An enhanced SAC algorithm with temporal sequence input, layer-normalized GRU, and squeeze-excitation blocks jointly optimizes drone trajectories and communication schedules for distributed-beamforming drone relays, reducing simulated AoI and drone energy.
AoI-Sensitive Data Forwarding with Distributed Beamforming in UAV-Assisted IoT
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abstract
This paper proposes a UAV-assisted forwarding system based on distributed beamforming to enhance age of information (AoI) in Internet of Things (IoT). Specifically, UAVs collect and relay data between sensor nodes (SNs) and the remote base station (BS). However, flight delays increase the AoI and degrade the network performance. To mitigate this, we adopt distributed beamforming to extend the communication range, reduce the flight frequency and ensure the continuous data relay and efficient energy utilization. Then, we formulate an optimization problem to minimize AoI and UAV energy consumption, by jointly optimizing the UAV trajectories and communication schedules. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results show that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms.
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Multi-AAV-enabled Distributed Beamforming in Low-Altitude Wireless Networking for AoI-Sensitive IoT Data Forwarding
An enhanced SAC algorithm with temporal sequence input, layer-normalized GRU, and squeeze-excitation blocks jointly optimizes drone trajectories and communication schedules for distributed-beamforming drone relays, reducing simulated AoI and drone energy.