In 5G and beyond network architectures, operators and content providers base their content distribution strategies on Heterogeneous Networks, where macro and small cells are combined to offer better Quality of Service to wireless users. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) joint transmissions are used to further improve performance. In this paper, we address the average delay minimization problem by first formulating it as a static optimization problem. Even though the problem is NP-hard we are able to solve it via an efficient algorithm that guarantees a 1 2 -approximation ratio. We then proceed to propose two fully distributed and dynamic caching policies for the same problem. The first one asymptotically converges to the static optimal solution under the Independent Reference Model (IRM). The second one provides better results in practice under real (nonstationary) request processes. Our online policies outperform existing dynamic solutions that are PHY-unaware.
In 5G and beyond network architectures, operators and content providers base their content distribution strategies on Heterogeneous Networks, where macro and small(er) cells are combined to offer better Quality of Service (QoS) to wireless users. On top of such networks, edge caching and Coordinated Multi-Point (CoMP) transmissions are used to further improve performance. The problem of optimally utilizing the cache space in dense and heterogeneous cell networks has been extensively studied under the name of "FemtoCaching." However, related literature usually assumes relatively simple physical layer (PHY) setups and known or stationary content popularity. In this paper, we address these issues by proposing a class of fully distributed and dynamic caching algorithms that take advantage of CoMP capabilities towards minimizing PHY-aware metrics, such as end-to-end (E2E) delay. Our policies outperform existing dynamic solutions that are PHY-unaware, under both synthetic and real (non-stationary) request processes, and converge to efficient centralized solutions, in static setups.Index Terms-Edge caching, joint transmission, CoMP, heterogeneous cellular networks, distributed algorithms.• We propose novel distributed CoMP-aware caching policies that are executed independently by each helper, without knowledge of global network topology or file popularities.• We show that these algorithms have desirable convergence properties to locally optimal allocations.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.