A Survey of Methods and Architectures for Strategy Design for Energy Efficient Data Offloading in 6G-Enabled Vehicular Edge Computing Networks Using Double Deep Q-Network

Abstract

The rapid evolution of intelligent transportation systems and 6G
communication networks has significantly increased the demand for
efficient data processing in vehicular environments. Vehicular Edge
Computing (VEC) has emerged as a promising paradigm to support
latency-sensitive and computation-intensive applications by offloading
tasks from vehicles to nearby edge servers. However, designing energy
efficient and adaptive offloading strategies remains a major challenge
due to high mobility, dynamic network conditions, and resource
constraints. Recent advancements in deep reinforcement learning,
particularly Double Deep Q-Network (DDQN), have provided effective
solutions to these challenges by enabling intelligent and stable decision
making. DDQN addresses the overestimation problem of traditional
DQN and improves convergence stability in dynamic environments. This
survey reviews recent methods and architectures for energy-efficient
data offloading in 6G-enabled vehicular edge computing networks,
focusing on DDQN-based approaches. The study analyses key
techniques, including single-agent DRL, multi-agent reinforcement
learning, and hybrid optimization frameworks. It also highlights
emerging trends such as hierarchical architectures and mobility-aware
strategies. The findings reveal that DDQN-based models significantly
enhance energy efficiency and reduce latency compared to conventional
approaches. Finally, open challenges and future research directions are
discussed to guide the development of scalable and intelligent offloading
strategies in next-generation vehicular networks.