Deep Learning-Based Joint Symbol Detection for NOMA

Emir, Ahmet; Kara, Ferdi; Kaya, Hakan

doi:10.1109/siu.2019.8806600

Cited by 12 publications

(6 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the contrary, the model-driven approach aims to improve the performance by building new models with new algorithmic improvements. Among the recent MA techniques, DL algorithms have been largely applied and studied for NOMA, which is a special case of RSMA, for signal detection [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70] and channel estimation [71], [72]. In [56], a DL-based multiuser detection scheme has been investigated for NOMA in a MIMO setting, where a low-complexity deep neural network (DNN) is proposed instead of the conventional SIC receiver.…”

Section: B Related Work and Motivationmentioning

confidence: 99%

Deep Learning-Based Signal Detection for Rate-Splitting Multiple Access Under Generalized Gaussian Noise

Kowshik

Raghavendra

Gurugopinath

et al. 2023

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

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-carrier modulation, over Nakagami-m fading and generalized Gaussian noise (GGN). The proposed DL detector completely eliminates the need for the use of successive interference cancellation (SIC), which suffers from disadvantages such as error propagation. In an orthogonal frequency division multiplexing setting, we show that the proposed DL detector outperforms the standard SIC receivers such as the least squares detector and the minimum mean-squared error receiver, and attains the performance of the optimal maximum likelihood detector, in terms of the symbol error rate (SER). Furthermore, we study the effects of the shaping parameter of GGN, hyperparameters of the DL network such as batch size and learning rate on the SER performance. INDEX TERMS Deep learning (DL), generalized Gaussian noise (GGN), long short-term memory (LSTM), non-orthogonal multiple access (NOMA), rate-splitting multiple access (RSMA).ANAGHA K. KOWSHIK (Graduate Student Member, IEEE) received the bachelor's degree (B.

show abstract

Section: B Related Work and Motivationmentioning

confidence: 99%

Deep Learning-Based Signal Detection for Rate-Splitting Multiple Access Under Generalized Gaussian Noise

Kowshik

Raghavendra

Gurugopinath

et al. 2023

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

show abstract

“…Moreover, [15] presented a DLbased SIC for MIMO-NOMA for the improved recovery of the transmitted signals from a combination of received signals; however, the performance of this system was not comparable with that of the conventional techniques that did not use DL methods. Few works such as [16], [17] handled DL-based SIC and channel estimation for mMIMO-NOMA, but they assumed perfect SIC with complete CSI of users. Moreover, the system model was either not completely stated as mMIMO-NOMA or was evaluated using only the MIMO network [14], [18], [19].…”

Section: R1a2b5b01001994)mentioning

confidence: 99%

“…The work proposed in [21] provided higher throughput, but × × × M. A. Aref et al [15] × × × J. Fu et al [13] × × × Y. Xie et al [20] × × × T.-H. Vu et al [21] × × S. Gao et al [16] × × × A. Emır et al [17] × × × C. Lin et al [18] × × × Y. Cao et al [19] × × × J.-M. Kang et al [14] × × C.-J. Chun et al [22] × × × J. Liao et al [23] × × Presented article the effect of incomplete CSI on the SIC process for NOMA users was not specifically investigated.…”

Section: R1a2b5b01001994)mentioning

confidence: 99%

Deep Learning aided SIC for Wavelet-based Massive MIMO-NOMA

Ahmad¹,

Shin²

2022

Preprint

View full text Add to dashboard Cite

<p>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 is presented. The proposed network applies deep learning (DL) to a discrete wavelet transform (DWT) based orthogonal frequency-division-multiplexing (OFDM) to reduce the noise and inter-channel interference (ICI) while maintaining compatibility with the existing networks. The transmission overhead is reduced because the presented network is trained to estimate and detect symbols with and without pilot signals.</p> <p>The results of this study demonstrate that our provided DL-based successive interference cancellation (SIC) method performs better than the conventional SIC-based signal detection in the mMIMONOMA network. The suggested wavelet-based mMIMO-NOMA is also compared to the traditional fast-Fourier-transform (FFT) based mMIMO-NOMA, particularly in terms of symbol error rate (SER).</p>

show abstract

“…Nowadays, DNN is used to mitigate or solve some problems in telecommunications area [27], [28], [29], in which it also includes the use of DNN applying in NOMA schemes [30], [31], [32], [33]. DNN is used in general to map input features to an output predicted values, through a set of mathematical operations performed by multiple connected layers, each containing multiple processing units called neurons.…”

Section: A Dnn-based Channel Estimatormentioning

confidence: 99%

MUSA Grant-Free Access Framework and Blind Detection Receiver

Aquino¹,

Barbosa²,

Chafii³

et al. 2021

JCIS

View full text Add to dashboard Cite

Recently, a non-orthogonal multiple access scheme called multi-user shared access (MUSA) was proposed to provide massive connection capability of low-complexity devices in the 5G networks. MUSA achieves higher spectral efficiency allowing independent devices to transmit data on the same physical layer time-frequency resources. Furthermore, MUSA introduces a grant-free transmission and a blind multi-user detection at the receiver, reducing the complexity on the transmit side. This approach is interesting for Internet of Things applications over mobile communication networks, where the devices have limited power and processing capacity. The references available in the literature about this multiple access scheme do not bring sufficient details about the MUSA multi-user detector. This limitation makes it difficult to evaluate the MUSA performance and to propose improvements for this new technique. The main goal of this paper is to provide a framework describing the entire communication chain using MUSA as multiple access. This paper also brings a proposal for a blind multi-user detection, where the information about the MUSA parameters and the channel state information are unknown at the receiver side. The performance of the MUSA multi-user detector is improved by a deep learning based processing that enhances the quality of the channel estimation provided by a initial minimum mean square error estimator. The proposed deep neural network architecture employed to improve the channel estimation allows more users to share the same time-frequency resources for a given target block error rate, increasing the overall spectrum efficiency of the system.

show abstract

Deep Learning-Based Joint Symbol Detection for NOMA

Cited by 12 publications

References 8 publications

Deep Learning-Based Signal Detection for Rate-Splitting Multiple Access Under Generalized Gaussian Noise

Deep Learning-Based Signal Detection for Rate-Splitting Multiple Access Under Generalized Gaussian Noise

Deep Learning aided SIC for Wavelet-based Massive MIMO-NOMA

MUSA Grant-Free Access Framework and Blind Detection Receiver

Contact Info

Product

Resources

About