… There have been some existing works on the power allocation for the single-carrier NOMA …
-carrier NOMA system is still unclear. In this paper, we propose a deep reinforcement learning …

… deep reinforcement learning (DRL)-based approach to addressing joint resource management
(JRM) in a practical multi-carrier nonorthogonal multiple access (MC-NOMA) … a deep …

… However, in grant-free NOMA systems, the collisions resulting from uncoordinated resource
… , we apply deep reinforcement learning (DRL) in the decision making for grant-free NOMA …

… maximization in GFNOMA systems. We then design a deep reinforcement learning (DRL)-based
distributed algorithm for each user to select its pilot sequence via learning from the past …

… configuration in GF-NOMA systems is challenging … -NOMA for mURLLC more complex.
To address this problem, we develop a novel learning framework for signature-based GF-NOMA …

… Considering the computational complexity and scalability, we propose a multi-agent deep
reinforcement learning based SBS state selection scheme, in which each SBS acts as an …

… allocation using deep reinforcement learning under a non-… deep actor-critic reinforcement
learning framework for efficient joint power and bandwidth allocation by adopting hybrid NOMA/…

… deep learning algorithm named environment trained deep learning (ETDL) and a reinforcement
learning algorithm named exploration attenuated deep deterministic policy gradient (EA-…

In this work, we examine an intelligent reflecting surface (IRS) assisted downlink non-orthogonal
multiple access (NOMA) scenario intending to maximize the sum-rate of users. The …

… multiple access and traditional NOMA. The performance of hybrid NOMA systems depends
on … channel assignment problem as a deep reinforcement learning task, to achieve better …