Joint Optimization of Base Station Clustering and Service Caching in User-Centric MEC
Langtian Qin, Hancheng Lu, Yao Lu, Chenwu Zhang, Feng Wu

TL;DR
This paper introduces a user-centric MEC framework that jointly optimizes base station clustering and service caching to significantly reduce delay and caching costs, using a novel algorithm based on Lyapunov optimization and GBD.
Contribution
It proposes a new joint optimization framework and algorithm for base station clustering and caching in MEC, addressing delay and cost reduction.
Findings
Reduces long-term delay by up to 93.75%.
Reduces caching cost by up to 53.12%.
Outperforms existing algorithms in simulations.
Abstract
Edge service caching can effectively reduce the delay or bandwidth overhead for acquiring and initializing applications. To address single-base station (BS) transmission limitation and serious edge effect in traditional cellular-based edge service caching networks, in this paper, we proposed a novel user-centric edge service caching framework where each user is jointly provided with edge caching and wireless transmission services by a specific BS cluster instead of a single BS. To minimize the long-term average delay under the constraint of the caching cost, a mixed integer non-linear programming (MINLP) problem is formulated by jointly optimizing the BS clustering and service caching decisions. To tackle the problem, we propose JO-CDSD, an efficiently joint optimization algorithm based on Lyapunov optimization and generalized benders decomposition (GBD). In particular, the long-term…
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Taxonomy
TopicsCaching and Content Delivery · Cooperative Communication and Network Coding · Advanced MIMO Systems Optimization
Methodstravel james
