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pro vyhledávání: '"Cousik, Tarun"'
The widespread proliferation of mmW devices has led to a surge of interest in antenna arrays. This interest in arrays is due to their ability to steer beams in desired directions, for the purpose of increasing signal-power and/or decreasing interfere
Externí odkaz:
http://arxiv.org/abs/2309.10904
We present DeepIA, a deep neural network (DNN) framework for enabling fast and reliable initial access for AI-driven beyond 5G and 6G millimeter (mmWave) networks. DeepIA reduces the beam sweep time compared to a conventional exhaustive search-based
Externí odkaz:
http://arxiv.org/abs/2101.01847
This paper presents DeepIA, a deep learning solution for faster and more accurate initial access (IA) in 5G millimeter wave (mmWave) networks when compared to conventional IA. By utilizing a subset of beams in the IA process, DeepIA removes the need
Externí odkaz:
http://arxiv.org/abs/2006.12653
The concept of CogRF, a novel tunable radio frequency (RF) frontend that uses artificial intelligence (AI) to meet mission requirements for beyond 5G and 6G systems, is introduced. CogRF utilizes AI as the core to control and operate RF system compon
Externí odkaz:
http://arxiv.org/abs/1909.06862
Publikováno v:
MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM).
This paper presents DeepIA, a deep learning solution for faster and more accurate initial access (IA) in 5G millimeter wave (mmWave) networks when compared to conventional IA. By utilizing a subset of beams in the IA process, DeepIA removes the need
Publikováno v:
2015 International Conference on Advances in Computing, Communications & Informatics (ICACCI); 2015, p675-679, 5p