Path loss and delay spread for the stairwell channel at 5 GHz

2018

Summary We present a newly introduced multiplicative based path loss model for the wireless channel. The model verified with experimental data at 2100 MHz collected across Cyprus in 6 existing microcells in urban, suburban, and rural areas. The new method uses the multiplicative least square fitting model that relates the decibel path loss to the distance with parameterized exponential‐type basis. The parameters are extracted from the real‐time measured values. The resulting path loss models apply to single‐cell arrangement with base antenna heights from 25 to 45 m and base‐to‐terminal distances from 0.1 to 2 km. The newly generated multiplicative base path loss model tested and compared with the best existing suitable models. The validity of the new model verified using relative error analysis. The new model can be used to determine accurately the path loss for microcells in the wireless coverage area as well as in the applications of data analysis.

Section: Multiplicative Based Exponential Path Loss Modelmentioning

confidence: 99%

Section: Multiplicative Based Exponential Path Loss Modelmentioning

confidence: 99%

Section: E Ecc-33 Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multiplicative based path loss model

Bilgehan

2018

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

Imoize

Alienyi³

2020

Summary Path loss prediction models occupy a central role in wireless signal propagation because of the continuous need to achieve reliable and high quality of service for subscribers satisfaction. However, the adoption of deterministic and empirical models for pathloss characterization presents a highly contending trade‐off between simplicity and accuracy. On the one hand, empirical models are relatively simple to apply but are mostly inaccurate and inconsistent. Deterministic models are more accurate but quite complex to develop, time‐consuming, and possess nonadaptable characteristics. Toward this end, this paper proposes to address the problems associated with the existing models (empirical and deterministic) through the introduction of machine learning algorithms to path loss predictions. The contribution of this paper is in threefold. First, experimental data were collected in multitransmitter scenarios via drive test in six base transceiver stations, and the pathloss of the received signal level was derived and analyzed. Two machine learning‐based path loss prediction models were then developed using the measured data as input variables. The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN). Further to this, the MLPNN and the RBFNN models were compared with the measured path loss, and the RBFNN appears to be more accurate with lower values of root mean squared errors (RMSEs) in comparison with the MLPNN. Finally, the proposed machine language‐based path loss prediction models (MLPNN and RBFNN) were compared against five existing empirical models, and again, the RBFNN shows the most accurate results.

An ensemble machine learning approach for enhanced path loss predictions for 4G LTE wireless networks

Akkaya

Sopuru

2022

Summary Accurate path loss prediction models are indispensable in modern wireless communication systems. In recent times, several path loss prediction models have been proposed to improve network performance. However, most of these models have not addressed the fundamental issues. The problem of deploying a single path loss prediction model that fits well in all wireless propagation environments remains. To address this problem, we present machine learning‐based ensemble methods to path loss predictions. Specifically, ensemble methods have been introduced to improve signal prediction accuracy and performance. Additionally, the radial basis function (RBF) and multilayer perceptron (MLP) neural network models were deployed and improved by adding more network parameters to their respective input layers. Results revealed that the RBF model with an increase in the number of centroids reduces the mean square error (MSE). Also, the Gaussian kernel function gives lower MSE compared to the multiquadric and inverse multiquadric functions. The bagging and blending ensemble path loss prediction models were introduced for optimal performance. An RBF neural network of different clusters was incorporated into the bagging algorithm as base learners. The RBF, MLP, blending, and bagging were examined using standard metrics for the training, testing, and validation dataset for model validation. The developed bagging ensemble path loss prediction model gave the lowest errors (MSE = 0.0011 dB, SSE = 0.6069 dB, MAE = 0.0245 dB, & R = 0.7484 dB) for the datasets. The bagging ensemble method acts as a variance and error reduction mechanism because it predicted path loss closest to measured data and is suitable for near precise path loss predictions.