A reliable reinforcement learning for resource allocation in uplink NOMA-URLLC networks

W Ahsan, W Yi, Y Liu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this paper, we propose a deep state-action-reward-state-action (SARSA) learning
approach for optimising the uplink resource allocation in non-orthogonal multiple access …
被引用次数：12 相关文章

A Reliable Reinforcement Learning for Resource Allocation in Uplink NOMA-URLLC Networks

W Ahsan, W Yi, Y Liu, A Nallanathan - arXiv e-prints, 2022 - ui.adsabs.harvard.edu
In this paper, we propose a deep state-action-reward-state-action (SARSA) $\lambda $
learning approach for optimising the uplink resource allocation in non-orthogonal multiple …

A Reliable Reinforcement Learning for Resource Allocation in Uplink NOMA-URLLC Networks

W Ahsan, W Yi, Y Liu, A Nallanathan - arXiv preprint arXiv:2201.06027, 2022 - arxiv.org
In this paper, we propose a deep state-action-reward-state-action (SARSA) $\lambda $
learning approach for optimising the uplink resource allocation in non-orthogonal multiple …
[PDF] qmul.ac.uk

[PDF][PDF] A Reliable Reinforcement Learning for Resource Allocation in Uplink NOMA-URLLC Networks

W Ahsan, W Yi, Y Liu, A Nallanathan - IEEE TRANSACTIONS ON …, 2022 - qmro.qmul.ac.uk
In this paper, we propose a deep state-actionreward-state-action (SARSA) λ learning
approach for optimising the uplink resource allocation in non-orthogonal multiple access …