An Intelligent Discontinuous Reception Scheme for Critical and Massive Machine-type Communications

In 5G and beyond, discontinuous reception (DRX) is a promising energy-saving technology for massive machine-type communications (mMTC) devices with intermittent connections. However, existing DRX solutions face challenges in effectively accommodating mMTC services with critical delay requirements, particularly in a dynamic wireless environment. To address this problem, this paper proposes an intelligent DRX scheme for critical mMTC. Firstly, we modify the conventional discontinuous reception model by introducing the critical delay flag. Then we optimize the DRX by Markov Decision Process and designe the energy-delay factor to achieve the simultaneous optimization. Finally, we extract the environmental features by convolutional neural network and solve the problem by deep Q-network. A communication simulation platform is established to verify the effectiveness of the proposed method. The simulation results show that the proposed method can effectively improve the energy efficiency in mMTC situations compared with state-of-the-art.