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Massive Machine-Type Communication (mMTC) is proposed by ITU to provide a large scale of connectivity services for billions of machine-type communications devices (MTCDs) via cellular networks. However, MTCDs are generally energy-constrained, security capability limited and short-packet transmission dominated. It has great challenges for massive MTCDs to perform short-packet transmission simultaneously while guaranteeing the confidentiality and energy efficiency of the information transmission. In this paper, we investigate the energy-efficient secure short-packet transmission of Non-Orthogonal Multiple Access (NOMA) assisted mMTC networks, in which MTCDs aim to transmit secrecy messages to the destination base station via trusted relays with a passive eavesdropper present. To realize secure, reliable and energy-efficient transmission, we maximize the secrecy energy efficiency (SEE) by implementing joint relay and power level selection. Furthermore, we formulate a decentralized stateless Q-learning-based multi-agent reinforcement learning (MARL) algorithm for the dynamic and complex mMTC systems. The simulation results show that adopting the proposed decentralized MARL algorithm and relaying with NOMA can improve the performance of SEE while reducing information exchange overhead. [ABSTRACT FROM AUTHOR]

The 5th-generation (5G) mobile networks and beyond need to support massive machine-type communications (MTC) devices with limited available radio resources. In this paper, we study the power-domain non-orthogonal multiple access (NOMA) technology to support energy-efficient massive MTC networks, where MTC devices exchange information using sporadic and low-rate short packets. We investigate the subchannel allocation and power control policy to maximize the achievable effective energy efficiency (EE) for uplink NOMA-based massive MTC networks, taking into account of short-packet communication characteristics. We model the subchannel allocation problem as a multi-agent Markov decision process and propose an efficient Q-learning algorithm to solve it. Furthermore, we obtain the optimal transmission power policy by approximating the achievable effective rate of uplink NOMA-based short packet communications. Compared with the existing OFDMA scheme, simulations validate that the proposed scheme can improve the achievable effective EE of massive MTC networks with 5.93%. [ABSTRACT FROM AUTHOR]