Abstract
As industrial plants increase the number of wirelessly connected sensors for fault detection, a key problem is to identify and obtain data from the sensors. Due to the large number of sensors, random access protocols exploiting non-orthogonal multiple access (NOMA) are a natural approach. In this paper, we develop new algorithms based on approximate message passing for sensor identification and channel estimation accounting for correlation in the activity probability of each sensor and observations of physical variables (e.g., temperature) by the access point. These algorithms form the basis for data decoding, while also identifying faulty machines and estimating local values of the temperature, which can lead to a reduction in the amount of data required to be transmitted. Numerical results show that for an expected activity probability of 0.35, our algorithms improve the normalized mean-square error of the channel estimate by up to 5dB and reduce the rate of sensor identification errors by a factor of four.
Highlights
T O improve efficiency and minimize economic losses, a key challenge in large-scale industrial plants is rapid identification of faulty or degraded machines
We develop an algorithm within the hybrid GAMP (HGAMP) framework called β-HGAMP from loopy BP (LBP) to reduce the complexity of the algorithm with limited loss in performance
This paper investigates the joint sensor identification and channel estimation problem for fault detection, where multiple sensors are placed on each machine
Summary
T O improve efficiency and minimize economic losses, a key challenge in large-scale industrial plants is rapid identification of faulty or degraded machines. For applications with a relatively small number of sensors, it would be reasonable to allocate distinct subcarriers to each sensor, making the identification trivial since detecting a signal in a subcarrier would indicate which sensor is active. It will not be the case when the—potentially very large—number of sensors that transmit within a given frame can vary dramatically. Doing so would lead to high resource requirements, most of which is not utilized. As a consequence, this scenario—known as random access—
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