Detection over diffusion networks: Asymptotic tools for performance prediction and simulation. Matta, V., Braca, P., Marano, S., & Sayed, A. H. In 2016 24th European Signal Processing Conference (EUSIPCO), pages 1503-1507, Aug, 2016.
Detection over diffusion networks: Asymptotic tools for performance prediction and simulation [pdf]Paper  doi  abstract   bibtex   
Exploiting recent progress [1]-[4] in the characterization of the detection performance of diffusion strategies over adaptive multi-agent networks: i) we present two theoretical approximations, one based on asymptotic normality and the other based on the theory of exact asymptotics; and ii) we develop an efficient simulation method by tailoring the importance sampling technique to diffusion adaptation. We show that these theoretical and experimental tools complement each other well, with their combination offering a substantial advance for a reliable quantitative detection-performance assessment. The analysis provides insight into the interplay between the network topology, the combination weights, and the inference performance, revealing the universal behavior of diffusion-based detectors over adaptive networks.
@InProceedings{7760499,
  author = {V. Matta and P. Braca and S. Marano and A. H. Sayed},
  booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
  title = {Detection over diffusion networks: Asymptotic tools for performance prediction and simulation},
  year = {2016},
  pages = {1503-1507},
  abstract = {Exploiting recent progress [1]-[4] in the characterization of the detection performance of diffusion strategies over adaptive multi-agent networks: i) we present two theoretical approximations, one based on asymptotic normality and the other based on the theory of exact asymptotics; and ii) we develop an efficient simulation method by tailoring the importance sampling technique to diffusion adaptation. We show that these theoretical and experimental tools complement each other well, with their combination offering a substantial advance for a reliable quantitative detection-performance assessment. The analysis provides insight into the interplay between the network topology, the combination weights, and the inference performance, revealing the universal behavior of diffusion-based detectors over adaptive networks.},
  keywords = {approximation theory;telecommunication network topology;adaptive multi-agent networks;diffusion strategies;asymptotic normality;sampling technique;network topology;Error probability;Steady-state;Adaptive systems;Monte Carlo methods;Limiting;Random variables;Europe;Distributed detection;adaptive network;diffusion;large deviations;exact asymptotics;importance sampling},
  doi = {10.1109/EUSIPCO.2016.7760499},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570256137.pdf},
}
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