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A Novel Deep Neural Network Based Approach for Sparse Code Multiple Access
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In this article, a deep neural network (DNN) was trained to decode SCMA modulated signals corrupted by additive white Gaussian noise (AWGN), and an autoencoder was established and trained to generate optimal SCMA codewords and reconstruct original bits.Abstract:
Sparse code multiple access (SCMA) has been one of non-orthogonal multiple access (NOMA) schemes aiming to support high spectral efficiency and ubiquitous access requirements for 5G wireless communication networks. Conventional SCMA approaches are confronting remarkable challenges in designing low complexity high accuracy decoding algorithm and constructing optimum codebooks. Fortunately, the recent spotlighted deep learning technologies are of significant potentials in solving many communication engineering problems. Inspired by this, we explore approaches to improve SCMA performances with the help of deep learning methods. We propose and train a deep neural network (DNN) called DL-SCMA to learn to decode SCMA modulated signals corrupted by additive white Gaussian noise (AWGN). Putting encoding and decoding together, an autoencoder called AE-SCMA is established and trained to generate optimal SCMA codewords and reconstruct original bits. Furthermore, by manipulating the mapping vectors, an autoencoder is able to generalize SCMA, thus a dense code multiple access (DCMA) scheme is proposed. Simulations show that the DNN SCMA decoder significantly outperforms the conventional message passing algorithm (MPA) in terms of bit error rate (BER), symbol error rate (SER) and computational complexity, and AE-SCMA also demonstrates better performances via constructing better SCMA codebooks. The performance of deep learning aided DCMA is superior to the SCMA.read more
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Journal ArticleDOI
Deep Learning Enhanced NOMA System: A Survey on Future Scope and Challenges
TL;DR: In this paper, Deep Learning aided NOMA is used for channel estimation and power allocation in a wireless communication system, which makes the system computationally simpler than the conventional system.
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Trainable Projected Gradient Detector for Sparsely Spread Code Division Multiple Access
TL;DR: A novel trainable multiuser detector called sparse trainable projected gradient (STPG) detector is proposed, which is based on the notion of deep unfolding, which enables us to treat massive SCDMA systems.
References
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Learning to Decode Linear Codes Using Deep Learning
TL;DR: In this paper, a deep learning method for improving the belief propagation algorithm is proposed, where weights are assigned to the edges of the Tanner graph and these edges are then trained using deep learning techniques.
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Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding
TL;DR: In this paper, a deep-learning-enabled mmWave massive MIMO framework for effective hybrid precoding is proposed, in which each selection of the precoders for obtaining the optimized decoder is regarded as a mapping relation in the deep neural network (DNN).
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Deep Learning Based MIMO Communications.
TL;DR: A novel physical layer scheme for single user Multiple-Input Multiple-Output (MIMO) communications based on unsupervised deep learning using an autoencoder is introduced and demonstrates significant potential for learning schemes which approach and exceed the performance of the methods which are widely used in existing wireless MIMO systems.
Book
Modern Computer Arithmetic
Richard P. Brent,Paul Zimmermann +1 more
TL;DR: Brent and Zimmermann as discussed by the authors present algorithms that are ready to implement in your favorite language, while keeping a high-level description and avoiding too low-level or machine-dependent details.
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Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines
TL;DR: The experimental results show that the proposed method can not only solve the two deficiencies of SAEs, but also achieve a superior performance to the existing methods.