Machine Learning-Based 5G RAN Slicing for Broadcasting Services

Mu, Junsheng; Jing, Xiaojun; Zhang, Yangying; Gong, Yi; Zhang, Ronghui; Zhang, Fangpei

doi:10.1109/tbc.2021.3122353

Cited by 17 publications

(12 citation statements)

References 28 publications

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“…In a radio portion, CNN-LSTM predicts the channel state for two types of 5G services. Additionally, a mathematical model based on deep Q-Networks was established for performance optimization in energy efficiency [33]. To give an effective result for IoT vertical slice, it was suggested in [34] to utilise machine learning techniques to lower the cost of slice selection and prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment

Mohammedali¹,

Kanakis²,

Al-Sherbaz³

et al. 2023

JCOMSS

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The 3G Partnership Project (3GPP) defined network slicing as a set of resources that could be scaled up and down to cover users' requirements. Machine learning and network slicing will be used together to manage and optimize resources efficiently. Sharing resources across multiple operators, such as towers, spectrum and infrastructure, can reduce the cost of 5G resources. In the proposed prototype, the end-user is connected to more than eight inter and intra-slices according to the demands. A set of slices is implemented over the 5G networks to provide an efficient service to the end-user using softwarization and virtualization technologies. Traffic is generated over multiple scenarios then End-to-End slicing traffic was analyzed after generating realtime traffic over the 5G networks. Also, all the features extracted from the traffic based on the flow behaviours and a set of elements selected from the datasets according to machine learning behaviours. Multiple machine learning algorithms are applied to our datasets using MATLAB classification application. After that, the best model is chosen to train and predict the slices using less CPU and training time to reduce the computational power in future networks and build a sustainable environment. Furthermore, the regression application predicts the slice type on the third dataset with the minimum squared error.

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Section: Literature Reviewmentioning

confidence: 99%

Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment

Mohammedali¹,

Kanakis²,

Al-Sherbaz³

et al. 2023

JCOMSS

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show abstract

“…The Internet of Things (IoT) and mobile networks are evolving rapidly to fulfil the need for ultra reliable and low latency performance, seamless connectivity, mobility, and intelligence [1][2][3][4]. It is estimated that over 50 billion devices are wirelessly connected to the internet, which can sense their surroundings and offer high-quality services.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices

Zhang

Lambotharan

Zheng

et al. 2023

IEEE Internet Things J.

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Due to great success of transformers in many applications such as natural language processing and computer vision, transformers have been successfully applied in automatic modulation classification. We have shown that transformer-based radio signal classification is vulnerable to imperceptible and carefully crafted attacks called adversarial examples. Therefore, we propose a defense system against adversarial examples in transformer-based modulation classifications. Considering the need for computationally efficient architecture particularly for Internet of Things (IoT)-based applications or operation of devices in environment where power supply is limited, we propose a compact transformer for modulation classification. The advantages of robust training such as adversarial training in transformers may not be attainable in compact transformers. By demonstrating this, we propose a novel compact transformer that can enhance robustness in the presence of adversarial attacks. The new method is aimed at transferring the adversarial attention map from the robustly trained large transformer to a compact transformer. The proposed method outperforms the state-of-the-art techniques for the considered white-box scenarios including fast gradient method and projected gradient descent attacks. We have provided reasoning of the underlying working mechanisms and investigated the transferability of the adversarial examples between different architectures. The proposed method has the potential to protect the transformer from the transferability of adversarial examples.

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“…On the contrary, data-driven model, such as artificial intelligence (AI), etc. [4,5], brings new impetus to wireless communication, relying on its powerful capacity of feature learning. As a key technology of wireless communication, automatic modulation recognition (AMR) is facing new opportunities and potential with the assistance of AI [6].…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised-based automatic modulation classification with domain adaptation for wireless IoT spectrum monitoring

et al. 2023

Self Cite

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Dramatic advances in wireless networks have led to the Smart IoT, which may enable new modes of information transfer. Generally, data-driven advanced artificial intelligence (AI) techniques can be used in smart IoT networks, which prones to a lot of unexpected challenges. Enabling efficient management of spectrum resources based on cognitive radio is considered to be an effective means to address the limited spectrum resources. As a key technology in CR, automatic modulation recognition (AMR) is developing towards intelligence with deep learning as the main approach. Deep learning (DL) has been widely applied to AMR for possible improvement of recognition accuracy, while the superb performance highly depends on high-quality and well-labeled datasets. Consequently, these requirements prone to poor performance in the environments where the datasets are not well-labeled. Motivated by this, domain adaptation is considered for AMR in this paper, and a novel network architecture is proposed therein, termed semi-supervised automatic modulation recognition (SemiAMR). Specifically, source domain high SNR data are mapped by the source encoder to the classification domain and classified by the classifier, where the source data with labels enable this training process. Next, the discriminator and the target domain encoder are trained by determining whether the data is from the target or source domain. Finally, the target domain encoder and classifier are combined and are used to infer the data labels of the target domain. Experimental results state that the accuracy of proposed SemiAMR achieves 1\% to 27\% improvements when compared with classical schemes under the target domain condition where there is no corresponding labeled data and signal-to-noise ratios (SNRs) varies from −20 dB to −4 dB.

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Machine Learning-Based 5G RAN Slicing for Broadcasting Services

Cited by 17 publications

References 28 publications

Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment

Management and Evaluation of the Performance of end-to-end 5G Inter/Intra Slicing using Machine Learning in a Sustainable Environment

Attention-Based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices

Semi-supervised-based automatic modulation classification with domain adaptation for wireless IoT spectrum monitoring

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