Towards Deep Learning Augmented Robust D-Band Millimeter-Wave Picocell Deployment-Reference-Cited by-同舟云学术

Towards Deep Learning Augmented Robust D-Band Millimeter-Wave Picocell Deployment

Published:2023-04-26 Issue:4 Volume:50 Page:62-64
ISSN:0163-5999
Container-title:ACM SIGMETRICS Performance Evaluation Review
language:en
Short-container-title:SIGMETRICS Perform. Eval. Rev.

Author:

Regmi Hem¹,Sur Sanjib¹

Affiliation:

1. University of South Carolina, Columbia, SC, USA

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.

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Link

https://dl.acm.org/doi/pdf/10.1145/3595244.3595266

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5. Argus

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