Quantization Analysis and Robust Design for Distributed Graph Filters
نویسندگان
چکیده
Distributed graph filters have recently found applications in wireless sensor networks (WSNs) to solve distributed tasks such as reaching consensus, signal denoising, and reconstruction. However, when implemented over WSNs, the should deal with network limited energy constraints well processing communication capabilities. Quantization plays a fundamental role improve latter but its effects on filtering are little understood. WSNs also prone random link losses due noise interference. In this instance, filter output is affected by both quantization error topological randomness error, which, if it not properly accounted design phase, may lead an accumulated through iterations significantly degrade performance. paper, we analyze how affects time-invariant time-varying graphs. We bring insights for two most common filters: finite impulse response (FIR) autoregressive moving average (ARMA) filter. Besides providing comprehensive analysis, devise theoretical performance guarantees stepsize fixed or changes dynamically iterations. For FIR filters, show that dynamic leads more reduction of than fixed-stepsize quantization. ARMA decreasing reduces zero at steady-state. addition, propose robust strategies minimize networks. Numerical experiments synthetic real data sets corroborate our findings different trade-offs between bits, order, robustness randomness.
منابع مشابه
Distributed Graph Filters
This work was carried out in the ASCI graduate school. ASCI dissertation series number 324.
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2022
ISSN: ['1053-587X', '1941-0476']
DOI: https://doi.org/10.1109/tsp.2021.3139208