Smart Packet Transmission Scheduling in Cognitive IoT Systems: DDQN Based Approach
نویسندگان
چکیده
The convergence of Artificial Intelligence (AI) can overcome the complexity network defects and support a sustainable green system. AI has been used in Cognitive Internet Things (CIoT), improving large volume data, minimizing energy consumption, managing traffic, storing data. However, smart packet transmission scheduling (TS) CIoT is dependent on choosing an optimum channel with minimum estimated Packet Error Rate (PER), delays caused by errors, subsequent retransmissions. Therefore, we propose Generative Adversarial Network Deep Distribution Q (GAN-DDQN) to enhance TS reducing distance between target action-value particles. Furthermore, GAN-DDQN training based reward clipping evaluate value each action for certain states avoid variations value. simulation results show that proposed increases throughput while power consumption Transmission Delay (TD) when compared fuzzy Radial Basis Function (fuzzy-RBF) Distributional Q-Network (DQN). provides high rate 38 Mbps, actor-critic fuzzy-RBF’s 30 Mbps DQN algorithm’s 19 Mbps.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3168549