论文标题
部分可观测时空混沌系统的无模型预测
On Distributed Detection in EH-WSNs With Finite-State Markov Channel and Limited Feedback
论文作者
论文摘要
我们考虑一个网络，负责求解由n传感器，融合中心（FC）和从FC到传感器的反馈通道组成的二进制分布式检测。每个传感器都能收集能量，并配备有限尺寸的电池以存储随机到达的能量。传感器通过正交褪色通道处理其观察结果并将其符号传输到FC。 FC融合了接收的符号并做出全球二进制决策。我们旨在制定自适应通道依赖性发射功率控制策略，以至于基于J-Diverence的检测度量在FC处最大化，但要受到总发射功率约束。 Modeling the quantized fading channel, the energy arrival, and the battery dynamics as time-homogeneous finite-state Markov chains, and the network lifetime as a geometric random variable, we formulate our power control optimization problem as a discounted infinite-horizon constrained Markov decision process (MDP) problem, where sensors' transmit powers are functions of the battery states, quantized channel gains, and the arrived energies.我们利用随机动态编程和拉格朗日方法来找到最佳和最佳的功率控制策略。我们证明，我们的次优政策提供了近距离的性能，其计算复杂性降低并且没有在传感器上施加信号开销。
We consider a network, tasked with solving binary distributed detection, consisting of N sensors, a fusion center (FC), and a feedback channel from the FC to sensors. Each sensor is capable of harvesting energy and is equipped with a finite size battery to store randomly arrived energy. Sensors process their observations and transmit their symbols to the FC over orthogonal fading channels. The FC fuses the received symbols and makes a global binary decision. We aim at developing adaptive channel-dependent transmit power control policies such that J-divergence based detection metric is maximized at the FC, subject to total transmit power constraint. Modeling the quantized fading channel, the energy arrival, and the battery dynamics as time-homogeneous finite-state Markov chains, and the network lifetime as a geometric random variable, we formulate our power control optimization problem as a discounted infinite-horizon constrained Markov decision process (MDP) problem, where sensors' transmit powers are functions of the battery states, quantized channel gains, and the arrived energies. We utilize stochastic dynamic programming and Lagrangian approach to find the optimal and sub-optimal power control policies. We demonstrate that our sub-optimal policy provides a close-to-optimal performance with a reduced computational complexity and without imposing signaling overhead on sensors.
