MADQN-Enhanced Computation Offloading and Resource Allocation for 6G Low-Altitude Economy Vehicular Networks

Air-to-ground communication networks in future sixth-generation (6G) networks are expected to leverage integrated sensing and communication (ISAC) to support the low-altitude economy (LAE). In such networks, a set of unmanned aerial vehicles (UAVs) acting as mobile edge computing (MEC) servers cooperatively process delay-sensitive tasks offloaded by multiple authorised vehicular user equipments (V-UEs). However, the diverse, stringent requirements of ISAC-enabled V-UE services require more intelligent and efficient resource allocation for the LAE-aided vehicle-to-everything (V2X) communications systems. To address this issue, we propose a digital twin (DT)-empowered multi-agent LAE MEC vehicular framework, where the DT technology enables real-time data collection, processing, monitoring, and optimisation in a virtual environment. Meanwhile, each V-UE may offload its delay-sensitive task to a UAV-assisted MEC server. We aim to minimise the long-term average total service delay (which may include the task processing delay and the transmission delay) of all V-UEs, the computation resource allocation at each UAV-assisted MEC server, the transmission power, and the allocation of resource blocks for all V-UEs. To solve the joint optimisation problem, we propose a Multi-agent deep Q-network-based Offloading and Resource allocation Optimisation (MORO) algorithm. Simulation results demonstrate that our proposed algorithm outperforms the benchmarks in terms of the convergence rate and the long-term average total service delay of all V-UEs.