Optimal downlink power allocation schemes for OFDM-NOMA-based Internet of things

• Published in: International journal of distributed sensor networks Vol. 18; no. 1; p. 155014772110647
• Main Authors: Gao, Ya, Yu, Fei, Zhang, Haoran, Shi, Yongpeng, Xia, Yujie
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2022; Wiley
• Subjects: water-filling algorithm; NOMA; 5G; UDN; total system throughput; IoT
• ISSN: 1550-1329; 1550-1477
• DOI: 10.1177/15501477211064741

With the continuous development of fifth-generation technology, the number of mobile terminal Internet of Things devices has increased exponentially. How to effectively improve the throughput of fifth-generation systems has become a challenge. In the Internet of Things networks, ultra-dense networks and non-orthogonal multiple access technology have drawn extensive attention in recent years, because they can achieve multiplexing from the space domain and power domain. To improve the throughput of the system, this article combines non-orthogonal multiple access with ultra-dense networks technology and considers the orthogonal frequency division multiplexing non-orthogonal multiple access–based ultra-dense networks with multiple base stations and multiple Internet of Things devices. In particular, first, we build the network model and channel model. Second, we construct the downlink transmission rate maximizing problem subject to the total power. Then, to solve this problem, we divide it into three sub-problems: device grouping, inter-sub-band power allocation, and intra-sub-band power allocation problems. Solving these sub-problems, we obtain the optimal power allocation schemes by jointly employing channel-state sorting–pairing algorithm, water-filling algorithm, and convex optimization theory. Finally, numerical simulations are conducted to validate the performance of our proposed optimal downlink power allocation scheme. Experimental results show that the total throughput of the system has been significantly improved.