Deep Learning-Based MIMO-NOMA With Imperfect SIC Decoding

Kang, Jae-Mo; Kim, Il-Min; Chun, Chang-Jae

doi:10.1109/jsyst.2019.2937463

Cited by 90 publications

(68 citation statements)

References 12 publications

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“…Currently, DL and DRL show a lot of promise for this problem. Recently, the studies in [49], [50] have developed DL-based precoding schemes for MIMO-NOMA systems and shown that these schemes are able to optimize energy efficiency, data rates and bit error rates. In these studies, deep neural networks are used and sophisticated training algorithms are designed to allow the neural networks to approximate the solution to the formulated precoding problem.…”

Section: A Ai-assisted Flexible Ran Slicing For 6gmentioning

confidence: 99%

6G Massive Radio Access Networks: Key Applications, Requirements and Challenges

Lee

Qin

Wang

et al. 2021

IEEE Open J. Veh. Technol.

118

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Driven by the emerging use cases in massive access future networks, there is a need for technological advancements and evolutions for wireless communications beyond the fifthgeneration (5G) networks. In particular, we envisage the upcoming sixth-generation (6G) networks to consist of numerous devices demanding extremely high-performance interconnections even under strenuous scenarios such as diverse mobility, extreme density, and dynamic environment. To cater for such a demand, investigation on flexible and sustainable radio access network (RAN) techniques capable of supporting highly diverse requirements and massive connectivity is of utmost importance. To this end, this paper first outlines the key driving applications for 6G, including smart city and factory, which trigger the transformation of existing RAN techniques. We then examine and provide in-depth discussions on several critical performance requirements (i.e., the level of flexibility, the support for massive interconnectivity, and energy efficiency), issues, enabling technologies, and challenges in designing 6G massive RANs. We conclude the article by providing several artificial-intelligencebased approaches to overcome future challenges.

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Section: A Ai-assisted Flexible Ran Slicing For 6gmentioning

confidence: 99%

6G Massive Radio Access Networks: Key Applications, Requirements and Challenges

Lee

Qin

Wang

et al. 2021

IEEE Open J. Veh. Technol.

118

View full text Add to dashboard Cite

show abstract

“…In addition, the time interval required for processing must be in a coherent time block during several hundreds of microseconds. To overcome these issues and improve the SE with scarce resources, a learning-based NOMA was investigated in some recent works [139], while an advanced deep learning algorithm was further developed for NOMA-based systems [140][141][142][143][144][145]. Particularly, a deep learning algorithm integrated in the NOMA system was proposed in [140], where the training and testing models are built for data encoding, decoding, and channel detection to enhance the SE.…”

Section: Learning-based Approachesmentioning

confidence: 99%

A Survey on Non-Orthogonal Multiple Access: From the Perspective of Spectral Efficiency and Energy Efficiency

et al. 2020

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Non-orthogonal multiple access (NOMA) is a promising technology for next-generation wireless networks with emerging demands on low latency, high throughput, and massive connectivity. Unlike orthogonal multiple access, NOMA allows multiple users to share the same radio resources, which significantly improves spectral efficiency (SE). To achieve green wireless communications for numerous networked devices, NOMA helps reduce energy consumption while satisfying rate fairness and quality-of-experience requirements. The goal of this paper is to introduce the innovative approaches for NOMA in terms of the SE and energy efficiency, and discuss emerging technologies involved with NOMA. Further, its challenges and future research directions are highlighted.

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“…Recall that as we are using non orthogonal modulation, multi-user detection becomes a cumbersome issue. In [22], the authors proposed a deep learning model to learn the coding/decoding process of MIMO-NOMA system in order to minimize the total mean square error of the users signals. In [23], the authors proposed a deep learning model to be used in sparse code multiple access (SCMA) system, which is a promising code-based NOMA technique, with the goal to minimize the bit error rate.…”

Section: Coding/decoding Scheme Representationmentioning

confidence: 99%

“…Most of the works that used fully connected layers addressed problems related to the physical medium in 5G systems [2,11,[15][16][17]21,22,24,26,56,60,[62][63][64][65]68,72,74,76]. This can be justified because physical information usually can be structured (e.g., CSI, channel quality indicator (CQI), radio condition information, etc.).…”

Section: Fully Connected Modelsmentioning

confidence: 99%

When 5G Meets Deep Learning: A Systematic Review

et al. 2020

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This last decade, the amount of data exchanged on the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high-speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. However, for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements, and each of these technologies brings its own design issues and challenges. In this context, deep learning models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contribution, this paper presents a systematic review about how DL is being applied to solve some 5G issues. Differently from the current literature, we examine data from the last decade and the works that address diverse 5G specific problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using deep learning models in 5G scenarios and identify several issues that deserve further consideration.

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Deep Learning-Based MIMO-NOMA With Imperfect SIC Decoding

Cited by 90 publications

References 12 publications

6G Massive Radio Access Networks: Key Applications, Requirements and Challenges

6G Massive Radio Access Networks: Key Applications, Requirements and Challenges

A Survey on Non-Orthogonal Multiple Access: From the Perspective of Spectral Efficiency and Energy Efficiency

When 5G Meets Deep Learning: A Systematic Review

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