Year: 2025
Type: article
Abstract: Content caching is a promising solution to overcome the backhaul traffic delay issue by caching content at the base station (BS). However, the performance of content caching is restricted by the limited BS cache storage. In this paper, we are the first to employ artificial intelligence generated content (AIGC) to enhance the content caching performance by empowering the BS's intelligence capability. Besides caching a popular file, our AIGC-enhanced BS can alternatively cache its prompt of smaller size to reconstruct the file whenever needed by mobile terminals. We reveal the fundamental tradeoff between caching storage saving and computation delay for AIGC to decide whether to cache a file or its prompt. In this regard, this paper investigates the new problem of joint hybrid caching, computation and communication resource allocation optimization to minimize the average system latency to serve mobile terminals. As the problem is a non-convex and involves mixed integer non-linear programming, we develop a divide-and-conquer algorithm to decompose the problem into two timescale levels. We theoretically prove that our AIGC-enhanced hybrid content caching outperforms the conventional content caching once the computation capacity is non-trivial. Extensive simulation results also validate the superiority of the proposed hybrid content caching.
Authors: Ding Xu, Lingjie Duan, Hongbo Zhu
Language: English
Topic: Caching and Content Delivery
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,645
Awards: G2449939794, G5298722671, G7290676604
OpenAlex ID: W4409233796