4 papers
Dual-Mind World Models: A General Framework for Learning in Dynamic Wireless Networks
Lingyi Wang, Rashed Shelim, Walid Saad +1
Despite the popularity of reinforcement learning (RL) in wireless networks, existing approaches that rely on model-free RL (MFRL) and model-based RL (MBRL) are data inefficient and…
A Theoretically-Grounded Codebook for Digital Semantic Communications
Lingyi Wang, Rashed Shelim, Walid Saad +1
The use of a learnable codebook provides an efficient way for semantic communications to map vector-based high-dimensional semantic features onto discrete symbol representations re…
World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks
Lingyi Wang, Rashed Shelim, Walid Saad +1
Traditional reinforcement learning (RL)-based learning approaches for wireless networks rely on expensive trial-and-error mechanisms and real-time feedback based on extensive envir…
DMWM: Dual-Mind World Model with Long-Term Imagination
Lingyi Wang, Rashed Shelim, Walid Saad +1
Imagination in world models is crucial for enabling agents to learn long-horizon policy in a sample-efficient manner. Existing recurrent state-space model (RSSM)-based world models…