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 …
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 …
A comprehensive survey on radio resource management in 5G HetNets: Current solutions, future trends and open issues
The 5G network technologies are intended to accommodate innovative services with a large
influx of data traffic with lower energy consumption and increased quality of service and user …
influx of data traffic with lower energy consumption and increased quality of service and user …
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 …
Multi-agent deep reinforcement learning based spectrum allocation for D2D underlay communications
Device-to-device (D2D) communication underlay cellular networks is a promising technique
to improve spectrum efficiency. In this situation, D2D transmission may cause severe …
to improve spectrum efficiency. In this situation, D2D transmission may cause severe …
Multiagent deep-reinforcement-learning-based resource allocation for heterogeneous QoS guarantees for vehicular networks
Vehicle-to-vehicle communications can offer direct information interaction, including security-
centered information and entertainment information. However, the rapid proliferation of …
centered information and entertainment information. However, the rapid proliferation of …
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 …
A review on resource allocation techniques in D2D communication for 5G and B5G technology
S Jayakumar - Peer-to-Peer Networking and Applications, 2021 - Springer
Device to Device communication is an important aspect of the fifth-generation (5G) and
beyond fifth-generation (B5G) wireless networks. 5G facilitates network connectivity among …
beyond fifth-generation (B5G) wireless networks. 5G facilitates network connectivity among …
A survey on essential challenges in relay-aided D2D communication for next-generation cellular networks
Abstract Device-to-device (D2D) communication was originally presented as an efficient
solution to boost conventional cellular network performance. Since the signals degrade …
solution to boost conventional cellular network performance. Since the signals degrade …
Energy-efficient power allocation and Q-learning-based relay selection for relay-aided D2D communication
X Wang, T Jin, L Hu, Z Qian - IEEE Transactions on Vehicular …, 2020 - ieeexplore.ieee.org
Device-to-device (D2D) communication is a promising paradigm to meet the requirement of
ultra-dense, low-latency and high-rate in the fifth-generation networks. However, energy …
ultra-dense, low-latency and high-rate in the fifth-generation networks. However, energy …