… -carrier NOMA system. However, how to optimally assign channels in the multi-carrier NOMA
system is still unclear. In this paper, we propose a deep reinforcement learning framework …

… We consider the uplink transmission scenario, where a set N = {1, 2,..., N} of devices compete
for bandwidth W with grant-free NOMA to communicate with a BS. The radius of the BS cell …

… munications (MTC), the future communication architecture needs to provide services for both
… , we propose a multi-agent deep reinforcement learning based SBS state selection scheme, …

… As the signals of multiple users are superimposed in GF-NOMA systems, each user is …
maximization in GFNOMA systems. We then design a deep reinforcement learning (DRL)-based …

… 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 …

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

… orthogonal multiple access (NOMA) have gained tremendous … (5G) communication systems
and beyond application, NOMA … been carried out to investigate the CR-NOMA system [5], [6]. …

… Furthermore, there are potential compatibility and affinity between the RIS and NOMA [15]. …
improve the NOMA gain, and it also enables the communication quality of NOMA users to be …

… non-orthogonal multiple access (NOMA) scenario intending to … Driven by the rising
deployment of deep reinforcement … Simulation results reveal that the IRS-assisted NOMA system …

… Access (NOMA) is a promising candidate for multiple access techniques of future wireless
communication, which integrates orthogonal multiple access and traditional NOMA. The …