For the last few decades, wireless communication has been facing a technological
revolution. High data rate and continuous connectivity are the necessities because the …

This paper proposes a deep learning (DL)-based joint channel estimation and signal
detection in multi-user orthogonal-frequency division multiplexing-non-orthogonal multiple …

In this letter, we propose a deep learning-aided multi-user detection (DeepMuD) in uplink
non-orthogonal multiple access (NOMA) to empower the massive machine-type …

Presently, Non-Orthogonal Multiple Access (NOMA) frequently uses Successive Interference
Cancellation (SIC) with channel estimation to detect the receivers' signal successfully …

In this letter, we propose a pilot-assisted receiver scheme based on learnable successive
interference cancellation (PA-LSIC) for uplink single-input multiple-output (SIMO) non …

In this paper, we propose a multi-user symbol detection in cooperative-non-orthogonal
multiple access (C-NOMA) schemes via deep learning (DL). We use a DL-based detection …

In this article, a odel-driven signal detection method with deep transfer learning (DTL) is
proposed for downlink multiple-input multiple-output non-orthogonal multiple access (MIMO …

This paper presents a massive multiple-input multiple-output (mMIMO) non-orthogonal
multiple access (NOMA) system's channel estimation and detection method which uses a …

In this study, a deep neural network (DNN) for a massive multiple-input-multiple-output
(mMIMO) nonorthogonal multiple access (NOMA) system's channel estimation and detection …

In this paper, we propose a long short-term memory-based deep learning (DL) architecture
for signal detection in uplink and downlink rate-splitting multiple access systems with multi …