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A Cross-Layer Perspective on Energy-Harvesting-Aided Green Communications Over Fading Channels

机译:衰落通道上能量收集辅助的绿色通信的跨层视角

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In this paper, we consider the power allocation of the physical layer and the buffer delay of the upper application layer in energy harvesting green networks. The total power required for reliable transmission includes the transmission power and the circuit power. The harvested power (which is stored in a battery) and the grid power constitute the power resource. The uncertainty of data generated from the upper layer, the intermittence of the harvested energy, and the variation of the fading channel are taken into account and described as independent Markov processes. In each transmission, the transmitter decides the transmission rate and the allocated power from the battery, and the rest of the required power will be supplied by the power grid. The objective is to find an allocation sequence of transmission rate and battery power to minimize the long-term average buffer delay under the average grid power constraint. A stochastic optimization problem is formulated accordingly to find such transmission rate and battery power sequence. Furthermore, the optimization problem is reformulated as a constrained Markov decision process (MDP) problem whose policy is a 2-D vector with the transmission rate and the power allocation of the battery as its elements. We prove that the optimal policy of the constrained MDP can be obtained by solving the unconstrained MDP. Then, we focus on the analysis of the unconstrained average-cost MDP. The structural properties of the average optimal policy are derived. Moreover, we discuss the relations between elements of the 2-D policy. Next, based on the theoretical analysis, the algorithm to find the constrained optimal policy is presented for the finite-state-space scenario. In addition, heuristic policies (two deterministic policies and a mixed policy) with low complexity are given for the general state space. Finally, simulations are performed under these policies to demonstrate their effectiveness.
机译:在本文中,我们考虑了能量收集绿色网络中物理层的功率分配和上层应用层的缓冲延迟。可靠传输所需的总功率包括传输功率和电路功率。收集的电能(存储在电池中)和电网电能构成了电能。从上层生成的数据的不确定性,收集的能量的间歇性以及衰落信道的变化都被考虑在内,并被描述为独立的马尔可夫过程。在每次传输中,发射器决定传输速率和电池分配的功率,其余所需功率将由电网提供。目的是找到传输速率和电池电量的分配顺序,以使在平均电网电量约束下的长期平均缓冲延迟最小。相应地制定了一个随机优化问题,以找到这种传输速率和电池功率序列。此外,将优化问题重新表述为约束马尔可夫决策过程(MDP)问题,该问题的策略是以电池的传输速率和功率分配为要素的二维矢量。我们证明通过求解无约束的MDP可以获得约束MDP的最优策略。然后,我们集中于对无约束平均成本MDP的分析。推导了平均最优策略的结构特性。此外,我们讨论了二维政策要素之间的关系。接下来,在理论分析的基础上,提出了用于有限状态空间场景的约束最优策略的算法。另外,针对一般状态空间给出了具有较低复杂度的启发式策略(两个确定性策略和混合策略)。最后,在这些策略下进行仿真以证明其有效性。

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