Large bandwidth at mm-wave is crucial for 5G and beyond but the high path loss (PL) requires highly accurate PL prediction network planning optimization. Statistical models with slope-intercept fit fall short in capturing large variations seen urban canyons, whereas ray-tracing, capable of characterizing site-specific features, faces challenges describing foliage street clutter associated refle...

In optimization of wireless networks, path loss prediction is great importance for adequate planning and budgeting in communications. For efficient reliable communications the tropics, determination or estimation channel parameters becomes important. Research this article employed different machine learning techniques—AdaBoost, support vector regression (SVR), back propagation neural networks (...

Path loss optimization is an important requirement in the design and implementation phase of mobile radio systems. The accuracy of the propagation model is improved by optimizing the model parameters to reflect the real time environment in a better manner. The paper aims at optimizing the proposed hybrid Walfisch–Ikegami path loss model for cellular signals in urban environment. The hybrid mode...

In order to estimate the signal parameters accurately for wireless multimedia services, it is necessary to estimate a system’s propagation characteristics through a medium. Propagation analysis provides a good initial estimate of the signal characteristics. The ability to accurately predict radio propagation behavior for wireless multimedia services is becoming crucial to system design. Since s...

This work presents results for the path loss due to foliage at 2.4 GHz using RF equipment and XBee-Pro ZB S2B transceiver modules in Agricultural fields (Corn, Paddy and Groundnut) and Gardens (Coconut garden with green grass, open lawn with dry green grass and wet green grass) targeting short-range, near ground RF propagation measurements for planning and deployment of Wireless Sensor Communic...

Path loss prediction is a crucial task for the planning of networks inmodern mobile communication systems. Learning machine-based models seem to be a valid alternative to empirical and deterministic methods for predicting the propagation path loss.As learningmachine performance depends on the number of input features, a good way to get a more reliable model can be to use techniques for reducing...