Twin Delayed DDPG based Dynamic Power Allocation for Mobility in IoRT
نویسندگان
چکیده
The internet of robotic things (IoRT) is a modern as well fast-evolving technology employed in abundant socio-economical aspects which connect user equipment (UE) for communication and data transfer among each other. For ensuring the quality service (QoS) IoRT applications, radio resources, example, transmitting power allocation (PA), interference management, throughput maximization etc., should be efficiently allocated UE. Traditionally, resource has been formulated using optimization problems, are then solved mathematical computer techniques. However, those problems generally nonconvex nondeterministic polynomial-time hardness (NP-hard). In this paper, one most crucial challenges management emitting an antenna called PA, considering that interfering multiple access channel (IMAC) considered. addition, UE natural movement behavior directly impacts condition between remote head (RRH) Additionally, we have considered two well-known mobility models i) random walk ii) modified Gauss-Markov (GM). As result, simulation environment more realistic complex. A data-driven model-free continuous action based deep reinforcement learning algorithm twin delayed deterministic policy gradient (TD3) proposed combination gradient, actor-critics, double Q-learning (DDQL). It optimizes PA stationary UE, movements according to model, on GM model. Simulation results show TD3 method outperforms model-based techniques like weighted MMSE (WMMSE) fractional programming (FP) algorithms, Q network (DQN) DDPG terms average sum-rate performance.
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ژورنال
عنوان ژورنال: Journal of communications software and systems
سال: 2023
ISSN: ['1845-6421', '1846-6079']
DOI: https://doi.org/10.24138/jcomss-2022-0141