4.7 Article

Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Electrical & Electronic

Deep Reinforcement Learning-Based Intelligent Reflecting Surface for Secure Wireless Communications

Helin Yang et al.

Summary: This paper investigates an intelligent reflecting surface (IRS)-aided wireless secure communication system, utilizing deep reinforcement learning to optimize beamforming strategies for enhancing system secrecy rate and QoS satisfaction probability. Post-decision state (PDS) and prioritized experience replay (PER) schemes are applied to improve learning efficiency and secrecy performance against multiple eavesdroppers in dynamic environments.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2021)

Article Computer Science, Information Systems

Dynamic Computation Offloading in IoT Fog Systems With Imperfect Channel-State Information: A POMDP Approach

Renchao Xie et al.

Summary: This article investigates the dynamic computation offloading problem in the IoT fog system, proposing a partially observable offloading scheme for IoT devices to make optimal offloading decisions with imperfect channel-state information. An offline algorithm based on deep recurrent Q-network (DRQN) is developed to find the optimal offloading solution. Extensive simulation experiments are conducted to evaluate the effectiveness of the proposed offloading scheme.

IEEE INTERNET OF THINGS JOURNAL (2021)

Article Engineering, Electrical & Electronic

Multiple Access in Cell-Free Networks: Outage Performance, Dynamic Clustering, and Deep Reinforcement Learning-Based Design

Yasser Al-Eryani et al.

Summary: In future cell-free wireless networks, a dynamic architecture and a heuristic interference cancellation signal detection method are proposed to serve a large number of devices simultaneously through distributed access points. The system aims to maximize the sum rate or the minimum rate, and a deep reinforcement learning model is introduced to solve the optimization problem efficiently. The proposed DRL model outperforms in terms of average per-user rate performance and achieves around 78% of the rate achievable through exhaustive search-based design in the system setting.

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS (2021)

Article Computer Science, Hardware & Architecture

Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells

Dagnachew Azene Temesgene et al.

Summary: To meet the growing demand for network capacity, mobile network operators are deploying dense infrastructures of small cells, leading to increased power consumption and environmental impact. Recent trends show a shift towards powering mobile networks with harvested ambient energy for environmental and cost benefits.

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING (2021)

Article Computer Science, Information Systems

Artificial Intelligence for Wireless Caching: Schemes, Performance, and Challenges

Muhammad Sheraz et al.

Summary: Wireless data traffic is growing unprecedentedly, which may impede network performance. Artificial Intelligence can be used to optimize network performance through data caching, addressing issues such as data transmission and access delay.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2021)

Article Engineering, Electrical & Electronic

Multi-Agent Deep Reinforcement Learning Based Spectrum Allocation for D2D Underlay Communications

Zheng Li et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2020)

Article Engineering, Electrical & Electronic

Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

Fengxiao Tang et al.

PROCEEDINGS OF THE IEEE (2020)

Article Engineering, Electrical & Electronic

Optimization for Reinforcement Learning: From a single agent to cooperative agents

Donghwan Lee et al.

IEEE SIGNAL PROCESSING MAGAZINE (2020)

Article Computer Science, Information Systems

Optimal Power Allocation for Full-Duplex Underwater Relay Networks With Energy Harvesting: A Reinforcement Learning Approach

Ranning Wang et al.

IEEE WIRELESS COMMUNICATIONS LETTERS (2020)

Article Computer Science, Hardware & Architecture

A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems

Walid Saad et al.

IEEE NETWORK (2020)

Article Chemistry, Multidisciplinary

Deep Learning at the Mobile Edge: Opportunities for 5G Networks

Miranda McClellan et al.

APPLIED SCIENCES-BASEL (2020)

Article Computer Science, Information Systems

Multiagent Deep Reinforcement Learning for Joint Multichannel Access and Task Offloading of Mobile-Edge Computing in Industry 4.0

Zilong Cao et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Engineering, Electrical & Electronic

Deep Reinforcement Learning for Distributed Dynamic MISO Downlink-Beamforming Coordination

Jungang Ge et al.

IEEE TRANSACTIONS ON COMMUNICATIONS (2020)

Review Automation & Control Systems

Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

Thanh Thi Nguyen et al.

IEEE TRANSACTIONS ON CYBERNETICS (2020)

Review Computer Science, Hardware & Architecture

A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective

Ali Shakarami et al.

COMPUTER NETWORKS (2020)

Article Computer Science, Information Systems

Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges

Lei Lei et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2020)

Article Computer Science, Information Systems

A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks

Lina Bariah et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

Cooperative Edge Caching: A Multi-Agent Deep Learning Based Approach

Yuming Zhang et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

Machine Learning Meets Computation and Communication Control in Evolving Edge and Cloud: Challenges and Future Perspective

Tiago Koketsu Rodrigues et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2020)

Article Engineering, Electrical & Electronic

Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks

Mohamed Sana et al.

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS (2020)

Article Computer Science, Information Systems

6G and Beyond: The Future of Wireless Communications Systems

Ian F. Akyildiz et al.

IEEE ACCESS (2020)

Article Engineering, Electrical & Electronic

Deep Reinforcement Learning Based Resource Allocation for V2V Communications

Hao Ye et al.

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY (2019)

Article Automation & Control Systems

A survey and critique of multiagent deep reinforcement learning

Pablo Hernandez-Leal et al.

AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS (2019)

Article Computer Science, Information Systems

Deep Deterministic Policy Gradient (DDPG)-Based Energy Harvesting Wireless Communications

Chengrun Qiu et al.

IEEE INTERNET OF THINGS JOURNAL (2019)

Article Computer Science, Information Systems

A Distributed Multi-Agent RL-Based Autonomous Spectrum Allocation Scheme in D2D Enabled Multi-Tier HetNets

Kamran Zia et al.

IEEE ACCESS (2019)

Proceedings Paper Computer Science, Information Systems

Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach

Navid Naderializadeh et al.

CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS (2019)

Proceedings Paper Computer Science, Hardware & Architecture

Hurts to Be Too Early: Benefits and Drawbacks of Communication in Multi-Agent Learning

Parinaz Naghizadeh et al.

IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2019) (2019)

Proceedings Paper Computer Science, Hardware & Architecture

Distributed Cooperative Spectrum Sharing in UAV Networks Using Multi-Agent Reinforcement Learning

Alireza Shamsoshoara et al.

2019 16TH IEEE ANNUAL CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE (CCNC) (2019)

Article Computer Science, Information Systems

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Nguyen Cong Luong et al.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2019)

Review Computer Science, Hardware & Architecture

Deep Reinforcement Learning for Mobile Edge Caching: Review, New Features, and Open Issues

Hao Zhu et al.

IEEE NETWORK (2018)

Article Telecommunications

Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks

Shangxing Wang et al.

IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING (2018)

Article Computer Science, Artificial Intelligence

Survey of Model-Based Reinforcement Learning: Applications on Robotics

Athanasios S. Polydoros et al.

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS (2017)

Article Computer Science, Hardware & Architecture

Settling the Complexity of Computing Two-Player Nash Equilibria

Xi Chen et al.

JOURNAL OF THE ACM (2009)