Abstract
Caching popular contents in advance is an important technique to achieve low latency and reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process, optimal content placement caching probabilities are obtained to maximize the average success probability (ASP) for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. In this paper, we first propose two online prediction (OP) methods for forecasting CP viz., popularity prediction model (PPM) and Grassmannian prediction model (GPM), where the unconstrained coefficients for linear prediction are obtained by solving constrained non-negative least squares. To reduce the higher computational complexity per online round, two online learning (OL) approaches viz., weighted-follow-the-leader and weighted-follow-the-regularized-leader are proposed, inspired by the OP models. In OP, ASP difference (i.e, the gap between the ASP achieved by prediction and that by known content popularity) is bounded, while in OL, sub-linear MSE regret and linear ASP regret bounds are obtained. With MovieLens dataset, simulations verify that OP methods are better for MSE and ASP difference minimization, while the OL approaches perform well for the minimization of the MSE and ASP regrets.
Highlights
W ITH the continuous development of various intelligent devices such as smart vehicles, smart home appliances, mobile devices, etc, and various sized innovative application services such as news updates, high quality video feeds, Manuscript received May 24, 2019; revised September 22, 2019; accepted November 19, 2019
For the Poisson point process (PPP) network where both the base stations (BS) and users are distributed as homogeneous PPP and content requests are characterized using a global content popularity (CP) profile, we compute the average success probability (ASP) caching measure as a function of CPs and content placement probabilities (CPPs)
The square-root of content popularity (SCP) prediction is intended to maximize the ASP. It can be seen from the observed ASP difference in (25) that as the estimation error is improved in the SCP vectors, the ASP difference decreases, i.e., the achievable ASP of Grassmannian prediction model (GPM) is better than that of popularity prediction model (PPM)
Summary
W ITH the continuous development of various intelligent devices such as smart vehicles, smart home appliances, mobile devices, etc, and various sized innovative application services such as news updates, high quality video feeds, Manuscript received May 24, 2019; revised September 22, 2019; accepted November 19, 2019. To learn CP independently across contents, online policies are presented for cache-awareness in [13], low complexity video caching in [1], [14], user preference learning in [15], etc. These works are employed for a particular system with the fixed number of BSs and users, i.e., the statistical performance of the network as whole is lacking with respect to content delivery in the physical layer. The CP changes dynamically in both time and space dimensions owing to randomness of user requests, and needs to be predicted for the efficient caching placements. In addition to PPP analysis, we investigate the CP prediction models under dynamic scenarios, and its effect on the caching, which have not been investigated in this context to the best of the authors’ knowledge
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
