Deep Learning-Based Content Caching in the Fog Access Points

Proactive caching of the most popular contents in the cache memory of fog-access points (F-APs) is regarded as a promising solution for the 5G and beyond cellular communication to address latency-related issues caused by the unprecedented demand of multimedia data traffic. However, it is still challenging to correctly predict the user’s content and store it in the cache memory of the F-APs efficiently as the user preference is dynamic. In this article, to solve this issue to some extent, the deep learning-based content caching (DLCC) method is proposed due to recent advances in deep learning. In DLCC, a 2D CNN-based method is exploited to formulate the caching model. The simulation results in terms of deep learning (DL) accuracy, mean square error (MSE), the cache hit ratio, and the overall system delay is displayed to show that the proposed method outperforms the performance of known DL-based caching strategies, as well as transfer learning-based cooperative caching (LECC) strategy, randomized replacement (RR), and the Zipf’s probability distribution.

[1]  Mugen Peng,et al.  Fog-computing-based radio access networks: issues and challenges , 2015, IEEE Network.

[2]  Jorge Cadima,et al.  Principal component analysis: a review and recent developments , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[3]  Choong Seon Hong,et al.  DeepMEC: Mobile Edge Caching Using Deep Learning , 2018, IEEE Access.

[4]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[5]  Konstantinos Poularakis,et al.  Approximation Algorithms for Mobile Data Caching in Small Cell Networks , 2014, IEEE Transactions on Communications.

[6]  Wai-Xi Liu,et al.  Content Popularity Prediction and Caching for ICN: A Deep Learning Approach With SDN , 2018, IEEE Access.

[7]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[8]  Supeng Leng,et al.  Social-Aware Edge Caching in Fog Radio Access Networks , 2017, IEEE Access.

[9]  Hong Ping Zhao,et al.  City-Wide Traffic Congestion Prediction Based on CNN, LSTM and Transpose CNN , 2020, IEEE Access.

[10]  Shangguang Wang,et al.  Fog Computing: An Overview of Big IoT Data Analytics , 2018, Wirel. Commun. Mob. Comput..

[11]  Hong Ping Zhao,et al.  Optimal Cache Resource Allocation Based on Deep Neural Networks for Fog Radio Access Networks , 2020 .

[12]  Ilyas Alper Karatepe,et al.  Big data caching for networking: moving from cloud to edge , 2016, IEEE Communications Magazine.

[13]  Ning Zhang,et al.  Online Proactive Caching in Mobile Edge Computing Using Bidirectional Deep Recurrent Neural Network , 2019, IEEE Internet of Things Journal.

[14]  Mugen Peng,et al.  Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues , 2018, IEEE Communications Surveys & Tutorials.

[15]  Ning Zhang,et al.  Content Popularity Prediction Towards Location-Aware Mobile Edge Caching , 2018, IEEE Transactions on Multimedia.

[16]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[17]  Vivienne Sze,et al.  Efficient Processing of Deep Neural Networks: A Tutorial and Survey , 2017, Proceedings of the IEEE.

[18]  Xiaodong Gu,et al.  Towards dropout training for convolutional neural networks , 2015, Neural Networks.

[19]  Taghi M. Khoshgoftaar,et al.  Survey on categorical data for neural networks , 2020, Journal of Big Data.

[20]  Di Yuan,et al.  Device Caching for Network Offloading: Delay Minimization With Presence of User Mobility , 2018, IEEE Wireless Communications Letters.

[21]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Alexandros G. Dimakis,et al.  Femtocaching and device-to-device collaboration: A new architecture for wireless video distribution , 2012, IEEE Communications Magazine.

[23]  Mehdi Bennis,et al.  Living on the edge: The role of proactive caching in 5G wireless networks , 2014, IEEE Communications Magazine.

[24]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[25]  Mehdi Bennis,et al.  Decentralized Asynchronous Coded Caching Design and Performance Analysis in Fog Radio Access Networks , 2020, IEEE Transactions on Mobile Computing.

[26]  Fu-Chun Zheng,et al.  A Mean Field Game-Based Distributed Edge Caching in Fog Radio Access Networks , 2020, IEEE Transactions on Communications.

[27]  Mohsen Guizani,et al.  Caching in Information-Centric Networking: Strategies, Challenges, and Future Research Directions , 2018, IEEE Communications Surveys & Tutorials.

[28]  Min Xu,et al.  The Fourth Industrial Revolution: Opportunities and Challenges , 2018 .

[29]  Kwang-Cheng Chen,et al.  Architecture Harmonization Between Cloud Radio Access Networks and Fog Networks , 2015, IEEE Access.

[30]  Xuemin Shen,et al.  Cooperative Edge Caching in User-Centric Clustered Mobile Networks , 2017, IEEE Transactions on Mobile Computing.