In previous study, deep learning and autoencoder have been applied for data detection of
NOMA systems, rather than the resource allocation of OFDMA/NOMA systems. In previous …

Innon-orthogonal multiple access (NOMA) systems, transmit power allocation plays a crucial
role in maximizing the sum rate, while satisfying quality of service requirements of users. In …

Deep learning (DL) techniques can significantly improve successive interference
cancellation (SIC) performance for the non-orthogonal multiple access (NOMA) system. The …

This study shows how the channel estimation based Deep Learning (DL) and a power
allocation method are together employed for multi-user detection in a Power domain Non …

Prior works either considered outage capacity of wireless video transmission systems but
did not consider NOMA which is a key technology for 5G ultra-reliable low-latency (URLLC) …

In this paper, we consider downlink power-domain non-orthogonal multiple access (NOMA)
in heterogeneous networks (HetNets) and propose resource allocation algorithms for …

As a key candidate technique for fifth-generation (5G) mobile communication systems, non-
orthogonal multiple access (NOMA) has attracted considerable attention in the field of …

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to satisfy
the huge access demand and data rate requirements of the next generation network. In this …

Energy-efficient resource allocation for multi-cell multi-carrier non-orthogonal multiple
access (MCMC-NOMA) is a challenging task due to the interference and other related …

Non-orthogonal multiple access (NOMA) technology allows multiple users to use the same
time-frequency resource to send signals, which can improve spectral efficiency and …