Autonomous Wireless Systems With Artificial Intelligence: A Knowledge Management Perspective

Gacanin, Haris

doi:10.1109/mvt.2019.2920162

Cited by 49 publications

(26 citation statements)

References 13 publications

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“…To solve the power violation problem of DNN we propose 6 Unless stated otherwise, all DNNs in this study have the same architecture 7 MSE is generally used as the loss function for a regression problem in a…”

Section: A) Trainingmentioning

confidence: 99%

“…cannot be fulfilled by the existing 5G technology. Recent developments in artificial intelligence (AI) has shifted the interest towards the development of "autonomous and connected intelligence" [6], [7]. Thereby, learning techniques will take a central stage in the sixth generation (6G) communication systems to design autonomous networks with the stringent application requirements of next generation usercentric communications [8].…”

Section: Introductionmentioning

confidence: 99%

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A Study on Deep Learning for Latency Constraint Applications in Beyond 5G Wireless Systems

2020

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The fifth generation (5G) of wireless communications has led to many advancements in technologies such as large and distributed antenna arrays, ultra-dense networks, software based networks and network virtualization. However, the need for a higher level of automation to establish hyper-low latency and hyper-high reliability for beyond 5G applications requires extensive research on machine learning with applications in wireless communications. Thereby, learning techniques will take a central stage in the sixth generation of wireless communications to cope up with the stringent application requirements. This paper studies the practical limitations of these learning methods in the context of resource management in nonstationary radio environment. Based on the practical limitations we carefully design and propose supervised, unsupervised, and reinforcement learning models to support rate maximization objective under user mobility. We study the effects of practical systems such as latency and reliability on the rate maximization with deep learning models. For common testing in the non-stationary environment, we present a generic dataset generation method to benchmark across different learning models versus traditional optimal resource management solutions. Our results indicate that learning models have practical challenges related to training limiting their applications. These models need an environment-specific design to reach the accuracy of an optimal algorithm. Such an approach is practically not realistic due to the high resource requirement needed for frequent retraining. INDEX TERMS Machine learning, low-latency application, learning model ageing, mobile communications, 6G.

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Section: A) Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Study on Deep Learning for Latency Constraint Applications in Beyond 5G Wireless Systems

2020

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“…Recently, teleported or autonomous robots can be wholly controlled through communication networks by human operators [8]. In contrast, autonomous robots and swarm robots, which are distributed independently, without the involvement of human beings, can perform a common task by sensing and transferring information via the Internet or through another means [5].…”

Section: Artificial Intelligence and Classification Of Roboticsmentioning

confidence: 99%

An AI‐based virtual simulation experimental teaching system in space engineering education

Shen

Peng

et al. 2020

Comp Applic In Engineering

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In recent years, cognitive computing has represented an artifical intelligence (AI)‐based application of the next‐generation interaction among human beings. This study analyses the cognitive internet of things and proposes a Soft K‐Means Fuzzy Clustering approach. The paper presents the data collection procedure from the space applications, the data analysis by cognitive computing, and the ways of overcoming problems of scalability and flexibility. The interactions between robot team members and people become necessary for many real‐life applications. This study mainly focuses on the precision and complexity of the model, time of computation, and scalability. Furthermore, it demonstrates the AI's ability to enhance the communication skills of the multirobot team by making complex activities to carry out all tasks more effectively.

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“…In recent years, deep learning (DL) is considered as a powerful tool, because it is expert in automatic feature extraction from huge amounts of data, instead of the complex and difficult design of man-made features [21]- [24]. For this reason, DL has been successfully applied in network traffic prediction [25]- [28], physical layer wireless techniques [30]- [35] and internet-of-things [36]- [40].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Robust Automatic Modulation Recognition Method for IoT Applications

2020

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In the scenarios of non-cooperative wireless communications, automatic modulation recognition (AMR) is an indispensable algorithm to recognize various types of signal modulations before demodulation in many internet of things applications. Convolutional neural network (CNN)-based AMR is considered as one of the most promising methods to achieve good recognition performance. However, conventional CNN-based methods are often unstable and also lack of generalized capabilities under varying noise conditions, because these methods are merely trained on specific dataset and can only work at the corresponding noise condition. Hence, it is hard to apply these methods directly in practical systems. In this paper, we propose a CNN-based robust automatic modulation recognition (RAMR) method to recognize three types of modulation signals, i.e., frequency shift key (FSK), phase shift key (PSK), and quadrature amplitude modulation (QAM). The proposed method is trained on a mixed dataset for extracting common features under varying noise scenarios. Simulation results show that our proposed generalized CNN-based architecture can achieve higher robustness and convenience than conventional ones. INDEX TERMS Automatic modulation recognition, deep learning, convolutional neural network, internet of things.

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Autonomous Wireless Systems With Artificial Intelligence: A Knowledge Management Perspective

Cited by 49 publications

References 13 publications

A Study on Deep Learning for Latency Constraint Applications in Beyond 5G Wireless Systems

A Study on Deep Learning for Latency Constraint Applications in Beyond 5G Wireless Systems

An AI‐based virtual simulation experimental teaching system in space engineering education

Deep Learning for Robust Automatic Modulation Recognition Method for IoT Applications

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