AMADRL: Privacy-Aware Attention-Based Multiagent Deep Reinforcement Learning for Optimizing Spectral Allocation in 6G Vehicular Networks

The emergence of 6G-enabled vehicle-to-everything (V2X) networks has created unprecedented demand for ultrareliable, low-latency spectrum allocation across heterogeneous entities, including vehicles, Internet of Things (IoT) devices, and industrial systems. Current spectrum allocation methods suffer from exponential computational complexity, extensive information sharing requirements, and poor scalability in dense networks. This article proposes attention-based multiagent deep reinforcement learning (AMADRL), a novel framework employing dual critic networks with multihead self-attention mechanisms for intelligent spectrum allocation. The dual critic architecture resolves individual-collective optimization conflicts through local critics for independent entity optimization and a global critic with attention-based coordination. Our approach significantly reduces information sharing requirements while handling heterogeneous quality of service (QoS) demands across diverse entity types. Comprehensive experimental evaluation comparing AMADRL against state-of-the-art baselines, including MADDPG, MAAC, QMIX, attention-based methods (A-DDPG and MHA-DQN), and game-theoretic approaches, reveals that AMADRL achieves superior performance across multiple metrics, including spectrum utilization efficiency, interference mitigation, and network scalability while preserving user privacy and satisfying strict latency constraints required by safety-critical and industrial use cases.