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

被引:66
作者
Han, Shujun [1 ]
Xu, Xiaodong [1 ]
Liu, Zilong [2 ]
Xiao, Pei [2 ]
Moessner, Klaus [3 ,4 ]
Tao, Xiaofeng [1 ]
Zhang, Ping [5 ]
机构
[1] Beijing Univ Posts & Telecommun, Natl Engn Lab Mobile Network Technol, Beijing 100876, Peoples R China
[2] Univ Surrey, Inst Commun Syst, Home 5 G Innovat Ctr, Guildford GU2 7XH, Surrey, England
[3] Tech Univ Chemnitz, Commun Engn, D-09107 Chemnitz, Germany
[4] Univ Surrey, Home 5G Innovat Ctr, Guildford GU2 7XH, Surrey, England
[5] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
基金
英国工程与自然科学研究理事会; 中国国家自然科学基金; 欧盟地平线“2020”;
关键词
Massive MTC; NOMA; short packet communication; energy efficiency; joint subchannel and power allocation; LATENCY WIRELESS COMMUNICATION; NONORTHOGONAL MULTIPLE-ACCESS; RESOURCE-ALLOCATION; TRANSMISSION; CHALLENGES; COMPUTATION; POWER;
D O I
10.1109/TVT.2019.2948761
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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%.
引用
收藏
页码:12066 / 12078
页数:13
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