A Low-Complexity Codebook Optimization Scheme for Sparse Code Multiple Access

Jiang, Chengxin; Wang, Yafeng; Cheng, Peng; Wang, Xiang

doi:10.1109/tcomm.2022.3150416

Cited by 10 publications

(13 citation statements)

References 35 publications

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“…4: Comparison of BERs between the obtained constellations and that from [15] with K = 3. from the obtained MD constellations) with several representative codebooks with K = 2. These benchmarking codebooks for comparison are that obtained from [15], the Star-QAM codebook [8], Chen's codebooks [18] and Jiang's codebook [19]. Overall, our obtained codebooks outperform the Star-QAM codebooks, Jiang's codebook and the codebooks from in [15], and achieve comparable (but slightly worse) BER performance with the codebooks in [18] for M = 4 and M = 8.…”

Section: Numerical Evaluationmentioning

confidence: 78%

Designing Enhanced Multidimensional Constellations for Code-Domain NOMA

Wen¹,

Liu²,

Luo³

et al. 2022

IEEE Wireless Commun. Lett.

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This paper presents an enhanced design of multidimensional (MD) constellations which play a pivotal role in many communication systems such as code-domain nonorthogonal multiple access (CD-NOMA). MD constellations are attractive as their structural properties, if properly designed, lead to signal space diversity and hence improved error rate performance. Unlike the existing works which mostly focus on MD constellations with large minimum Euclidean distance (MED), we look for new MD constellations with additional feature that the minimum product distance (MPD) is also large. To this end, a non-convex optimization problem is formulated and then solved by the convex-concave procedure (CCCP). Compared with the state-of-the-art literature, our proposed MD constellations 1 lead to significant error performance enhancement over Rayleigh fading channels whilst maintaining almost the same performance over the Gaussian channels. To demonstrate their application, we also show that these MD constellations give rise to good codebooks in sparse code multiple access systems.

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Section: Numerical Evaluationmentioning

confidence: 78%

Designing Enhanced Multidimensional Constellations for Code-Domain NOMA

Wen¹,

Liu²,

Luo³

et al. 2022

IEEE Wireless Commun. Lett.

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“…Carefully designed codebooks can lead to better performance. [25][26][27][28] Traditionally, message-passing algorithm (MPA) 29 was employed to provide near-optimal detection accuracy.…”

Section: Deep Learning For Single User Physical Layer Communicationmentioning

confidence: 99%

Deep SCMA receiver: A low‐complexity joint decoder and channel estimator for SCMA over time‐varying channels using RNNs

Gamal

Elsayed

Mehana

2023

Trans Emerging Tel Tech

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Sparse code multiple access (SCMA) is a promising non‐orthogonal multiple access scheme for cellular Internet of things (IoT) due to its ability to support massive connectivity, grant‐free transmission and scalability. Inspired by the recent developments of deep learning for physical layer communications, we present a design of an uplink SCMA receiver using deep learning. We propose the use of recurrent neural networks (RNNs) for joint channel estimation and multiuser data detection of uplink SCMA under time‐varying Rayleigh channel using a single deep learning structure. The use of RNNs enables the receiver to learn the time correlation between the received samples with a very low pilot density and with low complexity. Compared with the conventional SCMA receiver, the simulation results show that the proposed deep learning‐based receiver can achieve BER performance similar to that of the conventional SCMA receivers (such as sparse pilot channel estimator and message‐passing algorithm detector) with very low pilot density and with much lower complexity. Moreover, the proposed SCMA receiver shows good resilience to small changes in the receiver speed (second‐order channel statistics) which enables the proposed deep learning receiver to work over a reasonable range of receiver speeds without parameter tuning. Fine tuning the network parameters to capture the variations of the channel, during online transmission using small data sets and small training period, is also checked and provides additional BER benefits.

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“…In [62], CB design based on Reinforcement Learning (RL) was proposed which are suitable for large-scale SCMA schemes. In [63], a novel CB optimization method, namely joint bare bones particle swarm optimization (JBBPSO) was proposed to maximize the average mutual information.…”

Section: ) Designing Of Mother Constellationmentioning

confidence: 99%

A Tutorial on Decoding Techniques of Sparse Code Multiple Access

et al. 2022

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Sparse Code Multiple Access (SCMA) is a disruptive code-domain non-orthogonal multiple access (NOMA) scheme to enable future massive machine-type communication networks. As an evolved variant of code division multiple access (CDMA), multiple users in SCMA are separated by assigning distinctive sparse codebooks (CBs). Efficient multiuser detection is carried out at the receiver by employing the message passing algorithm (MPA) that exploits the sparsity of CBs to achieve error performance approaching to that of the maximum likelihood receiver. In spite of numerous research efforts in recent years, a comprehensive one-stop tutorial of SCMA covering the background, the basic principles, and new advances, is still missing, to the best of our knowledge. To fill this gap and to stimulate more forthcoming research, we provide a holistic introduction to the principles of SCMA encoding, CB design, and MPA based decoding in a self-contained manner. As an ambitious paper aiming to push the limits of SCMA, we present a survey of advanced decoding techniques with brief algorithmic descriptions as well as several promising directions.

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A Low-Complexity Codebook Optimization Scheme for Sparse Code Multiple Access

Cited by 10 publications

References 35 publications

Designing Enhanced Multidimensional Constellations for Code-Domain NOMA

Designing Enhanced Multidimensional Constellations for Code-Domain NOMA

Deep SCMA receiver: A low‐complexity joint decoder and channel estimator for SCMA over time‐varying channels using RNNs

A Tutorial on Decoding Techniques of Sparse Code Multiple Access

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