# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network  
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1 National University of Defense Technology, Wuhan 430030, China; [EMAIL_ADDRESS] (B.H.) 1  
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2 Information Support Force Engineering University, Wuhan 430030, China 21215 2  
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332125 2  
[PERSON]  
2 Information Support Force Engineering University, Wuhan 430030, China 2  
[PERSON]  
2 Information Support Force Engineering University, Wuhan 430030, China 2  
[PERSON]  
1 National University of Defense Technology, Wuhan 430030, China; [EMAIL_ADDRESS] (B.H.) 1  
[PERSON]  
1 National University of Defense Technology, Wuhan 430030, China; [EMAIL_ADDRESS] (B.H.) 1  
[PERSON]  
1 National University of Defense Technology, Wuhan 430030, China; [EMAIL_ADDRESS] (B.H.) 1  
[PERSON]  
1 National University of Defense Technology, Wuhan 430030, China; [EMAIL_ADDRESS] (B.H.) 1  
[PERSON]  
2 Information Support Force Engineering University, Wuhan 430030, China 2  
[PERSON]  
332125 2  
[PERSON]  
2 Information Support Force Engineering University, Wuhan 430030, China 2  
[PERSON]  
1 National University of Defense Technology, Wuhan 430030, China 2  
[PERSON]  
1 National University of Defense Technology, Wuhan 430030, China 2  
[PERSON]  
through collaborative deployment of satellite links, terrestrial base stations, and wireless relays. This network architecture leverages 5G and cluster communication to achieve full-area coverage in disaster-affected core areas while establishing high-bandwidth transmission channels with the rear command center through satellite, microwave, and fiber-optic links [7]. It should be noted that although this complex network topology significantly enhances survivability against infrastructure damage, it imposes dual challenges for deployment planning: On one hand, physical constraints such as terrain features, disaster evolution, and communication coverage radii must be dynamically balanced; on the other hand, interference and resource competition between heterogeneous devices need to be avoided while meeting differentiated service demands like low-latency video backhaul and high-reliability command scheduling. Constrained by the dynamic nature of field environments and the urgency of decision-making timelines, traditional deployment models relying on manual experience struggle to rapidly generate globally optimal solutions. This results in a temporal mismatch between communication hub activation efficiency and the critical rescue window period, which has become a core bottleneck restricting the improvement of emergency communication effectiveness. To overcome the above limitations, this paper proposes an intelligent deployment method based on deep reinforcement learning (DRL) to achieve rapid deployment optimization of emergency communication networks. The widespread applications of DRL in UAV deployment [8] have demonstrated its ability to effectively address complex state spaces and dynamically changing environments. By leveraging powerful deep neural networks (DNNs) for decision-making, DRL has proven capable of delivering excellent performance across numerous learning tasks even under conditions of severe domain knowledge scarcity. However, applying DRL to solve emergency communication network deployment challenges is non-trivial. Traditional DRL algorithms, such as Deep Q Network (DQN) [9], utilize convolutional neural networks (CNNs) to estimate Q values for each state-action pair. The fully connected layers in CNNs limit the DQN to handling only very limited action spaces, severely constraining the decision-making capabilities of the agent. To address the challenges of rapid deployment and efficient communication in emergency communication networks under disaster scenarios, this thesis proposes a DRL-based intelligent deployment method. Targeting the dynamic characteristics of disaster environments and the complexity of heterogeneous network organization, this method innovatively integrates a fully convolutional neural network with deep reinforcement learning algorithms.