Deep reinforcement learning for scheduling in cellular networks
Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in
both industry and academia. A common solution is to replace partial or even all modules in …
both industry and academia. A common solution is to replace partial or even all modules in …
Learn to schedule (LEASCH): A deep reinforcement learning approach for radio resource scheduling in the 5G MAC layer
Network management tools are usually inherited from one generation to another. This was
successful since these tools have been kept in check and updated regularly to fit new …
successful since these tools have been kept in check and updated regularly to fit new …
Delay-aware cellular traffic scheduling with deep reinforcement learning
Radio access network (RAN) in 5G is expected to satisfy the stringent delay requirements of
a variety of applications. The packet scheduler plays an important role by allocating …
a variety of applications. The packet scheduler plays an important role by allocating …
Buffer-aware wireless scheduling based on deep reinforcement learning
In this paper, the downlink packet scheduling problem for cellular networks is modeled,
which jointly optimizes throughput, fairness and packet drop rate. Two genie-aided heuristic …
which jointly optimizes throughput, fairness and packet drop rate. Two genie-aided heuristic …
Radio resource scheduling for 5G NR via deep deterministic policy gradient
SC Tseng, ZW Liu, YC Chou… - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
The fifth generation (5G) wireless system plays a crucial role to realize future network
applications with diverse services requirements. The 3rd Generation Partnership Project …
applications with diverse services requirements. The 3rd Generation Partnership Project …
Radio resource scheduling with deep pointer networks and reinforcement learning
This article presents an artificial intelligence (AI) adaptable solution to handle the radio
resource scheduling (RRS) task in 5G networks. RRS is one of the core tasks in radio …
resource scheduling (RRS) task in 5G networks. RRS is one of the core tasks in radio …
Knowledge-assisted deep reinforcement learning in 5G scheduler design: From theoretical framework to implementation
In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL)
algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with …
algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with …
Resource allocation in wireless networks with deep reinforcement learning: A circumstance-independent approach
In the conventional approaches using reinforcement learning (RL) for resource allocation in
wireless networks, the structure of the policy depends on network circumstances such as the …
wireless networks, the structure of the policy depends on network circumstances such as the …
5G resource scheduling for low-latency communication: A reinforcement learning approach
Q Huang, M Kadoch - 2020 IEEE 92nd Vehicular Technology …, 2020 - ieeexplore.ieee.org
The emergence of various applications is driving the continuous development of 5G mobile
networks as infrastructure. To better support real-time wireless services, the low-latency …
networks as infrastructure. To better support real-time wireless services, the low-latency …
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 …