Online Learning to Cache and Recommend in the Next Generation Cellular Networks
An efficient caching can be achieved by predicting the popularity of the files accurately. It is well known that the popularity of a file can be nudged by using recommendation, and hence it can be estimated accurately leading to an efficient caching strategy. Motivated by this, in this paper, we con...
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10504600/ |
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| author | Krishnendu S. Tharakan B. N. Bharath Vimal Bhatia |
| author_facet | Krishnendu S. Tharakan B. N. Bharath Vimal Bhatia |
| author_sort | Krishnendu S. Tharakan |
| collection | DOAJ |
| description | An efficient caching can be achieved by predicting the popularity of the files accurately. It is well known that the popularity of a file can be nudged by using recommendation, and hence it can be estimated accurately leading to an efficient caching strategy. Motivated by this, in this paper, we consider the problem of joint caching and recommendation in a 5G and beyond heterogeneous network. We model the influence of recommendation on demands by a Probability Transition Matrix (PTM). The proposed framework consists of estimating the PTM and use them to jointly recommend and cache the files. In particular, this paper considers two estimation methods namely a) <monospace>Bayesian estimation</monospace> and b) a genie aided <monospace>Point estimation</monospace>. An approximate high probability bound on the regret of both the estimation methods are provided. Using this result, we show that the approximate regret achieved by the genie aided <monospace>Point estimation</monospace> approach is <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(T^{2/3} \sqrt {\log T})$ </tex-math></inline-formula> while the <monospace>Bayesian estimation</monospace> method achieves a much better scaling of <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(\sqrt {T})$ </tex-math></inline-formula>. These results are extended to a heterogeneous network consisting of M small base stations (SBSs) with a central macro base station. The estimates are available at multiple SBSs, and are combined using appropriate weights. Insights on the choice of these weights are provided by using the derived approximate regret bound in the multiple SBS case. Finally, simulation results confirm the superiority of the proposed algorithms in terms of average cache hit rate, delay and throughput. |
| format | Article |
| id | doaj-art-eb6d2d3c78df4906ad59ab8be5a848ca |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-eb6d2d3c78df4906ad59ab8be5a848ca2025-08-20T02:53:06ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2024-01-01251152510.1109/TMLCN.2024.338897510504600Online Learning to Cache and Recommend in the Next Generation Cellular NetworksKrishnendu S. Tharakan0https://orcid.org/0000-0003-4172-8279B. N. Bharath1Vimal Bhatia2https://orcid.org/0000-0001-5148-6643School of Computing, Queen’s University, Kingston, ON, CanadaDepartment of Electrical, Electronics and Communication Engineering (EECE), Indian Institute of Technology Dharwad, Dharwad, IndiaDepartment of Electrical Engineering, Center for Advanced Electronics, Indian Institute of Technology Indore, Indore, IndiaAn efficient caching can be achieved by predicting the popularity of the files accurately. It is well known that the popularity of a file can be nudged by using recommendation, and hence it can be estimated accurately leading to an efficient caching strategy. Motivated by this, in this paper, we consider the problem of joint caching and recommendation in a 5G and beyond heterogeneous network. We model the influence of recommendation on demands by a Probability Transition Matrix (PTM). The proposed framework consists of estimating the PTM and use them to jointly recommend and cache the files. In particular, this paper considers two estimation methods namely a) <monospace>Bayesian estimation</monospace> and b) a genie aided <monospace>Point estimation</monospace>. An approximate high probability bound on the regret of both the estimation methods are provided. Using this result, we show that the approximate regret achieved by the genie aided <monospace>Point estimation</monospace> approach is <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(T^{2/3} \sqrt {\log T})$ </tex-math></inline-formula> while the <monospace>Bayesian estimation</monospace> method achieves a much better scaling of <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}(\sqrt {T})$ </tex-math></inline-formula>. These results are extended to a heterogeneous network consisting of M small base stations (SBSs) with a central macro base station. The estimates are available at multiple SBSs, and are combined using appropriate weights. Insights on the choice of these weights are provided by using the derived approximate regret bound in the multiple SBS case. Finally, simulation results confirm the superiority of the proposed algorithms in terms of average cache hit rate, delay and throughput.https://ieeexplore.ieee.org/document/10504600/Cache placementcontent deliveryrecommendationBayesian estimation |
| spellingShingle | Krishnendu S. Tharakan B. N. Bharath Vimal Bhatia Online Learning to Cache and Recommend in the Next Generation Cellular Networks IEEE Transactions on Machine Learning in Communications and Networking Cache placement content delivery recommendation Bayesian estimation |
| title | Online Learning to Cache and Recommend in the Next Generation Cellular Networks |
| title_full | Online Learning to Cache and Recommend in the Next Generation Cellular Networks |
| title_fullStr | Online Learning to Cache and Recommend in the Next Generation Cellular Networks |
| title_full_unstemmed | Online Learning to Cache and Recommend in the Next Generation Cellular Networks |
| title_short | Online Learning to Cache and Recommend in the Next Generation Cellular Networks |
| title_sort | online learning to cache and recommend in the next generation cellular networks |
| topic | Cache placement content delivery recommendation Bayesian estimation |
| url | https://ieeexplore.ieee.org/document/10504600/ |
| work_keys_str_mv | AT krishnendustharakan onlinelearningtocacheandrecommendinthenextgenerationcellularnetworks AT bnbharath onlinelearningtocacheandrecommendinthenextgenerationcellularnetworks AT vimalbhatia onlinelearningtocacheandrecommendinthenextgenerationcellularnetworks |