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 …
Learning to branch: Accelerating resource allocation in wireless networks
Resource allocation in wireless networks, such as device-to-device (D2D) communications,
is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are …
is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are …
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) …
Deep reinforcement learning-based resource allocation in cooperative UAV-assisted wireless networks
We consider the downlink of an unmanned aerial vehicle (UAV) assisted cellular network
consisting of multiple cooperative UAVs, whose operations are coordinated by a central …
consisting of multiple cooperative UAVs, whose operations are coordinated by a central …
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 …
Deep learning for radio resource allocation in multi-cell networks
The increased complexity and heterogeneity of emerging 5G and B5G wireless networks will
require a paradigm shift from traditional resource allocation mechanisms. Deep learning …
require a paradigm shift from traditional resource allocation mechanisms. Deep learning …
Spatial deep learning for wireless scheduling
The optimal scheduling of interfering links in a dense wireless network with full frequency
reuse is a challenging task. The traditional method involves first estimating all the interfering …
reuse is a challenging task. The traditional method involves first estimating all the interfering …
Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks
This work demonstrates the potential of deep reinforcement learning techniques for transmit
power control in wireless networks. Existing techniques typically find near-optimal power …
power control in wireless networks. Existing techniques typically find near-optimal power …
Learning to optimize: Training deep neural networks for wireless resource management
For decades, optimization has played a central role in addressing wireless resource
management problems such as power control and beamformer design. However, these …
management problems such as power control and beamformer design. However, these …
Deep learning for radio resource allocation with diverse quality-of-service requirements in 5G
To accommodate diverse Quality-of-Service (QoS) requirements in 5th generation cellular
networks, base stations need real-time optimization of radio resources in time-varying …
networks, base stations need real-time optimization of radio resources in time-varying …
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