Energy efficient management of two cellular access networks

Marsan, Marco Ajmone; Meo, Michela

doi:10.1145/1773394.1773406

Cited by 102 publications

(79 citation statements)

References 6 publications

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“…Each time period k ∈ K may correspond to several weeks, and the decisions x(b, k, t) can be mapped, e.g., to equipment orders or to the schedule of infrastructure deployment teams. Decommissioning or updating a base station is substantially different from turning it on and off in order to follow daily traffic fluctuation, as envisioned in "green networking" solutions [6], [7], [8]. Indeed, as discussed later, the two solutions are orthogonal and altogether compatible.…”

Section: System Modelmentioning

confidence: 99%

See 1 more Smart Citation

A Sharing- and Competition-Aware Framework for Cellular Network Evolution Planning

Francesco

Malandrino

Forde

et al. 2015

IEEE Trans. Cogn. Commun. Netw.

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Abstract-Mobile network operators are facing the difficult task of significantly increasing capacity to meet projected demand while keeping CAPEX and OPEX down. We argue that infrastructure sharing is a key consideration in operators' planning of the evolution of their networks, and that such planning can be viewed as a stage in the cognitive cycle. In this paper, we present a framework to model this planning process while taking into account both the ability to share resources and the constraints imposed by competition regulation (the latter quantified using the Herfindahl index). Using real-world demand and deployment data, we find that the ability to share infrastructure essentially moves capacity from rural, sparsely populated areas (where some of the current infrastructure can be decommissioned) to urban ones (where most of the next-generation base stations would be deployed), with significant increases in resource efficiency. Tight competition regulation somewhat limits the ability to share but does not entirely jeopardize those gains, while having the secondary effect of encouraging the wider deployment of next-generation technologies.

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Section: System Modelmentioning

confidence: 99%

“…This result allows us to efficiently tackle large-scale, real-world topologies, as we see in Sec. 6. Also notice that Property 3 refers to the combined complexity of our solution concept, i.e., all the algorithms described in Sec.…”

Section: Property 3 the Worst-case Combined Time Complexity Of All mentioning

confidence: 99%

A Sharing- and Competition-Aware Framework for Cellular Network Evolution Planning

Francesco

Malandrino

Forde

et al. 2015

IEEE Trans. Cogn. Commun. Netw.

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“…We assume that the daily traffic profile of the whole Hetnets is the same and repeats periodically, which can be approximated by a sinusoidal-like periodic behavior as follows [27]:…”

Section: Daily Network Traffic Profilementioning

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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“…In [5] a location-dependent traffic profiling study is conducted on real 3G network traces showing that 23-53% energy saving is possible via dynamic base station activation. Operators cooperation is investigated in [10], where optimal switch-off frequencies of base stations are computed in order to achieve balanced energy savings and roaming costs. Assuming a sinusoidal traffic profile, and following a threshold-based activation rule by each base station, an analysis of achievable energy savings is provided in [7].…”

Section: Introductionmentioning

confidence: 99%

Distributed base station activation for energy-efficient operation of cellular networks

Abbasi

Ghaderi

2013

Proceedings of the 16th ACM International Conference on Modeling, Analysis &Amp; Simulation of Wireless and Mobile Systems

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Dynamic base station activation (DBA) has recently emerged as a viable solution for reducing energy consumption in cellular networks. While most of the works on this topic focused on centralized decision making algorithms, in this paper we investigate distributive solutions. These solutions are particularly desirable due to importance of self-organization and self-optimization in future cellular networks. The goal of DBA is to achieve an optimal trade-off between network operator's revenue and operational cost while guaranteeing coverage for network users. The problem is posed as a network utility maximization aiming to find the optimal activation schedule of each base station. Using Lagrangian duality, the problem is decomposed into smaller subproblems, where each subproblem is solved locally at its associated base station. Controlled message passing among base stations ensures convergence to the global optimal solution. Moreover, this general solution is further extended to capture the combinatorial nature of DBA. Finally, numerical results are provided to demonstrate the behavior of our solution in terms of utility and cost trade-off and convergence in some example network scenarios.

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Energy efficient management of two cellular access networks

Cited by 102 publications

References 6 publications

A Sharing- and Competition-Aware Framework for Cellular Network Evolution Planning

A Sharing- and Competition-Aware Framework for Cellular Network Evolution Planning

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

Distributed base station activation for energy-efficient operation of cellular networks

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