Replacing Sensing-Based Semi-Persistent Scheduling with Deep Q-Networks

Eliminating packet collisions in congested urban vehicle platoons.

Multi-Agent Reinforcement Learning Formulation

Vehicles learn decentralized transmission policies by observing local spectrum energy patterns, cutting packet collisions by 70% under 100% channel loads.

Continuous Online Policy Adaptation

Basebands update policy weights on-the-fly as vehicles transition from open highways to dense concrete parking structures.

70% reduction in packet collision rates.
Adaptive spectrum policy learning.
Autonomous decentralized subchannel selection.

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