Integrated networking, caching, and computing for connected vehicles: A deep reinforcement learning approach
… learning algorithm that uses deep Q network to approximate the Q … , deep reinforcement
learning is used to obtain the resource allocation policy in vehicular networks with integrated …
learning is used to obtain the resource allocation policy in vehicular networks with integrated …
A deep reinforcement learning framework for spectrum management in dynamic spectrum access
Dynamic spectrum access (DSA) has the great potential to alleviate spectrum shortage and
promote network capacity. However, two fundamental technical issues have to be addressed…
promote network capacity. However, two fundamental technical issues have to be addressed…
IAB topology design: A graph embedding and deep reinforcement learning approach
… integrated access and backhaul (IAB) architecture, in which the same infrastructure and
spectral resources are shared to provide access … a combination of deep reinforcement learning …
spectral resources are shared to provide access … a combination of deep reinforcement learning …
Dynamic multichannel access based on deep reinforcement learning in distributed wireless networks
… learning (RL) is applied to solve the spectrum access problem in … Deep reinforcement
learning (DRL) have been applied to address large state and action spaces, by integrating deep …
learning (DRL) have been applied to address large state and action spaces, by integrating deep …
Deep reinforcement learning mechanism for dynamic access control in wireless networks handling mMTC
… random access procedure in current network deployments, and therefore can be successfully
integrated into the system. The main contributions of this paper are summarized as follows …
integrated into the system. The main contributions of this paper are summarized as follows …
Deep-reinforcement-learning-based resource allocation for content distribution in fog radio access networks
… a deep reinforcement learning (DRL)-based resource allocation scheme to improve content
distribution in a layered fog radio access … the integrated allocation of caching, computing, …
distribution in a layered fog radio access … the integrated allocation of caching, computing, …
Trajectory design and access control for air–ground coordinated communications system with multiagent deep reinforcement learning
… action probabilities and propose a multiagent deep reinforcement learning (MADRL)
approach, named air–ground probabilistic multiagent deep deterministic policy gradient (AG-…
approach, named air–ground probabilistic multiagent deep deterministic policy gradient (AG-…
Distributed deep reinforcement learning assisted resource allocation algorithm for space-air-ground integrated networks
… More, this process is optimized using distributed Deep Reinforcement Learning (DRL),
thereby reducing transmission delay and relieving the pressure of task offloading on space-…
thereby reducing transmission delay and relieving the pressure of task offloading on space-…
Deep reinforcement learning-based user association in sub6GHz/mmWave integrated networks
… of userto-multiple APs association in integrated sub-6GHz/mmWave systems, where each …
to make use of a Deep Reinforcement Learning (DRL) technique based on Deep QNetworks (…
to make use of a Deep Reinforcement Learning (DRL) technique based on Deep QNetworks (…
Reinforcement and deep reinforcement learning for wireless Internet of Things: A survey
… The results of this work prove that the integration of the RL into the blockchain system improves
delay performance even with 50% of malicious nodes in the routing environment. Liu et al…
delay performance even with 50% of malicious nodes in the routing environment. Liu et al…