Energy-Efficient Short Packet Communications for Uplink NOMA-Based Massive MTC Networks

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, whe...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2019-12, Vol.68 (12), p.12066-12078
Main Authors: Han, Shujun, Xu, Xiaodong, Liu, Zilong, Xiao, Pei, Moessner, Klaus, Tao, Xiaofeng, Zhang, Ping
Format: Article
Language:English
Subjects:
Algorithms
Computer simulation
Decoding
Electronic devices
Energy efficiency
Energy management
Error probability
joint subchannel and power allocation
Machine learning
Manganese
Markov processes
Massive MTC
Multiagent systems
NOMA
Nonorthogonal multiple access
Power control
Power management
Quality of service
Resource management
short packet communication
Uplink
Wireless networks
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Summary: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%.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2019.2948761