3,688 research outputs found

    Energy Efficient Relay-Assisted Cellular Network Model using Base Station Switching

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    Cellular network planning strategies have tended to focus on peak traffic scenarios rather than energy efficiency. By exploiting the dynamic nature of traffic load profiles, the prospect for greener communications in cellular access networks is evolving. For example, powering down base stations (BS) and applying cell zooming can significantly reduce energy consumption, with the overriding design priority still being to uphold a minimum quality of service (QoS). Switching off cells completely can lead to both coverage holes and performance degradation in terms of increased outage probability, greater transmit power dissipation in the up and downlinks, and complex interference management, even at low traffic loads. In this paper, a cellular network model is presented where certain BS rather than being turned off, are switched to low-powered relay stations (RS) during zero-to-medium traffic periods. Neighbouring BS still retain all the baseband signal processing and transmit signals to corresponding RS via backhaul connections, under the assumption that the RS covers the whole cell. Experimental results demonstrate the efficacy of this new BS-RS Switching technique from both an energy saving and QoS perspective, in the up and downlinks

    Cell sleeping for energy efficiency in cellular networks: Is it viable?

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    An approach advocated in the recent literature for reducing energy consumption in cellular networks is to put base stations to sleep when traffic loads are low. However, several practical considerations are ignored in these studies. In this paper, we aim to raise questions on the feasibility and benefits of base station sleeping. Specifically we analyze the interference and capacity of a coverage-based energy reduction system in CDMA based cellular networks using a simple analytical model and show that sleeping may not be a feasible solution to reduce energy consumption in many scenarios. © 2012 IEEE

    Green Cellular Networks: A Survey, Some Research Issues and Challenges

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    Energy efficiency in cellular networks is a growing concern for cellular operators to not only maintain profitability, but also to reduce the overall environment effects. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief survey of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. Since base stations consume a maximum portion of the total energy used in a cellular system, we will first provide a comprehensive survey on techniques to obtain energy savings in base stations. Next, we discuss how heterogeneous network deployment based on micro, pico and femto-cells can be used to achieve this goal. Since cognitive radio and cooperative relaying are undisputed future technologies in this regard, we propose a research vision to make these technologies more energy efficient. Lastly, we explore some broader perspectives in realizing a "green" cellular network technologyComment: 16 pages, 5 figures, 2 table

    Maximizing Profit in Green Cellular Networks through Collaborative Games

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    In this paper, we deal with the problem of maximizing the profit of Network Operators (NOs) of green cellular networks in situations where Quality-of-Service (QoS) guarantees must be ensured to users, and Base Stations (BSs) can be shared among different operators. We show that if NOs cooperate among them, by mutually sharing their users and BSs, then each one of them can improve its net profit. By using a game-theoretic framework, we study the problem of forming stable coalitions among NOs. Furthermore, we propose a mathematical optimization model to allocate users to a set of BSs, in order to reduce costs and, at the same time, to meet user QoS for NOs inside the same coalition. Based on this, we propose an algorithm, based on cooperative game theory, that enables each operator to decide with whom to cooperate in order to maximize its profit. This algorithms adopts a distributed approach in which each NO autonomously makes its own decisions, and where the best solution arises without the need to synchronize them or to resort to a trusted third party. The effectiveness of the proposed algorithm is demonstrated through a thorough experimental evaluation considering real-world traffic traces, and a set of realistic scenarios. The results we obtain indicate that our algorithm allows a population of NOs to significantly improve their profits thanks to the combination of energy reduction and satisfaction of QoS requirements.Comment: Added publisher info and citation notic

    A Novel Multiobjective Cell Switch-Off Framework for Cellular Networks

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    Cell Switch-Off (CSO) is recognized as a promising approach to reduce the energy consumption in next-generation cellular networks. However, CSO poses serious challenges not only from the resource allocation perspective but also from the implementation point of view. Indeed, CSO represents a difficult optimization problem due to its NP-complete nature. Moreover, there are a number of important practical limitations in the implementation of CSO schemes, such as the need for minimizing the real-time complexity and the number of on-off/off-on transitions and CSO-induced handovers. This article introduces a novel approach to CSO based on multiobjective optimization that makes use of the statistical description of the service demand (known by operators). In addition, downlink and uplink coverage criteria are included and a comparative analysis between different models to characterize intercell interference is also presented to shed light on their impact on CSO. The framework distinguishes itself from other proposals in two ways: 1) The number of on-off/off-on transitions as well as handovers are minimized, and 2) the computationally-heavy part of the algorithm is executed offline, which makes its implementation feasible. The results show that the proposed scheme achieves substantial energy savings in small cell deployments where service demand is not uniformly distributed, without compromising the Quality-of-Service (QoS) or requiring heavy real-time processing
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