A cross-layer optimization of the joint macro- and picocell deployment with sleep mode for green communications

Cai, Shijie; Xiao, Limin; Yang, Haibin; Jing, Wang; Zhou, Shidong

doi:10.1109/wocc.2013.6676373

Cited by 14 publications

(9 citation statements)

References 10 publications

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“…User QoS is added to the optimization problem in [37]. In this case, as the problem to solve is NP-hard, the authors propose a suboptimal, iterative and low-complexity solution.…”

Section: A Network Energy Efficiencymentioning

confidence: 99%

Energy sustainable paradigms and methods for future mobile networks: A survey

Piovesan

Gambı́n

Miozzo

et al. 2018

Computer Communications

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Abstract-In this survey, we discuss the role of energy in the design of future mobile networks and, in particular, we advocate and elaborate on the use of energy harvesting (EH) hardware as a means to decrease the environmental footprint of 5G technology. To take full advantage of the harvested (renewable) energy, while still meeting the quality of service required by dense 5G deployments, suitable management techniques are here reviewed, highlighting the open issues that are still to be solved to provide eco-friendly and cost-effective mobile architectures. Several solutions have recently been proposed to tackle capacity, coverage and efficiency problems, including: C-RAN, Software Defined Networking (SDN) and fog computing, among others. However, these are not explicitly tailored to increase the energy efficiency of networks featuring renewable energy sources, and have the following limitations: (i) their energy savings are in many cases still insufficient and (ii) they do not consider network elements possessing energy harvesting capabilities. In this paper, we systematically review existing energy sustainable paradigms and methods to address points (i) and (ii), discussing how these can be exploited to obtain highly efficient, energy self-sufficient and high capacity networks. Several open issues have emerged from our review, ranging from the need for accurate energy, transmission and consumption models, to the lack of accurate data traffic profiles, to the use of power transfer, energy cooperation and energy trading techniques. These challenges are here discussed along with some research directions to follow for achieving sustainable 5G systems.

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“…User QoS is added to the optimization problem in [37]. In this case, as the problem to solve is NP-hard, the authors propose a suboptimal, iterative and low-complexity solution.…”

Section: A Network Energy Efficiencymentioning

confidence: 99%

Energy sustainable paradigms and methods for future mobile networks: A survey

Piovesan

Gambı́n

Miozzo

et al. 2018

Computer Communications

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“…Some work [7], [6], [8], [9], [22] uses game theory to compute the base station sleep schedule such that the total power used is minimized subject to constraints on the quality of service received by the users. A similar problem is studied in [11] in the context of a heterogeneous network made up of macro and pico cells and is extended in [17] to the case of femtocells. For 3G networks, the minimum network energy use is computed in [35] where a base station is turned off whenever all of its users can be served by another active base station.…”

Section: Previous Work a Base Station Energy Managementmentioning

confidence: 99%

Planning Solar in Energy-Managed Cellular Networks

D'Amours

Girard²,

Sansò

2018

IEEE Access

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There has been a lot of interest recently on the energy efficiency and environmental impact of wireless networks. Given that the base stations are the network elements that use most of this energy, much research has dealt with ways to reduce the energy used by the base stations by turning them off during periods of low load.In addition to this, installing a solar harvesting system composed of solar panels, batteries, charge controllers and inverters is another way to further reduce the network environmental impact and some research has been dealing with this for individual base stations.In this paper, we show that both techniques are tightly coupled. We propose a mathematical model that captures the synergy between solar installation over a network and the dynamic operation of energy-managed base stations. We study the interactions between the two methods for networks of hundreds of base stations and show that the order in which each method is introduced into the system does make a difference in terms of cost and performance. We also show that installing solar is not always the best solution even when the unit cost of the solar energy is smaller than the grid cost. We conclude that planning the solar installation and energy management of the base stations have to be done jointly.Index Terms-Cellular networks, energy management, sleep mode, solar power arXiv:1809.07835v1 [cs.NI]

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“…It is known that BS switching of finding the optimal operation mode of the network system is a difficult combinatorial problem. Since this problem is NP hard and requires high-computational complexity as well as large signaling overhead, there are some works [13][14][15] considering low complexity algorithms to tackle such problem. These algorithms are all sub-optimal, yet proved to be very useful in the given scenarios.…”

Section: Contributionsmentioning

confidence: 99%

Dynamic switching off algorithms for pico base stations in heterogeneous cellular networks

Jin

Jiang

et al. 2015

J Wireless Com Network

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The densification of pico base stations (PBSs) in heterogenous cellular networks (Hetnets) causes redundant energy consumption of the whole network during low traffic periods. In order to address this problem, we take advantage of the traffic load fluctuation over time and area to adapt the necessary resource to the actual traffic demand by introducing sleep mode to the PBSs. To achieve this target, we propose two centralized algorithms (i.e., the heuristic algorithm and the progressive algorithm) in this paper to dynamically switch off the unnecessary PBSs, both of which can track the traffic variation well. We design a utility function of some key factors considered particularly for PBSs in Hetnets. These algorithms rely on the utility function to switch off the redundant PBSs, enabling the working mode of all PBSs be reconfigured periodically. The progressive algorithm is proposed to overcome the inefficiency of the heuristic algorithm. The simulations demonstrate that the execution time of the progressive algorithm is at most one third of that of the heuristic algorithm, which enables the network to respond to the traffic variation more promptly. Besides, the progressive algorithm can switch off more PBSs than the heuristic algorithm while slightly affects the network blocking probability, which indicates that the progressive algorithm has a better potential to save energy. Moreover, simulations also reveal that some key parameters all have nonnegligible influence on the performance of our algorithms. These parameters should be tuned well to trade spare network resource for energy saving.

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A cross-layer optimization of the joint macro- and picocell deployment with sleep mode for green communications

Cited by 14 publications

References 10 publications

Energy sustainable paradigms and methods for future mobile networks: A survey

Energy sustainable paradigms and methods for future mobile networks: A survey

Planning Solar in Energy-Managed Cellular Networks

Dynamic switching off algorithms for pico base stations in heterogeneous cellular networks

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