Path loss prediction is essential for network planning in any wireless communication system. For cellular networks, it is usually achieved through extensive received signal power measurements in the target area. When the 3D model of an area is available, ray tracing simulations can be utilized; however, an important drawback of such an approach is the high computational complexity of the simulations. In this paper, we present a fundamentally different approach for path loss distribution prediction directly from 2D satellite images based on deep convolutional neural networks. While training process is time consuming and completed offline, inference can be done in real time. Another advantage of the proposed approach is that 3D model of the area is not needed during inference since the network simply uses an image captured by an aerial vehicle or satellite as its input. Simulation results show that the path loss distribution can be accurately predicted for different communication frequencies and transmitter heights. INDEX TERMS Path loss, deep learning, convolutional neural networks.
Abstract:The multi-gate transistors such as Fin-FETs, Tri-gate FETs, and Gate-all-around (GAA) FETs are remarkable breakthrough in the electronic industry. 3D Transistor is taking the place of the conventional 2D planar transistor for many reasons. 3D transistors afford more scalability, energy efficient performance than planar transistors and increase the control on the channel region to reduce the short channel effect, which enables us to extend Moore's law to further extent. In this paper, we will present a review about their structure, operation, types and fabrication.
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