5g caching

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A novel context-aware caching

scheme for 5G networks

Dr. Noman Islam

13th International Conference - Mathematics, Actuarial, Computer Science & Statistics (MACS 13), IoBM, Karachi

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Samsung’s 5G rainbow

1. Very high data rates

2. High spectral efficiency

3. Speed during mobility conditions

4. High data transmission rates even at the boundary of a cell

5. Maximum number of concurrent connections 6. Reduced delay in communication

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Caching has been regarded as amongst the five most disruptive technologies for 5G

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Introduction

• _{Most of the requests on cellular networks are}

for videos

• _{Popular contents can be cached} • _{Where to cache?}

– _{Cache at the edge on small base stations or mobile}

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Objective

• _{The paper analyzes the current approaches}

available for caching in 5G networks

• _{Discusses a context-aware collaborative}

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Increasing demand for data

• _{Rise in computing devices, connectivity}

medium

• _{How to cope with soaring demands for data?} • _{Capacity can be increased by installing more}

access points or base stations

• _{There is a limit to the network densification} • _{Most of the contents are videos or social}

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Caching in 5G network

• _{If the popular contents (such as videos) can be}

proactively cached at the edges closer to the user, the backhaul network can be offloaded.

• _{Caching can not only avoid the network}

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Research gap

• _{Caching is a very important research problem}

in 5G networks

• _{Only one study is found that considered}

context parameters such as mobility of the nodes while performing caching.

• _{Context which is of paramount importance in}

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Collaborative filtering

• _{Collaborative filtering is based on the idea that}

people who liked similar things in past would likely to have same opinion about future items.

• _{Collaborative filtering approaches are classified as}

neighborhood based and latent factor approaches.

• _{The former finds a set of neighbors to a user or}

item. While in latent factor approach, the rating of a user is decomposed using matrix factorizing

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Bastug et al. (2014)

• _{Bastug et al. have proposed an approach}

based on recommendation system for caching in 5G network. A popularity matrix is

calculated by solving a least square problem. The regularized singular value decomposition was chosen to decompose the popularity

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Context-aware collaborative filtering

• _{In the established domain of CF,}

context-aware approaches already exist

• _{They are classified as contextual pre-filtering,}

contextual post-filtering and contextual modeling

• _{We suggest employing these techniques to}

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TF-based context-aware collaborative

filtering

• _{The demands for data items of a particular user are}

predictable based on a popularity matrix P

• _{Each small cell base station is equipped with storage}

capabilities M to cache the popular contents.

• _{As the storage available is small, a popularity matrix}

P is used to decide what particular contents to be cached. It is also assumed that the user arrives

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Popularity matrix

• _{We extend the popularity function}_P_such

that:

P: Users × Item × Context → Ratings

• _{Hence, the popularity matrix is a function of}

not only users and items, but also the context in which user issued the request for the

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• _{Six contextual information has been identified}

based on Schmidt et al. [27]: information about user, social information, user’s tasks, location, infrastructure and physical conditions.

• _{The contextual information can be obtained}

from various sources such as sensors and logs.

• _{The popularity matrix is indexed according to a}

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Tensor factorization

• _{Using tensor factorization, a low rank version of}

popularity matrix is constructed as shown in Figure.

• _{The tensor factorization decomposes the}

popularity matrix into factors of users, items and context inferred from popularity.

• _{There are a number of tensor factorization}

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Performing caching

• _{The resultant popularity matrix can be used}

for deciding the items to be cached proactively.

• _{The most popular files are greedily cached}

until there is not enough storage available.

• _{This helps in offloading the network during}

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Conclusion

• _{A collaborative filtering based context-aware caching}

scheme has been proposed for 5G networks.

• _{The proposed approach stores the popularity matrix}

as a combination of user, item and context’s rating.

• _{The popularity matrix is used to decide about}

caching the data items.

• _{The future work lies in the implementation of}

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References

• _{A. Karatzoglou, X. Amatriain, L. Baltrunas, and N.}

Oliver, "Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering," presented at Proceedings of the fourth ACM conference on Recommender systems, 2010.

• _{E. Bastug, M. Bennis, and M. r. Debbah, "Living on}

the edge: The role of proactive caching in 5G wireless networks," IEEE Communications