TY - JOUR
T1 - Caching with Time-Varying Popularity Profiles
T2 - A Learning-Theoretic Perspective
AU - Bharath, B. N.
AU - Nagananda, K. G.
AU - Gunduz, D.
AU - Poor, H. Vincent
N1 - Funding Information:
Manuscript received October 24, 2017; revised February 22, 2018; accepted May 4, 2018. Date of publication May 11, 2018; date of current version September 14, 2018. This work was supported in part by the U.S. National Science Foundation under Grants CCF-1420575 and ECCS-1647198, the European Research Council (ERC) under Starting Grant BEACON (agreement 677854), and DST/INT/UK/P-129/2016 and IIT Dharwad startup grant. The associate editor coordinating the review of this paper and approving it for publication was M. Grangetto. (Corresponding author: K. G. Nagananda.) B. N. Bharath is with the Department of Electrical Engineering, IIT Dharwad, Dharwad 580011, India (e-mail: bharathbn@iitdh.ac.in).
Publisher Copyright:
© 1972-2012 IEEE.
PY - 2018/9
Y1 - 2018/9
N2 - Content caching at the small-cell base stations (sBSs) in a heterogeneous wireless network is considered. A cost function is proposed that captures the backhaul link load, called the 'offloading loss,' which measures the fraction of the requested files that are not available in the sBS caches. As opposed to the previous approaches that consider time-invariant and perfectly known popularity profiles, caching with non-stationary and statistically dependent popularity profiles (assumed unknown, and hence, estimated) is studied from a learning-theoretic perspective. A probably approximately correct result is derived, which presents a high probability bound on the offloading loss difference, i.e., the error between the estimated and the optimal offloading loss. The difference is a function of the Rademacher complexity, the \beta -mixing coefficient, the number of time slots, and a measure of discrepancy between the estimated and true popularity profiles. A cache update algorithm is proposed, and simulation results are presented to show its superiority over periodic updates. The performance analyses for Bernoulli and Poisson request models are also presented.
AB - Content caching at the small-cell base stations (sBSs) in a heterogeneous wireless network is considered. A cost function is proposed that captures the backhaul link load, called the 'offloading loss,' which measures the fraction of the requested files that are not available in the sBS caches. As opposed to the previous approaches that consider time-invariant and perfectly known popularity profiles, caching with non-stationary and statistically dependent popularity profiles (assumed unknown, and hence, estimated) is studied from a learning-theoretic perspective. A probably approximately correct result is derived, which presents a high probability bound on the offloading loss difference, i.e., the error between the estimated and the optimal offloading loss. The difference is a function of the Rademacher complexity, the \beta -mixing coefficient, the number of time slots, and a measure of discrepancy between the estimated and true popularity profiles. A cache update algorithm is proposed, and simulation results are presented to show its superiority over periodic updates. The performance analyses for Bernoulli and Poisson request models are also presented.
KW - Caching
KW - probably approximately correct (PAC) learning
KW - time-varying popularity profiles
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U2 - 10.1109/TCOMM.2018.2835479
DO - 10.1109/TCOMM.2018.2835479
M3 - Article
AN - SCOPUS:85046729107
SN - 0090-6778
VL - 66
SP - 3837
EP - 3847
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
IS - 9
M1 - 8357917
ER -