Multi-level Quantization Scheme for Distributed Maximum Likelihood Estimation in Wireless Sensor Networks

GUO Li-li; GAO Fei; SUN Zhi-guo

doi:10.3969/j.issn.0372-2112.2016.11.029

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Multi-level Quantization Scheme for Distributed Maximum Likelihood Estimation in Wireless Sensor Networks

更新时间：2025-07-16

- Multi-level Quantization Scheme for Distributed Maximum Likelihood Estimation in Wireless Sensor Networks
- Acta Electronica Sinica Vol. 44, Issue 11, Pages: 2773-2779(2016)
- 作者机构：
  
  哈尔滨工程大学信息与通信工程学院,黑龙江,哈尔滨,150001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61271263, No.61101141)
- DOI：10.3969/j.issn.0372-2112.2016.11.029
  CLC： TN911.23
- Published Online：25 November 2016，
  
  Published：2016
- 稿件说明：
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GUO Li-li, GAO Fei, SUN Zhi-guo. Multi-level Quantization Scheme for Distributed Maximum Likelihood Estimation in Wireless Sensor Networks[J]. Acta Electronica Sinica, 2016, 44(11): 2773-2779.
DOI：

GUO Li-li, GAO Fei, SUN Zhi-guo. Multi-level Quantization Scheme for Distributed Maximum Likelihood Estimation in Wireless Sensor Networks[J]. Acta Electronica Sinica, 2016, 44(11): 2773-2779. DOI： 10.3969/j.issn.0372-2112.2016.11.029.

摘要

在无线传感器网络背景下的分布式估计中，由于传输网络对发送功率和传输带宽的限制，压缩信源冗余、降低通信数据量便成为一个重要的课题.为此，本文提出了一种基于多比特量化观测的分布式估计方法（MQS），利用渐进性能作为优化准则构造量化阈值优化问题，运用粒子群算法对其进行求解得到最优量化阈值，给出了克拉美罗下界的解析表达式，并与均匀量化方法（UQS）和未量化方法（NQS）进行对比.理论分析和仿真实验表明，MQS的性能优于UQS.当量化深度增大到3时，MQS的估计性能十分接近NQS的估计性能.

Abstract

In the context of distributed estimation in wireless sensor networks (WSN)

due to transmission power/bandwidth constrains

it is significant to reduce size of transmitted data.In this paper

a distributed estimation scheme

namely

multi-level quantization scheme (MQS) is proposed.The quantization threshold optimization problem is formulated by using asymptotic performance as an optimality criterion.The optimum quantization thresholds are obtained by resorting to particle swarm optimization algorithm.The explicit expression of the Cramr-Rao lower bound is derived.The proposed method is compared with uniform quantization scheme (UQS) and no quantization scheme (NQS).Theoretical analysis and simulation results demonstrate that the MQS scheme outperforms the UQS.Moreover

with 3-bit quantization

the MQS can provide estimation performance very close to that of the NQS.

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