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Consensus-Based Distributed Least-Mean Square Algorithm Using Wireless Ad Hoc Networks

Gonzalo Mateos, Ioannis Schizas and Georgios B. GiannakisECE Department, University of Minnesota

Acknowledgment: ARL/CTA grant no. DAAD19-01-2-0011

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Motivation Estimation using ad hoc WSNs raises exciting challenges

Communication constraints Limited power budget Lack of hierarchy / decentralized processing Consensus

Unique features Environment is constantly changing (e.g., WSN topology) Lack of statistical information at sensor-level

Bottom line: algorithms are required to be Resource efficient Simple and flexible Adaptive and robust to changes

Single-hop communications

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Prior Works Single-shot distributed estimation algorithms

Consensus averaging [Xiao-Boyd ’05, Tsitsiklis-Bertsekas ’86, ’97] Incremental strategies [Rabbat-Nowak etal ’05] Deterministic and random parameter estimation [Schizas etal ’06]

Consensus-based Kalman tracking using ad hoc WSNs MSE optimal filtering and smoothing [Schizas etal ’07] Suboptimal approaches [Olfati-Saber ’05], [Spanos etal ’05]

Distributed adaptive estimation and filtering LMS and RLS learning rules [Lopes-Sayed ’06 ’07]

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Problem Statement Ad hoc WSN with sensors

Single-hop communications only. Sensor ‘s neighborhood Connectivity information captured in Zero-mean additive (e.g., Rx, quantization) noise

Each sensor , at time instant Acquires a regressor and scalar observation Both zero-mean w.l.o.g and spatially uncorrelated

Least-mean squares (LMS) estimation problem of interest

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Centralized Approaches If , jointly stationary Wiener solution

If global (cross-) covariance matrices , available Steepest-descent converges avoiding matrix inversion

If (cross-) covariance info. not available or time-varying Low complexity suggests (C-) LMS adaptation

Goal: develop a distributed (D-) LMS algorithm for ad hoc WSNs

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A Useful Reformulation

Introduce the bridge sensor subset1) For all sensors , such that2) For , there must such that

Consider the convex, constrained optimization

Proposition [Schizas etal’06]: For satisfying 1)-2) and the WSN is connected, then

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Algorithm Construction Problem of interest

Two key steps in deriving D-LMS1) Resort to the alternating-direction method of multipliers

Gain desired degree of parallelization

2) Apply stochastic approximation ideasCope with unavailability of statistical

information

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Derivation of Recursions Associated augmented Lagrangian

Alternating-direction method of Lagrange multipliersThree-step iterative update process

Multipliers Dual iteration Local estimates Minimize w.r.t. Bridge variables Minimize w.r.t.

Step 1:Step 2:Step 3:

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Multiplier Updates Recall the constraints

Use standard method of multipliers type of update

Requires from the bridge neighborhood

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Local Estimate Updates Given by the local optimization

First order optimality condition

Proposed recursion inspired by Robbins-Monro algorithm

1) is the local prior error2) is a constant step-size

Requires Already acquired bridge variables Updated local multipliers

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Bridge Variable Updates

Similarly,

Requires from the neighborhood from the neighborhood in a startup phase

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D-LMS Recap and Operation In the presence of communication noise, for

Simple, fully distributed, only single-hop exchanges needed

Step 1:Step 2:

Step 3:

Sensor

Rxfrom

Tx

toBridge sensor

Txto

Rx

from

Steps 1,2:

Step 3:

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Further Insights Manipulating the recursions for and yields

Introduce the instantaneous consensus error at sensor

The update of becomes

Superposition of two learning mechanisms Purely local LMS-type of adaptation PI consesus loop tracks the consensus set-point

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Network-wide information enters through the set-point Expect increased performance with Flexibility

D-LMS ProcessorLocal LMS Algorithm

Sensor j

PI RegulatorTo

Consensus Loop

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Mean Analysis Independence setting signal assumptions for

(As1) is a zero-mean white random vector , with spectral radius

(As2) Observations obey a linear model where is a zero-mean white noise

(As3) and are statistically independent

Define and

Goal: derive sufficient conditions under which

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Dynamics of the MeanLemma: Under (As1)-(As3), consider the D-LMS algorithm initialized with .Then for , is given by the second-order recursion with and , where

Equivalent first-order system by state concatenation

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First-Order Stability ResultProposition: Under (As1)-(As3), the D-LMS algorithm whose positive step-sizes and relevant parameters are chosen such that , achieves consensus in the mean sense i.e.,

Step-size selection based on local information only Local regressor statistics Bridge neighborhood size

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Simulations node WSN, Regressors: i.i.d.Observations:

D-LMS: ,

True time-varying weight:

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Loop Tuning Adequately selecting actually does make a difference

Compared figures of merit: MSE (Learning curve): MSD (Normalized estimation error):

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Concluding Summary Developed a distributed LMS algorithm for general ad hoc WSNs Intuitive sensor-level processing

Local LMS adaptation Tunable PI loop driving local estimate to consensus

Mean analysis under independence assumptions step-size selection rules based on local information

Simulations validate mss convergence and tracking capabilities

Ongoing research Stability and performance analysis under general settings Optimality: selection of bridge sensors, D-RLS. Estimation/Learning performance Vs complexity tradeoff