Fusing Diverse Input Modalities for Path Loss Prediction: A Deep Learning Approach

Abstract

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).

Wireless Transmissions, Propagation and Channel Modelling for IoT Technologies: Applications and Challenges

“…In [171], [172], the authors followed DL or ML approaches to model the PL. Accordingly, [171] incorporated tabular data and images as inputs for CNN to perform PL prediction in urban areas.…”

“…In [171], [172], the authors followed DL or ML approaches to model the PL. Accordingly, [171] incorporated tabular data and images as inputs for CNN to perform PL prediction in urban areas. Hence, the vectors of tabular data were first manipulated and transformed into images, and then each feature was spread across several pixels, proportional to its calculated importance.…”

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

“…However, some researchers hope to use indirect methods to solve IoT WSN communication problems such as environmental modeling for wireless data transmission (radio propagation modeling) [213], [214]. The Computational Intelligence method to perform this feat including ANFIS [105], CNN [215], Random Forest [216], PSO (Particle Swarm Optimization) [217], Markov Chain [218], and more.…”

DeepChannel: Robust Multimodal Outdoor Channel Model Prediction in LTE Networks Using Deep Learning

“…In [18], the authors presented a deep learning model to predict the large-scale channel fading in mmWave by designing input features and neural-network architecture to capture topographical information around the base station coverage area using U-Net CNN architecture. In [19], the authors presented a deep learning framework to convert some tabular data to an image and, jointly with a high scale map, extract the high-importance features that impact the path loss hence predicting its values. In [20], the authors presented a model using image segmentation using image processing and image transformation.…”