能量收集通信系统优化的学习理论方法
A Learning Theoretic Approach to Energy Harvesting Communication System Optimization
摘要：
研究了一种点对点无线通信系统，其中发射机配备有能量收集装置和可充电电池。发射机的能量和数据到达都被建模为马尔可夫过程。延迟受限通信被认为是假设潜在的信道是块衰落与记忆，和瞬时的信道状态信息是可用的在发送器和接收器。在发射机的激活时间期间的预期总传输数据在关于在发射机处关于基础随机过程的可用信息的三组不同假设下被最大化。一个学习理论的方法，它不假设任何先验信息的马尔可夫过程管理的通信系统。此外，在线和离线优化问题进行了研究，为相同的设置。在在线优化问题中假设关于潜在随机过程的实现的全部统计知识和因果信息，而离线优化问题预先假设关于实现的非因果知识。比较所有三个框架中的最佳解决方案，由于缺乏发射机的基本马尔可夫过程的行为的信息的性能损失进行量化。
英文摘要：
A point-to-point wireless communication system in which the transmitter is equipped with an energy harvesting device and a rechargeable battery, is studied. Both the energy and the data arrivals at the transmitter are modeled as Markov processes. Delay-limited communication is considered assuming that the underlying channel is block fading with memory, and the instantaneous channel state information is available at both the transmitter and the receiver. The expected total transmitted data during the transmitter's activation time is maximized under three different sets of assumptions regarding the information available at the transmitter about the underlying stochastic processes. A learning theoretic approach is introduced, which does not assume any a priori information on the Markov processes governing the communication system. In addition, online and offline optimization problems are studied for the same setting. Full statistical knowledge and causal information on the realizations of the underlying stochastic processes are assumed in the online optimization problem, while the offline optimization problem assumes non-causal knowledge of the realizations in advance. Comparing the optimal solutions in all three frameworks, the performance loss due to the lack of the transmitter's information regarding the behaviors of the underlying Markov processes is quantified.
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