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A taxonomy of AI techniques for 6G communication networks

Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal multiple access (NOMA) scheme for the enabling of massive machine-type communication. In SCMA, the design of good sparse codebooks and efficient multiuser decoding have attracted tremendous research attention in the past few years. This letter aims to leverage deep learning to jointly design the downlink SCMA encoder and decoder with the aid of autoencoder. We introduce a novel end-to-end learning based SCMA (E2E-SCMA) design framework, under which improved sparse codebooks and low-complexity decoder are obtained. Compared to conventional SCMA schemes, our numerical results show that the proposed E2E-SCMA leads to significant improvements in terms of error rate and computational complexity.

https://repository.essex.ac.uk/34575/1/Liu_WCL_1Col.pdf

A Novel Multitask Learning Empowered Codebook Design for Downlink SCMA Networks

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.

/pdf/sparse-code-multiple-access-potentials-and-challenges-50727giouz.pdf

Sparse Code Multiple Access: Potentials and Challenges

A novel deep neural network based approach for sparse code multiple access

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.

Yong Zhang

Papers

A novel deep neural network based approach for sparse code multiple access

A Novel Deep Neural Network Based Approach for Sparse Code Multiple Access