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Abstract：
Natural disasters, including earthquakes, floods, landslides, tsunamis, wildfires, and hurricanes, have become more common in recent years due to rapid climate change. For Post-Disaster Management (PDM), authorities deploy various types of user equipment (UE) for the search and rescue operation, for example, search and rescue robots, drones, medical robots, smartphones, etc., via the Internet of Robotic Things (IoRT) supported by cellular 4G/LTE/5G and beyond or other wireless technologies. For uninterrupted communication services, movable and deployable resource units (MDRUs) have been utilized where the base stations are damaged due to the disaster. In addition, power optimization of the networks by satisfying the quality of service (QoS) of each UE is a crucial challenge because of the electricity crisis after the disaster. In order to optimize the energy efficiency, UE throughput, and serving cell (SC) throughput by considering the stationary as well as movable UE without knowing the environmental priori knowledge in MDRUs aided two-tier heterogeneous networks (HetsNets) of IoRT, the optimization problem has been formulated based on emitting power allocation and user association combinedly in this article. This optimization problem is nonconvex and NP-hard where parameterized (discrete: user association and continuous: power allocation) action space is deployed. The new model-free hybrid action space-based algorithm called multi-pass deep Q network (MP-DQN) is developed to optimize this complex problem. Simulations results demonstrate that the proposed MP-DQN outperforms the parameterized deep Q network (P-DQN) approach, which is well known for solving parameterized action space, DQN, as well as traditional algorithms in terms of reward, average energy efficiency, UE throughput, and SC throughput for motionless as well as moveable UE.
摘要：
自然灾害,包括地震,洪水,山体滑坡,海啸,野火,和飓风，近年来由于快速的气候变化变得越来越普遍。对于灾后管理(PDM)，当局为搜救行动部署各种类型的用户设备(UE)，例如,搜索和救援机器人，无人机,医疗机器人,智能手机,等。,通过蜂窝4G/LTE/5G及其他无线技术支持的机器人物联网(IoRT)。对于不间断通信服务，可移动和可部署资源单元（MDRU）已被用于基站因灾难而受损的地方。此外,由于灾难后的电力危机,通过满足每个UE的服务质量(QoS)来优化网络的电力是一个至关重要的挑战。为了优化能源效率，UE吞吐量，和服务小区(SC)吞吐量通过考虑固定以及可移动的UE，而不知道环境先验知识在MDRU辅助两层异构网络(HetsNet)的IoRT，本文提出了基于发射功率分配和用户关联相结合的优化问题。此优化问题是非凸的，并且是NP难的，其中部署了参数化（离散：用户关联和连续：功率分配）动作空间。开发了称为多通深度Q网络（MP-DQN）的新的无模型混合动作空间算法，以优化此复杂问题。仿真结果表明，提出的MP-DQN优于参数化深度Q网络(P-DQN)方法，这是众所周知的解决参数化的动作空间，DQN,以及奖励方面的传统算法，平均能源效率，UE吞吐量，和SC吞吐量的静止以及可移动的UE。
