Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks
Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …
costs, which have been considered to be a promising technique in the next-generation …
Deep reinforcement learning for user association and resource allocation in heterogeneous networks
Heterogeneous networks (HetNets) can offload the traffic and reduce the deployment cost,
which is regarded as a promising technique in next-generation cellular networks. Because …
which is regarded as a promising technique in next-generation cellular networks. Because …
A deep reinforcement learning for user association and power control in heterogeneous networks
Heterogeneous network (HetNet) is a promising solution to satisfy the unprecedented
demand for higher data rate in the next generation mobile networks. Different from the …
demand for higher data rate in the next generation mobile networks. Different from the …
Distributed deep reinforcement learning-based spectrum and power allocation for heterogeneous networks
This paper investigates the problem of distributed resource management in two-tier
heterogeneous networks, where each cell selects its joint device association, spectrum …
heterogeneous networks, where each cell selects its joint device association, spectrum …
A model-driven deep reinforcement learning heuristic algorithm for resource allocation in ultra-dense cellular networks
Resource allocation in ultra dense network (UDN) is an multi-objective optimization problem
since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) …
since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) …
Power allocation in multi-user cellular networks: Deep reinforcement learning approaches
The model-based power allocation has been investigated for decades, but this approach
requires mathematical models to be analytically tractable and it has high computational …
requires mathematical models to be analytically tractable and it has high computational …
Resource management in wireless networks via multi-agent deep reinforcement learning
We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …
A cell outage management framework for dense heterogeneous networks
In this paper, we present a novel cell outage management (COM) framework for
heterogeneous networks with split control and data planes-a candidate architecture for …
heterogeneous networks with split control and data planes-a candidate architecture for …
Deep-learning-based wireless resource allocation with application to vehicular networks
It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …
interference, improving network efficiency, and ultimately optimizing wireless communication …
Power allocation in multi-user cellular networks with deep Q learning approach
The model-driven power allocation (PA) algorithms in the wireless cellular networks with
interfering multiple-access channel (IMAC) have been investigated for decades. Nowadays …
interfering multiple-access channel (IMAC) have been investigated for decades. Nowadays …
相关搜索
- resource allocation reinforcement learning
- heterogeneous cellular networks
- deep reinforcement learning approaches
- deep reinforcement user association
- vehicular networks reinforcement learning
- multi-user cellular networks
- ultra-dense cellular networks
- power control user association
- heterogeneous qos reinforcement learning
- heterogeneous networks deep reinforcement
- deep reinforcement power allocation
- deep reinforcement heuristic algorithm
- resource management in wireless networks
- power control in heterogeneous networks
- dense heterogeneous networks
- resource allocation user association