Artificial Intelligence for Wireless Caching: Schemes, Performance, and Challenges

Sheraz, Muhammad; Ahmed, Manzoor; Hou, Xueshi; Li, Yong; Jin, Depeng; Han, Zhu; Jiang, Tao

doi:10.1109/comst.2020.3008362

Cited by 48 publications

(26 citation statements)

References 161 publications

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“…However, the tutorial dedicates only one part for ANN-aided edge caching and includes a small number of relevant works. An overview of AI-based wireless edge caching, including supervised, unsupervised, reinforcement and transfer learning is provided in [20]. Various challenges are highlighted, such as the dynamic environment due to mobility and fading.…”

Section: A Contributionsmentioning

confidence: 99%

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

et al. 2022

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Mobile networks experience a tremendous increase in data volume and user density due to the massive number of coexisting users and devices. An efficient technique to alleviate this issue is to bring the data closer to the users by exploiting cache-aided edge nodes, such as fixed and mobile access points, and even user devices. Meanwhile, the fusion of machine learning and wireless networks offers new opportunities for network optimization when traditional optimization approaches fail or incur high complexity. Among the various machine learning categories, reinforcement learning provides autonomous operation without relying on large sets of historical data for training. In this survey, reinforcement learning-aided mobile edge caching solutions are presented and classified, based on the networking architecture and optimization target. As sixth generation (6G) networks will be characterized by high heterogeneity, fixed cellular, fog, cooperative, vehicular, and aerial networks are studied. The discussion of these works reveals that there exist reinforcement learning-aided caching schemes with varying complexity that can surpass the performance of conventional policy-based approaches. Finally, several open issues are presented, stimulating further interest in this important research field.INDEX TERMS 6G, edge caching, heterogeneous networks, machine learning, mobile edge networks, reinforcement learning.

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Section: A Contributionsmentioning

confidence: 99%

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

et al. 2022

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“…where equation (5) indicates that the sum of the sizes of all contents cached in the MBS is less than or equal to its cache space, and similarly, equations ( 6) and ( 7) indicate the storage capacity constraints in the SBS and user devices. e cache hit ratio and total system delivery costs are important policy evaluation metrics in edge caching [1,7].…”

Section: Problem Formulationmentioning

confidence: 99%

“…erefore, the concept of edge caching was proposed to meet the massive demands for content distribution in mobile cellular networks [5,6]. With edge caching technology, the frequently requested content can be cached closer to the user side, such as macro base station (MBS), small base stations (SBS), edge server, and mobile devices [7].…”

Section: Introductionmentioning

confidence: 99%

Diversified Demands Integration-Based Proactive Caching Strategy towards Edge Networks

Jing

Liu

Yang

et al. 2022

Wireless Communications and Mobile Computing

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“…The cascade structure is poorly parallelized; the model is too complex, and the training is time-consuming. In addition, the temporal signal loses some timing features after the convolutional layer [11].…”

Section: Related Workmentioning

confidence: 99%

Automatic Recognition of Communication Signal Modulation Based on the Multiple‐Parallel Complex Convolutional Neural Network

Huang

et al. 2021

Wireless Communications and Mobile Computing

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This paper implements a deep learning-based modulation pattern recognition algorithm for communication signals using a convolutional neural network architecture as a modulation recognizer. In this paper, a multiple-parallel complex convolutional neural network architecture is proposed to meet the demand of complex baseband processing of all-digital communication signals. The architecture learns the structured features of the real and imaginary parts of the baseband signal through parallel branches and fuses them at the output according to certain rules to obtain the final output, which realizes the fitting process to the complex numerical mapping. By comparing and analyzing several commonly used time-frequency analysis methods, a time-frequency analysis method that can well highlight the differences between different signal modulation patterns is selected to convert the time-frequency map into a digital image that can be processed by a deep network. In order to fully extract the spatial and temporal characteristics of the signal, the CLP algorithm of the CNN network and LSTM network in parallel is proposed. The CNN network and LSTM network are used to extract the spatial features and temporal features of the signal, respectively, and the fusion of the two features as well as the classification is performed. Finally, the optimal model and parameters are obtained through the design of the modulation recognizer based on the convolutional neural network and the performance analysis of the convolutional neural network model. The simulation experimental results show that the improved convolutional neural network can produce certain performance gains in radio signal modulation style recognition. This promotes the application of machine learning algorithms in the field of radio signal modulation pattern recognition.

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Artificial Intelligence for Wireless Caching: Schemes, Performance, and Challenges

Cited by 48 publications

References 161 publications

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

A Survey on Reinforcement Learning-Aided Caching in Heterogeneous Mobile Edge Networks

Diversified Demands Integration-Based Proactive Caching Strategy towards Edge Networks

Automatic Recognition of Communication Signal Modulation Based on the Multiple‐Parallel Complex Convolutional Neural Network

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