A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz

Chi Nguyen, Adnan Ahmad Cheema

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
111 Downloads (Pure)


Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.
Original languageEnglish
Article number5100
Number of pages24
Issue number15
Early online date28 Jul 2021
Publication statusPublished online - 28 Jul 2021

Bibliographical note

Funding Information:
Funding: Miss Chi Nguyen PhD work is funded by Vice-Chancellor Research Scholarship awarded by the Ulster University, UK.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • path loss
  • path loss modelling
  • channel modelling
  • path loss prediction
  • AI
  • DNN
  • deep neural network
  • 5G


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