![]() Bommaraveni, Srikanth ![]() Scientific Conference (2019, November 03) Edge caching has received much attention as an effective solution to face the stringent latency requirements in 5G networks due to the proliferation of handset devices as well as data-hungry applications ... [more ▼] Edge caching has received much attention as an effective solution to face the stringent latency requirements in 5G networks due to the proliferation of handset devices as well as data-hungry applications. One of the challenges in edge caching systems is to optimally cache strategic contents to maximize the percentage of total requests served by the edge caches. To enable the optimal caching strategy, we propose an Active Learning approach (AL) to learn and design an accurate content request prediction algorithm. Specifically, we use an AL based Query-by-committee (QBC) matrix completion algorithm with a strategy of querying the most informative missing entries of the content popularity matrix. The proposed AL framework leverage's the trade-off between exploration and exploitation of the network, and learn the user's preferences by posing queries or recommendations. Later, it exploits the known information to maximize the system performance. The effectiveness of proposed AL based QBC content learning algorithm is demonstrated via numerical results. [less ▲] Detailed reference viewed: 92 (14 UL)![]() Korrai, Praveenkumar ![]() ![]() ![]() in International Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM), Poznan, Poland, June 2019 (2019) Detailed reference viewed: 235 (29 UL)![]() Vu, Thang Xuan ![]() ![]() in 2018 IEEE International Conference on Communications (ICC) (2018, May) Detailed reference viewed: 161 (17 UL)![]() Vu, Thang Xuan ![]() ![]() in 2018 IEEE Wireless Communications and Networking Conference (WCNC) (2018, May) Detailed reference viewed: 169 (19 UL) |
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