[PDF] arxiv.org

Deep learning for mmWave beam and blockage prediction using sub-6 GHz channels

M Alrabeiah, A Alkhateeb - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Predicting the millimeter wave (mmWave) beams and blockages using sub-6 GHz channels
has the potential of enabling mobility and reliability in scalable mmWave systems. Prior work …
被引用次数：267 相关文章 所有 7 个版本
[PDF] ieee.org

Deep learning-based mmWave beam selection for 5G NR/6G with sub-6 GHz channel information: Algorithms and prototype validation

MS Sim, YG Lim, SH Park, L Dai, CB Chae - IEEE Access, 2020 - ieeexplore.ieee.org
In fifth-generation (5G) communications, millimeter wave (mmWave) is one of the key
technologies to increase the data rate. To overcome this technology's poor propagation …
被引用次数：146 相关文章 所有 6 个版本

A deep learning-based low overhead beam selection in mmWave communications

H Echigo, Y Cao, M Bouazizi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Due to large amounts of available spectrum at high frequencies, millimeter-wave (mmWave)
technology has gained extensive research attention in 5G communications, whereas …
被引用次数：63 相关文章 所有 4 个版本
[PDF] arxiv.org

Deep learning assisted calibrated beam training for millimeter-wave communication systems

K Ma, D He, H Sun, Z Wang… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Huge overhead of beam training imposes a significant challenge in millimeter-wave
(mmWave) wireless communications. To address this issue, in this paper, we propose a …
被引用次数：57 相关文章 所有 7 个版本
[PDF] soton.ac.uk

Deep learning assisted mmwave beam prediction for heterogeneous networks: A dual-band fusion approach

K Ma, S Du, H Zou, W Tian, Z Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this paper, motivated by the inter-base station (BS) channel dependence due to the
shared wireless environment, we propose to fuse sub-6 GHz channel information and …
被引用次数：22 相关文章 所有 4 个版本
[PDF] arxiv.org

Millimeter wave base stations with cameras: Vision-aided beam and blockage prediction

M Alrabeiah, A Hredzak… - 2020 IEEE 91st vehicular …, 2020 - ieeexplore.ieee.org
This paper investigates a novel research direction that leverages vision to help overcome
the critical wireless communication challenges. In particular, this paper considers millimeter …
被引用次数：165 相关文章 所有 6 个版本
[PDF] arxiv.org

Learning site-specific probing beams for fast mmWave beam alignment

Y Heng, J Mo, JG Andrews - IEEE Transactions on Wireless …, 2022 - ieeexplore.ieee.org
Beam alignment–the process of finding an optimal directional beam pair–is a challenging
procedure crucial to millimeter wave (mmWave) communication systems. We propose a …
被引用次数：33 相关文章 所有 3 个版本
[PDF] arxiv.org

Machine learning for reliable mmwave systems: Blockage prediction and proactive handoff

A Alkhateeb, I Beltagy, S Alex - 2018 IEEE Global conference …, 2018 - ieeexplore.ieee.org
The sensitivity of millimeter wave (mmWave) signals to blockages is a fundamental
challenge for mobile mmWave communication systems. The sudden blockage of the line-of …
被引用次数：141 相关文章 所有 8 个版本
[PDF] ieee.org

Millimeter wave beam-selection using out-of-band spatial information

A Ali, N González-Prelcic… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Millimeter wave (mmWave) communication is one feasible solution for high data-rate
applications like vehicular-to-everything communication and next generation cellular …
被引用次数：269 相关文章 所有 6 个版本
[PDF] arxiv.org

MmWave beam prediction with situational awareness: A machine learning approach

Y Wang, M Narasimha… - 2018 IEEE 19th …, 2018 - ieeexplore.ieee.org
Millimeter-wave communication is a challenge in the highly mobile vehicular context.
Traditional beam training is inadequate in satisfying low overheads and latency. In this …
被引用次数：111 相关文章 所有 5 个版本