Abstract
Accurate channel models are essential to evaluate mobile communication system performance and optimize coverage for existing deployments. The introduction of various transmission frequencies for 5G imposes new challenges for accurate radio performance prediction. This paper compares traditional channel models to a channel model obtained using Deep Learning (DL)-techniques utilizing satellite images aided by a simple path loss model. Experimental measurements are gathered and compose the training and test set. This paper considers path loss modelling techniques offered by state-of-the-art stochastic models and a ray-tracing model for comparison and evaluation. The results show that 1) the satellite images offer an increase in predictive performance by ≈ 0.8 dB, 2) The model-aided technique offers an improvement of ≈ 1 dB, and 3) that the proposed DL model is capable of improving path loss prediction at unseen locations for 811 MHz with ≈ 1 dB and ≈ 4.7 dB for 2630 MHz.
Highlights
The fifth generation of mobile networks, 5G, seeks to expand the current mobile architecture with densification of base stations, known as Heterogeneous UltraDense Network (H-UDN), to offer improved capacity and coverage for users
We propose an improved model for path loss prediction for use in mobile communication systems based on a Deep Learning (DL) framework utilizing satellite imagery and position indicators
Accurate path loss prediction with improved generalization using satellite images can be achieved with the use of convoluted neural networks
Summary
The fifth generation of mobile networks, 5G, seeks to expand the current mobile architecture with densification of base stations, known as Heterogeneous UltraDense Network (H-UDN), to offer improved capacity and coverage for users. The densification results in low inter-site distances between terminals and base stations. Such a decrease in distance allows for improved radio conditions when operating at higher frequencies. A Heterogeneous mindset is set to replace the classical mindset where smaller base stations such as micro, pico, and even femtocells [1], [2] manage user data and the macrocells manage control signals and wide-area coverage. This approach poses a significant challenge in terms of network
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