A LOW-COMPLEXITY MAP DETECTOR FOR DISTRIBUTED NETWORKS

被引：0

作者：

Feitosa, Allan E. ^{[1
]}

Nascimento, Vitor H. ^{[1
]}

Lopes, Cassio G. ^{[1
]}

机构：

[1] Univ Sao Paulo, Dept Elect Syst Engn, Sao Paulo, Brazil

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2020年

基金：

巴西圣保罗研究基金会;

关键词：

adaptive networks; distributed detection; maximum a posteriori; network connectivity; PERFORMANCE; INTERNET;

D O I：

10.1109/icassp40776.2020.9054197

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

This work describes a generalization of our previous maximum likelihood (ML) detector to a maximum a posteriori (MAP) detector in distributed networks using the diffusion LMS algorithm. Nodes in the network must decide between two concurrent hypotheses concerning their environment, using local measurements and shared estimates from neighbors. The generalization is provided by a new approximation concerning the network connectivity, whose accuracy is shown by simulations. The new MAP detector inherits from our ML formulation an exponential decay rate in probability of error independent of the LMS step size, if it is sufficiently small.

引用

页码：5920 / 5924

页数：5

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