Secrecy Rate Maximization for THz-Enabled Femto Edge Users using Deep Reinforcement Learning in 6G
Author:
Affiliation:
1. Thapar Institute of Engineering and Technology,Computer Science and Engineering Department,Patiala,Punjab,India
2. Bennett University,School of Computer Science Engineering and Technology,Greater Noida,Uttar Pradesh,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9797887/9797888/09798370.pdf?arnumber=9798370
Reference20 articles.
1. Secure Communications in Tiered 5G Wireless Networks With Cooperative Jamming
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4. UAV-Enabled Secure Communications by Multi-Agent Deep Reinforcement Learning
5. Indoor Terahertz Communications: How Many Antenna Arrays Are Needed?
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2. From 5G to 6G Networks: A Survey on AI-Based Jamming and Interference Detection and Mitigation;IEEE Open Journal of the Communications Society;2024
3. Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications;IEEE Access;2024
4. Secrecy Rate Maximization in THz-Aided Heterogeneous Networks: A Deep Reinforcement Learning Approach;IEEE Transactions on Vehicular Technology;2023-10
5. Secure Underwater Distributed Antenna Systems: A Multi-Agent Reinforcement Learning Approach;IEEE/CAA Journal of Automatica Sinica;2023-07
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