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Ultra Dense Small Cell Networks: Turning Density Into Energy Efficiency

机译:超密集小型蜂窝网络:将密度转化为能源效率

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In this paper, a novel approach for joint power control and user scheduling is proposed for optimizing energy efficiency (EE), in terms of bits per unit energy, in ultra dense small cell networks (UDNs). Due to severe coupling in interference, this problem is formulated as a dynamic stochastic game (DSG) between small cell base stations (SBSs). This game enables capturing the dynamics of both the queues and channel states of the system. To solve this game, assuming a large homogeneous UDN deployment, the problem is cast as a game (MFG) in which the MFG equilibrium is analyzed with the aid of low-complexity tractable partial differential equations. Exploiting the stochastic nature of the problem, user scheduling is formulated as a stochastic optimization problem and solved using the drift plus penalty (DPP) approach in the framework of optimization. Remarkably, it is shown that by weaving notions from Lyapunov optimization and mean-field theory, the proposed solution yields an equilibrium control policy per SBS, which maximizes the network utility while ensuring users’ quality-of-service. Simulation results show that the proposed approach achieves up to 70.7% gains in EE and 99.5% reductions in the network’s outage probabilities compared to a baseline model, which focuses on improving EE while attempting to satisfy the users’ instantaneous quality-of-service requirements.
机译:在本文中,提出了一种用于联合功率控制和用户调度的新颖方法,用于以超高密度小蜂窝网络(UDN)中的每单位能量的比特数优化能量效率(EE)。由于干扰中的严重耦合,此问题被表述为小型小区基站(SBS)之间的动态随机博弈(DSG)。该游戏可以捕获系统队列和通道状态的动态。为了解决这个问题,假设部署了一个较大的同构UDN,该问题将作为一个游戏(MFG)进行,其中借助低复杂性易处理的偏微分方程对MFG平衡进行​​分析。利用问题的随机性,将用户调度表述为随机优化问题,并在优化框架中使用漂移加罚分(DPP)方法进行求解。值得注意的是,通过结合Lyapunov优化和均值场理论,提出的解决方案根据SBS产生了均衡控制策略,该策略在确保用户服务质量的同时最大化了网络效用。仿真结果表明,与基线模型相比,该提议的方法可实现EE最高70.7%的增长,网络中断概率减少99.5%的基线模型,该模型侧重于改善EE并尝试满足用户的即时服务质量要求。

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