[论文解读] A Cross-layer Perspective on Energy Harvesting Aided Green Communications over Fading Channels

Tian Zhang, Wei Chen+2 more|arXiv (Cornell University)|Nov 23, 2012

Energy Harvesting in Wireless Networks被引用 4

一句话总结

本文提出了一种用于能量采集绿色通信的跨层优化框架，联合最小化长期平均缓冲延迟，同时满足平均电网功率约束。通过将传输速率和电池功率分配建模为受限马尔可夫决策过程（MDP），作者推导出最优策略的结构特性，并提出低复杂度启发式策略，证明在特定条件下贪婪使用电池是最优的，且通过仿真验证了性能。

ABSTRACT

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 as well as 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 MDP problem whose policy is a two-dimensional 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 two-dimensional 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 with low-complexity are given for the general state space. Finally, simulations are performed under these policies to demonstrate the effectiveness.

研究动机与目标

• 在电网功率约束下，最小化能量采集无线网络中的长期平均缓冲延迟。
• 联合优化物理层传输速率与电池功率分配，同时考虑上层缓冲动态。
• 考虑随机数据到达、间歇性能量采集和衰落信道对系统性能的综合影响。
• 开发低复杂度启发式策略，在保证实际可行性的同时实现近似最优性能。
• 证明在推导出的MDP框架结构特性下，贪婪电池使用策略是最优的。

提出的方法

• 在随机能量和信道条件下，构建整合物理层功率分配与应用层缓冲延迟的随机优化问题。
• 通过拉格朗日松弛法将受限MDP问题重述为无约束MDP，以推导最优策略结构。
• 将系统建模为二维MDP，状态变量包括缓冲区大小、信道增益、队列状态、电池电量和能量到达情况。
• 证明最优策略在缓冲区大小和电池能量上为非递减，并利用凸性与动态规划技术推导其结构特性。
• 提出一种有限状态算法以精确计算最优策略，并设计三种启发式策略（两种确定性策略和一种混合策略）用于一般状态空间。
• 通过在各种策略下进行仿真验证框架性能，结果表明延迟显著降低且能量效率得到提升。

实验结果

研究问题

• RQ1 如何联合优化传输速率与电池功率分配，以最小化能量采集网络中的平均缓冲延迟？
• RQ2 在随机数据到达、衰落信道和间歇性能量采集条件下，最优策略表现出何种结构特性？
• RQ3 在何种条件下，贪婪电池功率分配策略对于最小化延迟和电网功率使用是最优的？
• RQ4 与仅考虑传输功率的模型相比，引入电路功耗后，最优功率分配策略有何不同？
• RQ5 低复杂度启发式策略是否能在保持实际可实现性的同时，实现近似最优性能？

主要发现

• 受限MDP的最优策略被证明等价于求解无约束MDP，通过拉格朗日松弛法可实现高效计算。
• 最优传输速率策略关于缓冲区大小为非递减，确保在数据积压增加时采用更高传输速率。
• 最优电池功率分配策略关于电池能量水平为非递减，且证明贪婪使用（优先使用电池而非电网）是最优策略。
• 在大折扣因子下，无论固定速率策略如何，最优策略均收敛于贪婪电池分配策略。
• 所提出的启发式策略以极低复杂度实现显著延迟降低，在仿真中优于基线策略。
• 仿真结果证实，该框架在真实随机信道和能量到达模型下，能有效平衡电网功率使用与缓冲延迟。