Dynamic proximity-aware resource allocation in vehicle-to-vehicle (V2V) communications
Towards low-latency and ultra-reliable vehicle-to-vehicle communication
Dynamic resource allocation for optimized latency and reliability in vehicular networks
Risk-sensitive task fetching and offloading for vehicular edge computing
Risk-based optimization of virtual reality over terahertz reconfigurable intelligent surfaces
Integrating LEO satellite and UAV relaying via reinforcement learning for non-terrestrial networks
V2V cooperative sensing using reinforcement learning with action branching
Cooperative perception in vehicular networks using multi-agent reinforcement learning
Cooperative Edge Caching via Federated Deep Reinforcement Learning in Fog-RANs
Communication-efficient and federated multi-agent reinforcement learning
Vehicular cooperative perception through action branching and federated reinforcement learning
Cooperative edge caching via multi agent reinforcement learning in fog radio access networks
Information freshness-aware task offloading in air-ground integrated edge computing systems