Energy-Source-Aware Cost Optimization for Green Cellular Networks with Strong Stability
Energy-Source-Aware Cost Optimization for Green Cellular Networks with Strong Stability
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DOI：
10.1109/tetc.2014.2386612
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发表时间：
2016-10
影响因子：
5.9
通讯作者：
Weixian Liao;Ming Li;Sergio Salinas;Pan Li;M. Pan
中科院分区：
文献类型：
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作者：
Weixian Liao;Ming Li;Sergio Salinas;Pan Li;M. Pan
过去十年见证了移动设备及其流量需求的爆炸性增长，从而导致蜂窝服务提供商的能源成本显着增加。服务提供商运营支出的主要组成部分来自使用电网电力或在没有电网电力时使用柴油发电机的蜂窝基站的运营，这也会由于巨大的碳足迹而对环境造成不利影响。因此，从服务提供商的角度来看，如何在满足蜂窝用户不断增长的流量需求的同时，有效降低基站的能源成本已成为一个迫切且具有挑战性的问题。在本文中，我们研究了蜂窝服务提供商的长期时间平均预期能源成本的最小化，同时保证网络的强大稳定性。特别是，我们首先通过共同考虑流量路由、链路调度和能源（即可再生能源、储能单元等）约束来制定问题。由于所提出的问题是时间耦合随机混合整数非线性规划问题，解决起来成本高昂，因此我们采用李亚普诺夫优化理论重新表述了该问题。开发了基于分解的算法来解决该问题，并证明了网络的强稳定性。然后我们推导并证明原始问题最优结果的下限和上限。仿真结果证明了所获得的边界的紧密性和所提出方案的有效性。
Last decade witnessed the explosive growth in mobile devices and their traffic demand, and thereby the significant increase in the energy cost of the cellular service providers. One major component of the service providers' operational expenditure comes from the operation of cellular base stations using grid power or diesel generators when grid power is absent, which also causes adverse environmental impact due to enormous carbon footprint. Therefore, from the service providers' perspective, how to effectively reduce the energy cost of base stations while satisfying cellular users' soaring traffic demands has become an imperative and challenging problem. In this paper, we investigate the minimization of the long-term time-averaged expected energy cost of cellular service providers while guaranteeing the strong stability of the network. In particular, we first formulate the problem by jointly considering flow routing, link scheduling, and energy (i.e., renewable energy resource, energy storage unit, and so on) constraints. Since the formulated problem is a time-coupling stochastic mixed-integer nonlinear programming problem, which is prohibitively expensive to solve, we reformulate the problem by employing Lyapunov optimization theory. A decomposition-based algorithm is developed to solve the problem and the network strong stability is proven. We then derive and prove both the lower and the upper bounds on the optimal result of the original problem. Simulation results demonstrate the tightness of the obtained bounds and the efficacy of the proposed scheme.