Radio Environment Map Construction with Spatially Distributed Sensors
| Autor: | Daoxing Guo, Bangning Zhang, Yi-Qun Xu, Bing Zhao |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Estimation theory Bayesian probability Monte Carlo method Inference 020206 networking & telecommunications Markov chain Monte Carlo 02 engineering and technology symbols.namesake 020210 optoelectronics & photonics Sensor node 0202 electrical engineering electronic engineering information engineering symbols Bayesian hierarchical modeling Wireless sensor network Algorithm |
| Zdroj: | WCNC Workshops |
| DOI: | 10.1109/wcncw49093.2021.9419993 |
| Popis: | In this paper, we propose an analysis framework based on spectrum data gathered by spatially distributed sensors to construct the radio environment map (REM). To the best of our knowledge, this is the first attempt to leverage the power of Bayesian analysis and Markov chain Monte Carlo (MCMC) in REM. Specifically, a three-stage Bayesian hierarchical model (BHM) is established to imitate the spectrum data generation process under spatially correlated shadow fading. Parameters of BHM are estimated with MCMC algorithm from data collected by the sensor network. Then, we address the space-dimension spectrum inference problem with the aim to interpolate the signal strength where there is no sensor node by composition sampling. We make spatial inference under two sensor location modes (square lattice located sensors and randomly located sensors) and two scenarios (with and without the information about the signal source), respectively. Simulation results demonstrate that although the randomly located sensors mode is suitable for parameter estimation, the inference performance is not better than the square lattice located sensors mode. Quantitative analysis of the inference performance confirms that the effectiveness of our data analysis framework is compelling. |
| Databáze: | OpenAIRE |
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