Reinforcement Learning Based RSS-Threshold Optimization for D2D-Aided HTC/MTC in Dense NOMA Systems-Reference-Cited by-同舟云学术

Reinforcement Learning Based RSS-Threshold Optimization for D2D-Aided HTC/MTC in Dense NOMA Systems

Published:2023-10 Issue:10 Volume:22 Page:6489-6503
ISSN:1536-1276
Container-title:IEEE Transactions on Wireless Communications
language:
Short-container-title:IEEE Trans. Wireless Commun.

Author:

Zhang Shangwei¹^ORCID,Wang Xiao¹,Shi Zhenjiang²^ORCID,Liu Jiajia¹^ORCID

Affiliation:

1. National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Cybersecurity, Northwestern Polytechnical University, Xi’an, Shaanxi, China

2. State Key Laboratory of Integrated Services Networks, School of Cyber Engineering, Xidian University, Xi’an, Shaanxi, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Basic Research Programs of Taicang

Fundamental Research Funds for the Central Universities

Xi’an Unmanned System Security and Intelligent Communications International Science and Technology Cooperation (ISTC) Center

Special Funds for Central Universities Construction of World-Class Universities (Disciplines) and Special Development Guidance

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/7693/10275776/10048778.pdf?arnumber=10048778

Reference37 articles.

1. Deep Reinforcement Learning for Throughput Improvement of the Uplink Grant-Free NOMA System

2. Optimal Resource Allocation Considering Non-Uniform Spatial Traffic Distribution in Ultra-Dense Networks: A Multi-Agent Reinforcement Learning Approach

3. On the Outage Probability of Device-to-Device-Communication-Enabled Multichannel Cellular Networks: An RSS-Threshold-Based Perspective

4. Compressed Random Access for Noncoherent Massive Machine-Type Communications with Energy Modulation

5. Downlink and Uplink Non-Orthogonal Multiple Access in a Dense Wireless Network

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