The continuous evolution of future mobile communication systems is heading towards the integration of communication and computing, with Mobile Edge Computing (MEC) emerging as a crucial means of implementing Artificial Intelligence (AI) computation. MEC could enhance the computational performance of wireless edge networks by offloading computing-intensive tasks to MEC servers. However, in edge computing scenarios, the sparse sample problem may lead to high costs of time-consuming model training. This paper proposes an MEC offloading decision and resource allocation solution that combines generative AI and deep reinforcement learning (DRL) for the communication-computing integration scenario in the 802.11ax Wi-Fi network. Initially, the optimal offloading policy is determined by the joint use of the Generative Diffusion Model (GDM) and the Twin Delayed DDPG (TD3) algorithm. Subsequently, resource allocation is accomplished by using the Hungarian algorithm. Simulation results demonstrate that the introduction of Generative AI significantly reduces model training costs, and the proposed solution exhibits significant reductions in system task processing latency and total energy consumption costs.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged to support computation-intensive tasks in 6G systems. Since the battery capacity of a UAV is limited, to serve as many users as possible, a joint design on UAV trajectory and offloading strategy with consideration for service fairness is essential to provide energy-efficient computation offloading to the users in UAV-MEC networks. Unfortunately, such a joint decision-making problem is not straightforward due to various task types required from users and various functionalities of different UAVs enabled by different application programs. Considering the above issues, we take energy efficiency and service fairness as the objective, and propose aMulti-AgentEnergy-Efficient jointTrajectory andComputationOffloading (MA-ETCO) scheme. To adapt to dynamic demands of users, we develop an optimization-embedding multi-agent deep reinforcement learning (OMADRL) algorithm. Each UAV autonomously learns the trajectory control decision based on MADRL to adapt to dynamic demands. Then, it will obtain the optimal computation offloading decision by solving a mixed-integer nonlinear programming problem. The computation offloading result, in turn, will be used as an indicator to guide UAVs’ trajectory design. Compared to relying solely on deep reinforcement learning, such an optimization-embedding way reduces action space dimension and improves convergence efficiency.

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution to support the computation-intensive tasks in the Internet of Things (IoT) networks. As for the operation of UAV-assisted MEC, jointly design of the UAV trajectory control and computation offloading strategies becomes the key for achieving high offloading efficiency, which is extremely challenging due to the uncertain and dynamic demands in the network. In this paper, aiming at maximizing the offloading task amount, we propose an Multi-Agent joint TrAjectory and Computation Offloading (MA-TACO) scheme, where all related factors including task type variety, quality of service (QoS) guarantee, and service fairness are taken into account. To facilitate each UAV to obtain the best joint strategy under dynamic network environment, considering the complex decisions with both continuous and discrete variables, we develop an Optimization-oriented Multi-Agent Deep Reinforcement Learning approach (OMADRL), where each UAV could autonomously learn the trajectory decision to adapt to the dynamic demands, and the offloading decision would be made by solving a mixed-integer programming problem based on the observations, which would be utilized to guide the trajectory learning. Comparing with solely relying on learning, such an optimization-oriented way could reduce the action space dimension and make each UAV achieve the best strategy faster. The simulation results indicate the effectiveness of the proposed scheme.

A competitive traveling salesmen problem is a variant of traveling salesman problem in that multiple agents compete with each other in visiting a number of cities. The agent who is the first one to visit a city will receive a reward. Each agent aims to collect as more rewards as possible with the minimum traveling distance. There is still not effective algorithms for this complicated decision making problem. We investigate an improved ant colony approach for the competitive traveling sales-men problem which adopts a time dominance mechanism and a revised pheromone depositing method to improve the quality of solutions with less computational complexity. Simulation results show that the proposed algorithm outperforms the state of art algorithms.