Bandwidth-constrained distributed estimation for wireless sensor Networks-part I: Gaussian case

By Alejandro Ribeiro, Georgios B. Giannakis. IEEE Transactions on Signal Processing, 2006

547 citations recorded by the source corpus.

We study deterministic mean-location parameter estimation when only quantized versions of the original observations are available, due to bandwidth constraints. When the dynamic range of the parameter is small or comparable with the noise variance, we introduce a class of maximum-likelihood estimators that require transmitting just one bit per sensor to achieve an estimation variance close to that of the (clairvoyant) sample mean estimator. When the dynamic range is comparable or larger than the noise standard deviation, we show that an optimum quantization step exists to achieve the best possible variance for a given bandwidth constraint. We will also establish that in certain cases the sample mean estimator formed by quantized observations is preferable for complexity reasons. We finally touch upon algorithm implementation issues and guarantee that all the numerical maximizations required by the proposed estimators are concave.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/3b76a10e26a20f3b59cd21ee822cc64b9d90bab4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

In Control and Systems Engineering

Citations in the corpus

Distributed Kalman filtering based on consensus strategies (2008, methodology): “well-known data fusion formulas; [14], [ 31 ], [32], [40], [39] address quantization issues and”
Distributed compression-estimation using wireless sensor networks (2006, methodology): “P1 P 2 IEEE SIGNAL PROCESSING MAGAZINE [40] JULY 2006 distortion is achieved by separate source and channel coding.”
Diffusion recursive least-squares for distributed estimation over adaptive networks (2008, unknown): “Interesting work in this regard using 1-bit communications has been proposed in the works [20], [21] in the context of other distributed procedures.”
Dealing with Range Anxiety in Mean Estimation via Statistical Queries (2016, background): “Identical and closely related models are often studied in the context of distributed statistical estimation with communication constraints (e.g. Luo, 2005; Rajagopal et al., 2006; Ribeiro and Giannakis, 2006; Zhang et al., 2013; Steinhardt and Duchi, 2015; Steinhardt et al., 2016; Suresh et al., 201”
Stochastic Event-Triggered Sensor Schedule for Remote State Estimation (2013, unknown): “3) The channel bandwidth shared by a large mount of sensors may be limited in some cases [3], [4], [5], [6],”