Deep neural network is a new type of learning algorithm, which has both global and local aspects and performs well in pattern recognition and computational speed. In recent years, deep neural network algorithm has been widely used in scientific research and real life, but its complexity, parallelism and other characteristics lead it to be a very challenging and innovative research area. This study briefly introduces the basic principles and theoretical knowledge of deep neural network algorithms, and mainly discusses their applications and Advancement of feature extraction in the field.

https://doi.org/10.1088/1742-6596/2146/1/012001

A Study on the Application and the Advancement of Deep Neural Network Algorithm

A general form of codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered equivalent to an autoencoder (AE)-based constellation design for multi-user multidimensional modulation (MU-MDM). Due to a constrained design space for optimal constellation, e.g., fixed resource mapping and equal power allocation to all codebooks, however, existing AE architectures produce constellations with suboptimal bit-error-rate (BER) performance. Accordingly, we propose a new architecture for MU-MDM AE and underlying training methodology for joint optimization of resource mapping and a constellation design with bit-to-symbol mapping, aiming at approaching the BER performance of a single-user MDM (SU-MDM) AE model with the same spectral efficiency. The core design of the proposed AE architecture is dense resource mapping combined with the novel power allocation layer that normalizes the sum of user codebook power across the entire resources. This globalizes the domain of the constellation design by enabling flexible resource mapping and power allocation. Furthermore, it allows the AE-based training to approach a global optimal MU-MDM constellations for CD-NOMA. Extensive BER simulation results demonstrate that the proposed design outperforms the existing CD-NOMA designs while approaching the single-user BER performance achieved by the equivalent SU-MDM AE within 0.3dB over the additive white Gaussian noise channel. 

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Deep Learning-Based Codebook Design for Code-Domain Non-Orthogonal Multiple Access: Approaching Single-User Bit-Error Rate Performance

A general form of codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered equivalent to a constellation design for multi-user multi-dimensional modulation (MU-MDM). Motivated by recent works on deep learning (DL)-based design of MDM, we propose new architectures for autoencoder and the underlying training methodology that aim at joint optimization of resource mapping and a constellation design with bit-to-symbol mapping, hopefully approaching a bit error rate (BER) performance of the equivalent single-user MDM (SU-MDM) model. The novelty and contribution of the paper lies in the proposed architectures of autoencoder and the underlying training framework for optimum codeword design for CD-NOMA, which without knowing other-user symbols, approaches the performance of SU-MDM with the same spectral efficiency. It includes the trainable architectures of DL-based designs for SU-MDM and MU-MDM, which can be equivalently compared to each other. They are implemented to demonstrate that the proposed design for MU-MDM can achieve the BER performance of DL-based single-user codebook design within 0.3 dB in the additive white Gaussian noise channel, thus serving as the best existing design that can be realized with its low-complex decoder.

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

In the forthcoming 5G technology, Sparse Code Multiple Access (SCMA) is the most promising scheme that aims at improving spectral efficiency further and providing massive connectivity. The challenge behind implementing SCMA scheme is: constructing optimized codebooks in order to obtain minimum BER while keeping the receiver complexity minimum. To address this problem, we resort to the usage of an efficient deep learning technique, autoencoders, that club the encoder and the decoder part and automatically learn the most optimum codeword that could give the least BER. In this paper, SCMA sparse autoencoder, which is a variant of the autoencoder, is proposed, that has better BER performance than a conventional autoencoder, without paying in terms of computational complexity.

Medini Singh

Papers

Sparse Autoencoder for Sparse Code Multiple Access