Learning-based semantic communication is vulnerable to adversarial perturbations introduced before semantic encoding or over wireless channels. This paper proposes TwinViT-DeepJSCC, a preventive-corrective semantic image transceiver operating under a fixed channel-use budget. Two Vision Transformer (ViT)-based deep joi...
Maedeh Fallahreyhani, P. Azmi, N. Mokari et al.· 0 citations
This work proposes a multi-agent reinforcement learning (MARL) approach for queue-level scheduling in TSN-enabled VEC and employs multi-agent proximal policy optimization (MAPPO) to enable coordinated yet autonomous scheduling decisions.
Bernardo A. C. Pereira, Marcos Carvalho, Fatih Temiz et al.· 0 citations
GenAI is proposed to act as an uncertainty-aware support layer for the RF-WPT scheduler rather than as a standalone forecasting or decision-making tool, and a case study showing that distribution-aware prediction can improve robust charging decisions over deterministic, ensemble, and non-learning baselines, particularl...
Amirhossein Azarbahram, O. M. Rosabal, David E. Ruíz-Guirola et al.· arXiv.org· 0 citations
Simulation results under different eMBB traffic loads show that Agentic-QDRL improves eMBB throughput, reduces eMBB queue buildup, and maintains URLLC delay reliability compared with classical DRL and heuristic baselines.
Ting-Nan Bao, Medhat H. M. Elsayed, Pedro Enrique Iturria-Rivera et al.· 0 citations
This paper proposes a multi-agent reinforcement learning (MARL) framework for TSN scheduling, where each TSN queue is modeled as an autonomous agent and the Heterogeneous-Agent Proximal Policy Optimization (HAPPO) algorithm is employed to explicitly model inter-agent dependencies and jointly optimize service delivery a...
Marcos Carvalho, Fatih Temiz, Shavbo Salehi et al.· 0 citations
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