ANN Prediction Models for Indoor Environment

Popescu, I.; Nikitopoulos, D.; Nafornita, Ioan; Constantinou, P.

doi:10.1109/wimob.2006.1696368

Cited by 17 publications

(20 citation statements)

References 6 publications

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“…Besides, the frequency-dependent component C is set to 20, which refers to the settings for indoor environments in [32]. Besides the RMSE and MAPE, the mean absolute error (MAE) and the correlation coefficient are also used to evaluate the performance of the models with the following expressions [16], [33].…”

Section: Comparison Of Path Loss Prediction Performance Based On mentioning

confidence: 99%

“…In recent years, the existing research results have proved that the path loss models based on machine learning can provide more accurate path loss prediction results than the empirical models, and are even more computationally efficient than deterministic ones [11]- [15]. In indoor environments, the ANN-based models proposed in [16] showed good prediction performance. In [17], a hybrid-empirical neural model was proved to be able to predict the path loss values accurately due to its ability to consider many influences.…”

Section: Introductionmentioning

confidence: 99%

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Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

et al. 2019

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Wireless communications in aircraft cabin environments have drawn widespread attention with the increase of application requirements. To ensure reliable and stable in-cabin communications, the investigation of channel parameters such as path loss is necessary. In this paper, four machine learning methods, including back propagation neural network (BPNN), support vector regression (SVR), random forest, and AdaBoost, are used to build path loss prediction models for an MD-82 aircraft cabin. Firstly, machine-learning-based models are designed to predict the path loss values at different locations at a fixed frequency. It is shown that these models fit the measured data well, e.g., at 2.4 GHz central frequency the root mean square errors (RMSEs) of BPNN, SVR, random forest, and AdaBoost predictors are 1.90 dB, 2.20 dB, 1.76 dB, and 2.12 dB. Subsequent research is engaged to forecast path loss at a new frequency based on available information at known frequencies. Additionally, to solve the data limitation problem at the new frequency, we propose a path loss prediction scheme combining empirical models and machinelearning-based models. This scheme uses estimated values generated by the empirical model according to prior information to expand the training set. To verify the performance of this scheme, measured samples at 2.4 GHz and 3.52 GHz, as well as samples generated by the empirical model are employed as the training set for the path loss prediction at 5.8 GHz. The RMSEs of BPNN, SVR, random forest, and AdaBoost models are 2.49 dB, 2.78 dB, 2.54 dB, and 3.76 dB. In contrast, without samples generated by the empirical model, the RMSEs of those models are 3.84 dB, 4.94 dB, 6.57 dB, and 6.77 dB. Results show that the proposed data expansion scheme improves prediction performance when there are few measurement samples at the new frequency. INDEX TERMS Aircraft cabin, data expansion, machine learning, path loss prediction, propagation characteristics.

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Section: Comparison Of Path Loss Prediction Performance Based On mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

et al. 2019

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“…Most of the existing AI-based indoor propagation models make use of MLPs [19], [25]. The motivation in the work done in [19] was to reduce the computational cost of ray tracing through a coarse-to-dense grid MLP-assisted scheme.…”

Section: Related Workmentioning

confidence: 99%

An Expedient, Generalizable and Realistic Data-Driven Indoor Propagation Model

Bakirtzis¹,

Chen²,

Qiu³

et al. 2021

Preprint

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Efficient and realistic indoor radio propagation modelling tools are inextricably intertwined with the design and operation of next generation wireless networks. Machine learning (ML)-based radio propagation models can be trained with simulated or real-world data to provide accurate estimates of the wireless channel characteristics in a computationally efficient way. However, most of the existing research on ML-based propagation models focuses on outdoor propagation modelling, while indoor data-driven propagation models remain site-specific with limited scalability. In this paper we present an efficient and credible ML-based radio propagation modelling framework for indoor environments. Specifically, we demonstrate how a convolutional encoder-decoder can be trained to replicate the results of a ray-tracer, by encoding physics-based information of an indoor environment, such as the permittivity of the walls, and decode it as the path-loss (PL) heatmap for an environment of interest. Our model is trained over multiple indoor geometries and frequency bands, and it can eventually predict the PL for unknown indoor geometries and frequency bands within a few milliseconds. Additionally, we illustrate how the concept of transfer learning can be leveraged to calibrate our model by adjusting its pre-estimate weights, allowing it to make predictions that are consistent with measurement data. <br>

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“…For instance, in [19], 600,000 indoor data samples were collected, and learning took several hours. Authors in [18][19][20][21][22][23][24][25] used ANNs to predict the propagation in indoor environments. In these studies, the main ANN inputs were the: distance between transceivers, number of walls, number of doors, number of windows, frequency of transmission, antenna gains, and even transmission power.…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements

Hosseinzadeh

Almoathen

Larijani

et al. 2017

BDCC

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Among the many technologies competing for the Internet of Things (IoT), one of the most promising and fast-growing technologies in this landscape is the Low-Power Wide-Area Network (LPWAN). Coverage of LoRa, one of the main IoT LPWAN technologies, has previously been studied for outdoor environments. However, this article focuses on end-to-end propagation in an outdoor-indoor scenario. This article will investigate how the reported and documented outdoor metrics are interpreted for an indoor environment. Furthermore, to facilitate network planning and coverage prediction, a novel hybrid propagation estimation method has been developed and examined. This hybrid model is comprised of an artificial neural network (ANN) and an optimized Multi-Wall Model (MWM). Subsequently, real-world measurements were collected and compared against different propagation models. For benchmarking, log-distance and COST231 models were used due to their simplicity. It was observed and concluded that: (a) the propagation of the LoRa Wide-Area Network (LoRaWAN) is limited to a much shorter range in this investigated environment compared with outdoor reports; (b) log-distance and COST231 models do not yield an accurate estimate of propagation characteristics for outdoor-indoor scenarios; (c) this lack of accuracy can be addressed by adjusting the COST231 model, to account for the outdoor propagation; (d) a feedforward neural network combined with a COST231 model improves the accuracy of the predictions. This work demonstrates practical results and provides an insight into the LoRaWAN's propagation in similar scenarios. This could facilitate network planning for outdoor-indoor environments.

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ANN Prediction Models for Indoor Environment

Cited by 17 publications

References 6 publications

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

Path Loss Prediction Based on Machine Learning Methods for Aircraft Cabin Environments

An Expedient, Generalizable and Realistic Data-Driven Indoor Propagation Model

A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements

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