Exploiting Tensor-Based Bayesian Learning for Massive Grant-Free Random Access in LEO Satellite Internet of Things
Author:
Affiliation:
1. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China
2. Institute for Digital Communications (IDC), Friedrich Alexander University Erlangen--Nuremberg, Erlangen, Germany
Funder
Natural Science Foundation of China
Zhejiang Provincial Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/26/10045341/09973387.pdf?arnumber=9973387
Reference48 articles.
1. Robust precoding design for multibeam downlink satellite channel with phase uncertainty
2. User Activity Detection and Channel Estimation for Grant-Free Random Access in LEO Satellite-Enabled Internet of Things
3. Making Two-Way Satellite Relaying Feasible: A Differential Modulation Based Approach
4. A Random Access Protocol for Pilot Allocation in Crowded Massive MIMO Systems
5. Robust frequency and timing synchronization for OFDM
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1. Improving User Capacity of Satellite Internet of Things via Joint User Grouping and Multi-Beam Processing;IEEE Transactions on Communications;2024-07
2. Low Earth Orbit (LEO) Satellites Role in Shaping 6G Networks Amidst Emerging Threats;2023 IEEE Future Networks World Forum (FNWF);2023-11-13
3. 卫星物联网: 挑战、 方案和发展趋势;Frontiers of Information Technology & Electronic Engineering;2023-06-16
4. Low Earth Orbit Satellite Security and Reliability: Issues, Solutions, and the Road Ahead;IEEE Communications Surveys & Tutorials;2023
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