Generative AI-aided Optimization for AI-Generated Content (AIGC) Services in Edge Networks

Abstract

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AI-Generated Content (AIGC) offers a promising solution to this challenge. However, the training and deployment of large AI models necessitate significant resources. To address this issue, we introduce an AIGCas-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks, ensuring ubiquitous access to AIGC services for Metaverse users. Nonetheless, a key aspect of providing personalized user experiences requires the careful selection of AIGC service providers (ASPs) capable of effectively executing user tasks. This selection process is complicated by environmental uncertainty and variability, a challenge not yet addressed well in existing literature. Therefore, we first propose a diffusion model-based AI-generated optimal decision (AGOD) algorithm, which can generate the optimal ASP selection decisions. We then apply AGOD to deep reinforcement learning (DRL), resulting in the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, which achieves efficient and effective ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it a promising approach for the future research on AIGC-driven services in Metaverse. The implementation of our proposed method is available at: https://github.com/Lizonghang/AGOD.

An Overview on Generative AI at Scale with Edge-Cloud Computing

“…Generated content that contradicts common sense tends to look strange to users. Sensitive content, copyright content, and trademarks should also be avoided in the generated content [148,25,26]. Automatic detection [43,63] of strange and/or forbidden AIGC is still an open problem.…”

Very large scale integration architectures of discrete transforms for digital signal processing applications

“…The performance of XL-MIMO systems can be substantially enhanced by employing machine learning and artificial intelligence techniques [207], [209]. This includes the development of intelligent channel estimation methods, beamforming strategies, user scheduling, and resource allocation schemes that adapt to the ever-changing wireless communication environment.…”

“…Additionally, AI-enabled user scheduling algorithms, such as the one proposed in [77], can optimize resource allocation for IoT networks by considering the high-density user distribution typical of XL-MIMO scenarios. Future research could delve deeper into the potential of generative AI, e.g., generative diffusion models [209], [210], in generating the optimal resource allocation schemes according to the user behavior and network conditions in XL-MIMO systems.…”

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“…Additionally, the authors in [156] proposed a diffusion model-based AI-generated optimal decision (AGOD) algorithm. This algorithm enables adaptive and responsive task allocation based on real-time environmental changes and user demands.…”

“…The algorithm's efficacy is further enhanced by the integration of DRL, as demonstrated in the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm. Compared to traditional SAC methods, the D2SAC algorithm shows a performance improvement of approximately 2.3% in terms of the task completion rate and 5.15% in terms of utility gained [156]. Unlike traditional task allocation methods, which assume that all tasks and their corresponding utility values are known in advance, D2SAC could address the selection of the most appropriate service provider, where tasks arrive dynamically and in real time.…”