Artificial Intelligence Generated Content (AIGC) powered by Generative Diffusion Models (GDMs) has emerged as a transformative paradigm for automated content creation. To satisfy the stringent latency requirements of AIGC services in many edge intelligence scenarios (e.g., smart cities), Mobile Edge Computing (MEC) provides critical computational support by deploying GDMs at edge servers (ES) close to end users. This paper investigates an MEC-enabled AIGC network comprising multiple ES, wireless access points (APs), and mobile users (UEs) with heterogeneous latency and accuracy demands. We formulate aJoint Communication Association and Computation Offloading (JCACO)game, where each UE strategically selects its serving AP, ES, and inference steps to minimize the overall service completion time while meeting accuracy constraints. The problem is challenging due to the network dynamics and the incomplete information. We prove that the JCACO game is apotential gameunder both complete and stochastic information settings, ensuring the existence of Nash Equilibrium (NE) in both cases. To derive the NE efficiently, we develop a distributedMulti-Agent Stochastic Learning(MASL) algorithm that provably converges to the NE with strict performance guarantees. Unlike conventional best-response schemes, MASL requires neither the knowledge of other players’ strategies nor global network information, making it fully distributed and adaptive to dynamic environments. We further provide a strict theoretical convergence analysis for MASL by usingOrdinary Differential Equations(ODEs). Simulation results demonstrate that MASL significantly reduces service completion time compared with benchmark methods while satisfying accuracy constraints, confirming its effectiveness and practicality for real-world MEC-enabled AIGC networks.

Generative Diffusion Model (GDM)-based AI-Generated Content (AIGC) services are rapidly emerging as powerful solutions for creating high-quality, personalized content across various domains, playing an essential role in shaping the future network landscapes. However, the traditional cloud-based implementation of AIGC services faces substantial challenges due to the heterogeneity of GDMs as well as their computation-intensive nature and the low-latency requirement. In this work, we investigate a Mobile Edge Computing (MEC)-empowered heterogeneous AIGC service scenario, where User Equipments (UEs) and Base Stations (BSs) deploy lightweight and heavyweight GDMs, respectively, to deliver different levels of AIGC services at the network edge. Specifically, when initiating an AIGC task, each UE can choose to process the task locally using lightweight GDMs, or offload and process the task on a BS using heavyweight GDMs. In such a scenario, we focus on the joint optimization of offloading, scheduling, and inferencing, aiming to minimize task delay and energy consumption, while maximizing content quality. To address the problem in an online and decentralized manner, we develop a Multi-Agent Proximal Policy Optimization (MAPPO)-based deep reinforcement learning algorithm in a centralized training and decentralized execution framework. Simulation results show that our proposed algorithm outperforms existing intelligent benchmarks, with the performance gains ranging from 5.1% to 13.9%.

AI-Generated Content (AIGC) based on Generative Diffusion Models (GDMs) is revolutionizing content creation and promoting substantial advancements in domains like autonomous driving and robotics. Leveraging progress in Mobile Edge Computing (MEC) and model compression techniques, GDMs are increasingly being deployed on Edge Servers (ESs) and User Equipments (UEs), which typically face resource limitations. In such MEC-enabled scenarios, designing an efficient inference scheme for GDMs still remains a significant challenge, due to the resource constraints on ESs and UEs as well as the personalized demands of AIGC users. In this work, we propose a novel hybrid inference scheme, which consists of two stages: public prompt generation and common-to-personalized inference. In the first stage, a Large Language Model (LLM) is adopted to generate public prompts derived from the common features of users' personal prompts. In the second stage, a common inference phase based on public prompts is first executed for all users (to produce common intermediate results), and then a personalized inference phase based on each user's personal prompts is performed for each individual user (to generate final contents). Clearly, by introducing the common inference phase, the total inference steps can be significantly reduced. In such a scheme, we further study a hybrid inference optimization problem to optimize both common and personalized inference steps, aiming to maximize the total Quality of Service (QoS), while minimizing delay and energy consumption. Simulation results show that our proposed scheme significantly outperforms existing benchmarks, with the performance gains ranging from 12.6 % to 102.2 %.

Artificial Intelligence-Generated Content (AIGC) has emerged as a transformative paradigm, enabling the autonomous creation of diverse content. By offloading model inference tasks to the network edge that is closer to mobile users (MUs), Mobile Edge Computing (MEC) has the potential to significantly enhance the performance of AIGC services. In practice, however, it is challenging to optimally manage MEC-empowered AIGC services, due to the lack of well-defined AIGC-specific metrics, as well as the dynamic workload and computation-intensive nature of AIGC services. In this paper, we first define a novel AIGC metric based on extensive real data experiments, and then study thejoint task offloading and resource allocationproblem in a generic MEC-empowered AIGC network, where MUs can offload model inference tasks to local or remote Base Stations (BSs), aiming at maximizing their Quality of Experience (QoE). The problem is challenging due to the fast and randomly changing of environments, as well as the necessity for real-time, asynchronous decision-making. To tackle these challenges, we propose two deep reinforcement learning algorithms based on the Proximal Policy Optimization (PPO) framework:Single-Layer PPO (SL-PPO)andMulti-Layer PPO (ML-PPO), designed for slow-changing and fast-changing environments, respectively. In the SL-PPO algorithm, both task offloading and resource allocation decisions are made simultaneously when tasks arrive. In the ML-PPO algorithm, the task offloading decision is made immediately when tasks arrive, while the resource allocation decision is deferred until tasks are scheduled for processing or transmission in the corresponding queues. Simulation results show that (i) both algorithms outperform existing methods in the literature, and can increase the average utility by up to 47% and 48.8%; (ii) both algorithms can effectively manage the trade-off between latency and energy consumption.

This paper proposes a Simulated Annealing enhanced Greed Vacancy Search Algorithm (eGVXSA) for solving packing equal circles in a damaged square. The damaged area is consisted of a number of small square regions that are randomly distributed within the container. The benefits present at its efficiency of consuming limited space. For this problem, Experiments have shown the efficiency over the original Greedy Vacancy Search Algorithm.