Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial
Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …
led to multiple successes in solving sequential decision-making problems in various …
Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption
The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite
important for network optimization. The current 5G and conceived 6G network in the future …
important for network optimization. The current 5G and conceived 6G network in the future …
Machine learning for 6G wireless networks: Carrying forward enhanced bandwidth, massive access, and ultrareliable/low-latency service
To satisfy the expected plethora of demanding services, the future generation of wireless
networks (6G) has been mandated as a revolutionary paradigm to carry forward the …
networks (6G) has been mandated as a revolutionary paradigm to carry forward the …
Multiagent deep reinforcement learning for vehicular computation offloading in IoT
X Zhu, Y Luo, A Liu, MZA Bhuiyan… - IEEE Internet of Things …, 2020 - ieeexplore.ieee.org
The development of the Internet of Things (IoT) and intelligent vehicles brings a comfortable
environment for users. Various emerging vehicular applications using artificial intelligence …
environment for users. Various emerging vehicular applications using artificial intelligence …
Multi-agent deep reinforcement learning for computation offloading and interference coordination in small cell networks
X Huang, S Leng, S Maharjan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Integrating mobile edge computing (MEC) with small cell networks has been conceived as a
promising solution to provide pervasive computing services. However, the interactions …
promising solution to provide pervasive computing services. However, the interactions …
Integration of D2D, network slicing, and MEC in 5G cellular networks: Survey and challenges
With the tremendous demand for connectivity anywhere and anytime, existing network
architectures should be modified. To cope with the challenges that arise due to the …
architectures should be modified. To cope with the challenges that arise due to the …
Applications of multi-agent reinforcement learning in future internet: A comprehensive survey
Future Internet involves several emerging technologies such as 5G and beyond 5G
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
Intelligent offloading and resource allocation in heterogeneous aerial access IoT networks
Aerial access networks, comprising a hierarchical model of high-altitude platforms (HAPs)
and multiple unmanned aerial vehicles (UAVs), are considered a promising technology to …
and multiple unmanned aerial vehicles (UAVs), are considered a promising technology to …
Deep-reinforcement-learning-based proportional fair scheduling control scheme for underlay D2D communication
In the last few years, we have witnessed the usage of billions of Internet-of-Things (IoT)-
enabled devices in different applications starting from e-healthcare, transportation …
enabled devices in different applications starting from e-healthcare, transportation …
Reinforcement Learning Based Physical Cross-Layer Security and Privacy in 6G
Sixth-generation (6G) cellular systems will have an inherent vulnerability to physical (PHY)-
layer attacks and privacy leakage, due to the large-scale heterogeneous networks with …
layer attacks and privacy leakage, due to the large-scale heterogeneous networks with …