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.

Mobile Edge Computing (MEC) is a promising approach for enhancing the quality-of-service (QoS) of AI-enabled applications in the B5G/6G era, by bringing computation capability closer to end-users at the network edge. In this work, we investigate the joint optimization of edge server (ES) deployment, service placement, and computation task offloading under the stochastic information scenario. Traditional approaches often treat these decisions as equal, disregarding the differences in information realization. However, in practice, the ES deployment decision must be made in advance and remain unchanged, prior to the complete realization of information, whereas the decisions regarding service placement and computation task offloading can be made and adjusted in real-time after information is fully realized. To address such temporal coupling between decisions and information realization, we introduce the stochastic programming (SP) framework, which involves a strategic-layer for deciding ES deployment based on (incomplete) stochastic information and a tactical-layer for deciding service placement and task offloading based on complete information realization. The problem is challenging due to the different timescales of two layers' decisions. To overcome this challenge, we propose a multi-timescale SP framework, which includes a large timescale (called period) for strategic-layer decision-making and a small timescale (called slot) for tactical-layer decision making. Moreover, we design a Lyapunov-based algorithm to solve the tactical layer problem at each time slot, and a Markov approximation algorithm to solve the strategic-layer problem in every time period. Simulation results demonstrate that our proposed solution significantly outperforms existing benchmarks that overlook the coupling between decisions and information realization, achieving up to 56% reduction in total system cost.

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%.

Mobile Edge Computing (MEC) is a promising approach for enhancing the quality-of-service (QoS) of AI-enabled applications in the B5G/6G era, via providing computation services at the network edge that approximate end-users. In this work, we focus on the joint optimization of edge server (ES) deployment, service placement, and computation task offloading under stochastic information scenario. In traditional solutions, these decisions are often treated equally without considering differences in the information realization. In practice, however, the ES deployment decision needs to be made in advance before the complete information is realized, while the service placement and computation task offloading decisions can be made after the complete information is realized. To capture the time coupling between different decisions and information realizations, we formulate a two-layer stochastic programming (SP) problem, which consists of a strategic-layer decision for ES deployment, and a tactical-layer decision for service placement and computation task offloading. The strategic-layer decision will be made based on the stochastic information (i.e., before the complete information is realized), while the tactical-layer decision will be made based on every information realization. The problem is very challenging due to the large number of information realizations and the corresponding tactical-layer decisions. To solve the problem effectively, we propose a Sample Average Approximate (SAA) method to approximate the optimal solution, which involves generating a large number of randomly sampled information scenarios and using their averages to estimate the expected value of the objective function. Numerical simulations show that our proposed SP approach outperforms the traditional solutions that do not consider the coupling between decisions and information realizations. Moreover, compared with the ideal benchmark solution that assumes complete information, our proposed SP approach only results in a small performance degradation of 1.03%$\sim$6.26%.

Mobile edge computing (MEC) is a promising approach to reduce the network traffic load and alleviate the back-haul congestion by pushing computation down to the network edge (e.g., base stations) that are close to the origin of data. However, when many mobile devices (MDs) offload tasks to a base station (BS) in a dynamic and stochastic environment (e.g., with time-varying wireless channels and uncertain task models), it is often challenging for MDs to make offloading decisions in decentralized manner. In this work, we consider a collaborative MEC scenario, where an MD can offload its task to the associated BS or to other BSs through the associated BS. In such a scenario, we study the joint computation offloading and resource allocation problem, aiming at minimizing the expected long-term delay, taking the energy consumption constraint into consideration. The problem is challenging due to time-varying system and distributed decisions. To solve the problem in an online and decentralized manner, we propose a deep reinforcement learning (DRL) based distributed online algorithm. By incorporating the double deep Q network and dueling deep Q network technique, the proposed algorithm can improve the performance of the whole system significantly. Simulation results show that the proposed DRL-based algorithm outperforms baseline methods and can reduce the average delay of tasks by 76.4%-91.2%.

This paper studies the optimal guaranteed cost consensus problem for multi-agent systems with time-varying actuator faults for both leaderless and leader-following cases. The fault-tolerant controller is designed to solve the consensus problem at the presence of actuator faults by transforming a robust control problem into an infinite horizon optimal control problem. Meanwhile, it is proved that the optimal upper bound for the global performance of the system exists. Finally, two simulation examples are presented to verify the effectiveness of the proposed consensus controller.