Full-duplex massive multiple input multiple output systems under hardware impairments. (October 2018)

• Record Type: Journal Article
• Authors: Long, Yin; Chen, Zhi
• Is Part Of: Computers & electrical engineering. Volume 71(2018)
• Page Start: 768
• Page End: 781
• Publication Date: 2018-10
• Subjects: M-Learning -- 5G -- Full-duplex -- Massive MIMO -- Random matrix theory -- Hardware impairments; 00-01 -- 99-00; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.07.001
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Mobile learning systems are investigated from the perspective of physical layer. Full-duplex massive MIMO is applied into mobile learning systems to boost system rate and reduce transmission delay. Full-duplex based mobile learning systems are studied in the presence of hardware imperfections. A novel method to determine the optimal number of mobile learning users for maximizing sum uplink rate is proposed. Abstract: In this paper, we study mobile learning (M-Learning) systems from the perspective of physical layer. Due to the characteristic of bidirectional propagation, full-duplex is very suitable for M-Learning systems. Therefore, we apply the full-duplex large-scale antenna technique into M-Learning systems. To be practical, we consider the full-duplex based M-Learning systems under hardware imperfections which pose a huge challenge to the analysis of the uplink achievable rate. According to the principle of minimum mean square error, by resorting to the large dimensional matrix theory, we derive the deterministic equivalence for the uplink achievable rate, which is solely dependent on the statistical channel state information. The deterministic equivalence is proven to be not only tight under high antenna dimension, but also accurate for moderate antenna dimension, as shown by simulations. Moreover, by using the deterministic equivalence, we propose a method to determine the optimal number of M-Learning users in the aim of maximizing uplink sum rate.