Deep Reinforcement Learning (DRL)
Integrated Access and Backhaul (IAB) networks
offer a versatile and scalable solution for expanding broadband
coverage in urban environments. However, optimizing the deploy-
ment of IAB nodes to ensure reliable coverage while minimizing
costs poses significant challenges, particularly given the location
constraints and the highly dynamic nature of urban settings. This
work introduces a novel Deep Reinforcement Learning (DRL)
approach for IAB network planning, considering urban con-
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In unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system, rotary-wing UAV can be dispatched to fly close to ground terminals (GTs) to execute their offloaded tasks. This can extend GTs’ computing capability and save their energy cost. However, to enhance the energy efficiency of UAV propulsion, ensure successful completion of each GT's mission, and maintain a stable UAV-GT uplinks, it is crucial to design a rational UAV 3D trajectory and mission offloading strategy.
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