Deep Learning Based Joint Detection and Decoding of Non-Orthogonal Multiple Access Systems

Sun, Fuqiang; Niu, Kai; Dong, Chao

doi:10.1109/glocomw.2018.8644090

Cited by 16 publications

(6 citation statements)

References 5 publications

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“…The simulated training data are generated dynamically to have a different noise level at each step. The same unfolding principle was studied in [170] order to propose a joint detection and decoding of channelcoded SCMA systems. The channel coding is randomly generated and is included in the training procedure which makes this proposition more suitable for practical applications.…”

Section: F Deep Learning Based Detectorsmentioning

confidence: 99%

Sparse Code Multiple Access: Potentials and Challenges

Rebhi

Hassan

Raoof

et al. 2021

IEEE Open J. Commun. Soc.

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The massive connectivity is among other unprecedented requirements which are expected to be satisfied in order to follow the perpetual increase of connected devices in the era of internet of things. In contrast to the family of conventional orthogonal multiple access schemes, the key distinguishing feature of non-orthogonal multiple access (NOMA) is its capacity to support the massive connectivity. Sparse code multiple access (SCMA) is one of the powerful schemes of code-domain NOMA (CD-NOMA) and is among the promising candidates of multiple access techniques to be employed in future generations of wireless communication systems thanks to the sparsity pattern of its codebooks. This technique has been actively investigated in recent years. In this paper, we provide a comprehensive survey of the state-of-the-art of SCMA. First, we will pinpoint SCMA place in the NOMA landscape including power-domain NOMA and CD-NOMA with the aim of justifying why SCMA is prominent. Then, its system architecture is highlighted and its basic principles are presented, afterwards a review of exiting codebook designs and available SCMA detectors is provided, before showing how resources are expected to be assigned, and how SCMA can be combined with other existing and emerging technologies. Finally, we present a range of future research trends and challenging open issues that should be addressed to optimize SCMA performance.

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Section: F Deep Learning Based Detectorsmentioning

confidence: 99%

Sparse Code Multiple Access: Potentials and Challenges

Rebhi

Hassan

Raoof

et al. 2021

IEEE Open J. Commun. Soc.

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“…only a signal points in constellation design were explicitly considered in both conventional and deep learning-based approaches [7][8][9][10], [15][16][17][18][19][20][21]. To avoid the complexity of dealing with both C and F b for joint optimization, previous studies relied on simple suboptimal approaches such as a constellation rotation [9,10].…”

Section: B Deep Learning-based Mu-mdm Design: Problem Formulationmentioning

confidence: 99%

“…Following the seminal paper by O'shea et al [5], an end-toend optimized architecture that exploits the similarity between a communication system and an autoencoder (AE) has gained considerable attention [6][7][8], [13][14][15][16][17][18][19][20][21]. One of the application areas for an AE-based optimization is a constellation (signal space diagram) design for multi-dimensional modulation (MDM).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the existing AE-based MU-MDM design schemes in [7] and [16][17][18] fall short in jointly optimizing the signal points in MU-MDM constellation and its bit-tosymbol mapping function. More specifically, they employ L2normalization or cross-entropy with one-hot vector representation as their loss function, which minimizes the symbol error rate (SER) only, without considering a bit-to-symbol mapping function.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-based Codebook Design for Code-domain Non-Orthogonal Multiple Access Approaching Single-User Bit Error Rate Performance

Han¹,

Seo²,

Abebe³

et al. 2021

Preprint

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The codebook design for code-domain nonorthogonal multiple access (CD-NOMA) can be considered as a constellation design for multi-user multi-dimensional modulation (MU-MDM). This paper proposes an autoencoder (AE)-based constellation design for MU-MDM with the objective of achieving a comparable bit error rate (BER) performance to singleuser multi-dimensional modulation (SU-MDM), i.e., alleviating performance degradation in non-optimal AE design caused by overloading multiple users. Recognizing that various constraints in a receiver structure degrade a BER performance of the codebook design in the existing CD-NOMA, the MU-MDM design aims at global optimization on the common ground with SU-MDM by leveraging the agnosticism of the neural network-based multi-user decoder obtained from AE training, while mitigating the power normalization constraint and exploiting dense resource mapping in the MU-MDM AE structure. Moreover, as opposed to the existing loss function for MU-MDM which has failed to minimize BER for different levels of signal-to-noise ratio (SNR), a hyperparameterized loss function and proper training procedures are introduced to jointly optimize the signal points for MU-MDM constellation and their bit-to-symbol mapping. It has been demonstrated that the proposed design achieves a single-user BER bound with only 0.2dB loss, equivalently outperforming the existing CD-NOMA designs, while maintaining their overloading factor.

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“…Code-domain NOMA which multiplexing signals in the code-domain was recently explored in [2] to provide connections in overcrowded regime. Artificial-intelligence NOMA in [3] has applied computational learning techniques, such as deep learning in typical NOMA wireless communication systems. Power-domain NOMA is a transmitting structure, proposed initially to improve the spectral efficiency (SE) of wireless networks, sharing the same orthogonal resource, in time and frequency, by combining superposition coding in the transmitter side, and successive interference cancellation (SIC) in the receiver side [4].…”

Section: Introductionmentioning

confidence: 99%

NOMA-Based Random Access in mMTC XL-MIMO

Alves

Abrão

2023

IEEE Access

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In machine-type communication (MTC), massive access attempts are generated and the massive MIMO is the key technology to support this demand. To support massive MTC (mMTC), the recent extra-large scale massive multiple-input multiple-output (XL-MIMO) architecture has been seen as a promising technology for providing very high-data rates in high-user density scenarios. Therefore, the large dimension is of the same order as the distances to the user equipment (UE) causing spatial nonstationarities and visibility regions (VRs) to occur across the huge XL array extension. We investigate the random access (RA) problem in crowded XL-MIMO scenarios; the proposed grant-based random access (GB-RA) protocol combining the advantage of non-orthogonal multiple access (NOMA) and strongest user collision resolutions in extra-large arrays (SUCRe-XL) named NOVR-XL can allow access of multiple colliding users in the same XL subarray (SA) selecting the same pilot sequence. The proposed NOVR-XL GB-RA protocol is able to provide a reduction in the number of attempts to access the network, while improving the average sum-rate, as the number of SA increases.

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Deep Learning Based Joint Detection and Decoding of Non-Orthogonal Multiple Access Systems

Cited by 16 publications

References 5 publications

Sparse Code Multiple Access: Potentials and Challenges

Sparse Code Multiple Access: Potentials and Challenges

Deep Learning-based Codebook Design for Code-domain Non-Orthogonal Multiple Access Approaching Single-User Bit Error Rate Performance

NOMA-Based Random Access in mMTC XL-MIMO

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