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Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users
TL;DR: In this article, an algorithm that combines the machine learning framework of echo state networks with sublinear algorithms is proposed to predict the content request distribution and mobility pattern of each user, determine which content to cache at remote radio heads and BBUs.
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Abstract: In this paper, the problem of proactive caching is studied for cloud radio access networks (CRANs). In the studied model, the baseband units (BBUs) can predict the content request distribution and mobility pattern of each user, determine which content to cache at remote radio heads and BBUs. This problem is formulated as an optimization problem which jointly incorporates backhaul and fronthaul loads and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks with sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs can predict each user's content request distribution and mobility pattern while having only limited information on the network's and user's state. In order to predict each user's periodic mobility pattern with minimal complexity, the memory capacity of the corresponding ESN is derived for a periodic input. This memory capacity is shown to be able to record the maximum amount of user information for the proposed ESN model. Then, a sublinear algorithm is proposed to determine which content to cache while using limited content request distribution samples. Simulation results using real data from Youku and the Beijing University of Posts and Telecommunications show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 27.8% and 30.7%, respectively, compared to random caching with clustering and random caching without clustering algorithm.
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Citations
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial
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Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey
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A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms: Transparent Computing, Mobile Edge Computing, Fog Computing, and Cloudlet
TL;DR: A comprehensive survey of emerging computing paradigms from the perspective of end-edge-cloud orchestration is presented to discuss state-of-the-art research in terms of computation offloading, caching, security, and privacy.
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A Very Brief Introduction to Machine Learning With Applications to Communication Systems
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References
Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication
Herbert Jaeger,Harald Haas +1 more
TL;DR: A method for learning nonlinear systems, echo state networks (ESNs), which employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains is presented.
Next Generation 5G Wireless Networks: A Comprehensive Survey
TL;DR: This survey makes an exhaustive review of wireless evolution toward 5G networks, including the new architectural changes associated with the radio access network (RAN) design, including air interfaces, smart antennas, cloud and heterogeneous RAN, and underlying novel mm-wave physical layer technologies.
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Effective capacity: a wireless link model for support of quality of service
Dapeng Wu,Rohit Negi +1 more
TL;DR: This paper proposes and develops a link-layer channel model termed effective capacity (EC), which first model a wireless link by two EC functions, namely, the probability of nonempty buffer, and the QoS exponent of a connection, and proposes a simple and efficient algorithm to estimate these EC functions.
A Practical Guide to Applying Echo State Networks
Mantas Lukoševičius
- 01 Jan 2012
TL;DR: Practical techniques and recommendations for successfully applying Echo State Network, as well as some more advanced application-specific modifications are presented.
942
Minimum Complexity Echo State Network
Ali Rodan,Peter Tino +1 more
TL;DR: It is shown that a simple deterministically constructed cycle reservoir is comparable to the standard echo state network methodology and the (short-term) of linear cyclic reservoirs can be made arbitrarily close to the proved optimal value.
748