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首页> 外文期刊>IEEE Journal on Selected Areas in Communications >Power-Delay Tradeoff With Predictive Scheduling in Integrated Cellular and Wi-Fi Networks
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Power-Delay Tradeoff With Predictive Scheduling in Integrated Cellular and Wi-Fi Networks

机译:集成蜂窝和Wi-Fi网络中的功率调度与预测调度

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The explosive growth of global mobile traffic has led to rapid growth in the energy consumption in communication networks. In this paper, we focus on the energy-aware design of the network selection, subchannel, and power allocation in cellular and Wi-Fi networks, while taking into account the traffic delay of mobile users. Based on the two-timescale Lyapunov optimization technique, we first design an online Energy-Aware Network Selection and Resource Allocation (ENSRA) algorithm, which yields a power consumption within bound of the optimal value, and guarantees an traffic delay for any positive control parameter . Motivated by the recent advancement in the accurate estimation and prediction of user mobility, channel conditions, and traffic demands, we further develop a novel predictive Lyapunov optimization technique to utilize the predictive information, and propose a Predictive Energy-Aware Network Selection and Resource Allocation (P-ENSRA) algorithm. We characterize the performance bounds of P-ENSRA in terms of the power-delay tradeoff theoretically. To reduce the computational complexity, we finally propose a Greedy Predictive Energy-Aware Network Selection and Resource Allocation (GP-ENSRA) algorithm, where the operator solves the problem in P-ENSRA approximately and iteratively. Numerical results show that GP-ENSRA significantly improves the power-delay performance over ENSRA in the large delay regime. For a wide range of system parameters, GP-ENSRA reduces the traffic delay over ENSRA by 20–30% under the same power consumption.
机译:全球移动业务的爆炸性增长导致通信网络能耗的快速增长。在本文中,我们重点研究蜂窝和Wi-Fi网络中网络选择,子信道和功率分配的节能设计,同时考虑到移动用户的流量延迟。基于两时间尺度Lyapunov优化技术,我们首先设计了一个在线能源感知网络选择和资源分配(ENSRA)算法,该算法可产生最佳值范围内的功耗,并保证任何正控制参数的流量延迟。基于对用户移动性,信道状况和流量需求进行准确估计和预测的最新进展的推动,我们进一步开发了一种新颖的预测Lyapunov优化技术以利用预测信息,并提出了预测能源感知网络选择和资源分配( P-ENSRA)算法。我们从功率延迟折衷的角度来描述P-ENSRA的性能界限。为了降低计算复杂度,我们最终提出了一种贪婪预测能源感知网络选择和资源分配(GP-ENSRA)算法,其中,运营商可以近似并迭代地解决P-ENSRA中的问题。数值结果表明,在大延迟情况下,GP-ENSRA大大提高了ENSRA的功率延迟性能。对于广泛的系统参数,在相同功耗下,GP-ENSRA可以将ENSRA上的流量延迟降低20%到30%。

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