On Distributed Detection in EH-WSNs With Finite-State Markov Channel and Limited Feedback
On Distributed Detection in EH-WSNs With Finite-State Markov Channel and Limited Feedback
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DOI：
10.1109/tgcn.2023.3264506
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发表时间：
2022-10
影响因子：
4.8
通讯作者：
Ghazaleh Ardeshiri;A. Vosoughi
中科院分区：
文献类型：
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作者：
Ghazaleh Ardeshiri;A. Vosoughi
我们考虑一个网络的${N}$传感器，任务是解决二进制分布式检测，融合中心（FC），和反馈通道从FC传感器。每个传感器都能够收集能量，并配备有限大小的电池来存储随机到达的能量。传感器处理它们的观察结果，并通过正交和马尔可夫时间相关衰落信道将它们的符号发送到FC。FC融合接收到的符号并做出全局二进制判决。我们的目标是开发自适应信道相关的发射功率控制策略，使${J}$ -发散为基础的检测度量在FC最大化，总发射功率约束。建模量化衰落信道，能量到达，电池动态的时间齐次有限状态马尔可夫链，和网络的生命周期作为一个几何随机变量，我们制定我们的功率控制优化问题作为折扣无限时域约束马尔可夫决策过程（MDP）的问题，其中传感器的发射功率是电池状态，量化信道增益和到达的能量的函数。我们利用随机动态规划和拉格朗日方法来找到最佳和次佳的功率控制策略。我们证明，我们的次优策略提供了一个接近最优的性能，降低了计算复杂性，而不施加信号开销的传感器。
We consider a network of ${N}$ sensors, tasked with solving binary distributed detection, 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 and Markovian time correlated 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 quantized fading channel, energy arrival, and 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.