Deep Learning for Moving Blockage Prediction using Real mmWave Measurements

Author:

Wu Shunyao1,Alrabeiah Muhammad1,Hredzak Andrew1,Chakrabarti Chaitali1,Alkhateeb Ahmed1

Affiliation:

1. Arizona State University,School of Electrical, Computer and Energy Engineering,Tempe,AZ,85287

Funder

National Science Foundation

Publisher

IEEE

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Spectral-Based Proactive Blockage Detection for Sub-THz Communications;IEEE Communications Letters;2024-07

2. A Custom Loss Function for Machine Learning-based Resource Allocation Policies;2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN);2024-05-05

3. Blockage Attenuation and Duration Over Reflected Propagation Paths in Indoor Terahertz Deployments;Lecture Notes in Computer Science;2024

4. A New Blockage Detection Approach for 6G THz Systems;Lecture Notes in Computer Science;2024

5. ML-Assisted Resource Allocation Outage Probability: Simple, Closed-Form Approximations;2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS);2023-12-17

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