Neural network policy agents scheduling phased array beam directions at sub-millisecond intervals to maximize joint communication and radar performance.
ISACOrchestrator models beam scheduling as a Markov Decision Process (MDP). Neural policy networks observe user channel states, vehicle kinematics, and queue lengths, selecting beam steering vectors that maximize joint reward functions.
Trained reinforcement learning policies execute on embedded TensorRT and FPGA accelerators in under 250 microseconds, keeping pace with rapid 1 kHz 6G radio frame slots.