Power-efficient dynamic quantization for multisensor HMM state estimation over fading channels
Power-efficient dynamic quantization for multisensor HMM state estimation over fading channels
复制标题
衰落信道上多传感器 HMM 状态估计的节能动态量化
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
S. Dey
中科院分区：
文献类型：
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作者：
Nader Ghasemi;S. Dey
在本文中，我们解决的问题，设计功率有效的量化器的隐马尔可夫模型的状态估计，使用多个传感器通信到一个融合中心，通过易出错的随机时变平坦衰落信道建模的有限状态马尔可夫链。我们的目标是最小化的均方估计误差和预期的总功耗的长期平均值之间的权衡。我们制定的问题作为一个随机控制问题，使用马尔可夫决策过程。在传感器测量噪声的一些温和的假设下，离散化的动作空间（量化阈值和传输功率电平）版本的优化问题形成了一个单链马尔可夫决策过程的平稳政策。离散化问题的解决方案提供了最佳的量化阈值和功率电平，通过反馈通道传送回传感器。此外，为了提高量化系统的性能，我们采用无梯度随机优化技术来确定最优量化阈值集，从该最优量化阈值集确定最优量化级别。在各种信道条件和传感器测量质量下研究了估计误差/总传输功率权衡的性能结果。
In this paper, we address the problem of designing power efficient quantizers for state estimation of hidden Markov models using multiple sensors communicating to a fusion centre via error-prone randomly time-varying flat fading channels modelled by finite state Markov chains. Our objective is to minimize a tradeoff between the long term average of mean square estimation error and expected total power consumption. We formulate the problem as a stochastic control problem by using Markov decision processes. Under some mild assumption on the measurement noise at the sensors, the discretized action space (quantization thresholds and transmission power levels) version of the optimization problem forms a unichain Markov decision process for stationary policies. The solution to the discretized problem provides optimal quantization thresholds and power levels to be communicated back to the sensors via a feedback channel. Moreover, in order to improve the performance of the quantization system, we employ a gradient- free stochastic optimization technique to determine the optimal set of quantization thresholds from which optimal quantization levels are determined. The performance results for estimation error/total transmission power tradeoff are studied under various channel conditions and sensor measurement qualities.