Open AccessJoint caching and sleeping optimisation for D2D‐aided ultra‐dense network
Author(s) -
Pei Li,
Shen Gao,
Yaoyue Hu,
Zhiwen Pan,
Xiaohu You
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.0466
Subject(s) - computer science , mathematical optimization , energy consumption , base station , optimization problem , joint (building) , efficient energy use , scheme (mathematics) , power (physics) , power consumption , computer network , distributed computing , algorithm , mathematics , engineering , architectural engineering , ecology , mathematical analysis , physics , electrical engineering , quantum mechanics , biology
Device‐to‐device (D2D) communication provides the communication of the users in the vicinity and thereby decreases end‐to‐end delay and power consumption. More importantly, D2D communication enables offloading the traffic load of the base station (BS), and it is very suitable for caching, especially in ultra dense networks (UDNs). In this paper, a joint optimisation problem of collaborative caching and sleeping strategy based on energy‐delay tradeoff for D2D‐aided UDN is investigated. To solve the joint optimization problem, we decompose it into sleeping sub‐problem and collaborative caching sub‐problem. For sleeping subproblem, the optimal sleeping ratio is first derived, then, a delay‐aware sleeping strategy is proposed to obtain the local optimal sleeping scheme. For the collaborative caching subproblem, the suboptimal solution is obtained by distributed iterative method. In each iteration step, the suboptimal solution is derived by solving the combinatorial optimisation problem under Karush‐Kuhn‐Tucker conditions. Simulation results show that the proposed algorithms can coverage to the global solution. It also demonstrate that increasing caching capacity shortened mean delay, and benefitting from collaborative caching, delay performance and energy saving can be improved significantly. Moreover, it can be seen that combining the sleeping strategy with collaborative caching further reduced energy consumption by a considerable amount.