technologies coexist; with the use of vertical handovers, the users could switch between the different operating ones. Our work aims to make the used protocols for vertical handover collaborative, in such a way to reduce the overall network energy consumption. Our study is based on Media Independent Handover (MIH) protocol [2]. We bring modifications into this later for energy saving purposes in order to create a green collaborative MIH protocol. The proposed enhancements on MIH protocol allow gathering detailed information about network state and using them as statistics and indicators to manage network density according to traffic load and users location. Our experiments are performed using NS2.29 with National Institute of Standards and Technology (NIST) add-on which implements the MIH protocol (802.21). By comparing the results while using our proposed contribution and the conventional MIH protocol, we can see that the modifications done allow a significant improvement in terms of energy saving while maintaining reliability. Copyright © 2015 John Wiley & Sons, Ltd.

Reducing energy consumption of mobile communication networks has gained significant attentions since it takes a major part of the total energy consumption of information and communication technology (ICT). In this paper, we consider 5G networks with heterogeneous macro cells and small cells, where data and control planes are separated. We consider two types of data traffic, i.e., low rate data traffic and high rate data traffic. In basic separation architecture, a macro cell base station (MBS) manages control signals, while a small cell base station (SBS) manages both low rate data traffic and high rate data traffic. In the considered modified separation architecture, an MBS manages control signals and low rate data traffic, while an SBS manages high rate data traffic. Then, an efficient energy saving scheme for base stations (BSs) is proposed, where the state of a BS is determined depending on the number of user equipments (UEs) that request high rate data traffic and the number of UEs that exist under the overlapping areas commonly covered by the considered BS and the neighbor BSs. We formulate an optimization problem for the proposed energy saving scheme and obtain the solution using particle swarm optimization (PSO). Numerical results show that the proposed energy saving scheme in the modified separated network architecture has better energy efficiency compared to the conventional energy saving schemes in both basic and modified separated network architectures. Also, the proposed energy saving scheme has lower aggregate delay.