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Joint Optimization for Cell Configuration and Offloading in Heterogeneous Networks

机译:异构网络中单元配置和卸载的联合优化

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To steadily gaining benefit from the exponential growth in mobile traffic, operators are eager to find solutions to maximize profits. Two very attractive strategies have been proposed to complement the existing macro cellular architecture: deploying low power bases stations and offloading data traffic to other networks. Each strategy has different costs and yields different benefits for operators. The offloading option could be cheaper in the short run; nevertheless, it might be more expensive in the long run than cell densification due to the varying cost. On the other hand, small cells, since having to be deployed in advance, may be underutilized or not fully meet future demands. In the latter case, offloading techniques can be used to increase capacity with additional costs. Further, uncertainty of future data demands and electricity prices also impact operators profitability, making the best network strategy difficult to achieve. To address such problem, an optimal cell configuration algorithm is proposed by formulating a stochastic programming model that considers both network design and data offloading. This algorithm can maximize the profit, under future demand and price uncertainty. Numerical studies are extensively performed in which the results show that operators' profits can be improved with our proposed algorithm.
机译:为了稳步获益于移动流量的指数增长,运营商渴望找到最大限度地利用利润的解决方案。已经提出了两个非常有吸引力的策略来补充现有的宏蜂窝架构:部署低功耗基站并将数据流量卸载到其他网络。每个策略都有不同的成本,对运营商产生不同的利益。卸载选项在短期内可能更便宜;然而,由于成本变化,在长远而不是细胞致密化可能更昂贵。另一方面,由于必须提前部署的小细胞可能未充分利用或不完全满足未来的需求。在后一种情况下,卸载技术可用于增加具有额外成本的容量。此外,未来数据需求和电力价格的不确定性也会影响运营商的盈利能力,使得最佳的网络战略难以实现。为了解决这样的问题,通过制定考虑网络设计和数据卸载的随机编程模型来提出最佳小区配置算法。该算法可以在未来的需求和价格不确定性下最大化利润。广泛地进行数值研究,其中结果表明,通过所提出的算法可以提高运营商的利润。

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