Hem Regmi
Sanjib Sur
Abstract
D-band millimeter-wave, a key wireless technology for beyond 5G networks, promises extremely high data rate, ultra-low latency, and enables new Internet of Things applications. However, massive signal attenuation, complex response to building structures, and frequent non-availability of the Line-Of-Sight path make D-band picocell deployment challenging. To address this challenge, we propose a deep learning-based tool, that allows a network deployer to quickly scan the environment from a few random locations and predict Signal Reflection Profiles everywhere, which is essential to determine the optimal locations for picocell deployment.
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