The frontiers of deep reinforcement learning for resource management in future wireless HetNets: Techniques, challenges, and research directions
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
Deep reinforcement learning for radio resource allocation and management in next generation heterogeneous wireless networks: A survey
Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …
massive heterogeneity in terms of the types of network architectures they incorporate, the …
RIS-assisted UAV communications for IoT with wireless power transfer using deep reinforcement learning
Many of the devices used in Internet-of-Things (IoT) applications are energy-limited, and
thus supplying energy while maintaining seamless connectivity for IoT devices is of …
thus supplying energy while maintaining seamless connectivity for IoT devices is of …
3D UAV trajectory and data collection optimisation via deep reinforcement learning
Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the
network performance and coverage in wireless communication. However, due to the …
network performance and coverage in wireless communication. However, due to the …
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 …
Reconfigurable intelligent surface-assisted multi-UAV networks: Efficient resource allocation with deep reinforcement learning
KK Nguyen, SR Khosravirad… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
In this paper, we propose reconfigurable intelligent surface (RIS)-assisted unmanned aerial
vehicles (UAVs) networks that can utilise both advantages of UAV's agility and RIS's …
vehicles (UAVs) networks that can utilise both advantages of UAV's agility and RIS's …
Transmit power pool design for grant-free NOMA-IoT networks via deep reinforcement learning
Grant-free non-orthogonal multiple access (GF-NOMA) is a potential multiple access
framework for short-packet internet-of-things (IoT) networks to enhance connectivity …
framework for short-packet internet-of-things (IoT) networks to enhance connectivity …
A deep reinforcement learning scheme for sum rate and fairness maximization among d2d pairs underlaying cellular network with noma
Device-to-device (D2D) communication is an emerging technology in 5G and the upcoming
6G networks due to its properties to enhanced sum rate. Despite these advantages, co …
6G networks due to its properties to enhanced sum rate. Despite these advantages, co …
Real-time energy harvesting aided scheduling in UAV-assisted D2D networks relying on deep reinforcement learning
Unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) communications can be
deployed flexibly thanks to UAVs' agility. By exploiting the direct D2D interaction supported …
deployed flexibly thanks to UAVs' agility. By exploiting the direct D2D interaction supported …
Distributed deep deterministic policy gradient for power allocation control in D2D-based V2V communications
Device-to-device (D2D) communication is an emerging technology in the evolution of the 5G
network enabled vehicle-to-vehicle (V2V) communications. It is a core technique for the next …
network enabled vehicle-to-vehicle (V2V) communications. It is a core technique for the next …