With the advancement of industrial intelligence, control systems are increasingly demanding higher real-time performance and accuracy, particularly in the face of growing network data volumes. Semantic communication has shown considerable promise in improving transmission efficiency and minimizing latency, making it a promising approach for future communication systems. However, due to the lack of a unified and comprehensive theoretical framework, the relationship between semantic communication and control performance remains unexplored, as well as semantic-aware resource allocation. To deal with these challenges, we first analyze and derive the relationship between communication delay and control stability and define the transmission delay in semantic communication systems using the DeepSC model as an example. Then, we formulate a semantic-aware resource allocation problem aimed at maximizing semantic similarity through joint optimization of channel assignment, power allocation, and semantic compression ratio across multiple devices. To overcome the inefficiencies of traditional mathematical methods and deep reinforcement learning (DRL) algorithms in solving complex optimization problems, we propose a two-stage Generative Diffusion Model (GDM)empowered DRL algorithm framework which decouples the solution space by leveraging GDM to generate resource allocation scheme and optimal semantic compression parameter is solved subsequently with exhausted searching. The simulation results confirm the effectiveness of the proposed two-stage approach and highlight the advantages of the two-stage GDM-enhanced DRL algorithm over both the single-stage approach and the standalone DRL algorithm.

In the Metaverse, real-time, concurrent services such as virtual classrooms and immersive gaming require local graphic rendering to maintain low latency. However, the limited processing power and battery capacity of user devices make it challenging to balance Quality of Experience (QoE) and terminal energy consumption. In this paper, we investigate a multi-objective optimization problem (MOP) regarding power control and rendering capacity allocation by formulating it as a multi-objective optimization problem. This problem aims to minimize energy consumption while maximizing Meta-Immersion (MI), a metric that integrates objective network performance with subjective user perception. To solve this problem, we propose a Multi-Objective Multi-Agent Evolutionary Reinforcement Learning with User-Object-Attention (M2ERL-UOA) algorithm. The algorithm employs a prediction-driven evolutionary learning mechanism for multi-agents, coupled with optimized rendering capacity decisions for virtual objects. The algorithm can yield a superior Pareto front that attains the Nash equilibrium. Simulation results demonstrate that the proposed algorithm can generate Pareto fronts, effectively adapts to dynamic user preferences, and significantly reduces decision-making time compared to several benchmarks.

Expanding industrial components and network density raise challenges in the domain of mobility management of multiagent systems (MASs), such as multirobot cooperative transportation. This article investigates the heterogeneous network (HetNet) handover problem in the industrial context involves jointly optimizing data rate, block rate, and handover frequency among large-scale mobile user Terminals. Specifically, by introducing user-centric conditional handover features, we leverage Pareto-efficient solutions to address the multiobjective optimization problem of balancing data rate and block probability. The optimization problem is reformulated into a multiagent learning-based Markov cooperative game to cope with dynamic context conditions, introducing a handover penalty factor to enhance service continuity. Furthermore, we develop a Pareto-efficient multiagent transformer with efficient advantage decomposition, leveraging sequential modeling, and distributed computing power of MAS. Extensive simulations demonstrate the superiority of the proposed algorithm, implementing user-centric optimal handover decisions, while also obtaining an additional fairness gain.