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This paper presents an alternative procedure for the prediction of propagation path loss in urban environments. It is based on Neural Network (NN) algorithms and uses the detailed environment profile instead of the mean values of its structural parameters. The general performance of the NN shows its effectiveness to yield results with satisfactory accuracy in short time. The received results are compared to the respective ones yielded by the Ray-Tracing model and exhibit satisfactory accuracy either for uniform or for non-uniform distribution of the manmade structured environment.

Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. In this paper, we present a brief summary of ML methods, as well as an up-to-date review of ML approaches in 6G wireless communication systems. These methods include supervised, unsupervised and reinforcement techniques. Additionally, we discuss open issues in the field of ML for 6G networks and wireless communications in general, as well as some potential future trends to motivate further research into this area.

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Tabular data and images have been used from machine learning models as two diverse types of inputs, in order to perform path loss predictions in urban areas. Different types of models are applied on these distinct modes of input information. The work at hand tries to incorporate both modes of input data within a single prediction model. It therefore manipulates and transforms the vectors of tabular data into images. Each feature of the tabular data vector is spread into several pixels, corresponding to the calculated importance of the particular feature. The resulting synthetic images are then fused with images representing selected regions of the area's map. Compound pseudoimages, having channels of both map-based and tabular data-based images, are then being used as inputs for a Convolutional Neural Network (CNN), which predicts the path loss value at a specific point of the area of interest. The results are clearly better than those obtained from models based on a single mode of input data, as well as from the results produced by other bimodal-input approaches. This approach could be applied for path loss prediction in urban environments for several state-of-art wireless networks like 5G and Internet of Things (IoT).

In this paper we present an alternative procedure for the prediction of propagation path loss in urban environments, which is based on Artificial Neural Networks (ANN). The goal of this work is to synthesize and model ANNs which would require entering at the input nodes a detailed and the same time small amount of information about the propagation environment. We apply the Differential Evolution (DE) algorithm, in conjunction with the LevenbergMarquardt backpropagation algorithm in order to train different ANNs. The combined DE-LM method achieves better convergence of neural network weight optimization. We present two different ANN design cases with different number of input nodes. The general performance of the both ANNs shows their effectiveness to yield results with satisfactory accuracy in short time. The received results are compared to the respective ones yielded by the Ray-Tracing model and exhibit satisfactory accuracy.