On the Usefulness of the Generalised Additive Model for Mean Path Loss Estimation in Body Area Networks

Laskowski, Michal; Ambroziak, Slawomir J.; Correia, Luıs M.; Swider, Krzysztof

doi:10.1109/access.2020.3025118

Cited by 12 publications

(8 citation statements)

References 39 publications

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“…Throughout the campaigns, the intent was to place the Tx at one end of the corridor and moving the Rx away from the Tx, having a Tx-Rx separation distance of 2 − 24 meters with an incremental step of 2 meters a time. The reference Tx-Rx distance was 1 meter, as is recommended by most research experts in this field [63]- [67]. Note that to satisfy the far-field requirements, the distance from the Tx should be much greater than the wavelength of the lowest operational frequency, which already exists since the wavelength of the SHF signals is in the range of millimeters.…”

Section: Measurement Setup and Data Collection Methodsmentioning

confidence: 99%

An Efficient Approach of Improving Path Loss Models for Future Mobile Networks in Enclosed Indoor Environments

Elmezughi

Afullo

2021

IEEE Access

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Path loss is the primary aspect that determines the overall coverage of networks. Designing reliable wireless communication systems requires accurate path loss prediction models. Future wireless mobile systems will rely mainly on the super-high frequency (SHF) and the millimeter-wave (mmWave) frequency bands due to the massive available bandwidths that will meet projected users' demand, such as the needs of the fifth-generation (5G) wireless systems and other high-speed multimedia services. However, these bands are more sensitive and exhibit a different propagation behavior compared to the frequency bands below 6 GHz. Hence, improving the existing models and developing new models are vital for characterizing the wireless communication channel in both indoor and outdoor environments for future SHF and mmWave services. This paper proposes an efficient improvement of the well-known close-in (CI) free space reference distance model and the floating-intercept (FI) model. Real measured data was taken for both line-of-sight (LOS) and non-line-of-sight (NLOS) communication scenarios in a typical indoor corridor environment at three selected frequencies within the SHF band, namely 14 GHz, 18 GHz, and 22 GHz. The research finding of this work reveals that the proposed models have better performance in terms of their accuracy of fitting real measured data collected from measurement campaigns. In addition, this work studies the impact of the angle of arrival and the antenna heights on the current and improved CI and FI models. The results show that the improved models provide better stability and sensitivity to the change of these parameters. Furthermore, the mean square error between the models and their improved versions were presented. Finally, this paper shows that shadow fading's standard deviation can have a notable reduction in both the LOS and NLOS scenarios (especially in the NLOS), which means higher precision in predicting the path loss.INDEX TERMS Path loss, propagation measurements, 5G, angle of arrival, antenna height.

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Section: Measurement Setup and Data Collection Methodsmentioning

confidence: 99%

An Efficient Approach of Improving Path Loss Models for Future Mobile Networks in Enclosed Indoor Environments

Elmezughi

Afullo

2021

IEEE Access

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“…Non-linear regression models extend to include step functions, exponential, local regression, smoothing, regression splines, and polynomial regression into the Familia. Otherwise, the Generalized Additive Models (GAMs) [ 48 ] maintain the additivity of the original predictors , and the relation between every feature and the response y is expressed using nonlinear functions such as …”

Section: Background Of ML Algorithmsmentioning

confidence: 99%

Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey

Swain

Bhushan

Viriyasitavat

2022

Arch Computat Methods Eng

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Machine Learning (ML) has been categorized as a branch of Artificial Intelligence (AI) under the Computer Science domain wherein programmable machines imitate human learning behavior with the help of statistical methods and data. The Healthcare industry is one of the largest and busiest sectors in the world, functioning with an extensive amount of manual moderation at every stage. Most of the clinical documents concerning patient care are hand-written by experts, selective reports are machine-generated. This process elevates the chances of misdiagnosis thereby, imposing a risk to a patient's life. Recent technological adoptions for automating manual operations have witnessed extensive use of ML in its applications. The paper surveys the applicability of ML approaches in automating medical systems. The paper discusses most of the optimized statistical ML frameworks that encourage better service delivery in clinical aspects. The universal adoption of various Deep Learning (DL) and ML techniques as the underlying systems for a variety of wellness applications, is delineated by challenges and elevated by myriads of security. This work tries to recognize a variety of vulnerabilities occurring in medical procurement, admitting the concerns over its predictive performance from a privacy point of view. Finally providing possible risk delimiting facts and directions for active challenges in the future.

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“…The GoF (Goodness of Fit) of the developed linear model with the empirical data was determined by calculating the determination coefficient R 2 , which takes values in the range <0; 1>, where the value 1 means a perfect fit of the model to the empirical data [25]. R 2 was calculated for all the data and the model values that are consistent with the mathematical calculations of the model, but not with their physical interpretation (e.g., the BLER less than 0% or greater than 100%).…”

Section: Measurement Campaign Nomentioning

confidence: 99%

Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

Cwalina

Rajchowski

Olejniczak

et al. 2021

Sensors

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Following the continuous development of the information technology, the concept of dense urban networks has evolved as well. The powerful tools, like machine learning, break new ground in smart network and interface design. In this paper the concept of using deep learning for estimating the radio channel parameters of the LTE (Long Term Evolution) radio interface is presented. It was proved that the deep learning approach provides a significant gain (almost 40%) with 10.7% compared to the linear model with the lowest RMSE (Root Mean Squared Error) 17.01%. The solution can be adopted as a part of the data allocation algorithm implemented in the telemetry devices equipped with the 4G radio interface, or, after the adjustment, the NB-IoT (Narrowband Internet of Things), to maximize the reliability of the services in harsh indoor or urban environments. Presented results also prove the existence of the inverse proportional dependence between the number of hidden layers and the number of historical samples in terms of the obtained RMSE. The increase of the historical data memory allows using models with fewer hidden layers while maintaining a comparable RMSE value for each scenario, which reduces the total computational cost.

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On the Usefulness of the Generalised Additive Model for Mean Path Loss Estimation in Body Area Networks

Cited by 12 publications

References 39 publications

An Efficient Approach of Improving Path Loss Models for Future Mobile Networks in Enclosed Indoor Environments

An Efficient Approach of Improving Path Loss Models for Future Mobile Networks in Enclosed Indoor Environments

Appositeness of Optimized and Reliable Machine Learning for Healthcare: A Survey

Channel State Estimation in LTE-Based Heterogenous Networks Using Deep Learning

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