Deep Learning Based Successive Interference Cancellation Scheme in Nonorthogonal Multiple Access Downlink Network

Sim, Isaac; Sun, Young Ghyu; Lee, Donggu; Kim, Soo Hyun; Lee, Ji-Young; Kim, Jae Hyun; Shin, Yoan; Kim, Jin Young

doi:10.3390/en13236237

Cited by 17 publications

(13 citation statements)

References 19 publications

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“…This is one of the most critical factors degrading the total user performance due to the inefficiency of current SIC techniques and restricting the user capacity. Inspired by [292], the combination of a novel DL approach seeking the perfect SIC with DL-assisted RIS phase optimization can address the aforementioned challenges in scenarios using the downlink-NOMA scheme. Novel DL algorithms suitable for these scenarios can be tested with DNN or CNN architectures and appropriate loss functions can be developed.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

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Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use-cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer (PHY) methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising PHY concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output (MIMO) systems, sophisticated multi-carrier (MC) waveform designs, reconfigurable intelligent surface (RIS)-empowered communications, and PHY security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based MIMO by sharing user-friendly code snippets, which might be useful for interested readers.

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Section: Discussionmentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

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“…The proposed CNN includes three CNN layers followed by the pooling layers and two dense layers, and it ends with the output layer with the softmax activation function. For training, we use the following loss function with

norm [ 40 ]:

where

is the constellation symbol, and

refers to weight and bias. Figure 8 shows the general framework of the multiuser communication system using CNN and NOMA.…”

Section: Deep Learning-based Receivermentioning

confidence: 99%

Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems

Asif

Affan

Tarhuni

et al. 2022

Sensors

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Due to the growing number of users, power, and spectral effectiveness, most communication systems are complex and difficult to implement on a large scale. Artificial Intelligence (AI) has played an outstanding role in the implementation of theoretical systems in the real world, with less complexity achieving better results. In this direction, we compare the Non-Orthogonal Multiple Access (NOMA) technique for a multiuser Visible Light Communication (VLC) system with Successive Interference Cancellation (SIC) for two types of detectors: (1) the deep learning-based system and (2) the traditional maximum likelihood (ML) decoder-based system. For multiplexing, we compare the variations of novel Orbital Angular Momentum (OAM) multiplexing and Orthogonal Frequency Division Multiplexing (OFDM) with Index Modulation (IM). In this article, we implement OFDM-IM and OAM-IM for four users for the Gaussian fading MIMO Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) VLC channels. The suggested systems’ bit error rate (BER) performances are compared in simulations for a wide range of Signal-to-Noise Ratios (SNRs), which shows that deep learning-based systems outperform the ML-based system for both users to ensure better decoding at the receiver end, especially at higher SNR values. The detection error is lower in a deep learning-based system at around 20% and around 30% for low SNR and high SNR values, respectively.

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“…Although the DNN based power allocation scheme could achieve near-optimal performance in terms of energy efficiency at a much lower complexity, only a very small number of users is considered in their work. In another attempt, Sim et al proposed a convolutional neural network-based SIC for the downlink of the NOMA system to alleviate the sum-rate loss caused by imperfect SIC [28]. However, the maximum number of users considered in [28] is limited to four.…”

Section: Motivations and Contributionsmentioning

confidence: 99%

“…In another attempt, Sim et al proposed a convolutional neural network-based SIC for the downlink of the NOMA system to alleviate the sum-rate loss caused by imperfect SIC [28]. However, the maximum number of users considered in [28] is limited to four. In [29], a deep learning assisted receiver was developed for the uplink of the Faster than Nyquist (FTN) based NOMA system.…”

Section: Motivations and Contributionsmentioning

confidence: 99%

Deep Neural Network (DNN) for Efficient User Clustering and Power Allocation in Downlink Non-Orthogonal Multiple Access (NOMA) 5G Networks

Kumaresan

Tan

2021

Symmetry

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Non-orthogonal multiple access (NOMA) emerges as a promising candidate for 5G, which radically alters the way users share the spectrum. In the NOMA system, user clustering (UC) becomes another research issue as grouping the users on different subcarriers with different power levels has a significant impact on spectral utilization. In previous literature, plenty of works have been devoted to solving the UC problem. Recently, the artificial neural network (ANN) has gained considerable attention due to the availability of UC datasets, obtained from the Brute-Force search (BF-S) method. In this paper, deep neural network-based UC (DNN-UC) is employed to effectively characterize the nonlinearity between the cluster formation with channel diversity and transmission powers. Compared to the ANN-UC, the DNN-UC is more competent as UC is a non-convex NP-complete problem, which cannot be entirely captured by the ANN model. In this work, the DNN-UC is first trained with the training samples and then validated with the testing samples to examine its mean square error (MSE) and throughput performance in an asymmetrical fading NOMA channel. Unlike the ANN-UC, the DNN-UC model offers greater room for hyper-parameter optimizations to maximize its learning capability. With the optimized hyper-parameters, the DNN-UC can achieve near-optimal throughput performance, approximately 97% of the throughput of the BF-S method.

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Deep Learning Based Successive Interference Cancellation Scheme in Nonorthogonal Multiple Access Downlink Network

Cited by 17 publications

References 19 publications

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems

Deep Neural Network (DNN) for Efficient User Clustering and Power Allocation in Downlink Non-Orthogonal Multiple Access (NOMA) 5G Networks

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